Technology & Innovation Volume 18, Number 1

ISSN 1949-8241 • E-ISSN 1949-825X

Volume 18, Number 1

Evolution of N e u r o i m a g i n g Te c h n o l o g y Fiber Bundle Length in Cerebral White Matter

21

Evaluation of Neuromodulation Using MRI

39

Imaging the Perivascular Space

63

EDITORS-IN-CHIEF PAUL R. SANBERG University of South Florida Tampa, FL

ERIC R. FOSSUM Dartmouth College Hanover, NH

SENIOR EDITORS HOWARD J. FEDEROFF University of California, Irvine Irvine, CA

NASSER ARSHADI University of Missouri – Saint Louis St. Louis, MO

EDITORIAL STAFF Judy Lowry, Managing Editor

Kimberly Macuare, Assistant Editor

EDITORIAL BOARD Shantikumar Nair, Amrita University, India

Steven J. Kubisen, The George Washington University

Sethuraman Panchanathan, Arizona State University

Jarett Rieger, H. Lee Moffitt Cancer Center & Research Institute

David Winwood, Association of University Technology Managers

Christopher Fasel, Idaho State University

Jay Gogue, Auburn University

Sharon Heise, Institute for Human & Machine Cognition

Rivka Carmi, Ben-Gurion University of the Negev, Israel

A. Alan Moghissi, Institute for Regulatory Science

Ernest B. Izevbigie, Benson Idahosa University, Nigeria

Cama McNamara, Inventor’s Digest

Mark Rudin, Boise State University

Ken S. Lee, Jackson State University

Gloria Waters, Boston University

Christy Wyskiel, Johns Hopkins University

Farnam Jahanian, Carnegie Mellon University

Solomon H. Snyder, Johns Hopkins University

Joseph Jankowski, Case Western Reserve University

Mary Rezac, Kansas State University

Shinn-Zong (John) Lin, China Medical University, Taiwan

Paul DiCorleto, Kent State University

Todd Headley, Colorado State University

Norman R. Augustine, Lockheed Martin Corporation

Scot Hamilton, Columbia University

Kalliat T. Valsaraj, Louisiana State University

Alice Li, Cornell University

Richard Kordal, Louisiana Tech University

Donna M. DeCarolis, Drexel University

Robert S. Langer, Massachusetts Institute of Technology

Marti Van Scott, East Carolina University

Rebecca Mahurin, Montana State University

Todd Sherer, Emory University

Vimal Chaitanya, New Mexico State University

John W. Newcomer, Florida Atlantic University

Kurt H. Becker, New York University

Tachung (T.C.) Yih, Florida Gulf Coast University

Lesley Rigg, Northern Illinois University

Tristan J. Fiedler, Florida Institute of Technology

James G. Conley, Northwestern University

Andres G. Gil, Florida International University

Arlene A. Garrison, Oak Ridge Associated Universities

Lawrence O. Gostin, Georgetown University Law Center

Lonnie G. Thompson, The Ohio State University

John J. Kopchick, Ohio University

Karen J.L. Burg, University of Georgia

Steven Price, Oklahoma State University

Derek E. Eberhart, University of Georgia

Neil A. Sharkey, The Pennsylvania State University

Richard C. Willson, University of Houston

Curtis R. Carlson, The Practice of Innovation

Lesley Millar-Nicholson, University of Illinois at Urbana-Champaign

Kenneth J. Blank, Rowan University S. David Kimball, Rutgers, The State University of New Jersey

Taunya Phillips Walker, University of Kentucky Mary Shire, University of Limerick, Ireland

Raymond C. Tait, Saint Louis University

William M. Pierce, Jr., University of Louisville

Arthur Molella, Smithsonian Lemelson Center

Patrick O’Shea, University of Maryland

Arthur J. Tipton, Southern Research Institute

Louis A. Carpino, University of Massachusetts – Amherst

Christos Christodoulatos, Stevens Institute of Technology

James P. McNamara, University of Massachusetts Medical School

Robert V. Duncan, Texas Tech University Stephen Klasko, Thomas Jefferson University Richard A. Houghten, Torrey Pines Institute for Molecular Studies Woody Maggard, University at Buffalo – State University of New York Stephen Z. Cheng, The University of Akron Richard P. Swatloski, The University of Alabama Richard B. Marchase, The University of Alabama at Birmingham Frederic Zenhausern, The University of Arizona Jim Rankin, University of Arkansas Linda P. B. Katehi, University of California, Davis M. J. Soileau, University of Central Florida Patrick A. Limbach, University of Cincinnati Inge Wefes, University of Colorado – Denver/AMC Jeff Seemann, University of Connecticut Mathew Willenbrink, University of Dayton David S. Weir, University of Delaware Paula Heldt, University of Evansville David P. Norton, University of Florida

Kenneth J. Nisbet, University of Michigan Henry C. Foley, University of Missouri – Columbia Lawrence Dreyfus, University of Missouri – Kansas City Prem S. Paul, University of Nebraska-Lincoln Zachary Miles, The University of Nevada, Las Vegas Kumi Nagamoto-Combs, The University of North Dakota John Kantner, University of North Florida Thomas McCoy, University of North Texas James H. Bratton, The University of Oklahoma Lynne U. Chronister, The University of South Alabama Judy Genshaft, University of South Florida Gordon C. Cannon, University of Southern Mississippi T. Taylor Eighmy, The University of Tennessee, Knoxville Thomas Parks, The University of Utah William Barker, University of Wisconsin – Madison H. Holden Thorp, Washington University in St. Louis Keith H. Pickus, Wichita State University Robert E. W. Fyffe, Wright State University T. Kyle Vanderlick, Yale University

National Academy of Inventors. Technology and Innovation, University of South Florida Research Park, 3702 Spectrum Boulevard, Suite 165, Tampa, F. 33612-9445 USA. Tel: +1-813-974-1347; Fax: +1-813-974-4962; tijournal@academyofinventors.org; www. academyofinventors.org.

PUBLISHING INFORMATION Technology and Innovation, Journal of the National Academy of Inventors (ISSN: 1949-8241) is published by the National Academy of Inventors, University of South Florida Research Park, 3702 Spectrum Boulevard, Suite 165, Tampa, F. 33612-9445 USA. Tel: +1-813-974-1347; Fax: +1-813-974-4962; tijournal@ academyofinventors.org; www.academyofinventors.org Subscriptions: Technology and Innovation (T&I) is published 4 times a year. For subscription information, please visit our website or contact tijournal@academyofinventors.org.

Advertisement: T&I will accept advertisements. All advertisements are subject to approval by the editors.

For details and rates, please contact tijournal@academyofinventors.org.

Disclaimer: While every effort is made by the publisher, editors, and editorial board to see that no inaccurate or misleading data, opinion, or statement appears in T&I, they wish to make it clear that the data and opinions appearing in the articles and advertisements contained herein are the sole responsibility of the contributor or advertiser concerned. Therefore, the publisher, editors, editorial board, their respective employees, officers, and agents accept no responsibility or liability whatsoever for the effect of any such inaccurate or misleading opinion, data, or statement.

Copyright Notice: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. By submitting a manuscript, the authors agree that the copyright for their article is transferred to the publisher if and when the article is accepted for publication. However, assignment of copyright is not required from authors who work for organizations that do not permit such assignment. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints, photographic reproductions, microform, or any other reproductions of similar nature and translations. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, electrostatic, magnetic type, mechanical, photocopying, recording, or otherwise, without permission in writing from the copyright holder.

Photocopying information for users in the USA: For permission to reuse copyrighted content from T&I, please access www.copyright.com or contact Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, telephone +1-855-239-3415 (Monday-Friday, 3 AM to 6 PM Eastern Time), fax +1-978-6468600. Copyright Clearance Center is a not-for-profit organization that provides copyright licensing on behalf of the National Academy of Inventors. The copyright owner’s consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the publisher for such copying. In case of doubt, please contact T&I at tijournal@ academyofinventors.org.

Copyright © 2016 National Academy of Inventors® Printed in the USA

Cover Photo: Visual depiction of whole brain fiber bundles in cerebral white matter: an illustration of tractography modeling of white matter. This image shows a whole brain tractography model (lateral view), which consists of a complex pattern of connections. (Figure 2 from: Application of A Novel Quantitative Tractography Based Analysis of Diffusion Tensor Imaging to Examine Fiber Bundle Length in Human Cerebral White Matter, by Laurie M. Baker, Ryan P. Cabeen, Sarah Cooley, David H. Laidlaw, and Robert H. Paul, p. 24)

Volume 18, Number 1, 2016

Pages 1 – 82

ISSN 1949-8241 E-ISSN 1949-825X

CONTENTS SPECIAL ISSUE: EVOLUTION OF NEUROIMAGING TECHNOLOGY Introduction: Evolution of Neuroimaging Technology

Robert H. Paul

1

Reducing CSF Partial Volume Effects to Enhance Diffusion Tensor Imaging Metrics of Brain Microstructure

Lauren E. Salminen, Thomas E. Conturo, Jacob D. Bolzenius, Ryan P. Cabeen,

Erbil Akbudak, and Robert H. Paul

5

Application of A Novel Quantitative Tractography-Based Analysis of Diffusion Tensor Imaging to Examine Fiber Bundle Length in Human Cerebral White Matter

Laurie M. Baker, Ryan P. Cabeen, Sarah Cooley, David H. Laidlaw, and Robert H. Paul

21

Diffusion Imaging Fiber Bundles

Song Zhang

31

Assessing the Structural and Functional Effects of Neuromodulation Using Magnetic Resonance Imaging

David F. Tate, Jacob D. Bolzenius, Carmen Velez, Elisabeth A. Wilde, Sylvain Bouix,

Carlos A. Jaramillo, Jeffrey D. Lewis, and Michael Weisend

39

Using Pittsburgh Compound B for PET Imaging Across the Alzheimer’s Disease Spectrum

Ann D. Cohen

51

The Emerging Field of Perivascular Flow Dynamics: Biological Relevance and Clinical Applications

Jacob Huffman, Sarah Phillips, George T. Taylor, and Robert Paul

63

REGULAR FEATURES The Pillars of Patent Quality

Alex Camarota

75

The NAI Profile: An Interview with Dr. Frances Arnold

Frances Arnold and Kimberly A. Macuare

79

Aims and Scopes

i

Preparation of Manuscripts

ii

Ethics Statement

ii www.technologyandinnovation.org

Technology and Innovation, Vol. 18, pp. 1-4, 2016 Printed in the USA. All rights reserved.

Copyright © 2016 National Academy of Inventors.

ISSN 1949-8241 • E-ISSN 1949-825X DOI: http://dx.doi.org/10.21300/18.1.2016.1 www.technologyandinnovation.org

EVOLUTION OF NEUROIMAGING TECHNOLOGY IN THE MODERN ERA Robert H. Paul Missouri Institute of Mental Health and Department of Psychology, University of Missouri – Saint Louis, St. Louis, MO, USA Clinical applications in brain science have progressed at a glacial pace when compared to other medical disciplines. Treatments for most neurodegenerative brain diseases are limited, and cure strategies remain underdeveloped. Pressure to improve clinical outcomes in the neurological sciences is exacerbated by an aging population at risk for degenerative brain diseases. Fortunately, technical advances in the field of neuroimaging offer new promise, with enhanced characterization of microstructural anatomy, network connectivity, and functional biomarkers of health and disease. Articles highlighted in this issue describe cutting-edge applications targeting these outcomes using diffusion tensor imaging, diffusion-based tractography, and positron emission tomography. Finally, the glymphatic system is reviewed as a target for future neuroimaging investigation in clinical populations such as those with Alzheimer’s disease. Integration of these methods with new advances in computational science will inform mechanisms of healthy and dysfunctional brain mechanisms and ideally lead to new targeted therapeutic interventions.

Key words: Neuroimaging; Alzheimer’s disease; Aging

The human brain remains one of the most puzzling mysteries in the known universe. Encased in bone and vulnerable to slight homeostatic disruption, the brain is not easily examined by observational methods or invasive experimental procedures. Early perspectives of basic structure-function relationships were informed by clinical evaluation of individuals who had survived traumatic brain injury, such as Mr. Phineas Gage (3). However, the resulting models of brain organization and physiology were incomplete due to heterogeneity in lesion location and severity across individuals and limited capacity to measure the impact of focal lesions on larger networks described in histopathological studies. New technical insights were needed to bridge the science from his-

topathological bench work to in vivo examinations of complex human behavior. The requisite technology in brain science would not be available for nearly a century after the clinical description of Mr. Gage.

By contrast, progress in the prevention, diagnosis, and treatment of diseases peripheral to the central nervous system (CNS) progressed steadily over this time period, with more rapid advances after the mid1900s. Cardiovascular and cerebrovascular diseases previously known as fatal became manageable for many individuals with medications, surgery, and/or changes in lifestyle factors related to disease onset and progression (e.g., smoking, obesity). Similar breakthroughs in the prevention and treatment of other disease areas (e.g., diabetes) eclipsed the pace

_____________________ Accepted December 10, 2015.

Address correspondence to Robert H. Paul, PhD, Missouri Institute of Mental Health, 4366 World Parkway Circle, St. Louis, Missouri 63134, USA. Tel: 1-314-516-8403; E-mail: Robert.Paul@mimh.edu

1

2 PAUL of discovery in brain science. The discrepancy in treatment options for conditions above vs. below the neck contributed to a growing population of adults living longer lifespans, including a growing number of individuals with age-associated neurodegenerative diseases (8). The limited treatment options for the projected expansion of adults with neurodegenerative diseases of the brain such as Alzheimer’s disease, growing healthcare costs, and increasing numbers of uninsured individuals all pointed towards an emerging health crisis.

Political pressure culminated in a Presidential proclamation signed in 1989 declaring the period from 1990-1999 as the “Decade of the Brain” (5).

The initiative targeted 14 areas in the neurological sciences primed for breakthroughs in prevention, treatment, and cures for the most vexing and common neurological conditions. The Decade ended with few treatments and no interventions capable of reversing or halting common forms of neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, or vascular dementia. However, innovative work in the field of neural bioinformatics and neuroimaging flourished during this period, and enthusiasm was high that neuroimaging technology seeded during the 1990s would significantly alter the cadence of brain science outcomes in the near future (10). This prediction proved accurate as borne out through subsequent research that leveraged a historical foundation in magnetic resonance imaging (MRI).

Early MRI systems introduced in the 1970s provided researchers and clinicians with vastly improved spatial resolution of brain anatomy compared to radiographic methods of the past. High field MRI using 1.5 Tesla (15,000 Gauss) became common in both research and clinical settings, followed by the introduction of 3 Tesla systems. The image resolution of 3 Tesla MRI is approximately 16 times that of 1.5 Tesla systems, allowing for significantly improved signal-to-noise ratio and improved anatomical detail.

Eventually, 4 Tesla and even 7 Tesla systems were introduced at select research centers, the latter providing a magnetic field 140,000 times that of earth’s gravitational force. The improvements in image acquisition at higher field strengths combined with robust

post-processing algorithms improved visualization of both healthy and pathological brain tissue (11).

Anatomical detail provided by high field MRI opened a new world of brain structure-function relationships supported by the demarcation of tissue classification into cortical gray matter, subcortical white matter, and subcortical gray matter. Volumes of brain regions (e.g., frontal lobe) and specific nuclei (e.g., caudate nucleus) could be readily quantified and contrasted between patient groups and healthy controls or analyzed within groups to determine the degree of shared variance between brain volumes and measures of cognition, personality, or emotion.

Landmark studies revealed reduced hippocampal volume in the earliest stages of Alzheimer’s disease (6), microvascular infarcts in subcortical white matter (2), as well as numerous other clinically relevant findings.

However, the focus on specific nuclei and regional lobes belied the anatomical complexity of the brain previously characterized in histological studies. Work dating back to Golgi and Cajal (1) brilliantly revealed the multiplex architecture of neural networks using relatively crude methods in the 1800s, yet modern neuroimaging studies restricted analyses to focal brain regions. In effect, the field defaulted to a digitized version of phrenology, ascribing complex and diverse human behaviors to isolated brain volumes.

New technology was needed to measure brain network integrity on a larger scale.

The introduction of diffusion tensor imaging (DTI) opened a new technical portal away from the hyper-focused regionalization of volumetric studies.

DTI measures the rate and direction of water flow (i.e., hydrogen) in the brain, both of which are altered by neuronal damage (9). Common DTI outputs include scalar metrics of water diffusion, such as fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity (see Baker et al. in this issue for review). Refinement of DTI pulse sequences during the Decade of the Brain allowed researchers to visualize network connections by measuring the curvature of the diffusion metrics along known anatomical fasciculi. For the first time, the structural integrity of the brain (later referred to as the connectome) (12) could be defined. Subsequent studies developed methods to quantify the integrity of brain white matter tracts

EVOLUTION OF NEUROIMAGING TECHNOLOGY

identified using specialized processing methods for DTI outcomes. One such method, quantified fiber bundle length, is described in the current issue

by Zhang. Compelling evidence demonstrates the sensitivity of quantified fiber bundle length to perturbations in cognition and to genetic risk alleles associated with reduced brain integrity (see Baker et al. in this issue). Technical improvements to DTI applications continue to develop, including advances in pulse sequences and post-processing data computations. In the current issue of this journal, Salminen et al. describe a method to improve the DTI signal by suppressing artifact generated by cerebrospinal fluid (CSF). Application of this CSF-suppression method offers improved anatomical precision of DTI-informed network models and more robust characterization of the scalar indices.

A notable limitation of both structural MRI and DTI is the absence of direct functional information about the brain. Functional information is most commonly derived from functional MRI (fMRI) or positron emission tomography (PET). fMRI was introduced in the early part of the Decade of the Brain at the Society for Magnetic Resonance in Medicine annual meeting in 1991. The fMRI signal is generated by changes in the blood deoxyhemoglobin concentration associated with cognitive activity completed by the individual while inside the MR unit. The spatial resolution of fMRI for cortical functions is quite good, but poor temporal resolution and high data processing demands have restricted widespread adoption in clinical settings.

By contrast, PET has long been the workhorse of clinical functional brain imaging. PET is capable of detecting functional properties of the brain at the level of proteins or brain regions depending on the selection of isotopes or ligands. The degree of anatomical specificity provided by PET represents an advantage over fMRI. Ligands are available for specific neurotransmitters (e.g., dopamine) and specific proteins, such as amyloid. In this issue, Cohen reviews the research and clinical relevance of Pittsburgh Compound-B (PiB), a PET ligand that selectively binds to the amyloid plaques characteristic of neuropathology related to Alzheimer’s disease (13). PiB imaging is at the forefront of modern neuroimaging

3

innovation, supported by evidence of abnormal PiB amyloid binding among older individuals at risk for future diagnosis of Alzheimer’s disease (reviewed by Cohen).

In addition to serving as markers of disease mechanisms, neuroimaging tools are sensitive to changes in brain integrity following treatment. Tate et al. review evidence of changes in PET, fMRI, and DTI indices following neuromodulation methods.

PET imaging targeting the dopamine system reveals a potential mechanism of action of repetitive transcranial magnetic stimulation, and DTI studies reveal improvement in fractional anisotropy in the brain ipsilateral to the treated hemisphere (as reviewed

by Tate et al.). Additional randomized clinical trials are required to define the efficacy of neuromodulation and the impact of treatment on brain network function, yet the innovative methods represent an intriguing non-pharmacological approach or adjuvant treatment to maximize current pharmacological interventions.

This special issue of Technology and Innovation concludes with a contribution from Huffman et al. introducing a new frontier in imaging with high research and clinical relevance. The glymphatic and perivascular waste clearance systems identified recently in the brain have sparked both controversy and innovation (7). Once considered anatomically distinct from the periphery, the CNS is linked to the periphery through pathways that facilitate clearance of waste products across the blood-brain-barrier (BBB) (7). As reviewed by Huffman et al., identification of these pathways has opened a new world of discovery regarding communication between the CSF and plasma. Neuroimaging methods applied to animal models reveal waste products from metabolic functions and break-down of amyloid clear the CNS through these pathways, and, therefore, damage to this system may play a pivotal role in the pathogenesis of neurodegenerative diseases such as Alzheimer’s disease. Unfortunately, neuroimaging methods to examine this system are currently limited to animal studies. New innovation is required to translate these methods to human application and successfully define the relevance of disturbed glymphatic flow dynamics to human brain models.

4 PAUL The primary governor of progress in brain science is the extraordinary complexity of the brain.

Comprised of a dizzying number of interconnections, the brain is estimated to include as many as 1011 x 1014 synapses. Until recently, the computational methods required to capture the structural and functional complexity of the human brain in vivo were non-existent.

Neuroimaging technology now permits microscopic analyses and visualization of complete tracts and systems. Functional neuroimaging methods using biologically-specific ligands such as PiB reliably identify individuals at risk for developing dementia, with new advances coming from ligands for tau and other neuropathological markers of disease. Finally, it is likely that future models of brain structure-function will incorporate waste clearance dynamics occurring through central-peripheral exchange. Once established, treatment (e.g., neuromodulation) and cure strategies can be strategically targeted against specific mechanisms of brain dysfunction.

Ambitious initiatives are underway to define the human brain connectome (12). Detailed mapping of brain circuitry provided from the connectome project will set a new water mark, with an emphasis on complete and integrated brain circuits. Delineation of brain phenotypes and endophenotypes will emerge from the integration of multiple imaging modalities, such as DTI/diffusion spectrum imaging and resting state fMRI. The neuroimaging field has already shifted in this new direction (4). The rich outcomes generated from these studies are critical to develop cost-effective options for personalized medicine and optimal patient outcomes. Undoubtedly, progress will be governed by the pace of innovation in computational science and federal funding for new research. A new political stage was set in April of 2013, with President Obama announcing the launch of the BRAIN Initiative with coordinated funding from multiple federal sources to support interdisciplinary and highly integrated brain science research.

Time will determine whether the new initiative is sufficiently funded to support the development of treatments that can arrest and/or reverse neurological disease. There is little doubt, however, that the neuroimaging technology described in this issue will play a role in this next wave of strategic brain science aimed at improving the lives of individuals affected by a neurological illness worldwide.

REFERENCES 1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

Albright T.D.; Jessell, T.M.; Kandel, E.R.; Posner, M.I. Progress in the neural sciences in the century after Cajal (and the mysteries that remain). Ann. N. Y. Acad.

Sci. 929 (1):11-40; 2001.

Coutts, S.B.; Hill, M.D.; Simon, J.E.; Sohn, C.H.; Scott, J.N.; Demchuk, A.M.; VISION Study Group. Silent ischemia in minor stroke and TIA patients identified on MR imaging. Neurology 65(4):513-7; 2005. de Schotten, M.T.; Dell’Acqua, F.; Ratiu, P.; Leslie, A.; Howells, H.; Cabanis, E.; Iba-Zizen, M.T.; Plaisant, O; Simmons, A; Dronkers, N.F.; Corkin, S; Catani, M. From Phineas Gage and Monsieur Leborgne to H.M.: revisiting disconnection syndromes. Cereb. Cortex 25(12):4812-4827; 2015.

GadElkarim, J.J.; Schonfeld, D.; Ajilore, O.; Zhan, L.; Zhang, A.F.; Feusner, J.D.; Thompson, P.M.; Simon, T.J.; Kumar, A.; Leow, A.D. A framework for quantifying node-level community structure group differences in brain connectivity networks. Med. Image Comput.

Comput. Assist. Interv. 15(pt 2):196-203; 2012.

Goldstein, M. Decade of the brain. An agenda for the nineties. West. J. Med. 161(3):239-241; 1994.

Jack, C.R., Jr.; Petersen, R.C.; Xu, Y.C.; O’Brien, P.C.; Smith, G.E.; Ivnik, R.J.; Boeve, B.F.; Waring, S.C.; Tangalos, E.G.; Kokmen, E. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52(7):1397-403; 1999.

Iliff, J.J.; Lee, H.; Yu, M.; Feng, T.; Logan, J; Nedergaard, M.; Benveniste, H. Brain-wide path-way for waste clearance captured by contrast-en-hanced MRI. J. Clin.

Invest. 123(3):1299-309; 2013.

National Institute on Aging; World Health Organization. Global health and aging. Bethesda, MD: National Institute on Aging; 2011.

Shrager, R.I.; Basser, P.J. Anisotropically weighted MRI. Magn. Reson. Med. 40(1):160-5; 1998.

Tandon, P.N. The decade of the brain: a brief review.

Neurol. India 48(3):199-207; 2000. van der Kolk, A.G.; Hendrikse, J.; Zwanenburg, J.J.; Visser, F.; Luijten, P.R. Clinical applications of 7T MRI in the brain. Eur. J. Radiol. 82(5):708-718; 2013.

Van Essen, D.C.; Smith, S.M.; Barch, D.M.; Behrens, T.E.; Yacoub, E.; Ugurbil, K.; WU-Minn HCP Consortium. The WU-Minn Human Connectome Project: an overview. Neuroimage 80:62-79; 2013.

Vinters, H.V.; Miller, B.L.; Pardridge, W.M. Brain amyloid and Alzheimer disease. Ann. Intern. Med. 109(1):41-54; 1988.

Technology and Innovation, Vol. 18, pp. 5-20, 2016 Printed in the USA. All rights reserved.

Copyright © 2016 National Academy of Inventors.

ISSN 1949-8241 • E-ISSN 1949-825X http://dx.doi.org/10.21300/18.1.2016.5 www.technologyandinnovation.org

REDUCING CSF PARTIAL VOLUME EFFECTS TO ENHANCE DIFFUSION TENSOR IMAGING METRICS OF BRAIN MICROSTRUCTURE Lauren E. Salminen1, Thomas E. Conturo2, Jacob D. Bolzenius3, Ryan P. Cabeen4, Erbil Akbudak2, Robert H. Paul3 1

Department of Psychology, University of Missouri – Saint Louis, St. Louis, MO, USA Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA 3 Missouri Institute of Mental Health, St. Louis, MO, USA 4 Computer Science Department, Brown University, Providence, RI, USA

2

Technological advances over recent decades now allow for in vivo observation of human brain tissue through the use of neuroimaging methods. While this field originated with techniques capable of capturing macrostructural details of brain anatomy, modern methods such as diffusion tensor imaging (DTI) that are now regularly implemented in research protocols have the ability to characterize brain microstructure. DTI has been used to reveal subtle micro-anatomical abnormalities in the prodromal phase ofº various diseases and also to delineate “normal” age-related changes in brain tissue across the lifespan. Nevertheless, imaging artifact in DTI remains a significant limitation for identifying true neural signatures of disease and brain-behavior relationships. Cerebrospinal fluid (CSF) contamination of brain voxels is a main source of error on DTI scans that causes partial volume effects and reduces the accuracy of tissue characterization. Several methods have been proposed to correct for CSF artifact though many of these methods introduce new limitations that may preclude certain applications. The purpose of this review is to discuss the complexity of signal acquisition as it relates to CSF artifact on DTI scans and review methods of CSF suppression in DTI. We will then discuss a technique that has been recently shown to effectively suppress the CSF signal in DTI data, resulting in fewer errors and improved measurement of brain tissue. This approach and related techniques have the potential to significantly improve our understanding of “normal” brain aging and neuropsychiatric and neurodegenerative diseases. Considerations for next-level applications are discussed.

Key words: MRI; DTI; Partial volume effects; CSF suppression

INTRODUCTION Advances in radiology over the past several decades have drastically increased the ability to examine the living human brain in detail. The introduction of computed tomography (CT) allowed for low-resolution three-dimensional reconstruction of brain

tissue and represented a landmark improvement in medical care potential (37). The subsequent technique of magnetic resonance imaging (MRI) has continued to expand the potential of in vivo imaging technology (45). Clinical MRI is now commonly performed using MR systems operating at a1.5 Tesla (T) field strength,

_____________________ Accepted December 10, 2015.

Address correspondence to Lauren E. Salminen, Department of Psychology, University of Missouri – Saint Louis, One University Boulevard, Stadler Hall, Saint Louis, MO 63121, USA. Tel: +1 (314) 516-8440; Fax: (314) 516-5392; E-mail: LSalminen@mail.umsl.edu

5

6

SALMINEN ET AL.

with 3T and higher fields becoming ever increasingly common for both clinical and research applications (58,86). High-field imaging provides an invaluable tool for identification of various brain disorders with the goal of informing therapeutic methods and eventual cures.

In conjunction with the notable potential of these technologies to answer important questions about brain health, key technical components merit attention to optimize analytical procedures in both clinical and research settings. Examples of these components include the MR pulse sequence parameters, traditional methods of maximizing spatial accuracy of resulting images, and modern advances that exploit patterns of molecular movements within tissues to illustrate cellular integrity in greater detail, with particular regard to diffusion tensor imaging (DTI). This review will discuss foundational elements of MRI technology as it relates to diffusion MRI, explore potential sources of error when using traditional methods (e.g., cerebrospinal fluid (CSF) contamination), and propose an acquisition and post-processing approach to better manage artifacts associated with this phenomenon.

SOURCES OF VARIANCE IN THE MR SIGNAL Structural imaging techniques are based on the nuclear spin of a hydrogen nucleus composed of a single charged proton. Viewing this phenomenon classically, the combination of nuclear spin and electrical charge can be considered to generate a “magnetic moment” that aligns with the external magnetic field (B0). Because of these nuclear moments, a large ensemble of nuclear spins (e.g., water) generates a net magnetization. In a constant magnetic field, the net magnetization is a vector that has points along the z axis that runs parallel to B0 (i.e., the longitudinal direction) and a transverse component that lies in the x-y plane that is perpendicular to B0. In MRI, this net magnetization is detected by tipping the net magnetization vector from the longitudinal direction into the x-y plane (i.e., transverse plane), where the MRI scanner can detect it independent of the main magnetic field (B0). The magnetization is tipped by applying a radiofrequency (RF) pulse that has a time-varying field that oscillates in the x-y

plane. The RF pulse is applied at the characteristic frequency (Larmor frequency) of the hydrogen nucleus. The Larmor frequency changes in proportion to changes in magnetic field strength. A 90° RF pulse tips the magnetization entirely into the transverse (x-y) plane. The longitudinal magnetization is zero after the 90° pulse. Typical pulse sequences utilize a 90° RF pulse to maximize signal detection. Once the RF pulse is turned off, the longitudinal magnetization begins to grow back through a process called longitudinal relaxation (T1 relaxation). At the same time, the transverse magnetization vector rotates in the transverse plane at the Larmor frequency, and the transverse vector magnitude (length) decays to zero by a faster process called transverse relaxation (T2 relaxation) (14,29,86).

There are different physical mechanisms that influence T1 and T2 relaxation times (14). Pertinent to this review, relaxation times for both T1 and T2 vary by tissue composition according to the surrounding chemical environment of water protons (91). Water nuclei in fat and water have similar Larmor frequencies; therefore, fat is visible on MR images. However, due to the different physical environments between fat and water, fat has a short T1 and T2 (i.e., fast longitudinal and transverse relaxation). By contrast, brain gray matter and white matter have an intermediate T1 and T2, while pure water and CSF have a long T1 and T2 due to intrinsic biophysical factors (13,41,80,91).

Image Formation and Contrast Image formation relies on the spatial encoding of the signal generated by the net transverse magnetic vector rotating in the transverse plane (91). The MRI signal is detected by an RF receiver coil that is similar to the RF coil used to tip the net magnetization (RF transmit coil). The RF receiver coil simply “listens” to the transverse magnetization vector after the RF transmit coil is turned off. Each time the transverse magnetization rotates 360° in the transverse plane, a peak voltage is generated in the receiver coil and then digitized. The MR signal then becomes an oscillating voltage that has a frequency equal to the Larmor frequency but contains no information indicating the location of signal generation in the brain.

The signal is spatially encoded by applying external

CSF SUPPRESSION METHODS FOR DTI

magnetic field gradients across the brain that alter the strength of B0 in a predictable pattern that causes a change in the resonance Larmor frequency according to position. As a result, the transverse magnetization rotates at different frequencies at different positions across the brain. The variations in signal frequency are transmitted through the RF receiver coil and distinguished as spatial frequencies using a Fourier Transform (FT). A similar second spatial encoding is completed in the perpendicular direction across the brain. The signals are digitized and stored as raw data points on a two-dimensional grid (“k-space”) and then reconstructed by the FT to produce an image.

Each discrete data point in the reconstructed image is called a pixel (i.e., a picture element). The brightness of each pixel corresponds to the sum of the MRI signals in the corresponding small rectangular volume of tissue called a voxel (i.e., the volume element). At each point in the image, the brightness is proportional to the signal intensity generated by the total transverse magnetization in that corresponding voxel of the brain. Thus, the image intensity depends on the number (density) of hydrogen nuclei and the T1 and T2 in a specific area of brain tissue (29,91).

Differences in relaxation times between tissues (e.g., gray and white matter, CSF) produce different signal intensity contrasts in the image. The degree to which an MRI scan is sensitive to differences in T1 or T2 is controlled by the operator of the scanner. Most images are created using a “spin-echo,” which is a basic pulse sequence that consists of operator-modifiable parameters such as echo time (TE) and repetition time (TR). TE is the time between the initial RF pulse and the peak of the signal, whereas TR is the time between consecutive RF pulses. A pulse sequence with short TR and TE is sensitive to differences in T1, and the resulting image is called a T1-weighted image.

In such an image, tissues with a long T1 (e.g., CSF) appear dark. A pulse sequence with long TR and TE produces a T2-weighted image, in which tissues with a long T2 (e.g., CSF) appear bright (14,54,91). Thus, selecting the correct pulse sequence setting is critical for determining the relative visibility of certain brain structures and neuropathology.

Diffusion-weighted imaging (DWI) is an alternative approach to capture tissue contrast that depends

7

on the random microscopic movements of water molecules (i.e., diffusion) in brain tissue (26,46,84 92).

In DWI, strong magnetic field gradients are applied during a T2-weighted spin-echo pulse sequence to make the sequence sensitive to water diffusion. After applying the RF pulse to tip the net magnetization into the transverse plane, a diffusion-encoding gradient is applied along one direction, such that the B0 field is higher in one direction and lower in the opposite direction. As water molecules begin to move around randomly in the tissue, some molecules will move to areas with higher resonant frequencies, and others will move to areas with lower resonant frequencies.

The net result is a direct interference in the MR signal that causes signal loss. Signal loss is more severe if diffusion is faster and the diffusion gradients are stronger.

Adjusting the strength of the diffusion-encoding gradient adjusts the strength of the diffusion weighting (i.e., b value). Higher b values indicate stronger diffusion sensitivity, up to a b value given by bD = 1, where D is the diffusion coefficient or “diffusivity” (D ~ 10-3 mm2/s in brain tissue). Thus, a b value of 1000 s/mm2 is typically used in DWI. A higher b value will reduce the signal intensities and produce a darker image at that tissue location (18,26). Because DWI is also T2-weighted, “T2-shine through” effects can occur from the bright signal intensities of free-water.

These T2 effects can be eliminated by calculating the diffusion coefficient (D) from images with two different b values (typically from a T2-weighted image with b~0 and a DWI) (70). Because the diffusion coefficient depends on complex factors in tissue, it is sometimes referred to as the apparent diffusion coefficient (ADC). Thus, the observed D in tissues represents the effective rate of diffusion in an image voxel. To measure D, DWI sequences sometimes utilize at least two different b-values to plot the best-fit D using the log of the signal intensity measured in a specific tissue (26).

Diffusion encoding gradients can be applied along three orthogonal axes to produce three different DWI contrasts that provide information regarding the directionality of water motion. Water molecules that diffuse in tissues composed of fibers (e.g., white matter) move rapidly along the fiber, but slowly across

8

SALMINEN ET AL.

the fiber due to fiber walls that act as barriers (80). In these tissues, water diffusion is anisotropic (i.e., varies along different directions). Water molecules in pure water do not encounter barriers and travel quickly and equally in all directions (isotropic diffusion). For tissue that is generally isotropic (e.g., gray matter and CSF), water molecules follow a random pattern of motion, and the signal loss is not dependent on the direction of the diffusion-encoding gradient. In these tissues, a single scalar D is sufficient to characterize diffusion (26). By contrast, the signal loss in anisotropic tissue (e.g., white matter) strongly depends on the direction of white matter fibers and the diffusion-encoding gradients, which complicate interpretation of DWI contrast. Thus, the directionally-averaged D (or mean diffusivity (MD) image) is often calculated in clinical practice, which is the average of the computed D images obtained from the orthogonal encoding directions. Sometimes the average DWI or isotropic DWI (DWIiso) is also shown, which is the geometric mean of the corresponding DWIs. In both MD and DWIiso, the effects of white matter fiber direction have been removed. For a more complete description of the directional motion of water molecules in anisotropic tissues, a diffusion tensor is required (9,10,19).

DIFFUSION TENSOR IMAGING Derivation of the diffusion tensor from the DWIs allows for the quantification of water diffusion in living brain tissue, thus providing information about the underlying tissue microstructure. The diffusion tensor characterizes the three-dimensional spread of diffusing water molecules from a point source using three-dimensional ellipsoids. Various biological factors influence the shape of the diffusion ellipsoid, including the microstructural composition of human brain tissue. DTI scan protocols require the application of at least six non-collinear diffusion-encoding gradients and a reference image (typically a b~0 image) to capture the full extent of directional water mobility in anisotropic tissue voxels (an anisotropic tissue voxel is a voxel that is, for example, 2x2x5mm in dimensions) (19,63). Modern research scan protocols tend to utilize many more directions (up to 64) to increase the sensitivity of the signal and improve measurement accuracy (35).

The majority of work utilizing DTI has focused on white matter integrity, as changes in white matter microstructure can be readily delineated using DTI metrics. White matter primarily consists of myelinated and unmyelinated axon fibers that restrict water movement in directions perpendicular to the fibers, thus increasing anisotropy (56). Damage to axons and/or the myelin sheath reduces anisotropy and increases the rate of diffusion in directions perpendicular to the fibers, which is a common result of aging and disease (4,56,82). DTI can also be used to evaluate changes in gray matter microstructure, though the biological interpretation of gray matter diffusion is less clear than in white matter due to the high level of isotropic diffusion that is evident in normal-appearing gray matter (4,28). Increases in gray matter diffusion have been reported in previous studies of aging (59,68,75,76), yet the mechanisms underlying these changes have not been fully delineated.

Fractional anisotropy (FA) and MD are traditional DTI scalar metrics that measure diffusion processes by quantifying the degree of directionality of water diffusion and the directionally-averaged rate of water movement within an image voxel, respectively (15). Damage to cellular microstructure (e.g., axon degeneration, myelin loss, etc.) alters the movements of water molecules and typically results in increased MD and decreased FA (3). Axial diffusivity (AD) and radial diffusivity (RD) are additional DTI metrics that measure water diffusion that occurs parallel and perpendicular to axon fibers (60). Increased RD and decreased AD are believed to reflect reduced integrity as a result of demyelination, axon damage, fiber rarefaction, and/or gliosis (11).

TECHNICAL LIMITATIONS OF DTI Numerous factors influence accuracy and precision of DTI data, including imperfections in scanner hardware (e.g., RF coil, magnetic field gradients), selection of operator controlled parameters (e.g., pulse sequence, number of acquisitions, number of diffusion-encoding gradients), and patient variables (e.g., motion) (55). As a result, quality control (QC) of imaging data is critical for obtaining accurate DTI measurements. Various QC procedures can be implemented to minimize errors from multiple sources,

CSF SUPPRESSION METHODS FOR DTI

and these procedures are often selected according to the research question of interest. Despite these efforts, certain image artifacts remain after correction, and the presence of these artifacts significantly limits research investigations utilizing diffusion indices. Below, we review partial volume effects as a key limitation to DTI applications, specifically in the use of DTI to understand normal and abnormal brain conditions.

Partial Volume Effects Because the voxel signal is a sum of all tissue signals within the voxel (i.e., all transverse magnetization vectors), finite image resolution inevitably causes a mixture of signals at the interface of two tissues.

Qualitatively, this partial volume effect (PVE) can cause loss of edge contrast between tissues occupying the same voxel and can even obscure small lesions near the interface between tissues (72). Quantitatively, PVEs can cause errors in volumetric measurements using structural MRI or region-of-interest (ROI) measurements using DTI. The PVE is more severe when the signal difference between the two tissues is greater, and when the tissue interface makes a shallow angle with respect to the edge of the voxel. Such effects are particularly strong when using a slice thickness that is larger than the in-plane voxel dimensions (87) (e.g., non-isotropic voxel dimensions).

A main source of PVEs is CSF contamination of gray matter on the surface of the cortical ribbon (42).

The CSF-gray matter PVE is particularly strong due to the high contrast between CSF and gray matter on T1 and T2 -weighted images, and because of the undulations of the cortical ribbon that lead to unpredictably shallow interface angles. Further, CSF-gray matter PVEs manifest as errors in gray matter volumetric measurements and ROIs that are proportional to the ratio of surface area to volume of a measured tissue, which is higher for gray matter. These artifacts are of greater concern in studies of abnormal brain integrity in normal aging and age-related disorders, as tissue atrophy increases the magnitude of the PVE (12,88).

Thus, DTI examinations of clinical populations may yield variable conclusions across studies as a result of latent CSF PVEs. Further, voxels containing more than one type of tissue will exhibit heterogeneous dif-

9

fusion properties and produce biased measurements.

Signal contamination causes an overestimation of MD and underestimation of FA (78) and represents a major limitation for studies investigating brain microstructure. Specifically, CSF PVEs have been shown to reduce the biological approximation of DTI parameters by as much as 15-60% in previous studies (6,7,44).

Voxel and pixel size are important operator-controlled parameters that influence PVEs. Larger voxels are more likely to include multiple brain structures and therefore have a greater propensity for CSF contamination (2). This effect is often observed in gray matter, where larger voxels are sometimes used to enhance signal intensity of subcortical nuclei, though the effect may also be observed in deep white matter regions. Reducing the size of the image voxels will reduce PVEs, yet this comes at a cost to the signalto-noise ratio (SNR) (78,95). Since these factors have the potential to alter imaging analytical approaches, an investigation into methods that can mitigate CSF PVEs is warranted.

STRATEGIES TO REDUCE CSF CONTAMINATION Initial attempts to control CSF PVEs relied on fluid attenuated inversion recovery (FLAIR) (27).

The FLAIR sequence consists of an inversion pulse at the beginning of the pulse sequence that tips the net longitudinal magnetization 180째 into the z plane.

The longitudinal magnetization then undergoes T1 relaxation to become positive. After the inversion time (TI), signal is generated using a 90째 RF pulse (described earlier in this review). The TI interval is chosen so that the CSF longitudinal magnetization recovers exactly at the time of the 90째 RF pulse, which ensures that the signal is not generated from CSF. Because CSF has a longer T1 than gray or white matter, the longitudinal magnetization of brain tissues recovers to a positive value prior to the 90째 pulse, thus generating the signal. FLAIR also utilizes a long TE to produce T2-hyperintense signals in fluid-filled lesions near the ventricles (21,27). However, recovery of longitudinal magnetization in brain tissue is incomplete and therefore reduces SNR. The FLAIR approach can also be used to suppress CSF signals in DTI by preparing the magnetization

10

SALMINEN ET AL.

with a 180° RF pulse prior to the conventional DTI pulse sequence (44,64). While several studies have reported that FLAIR increases the accuracy of DTI and DTI-based tractography measurements (17,64), it also reduces SNR, reduces the number of slices, increases scan time, and has limited utility at higher magnetic field strengths (e.g., > 3T) (8,53). Further, this technique is seldom utilized clinically, as FLAIRprepared DTI sequences require cardiac gating to avoid motion artifact from cardiac pulsations (53).

Sub-Voxel Modeling as a Solution to CSF PVEs Other previous efforts to resolve the problem of CSF PVEs have relied on fitting a tissue tensor and a CSF tensor (or CSF diffusion coefficient) to the DWI data to account for mixed diffusion signals within a voxel (53,65,66,69). The most common approach is referred to as the “free water elimination” (FWE) method of Pasternak et al. (65). This approach assumes that there are two subregions within a voxel that demonstrate characteristic tensor signals for brain tissue and free-water. By calculating separate tensors for CSF versus brain tissue, the CSF contamination can be controlled. This FWE method uses a single b value for diffusion weighting, and thus can be used to analyze existing data acquired with typical research DTI scan protocols. A variant of the FWE method was also developed to give better estimates of the free water component by acquiring and fitting multiple “shells” of DWI data, with each shell providing tensor information and having a different b value (66). This method also uses a different neighboring voxel regularization scheme. While effective (53), this multi-shell approach increases scan time significantly (66).

FWE imposes constraints to a regularization bitensor model fitting to yield continuity between tissue tensors of adjacent voxels. It has been stated that these FWE methods enable estimation of the free water fraction and the distinction between water from CSF versus water from vasogenic edema (66). Because the latter source of water signal is a result of physiologic changes (e.g., aging, stroke, etc.) rather than partial volume artifact, this method was thought to be suited to removing CSF signal in brain scans of older individuals who may have pathologic sources of tissue

water. Technically, the FWE approaches are limited by smoothing constraints that reduce sensitivity to subtle microstructural abnormalities (47) and therefore may not be ideal for certain research questions examining brain changes in the early stages of disease.

A significant aspect of the sub-voxel modeling approaches described above is the modeling of free water and tissue signal components, and the assumption that each component signals a non-monoexponential pattern of signal decay (57). The exponentiality of signal decay likely depends on the range of b values used and the dependence of tissue and CSF signal on the b value (22,33,52,57). One view postulates that true diffusion decay of brain parenchymal tissue is in fact non-monoexponential (34,51,52), independent of the CSF signal. A potential limitation of these approaches that is not commonly discussed is that CSF in sulci and ventricles often exhibits bulk flow and pulsatile motion (49) and therefore may have a more complex signal decay behavior compared to stationary water in a container. Gaussian diffusion, in which molecular water displacements have a Gaussian distribution, forms the basis for the ellipsoid model of diffusion.

Several techniques have been developed to measure non-Gaussian diffusion, such as q-space imaging (5), diffusion kurtosis imaging (34), and diffusion spectrum imaging (90). While each of these approaches has provided new insights into image analysis and our understanding of neurophysiology, the theory of diffusion-weighted signal decay in brain tissue has not gained categorical acceptance, possibly due to variations in the intra- and extracellular volume fractions used across methods (52).

Alternative Techniques to Suppress the CSF Signal As an alternative to modeling the CSF signal component in the voxel, the CSF signal can be suppressed using the diffusion-encoding gradients (as opposed to using FLAIR). In developing such a model-independent approach, our group recently examined CSF suppression and multiexponential decay in the gray matter of older individuals (75). We analyzed DWIiso images (computed as the geometric means of the DWIs) efficiently acquired at 4 b values based on the following pulse sequence: 5 acquisitions at

CSF SUPPRESSION METHODS FOR DTI

b~0, 3 perpendicular directions at b = 680 s/mm2, 24 intermediate directions at b = 996 s/mm2, and 4 tetrahedral directions at b = 1412 s/mm2 (75). To assess the potential effect of CSF PVEs on the decay curve and the exponential nature of the signal decay, signal intensities were graphed as a function of b values for several bilateral ROIs, including perisulcal gray matter (e.g., superior temporal gray matter), periventricular gray matter (e.g., caudate), and gray matter distant from CSF (e.g., putamen). Semilog plots of the DWIiso signal intensity (geometric means) versus b value in perisylvian gray matter revealed that the b~0 signal was above the straight-line fit of the three b ≥ 680 s/mm2 points (Figure 1). Further, the b ≥ 680 s/mm2 points were observed to follow a straight line, indicating that CSF contamination is a source of non-monoexponential decay in gray matter that can be suppressed with the removal of the b~0 data. Importantly, this result also indicates that the remaining data points from b ≥ 680 to 1412 s/mm2 followed a monoexponential pattern of decay, demonstrating that a tensor can be accurately fit to this range of b values without including b~0 data.

CSF suppression and tissue exponential decay were further tested by comparing MD that was calculated from the results of three separate tensor-fitting schemes: 1) the standard scheme in which all four b values were included; 2) the nobase scheme in which

11

only b~0 data were eliminated; and 3) the high-b scheme in which only the two highest b values were included. The results for the different schemes are compared in Figure 2. First, MD measured by the standard method was compared to MD from the nobase method in the right superior temporal gray matter (Figure 2a). Each data point in the scatterplot represents a different subject. The deviation from the line of identity is due to the CSF effect. A similar deviation occurred for the standard versus high-b methods (Figure 2b). In contrast, the nobase and high-b results fell on the line of identity (Figure 2c), which indicates that the CSF effect was eliminated and that the b values between 680-1412 s/mm2 sample the same exponential decay curve. The smallest difference across the three different fitting schemes was observed for the putamen (1%, d < .4), which was expected given its distal location to CSF areas.

Moderate differences were observed in the caudate between standard and nobase, and between standard and high-b methods (11%, d < .75). Even larger differences were observed in sulcal gray matter areas (15%, d > 1.0). Differences between nobase and high-b were negligible for nearly all brain regions (1-2%), and these minor differences were likely a result of the additional random noise caused by the narrower range of b values in the high-b analyses (note that all CSF correction methods have increased

Figure 1. Diffusion-weighted signal decay curve for right superior temporal gray matter (GM) in one subject from the study by Salminen et al. (25). The isotropic DWI (DWIiso) image was computed from the geometric mean of the DWIs at each of the four b values (b~0 and b= 680, 996, and 1412 s/mm2). The DWIiso signal intensity was measured from the geometric mean images using a ROI in superior temporal GM. The log of the DWIiso is graphed versus b value so that the monoexponentiality of signal decay can be assessed by the fit of the data to a straight line. The solid red line is fit to the three b ≥ 680 s/mm2 data points. The graph shows that the b~0 data point is artifactually above the dashed red line that is extrapolated from the fit to the other three data points. The DWIiso images at b~0 showed bright CSF signal, while the three DWIiso images with b ≥ 680 had absent CSF signal (not shown). Thus, the deviation in the b~0 data point indicates partial volume averaging with CSF in the adjacent sulci and Sylvian fissure. The observation that the three data points with b ≥ 680 fall on a straight line indicates monoexponential decay of gray matter after removing CSF effects.

The artifactual deviation in the b~0 data due to CSF effects is somewhat muted by the large ROI, which contains some voxels that are not adjacent to CSF (for voxels adjacent to CSF spaces, the effect is stronger). The log of the ventricular CSF signal deviated from linearity at all data points (not shown), indicating a more complex signal behavior in CSF. The signal intensity and its log are in arbitraty units [a.u.].

12

SALMINEN ET AL.

SNR). For example, the left putamen demonstrated a subtle negative percent change between nobase and high-b (-1.8%). Further study is needed to determine whether this very small difference is due to a subtle effect of CSF-like signals (e.g., Virchow-Robin spaces).

Similar phenomena can occur in white matter. A semilog graph of the isotropic DWI signal versus b value in white matter anterior to the temporal horns of

the lateral ventricle (Figure 3) shows deviation of the b~0 data point, while the b â&#x2030;Ľ 680 s/mm2 data points fall on a straight line. This effect is indicative of a CSF PVE in this white matter region and monoexponential decay in the underlying white matter parenchyma over the range of b values from 680 â&#x20AC;&#x201C; 1412 s/mm2.

This monoexponentiality is important because it enables accurate tensor measurement over a subset

Figure 2. Scatterplots demonstrating the difference between mean diffusivity (MD) measurements in superior temporal gray matter of the right hemisphere (rh.superiortemporal) using the three different schemes: standard (all four b values), nobase (no b~0 data), and highb (no b~0 data and no b â&#x2030;Ľ 680 s/mm2 data). The diagonal black dashed lines are lines of identity. Figures 2a and 2b show that the standard MD is overestimated with respect to the other measures. The agreement between nobase and high-b in Figure 2c indicates that nobase effectively corrects for CSF PVEs. In Figure 2c, the slope is 0.965 +/- 0.041, and the y-intercept is 0.0293 x 10-3 +/- 0.0505 x 10-3 (+/- 1.0 standard error in each case), indicating agreement in the nobase and hi-b measures. Note that MD values are in units of mm2/s. Each data point represents a ROI measurement for one participant in the study. Linear regression lines are shown (solid red), with regression equations and R2 statistics given as text.

CSF SUPPRESSION METHODS FOR DTI

of b values that exclude the b~0 data. It also enables tensor fitting using a reduced number of encoding directions at each b value, thus shortening the scan time.

Recently, Baron and Beaulieu (8) independently reported a similar approach to improve the accuracy of DTI tractography using a non-zero b-minimum (bmin = 425 s/mm2) and a relatively short TR (TR = 3.0 s). This study compared the effects of both parameters on fiber tracking. Specifically, they determined the effects on pathway volume and diffusion scalar metrics in pathways vulnerable to CSF PVEs (such as the fornix) and also tracts that are less sensitive to CSF (such as the superior longitudinal fasciculus, SLF). The combination of a short TR and bmin = 425 s/mm2 resulted in more than a 50% increase in pathway volume for the crura and body of the fornix, and a 14% increase in SLF volume. As a result, 30% higher FA and 36% lower MD were observed in the crura. These results are consistent with the trends that would be expected if CSF contamination of tissue voxels were reduced. Percentage increases were not reported using a non-zero b-minimum alone without a short TR. DTI accuracy was also improved for the body of the fornix and the SLF, though these improvements were less robust. Interestingly, using a non-zero b-minimum without adjusting TR produced nearly equivalent volumetric increases as

13

the combined model in both tracts. Furthermore, reducing TR alone did not significantly impact SLF measurements, suggesting a greater effect of b~0 data on CSF PVEs in white matter.

The significance of the above two methods that omit the acquisition or analysis of b~0 is very high.

CSF contamination significantly reduces the accuracy of DTI measurements and DTI-based tractography.

Artificially low FA values in voxels adjacent to CSF can bias DTI measurements or cause tractography to fail in these regions due to FA falling below the tracking threshold (8). DTI measurements and tractography of white matter fiber bundles, such as the fornix, cingulum, uncinate fasciculus, and corpus callosum, may be particularly sensitive to CSF PVEs given their anatomical proximity to CSF spaces.

Abnormalities in each of these structures have been identified in normal aging and in various neurological conditions (e.g., dementia, multiple sclerosis, schizophrenia, etc.) (31,43,96), and accurate DTI measurements are critical for understanding the implications of these structural alterations. The fornix is of particular interest in studies of neurodegenerative and neuropsychiatric disease, as it is a major white matter projection from the hippocampus that directly passes through the ventricles. As such, it is highly susceptible to CSF contamination and requires a corrective technique to remove CSF signal. Few

Figure 3. Semilog graph of the isotropic DWI signal (DWIiso) versus b value in white matter. Measurement, analysis, and graphical display as described in Figure 1, except that the ROI in Figure 3 is in white matter posterior to the left temporal pole. Partial volume averaging with CSF is evident by the upward deviation of the b~0 data point relative to the dashed black line, due to the adjacent temporal horn of the lateral ventricle. Like the gray matter ROI in Figure 1, the three data points with b â&#x2030;Ľ 680 s/mm2 fall on the straight solid red line, suggesting monoexponential decay over the b value range of 680-1412 s/mm2.

14

SALMINEN ET AL.

studies of the fornix have employed such a method, however, which is likely due to limited recognition of CSF PVEs and the limitations of prior corrective techniques in DTI. The outcomes reported by Baron and Beaulieu (8) indicate that CSF suppression using a non-zero b-minimum and shorter TR causes fewer voxels to be missed during tractography, translating to greater accuracy of diffusion measurements. Our study (75) also reveals that removal of b~0 data from the analysis results in more sensitive detection of age effects in gray matter of the right temporal lobe (Figure 2), and removes CSF effects from white matter regions adjacent to CSF spaces (Figure 3).

To date, there has been limited research on water diffusion in gray matter using CSF suppression techniques. Interpretation of diffusion measurements in gray matter is less straightforward compared to interpretations of white matter measurements due to greater isotropic diffusion and cellular heterogeneity inherent in gray matter. Deep gray matter areas are also occupied by fluid-filled Virchow-Robin spaces (VRSs) that contribute relatively high signal intensity on b~0 images and may alter overall voxel diffusion measurements. The results of our study offer resolution to CSF contamination of gray matter through the removal of b~0 data and tensor fitting of data ranging in b values from 680 to 1412 s/mm2 (75). We also postulate that our technique may be effective at suppressing water-like signal in VRSs in addition to CSF. Enlarged VRSs (>2mm) are commonly observed in the basal ganglia of older adults (97). In our opinion, it is still preferred to acquire b~0 data (at little cost in scan time) because it can be included in the analysis of regions not affected by CSF-like PVEs, thus increasing SNR. Collectively, the two methods described by Salminen et al. (75) and Baron and Beaulieu (8) can be considered no-b-zero (NBZ) approaches to CSF suppression. Unlike the previously discussed methods, NBZ methods do not depend on a model of CSF or tissue signal (i.e., are model-independent), and CSF is suppressed regardless of CSF flow pattern or T1 value.

The observation that CSF contamination was evident in both NBZ studies suggests that outcomes of earlier investigations utilizing DTI have likely been impacted to some degree by CSF PVEs in certain

brain regions. This is particularly true in studies of older individuals in whom age-related physiologic factors (e.g., ventricular enlargement, sulcal expansion) increase the propensity for CSF PVEs. The CSF suppression techniques proposed by our group (75) and Baron and Beauleiu (8) offer promising alternatives to former correction methods by robustly reducing the CSF signal to negligible levels in both gray and white matter, thereby improving the biological approximation of the DTI measurements. As such, there are several implications for using this technique in conjunction with numerous imaging modalities and data processing schemes. For the remainder of this review, we will discuss potential uses of CSFsuppressed DTI in general, and the NBZ approaches in particular.

NEW APPLICATIONS OF CSF-SUPPRESSED DTI AND FUTURE INNOVATION To date, CSF-suppressed DTI using a model-free NBZ approach has been utilized in only two studies (8,75), both of which focused on older adults without neurologic disease. The potential for CSF suppression to improve the accuracy of DTI metrics in neurological populations represents an important direction for future research. Small vessel ischemic disease is common among older individuals and is related to poorer cognitive performance in otherwise healthy adults, which may progress to vascular dementia in some individuals (61). Further, vascular disease has been hypothesized to represent a risk factor for the subsequent development of Alzheimerâ&#x20AC;&#x2122;s disease (AD) (36,50). CSF-suppressed DTI, particularly using a NBZ approach, may help identify key mechanisms of neurological dysfunction in these clinical conditions.

Similarly, while standard DTI has been utilized to examine multiple sclerosis and HIV-associated brain dysfunction, the neuroimaging signatures associated with these conditions are significantly complicated by acute inflammatory fluctuations (16,71,74), resulting in variable outcomes across studies. Application of CSF-suppressed DTI has the potential to more accurately characterize tissue microstructure in these conditions to improve clinical assessment and patient care strategies.

CSF SUPPRESSION METHODS FOR DTI

Multimodal MR Imaging An additional opportunity for future applications of CSF-suppressed DTI includes multimodal imaging. The neuroimaging field is evolving towards the integration of multiple forms of imaging outcomes from various modalities. Multimodal imaging capitalizes on the strengths of different modalities that are uniquely valuable at detecting abnormalities in brain tissue across multiple neural systems. While many studies have applied different MR imaging sequences in parallel within a population (e.g., anatomical MRI, DTI, magnetic resonance spectroscopy (MRS), and resting state functional MRI (rs-fMRI)) (25,30,32,79,81,94), integration of outcomes from different MRI modalities into a unified and meaningful explanatory model of brain structure/function represents a future innovation in brain imaging.

Ajilore et al. (1) revealed the relative power of multimodal MR imaging compared to single modality imaging by integrating rs-fMRI with DTI to create a functional-by-structural hierarchical (FSH) map. This study revealed clinically relevant brain abnormalities that were not evident when either DTI or rs-fMRI outcomes were analyzed separately. These results demonstrate enhanced sensitivity of multimodal imaging to detect alterations in the brain connectome over single modality imaging. Implementing CSF-suppressed DTI has the potential to enhance FSH mapping by removing CSF contamination from voxels along white matter track lines that are adjacent to CSF spaces, resulting in a more accurate depiction of tract anatomy and more accurate measurement of tract metrics, such as tract anisotropy and mean fiber bundle length.

Machine Learning Another area of innovation involves machine learning approaches. Machine learning is an area of artificial intelligence that can detect spatially distributed patterns within a group (48). Earlier imaging studies utilized machine learning techniques such as support vector machine (SVM) analysis to classify patterns of brain activation across individuals (62,67,85). New applications of machine learning include pattern recognition through high-level, multidimensional clustering. The advantage of this

15

technique is that it automatically detects regularities in imaging data across disparate datasets (77). These regularities can be used to generate predictions for ascribing cognitive states to patterns of brain activity. As such, machine learning allows for automated pattern recognition that can be used as a diagnostic tool for complex diseases (62). SVM has been used to facilitate differential diagnoses of complex diseases (38,40), distinguish mild forms of AD from aging, distinguish individuals with clinically asymptomatic conditions from normal controls (39,73), define behavioral subtypes of clinical conditions (24), and reliably fit functional activity signals to genetic networks (20,89,93).

Machine learning is not without limitations.

Specifically, while machine learning is designed to manage complex and heterogeneous data, the method becomes less reliable when there are image artifacts or variations in imaging acquisition procedures (23,83).

Machine learning also requires large data sets, often acquired at multiple project centers. CSF-suppressed DTI may be one solution for reducing the variance of multicenter DTI data, as some of the variance could be a result of CSF PVEs and inconsistent CSFcorrection strategies. Studies are needed to confirm whether CSF-suppressed DTI effectively stabilizes the variance of DTI metrics and improves the accuracy of future prediction models.

CONCLUSIONS In the midst of remarkable technological advancement and innovation, our ability to improve health and the human condition has increased dramatically, but the human brain remains the greatest mystery in medical science. While advances in medical oncology, pathobiology, immunology, and microbiology provide demonstrable benchmarks toward the understanding and treatment of diseases such as cancer, diabetes, HIV, ebola, etc., the understanding and treatment of many brain disorders remain elusive.

Noninvasive neuroimaging is one of the most powerful tools in our arsenal to reveal biological signatures of human brain pathology. CSF-suppressed DTI, and in particular the NBZ approaches, offer a new frontier of imaging research that may yield measurements of increased biological value. By reducing signal artifact

16

SALMINEN ET AL.

and measurement bias, we can remove more barriers of uncertainty, which will allow us to unveil the dynamic physiological and microstructural mechanisms of the normal and disordered brain. Pushing the conventions of neuroimaging technology will continue to boost the value of data it yields and help solve the mysteries of this remarkably complex organ.

ACKNOWLEDGMENTS This work was supported by the National Institutes of Health/National Institute of Neurological Disorders and Stroke grant numbers R01 NS052470 and R01 NS039538 and the National Institutes of Health/ National Institute of Mental Health grant number R21 MH105822. Recruitment database searches were supported in part by the National Institutes of Health/ National Center for Research Resources grant number UL1 TR000448. The authors declare no conflicts of interest.

REFERENCES 1.

2.

3.

4.

5.

6.

7.

8.

Ajilore, O.; Zhan, L.; GadElkarim, J.; Zhang, A.; Feusner, J. D.; Yang, S.; Thompson, P. M.; Kumar, A.; Leow, A. Constructing the resting state structural connectome. Front. Neuroinform. 7:1-8; 2013.

Alexander, A. L.; Hasan, K. M.; Lazar, M.; Tsuruda, J. S.; Parker, D. L. Analysis of partial volume effects in diffusion‐tensor MRI. Magn. Reson. Med. 45(5):77080; 2001.

Alexander, A. L.; Lee, J. E.; Lazar, M.; Field, A. S. Diffusion tensor imaging of the brain. Neurotherapeutics 4(3):316-329; 2007.

Amiry-Moghaddam, M.; Ottersen, O. P. The molecular basis of water transport in the brain. Nat. Rev. Neurosci. 4(12):991-1001; 2003.

Assaf, Y.; Mayzel-Oreg, O.; Gigi, A.; Ben-Bashat, D.; Mordohovitch, M.; Verchovsky, R.; Reider-Groswasser, I. I.; Hendler, T.; Graif, M.; Cohen, Y.; Korczyn, A. D. High b value q-space-analyzed diffusion MRI in vascular dementia: a preliminary study. J. Neurol. Sci. 203:235-239; 2002.

Ballester, M. A.; Zisserman, A.; Brady, M. Segmentation and measurement of brain structures in MRI including confidence bounds. Med. Image Anal. 4(3):189-200; 2000.

Ballester, M. A.; Zisserman, A. P.; Brady, M. Estimation of the partial volume effect in MRI. Med. Image Anal.

6(4):389-405; 2002.

Baron, C. A.; Beaulieu, C. Acquisition strategy to reduce cerebrospinal fluid partial volume effects for

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

improved DTI tractography. Magn. Reson. Med. 73(3):1075-1084; 2015.

Basser, P. J.; Mattiello, J.; LeBihan, D. MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1):259; 1994.

Basser, P. J. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed. 8(7):333-44; 1995.

Bennett, I. J.; Madden, D. J.; Vaidya, C. J.; Howard, D. V.; Howard, J. H. Age‐related differences in multiple measures of white matter integrity: a diffusion tensor imaging study of healthy aging. Hum. Brain Mapp.

31(3):378-390; 2010.

Berlot, R.; Metzler-Baddeley, C.; Jones, D. K.; O’Sullivan, M. J. CSF contamination contributes to apparent microstructural alterations in mild cognitive impairment. Neuroimage 92:27-35; 2014.

Bottomley, P. A.; Foster, T. H.; Argersinger, R. E.; Pfeifer, L. M. A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1–100 MHz: dependence on tissue type, NMR frequency, temperature, species, excision, and age. J. Med. Phys. 11(4):425-448; 1984.

Brown, M. A.; Semelka, R. C. MRI: basic principles and applications. Hoboken, NJ: John Wiley & Sons; 2011.

Burzynska, A. Z.; Preuschhof, C.; Bäckman, L.; Nyberg, L.; Li, S. C.; Lindenberger, U.; Heekeren, H. R. Agerelated differences in white matter microstructure: region-specific patterns of diffusivity. Neuroimage 49(3):2104-2112; 2010.

Chang, L.; Wong, V.; Nakama, H.; Watters, M.; Ramones, D.; Miller, E. N.; Cloak, C.; Ernst, T. Greater than age-related changes in brain diffusion of HIV patients after 1 year. J. Neuroimmune Pharmacol.

3(4):265-274; 2008.

Chou, M. C.; Lin, Y. R.; Huang, T. Y.; Wang, C. Y.; Chung, H. W.; Juan, C. J.; Chen, C. Y. FLAIR diffusion-tensor MR tractography: comparison of fiber tracking with conventional imaging. AJNR 26(3):591597; 2005.

Conturo, T. E.; McKinstry, R. C.; Aronovitz, J. A.; Neil, J. J. Diffusion MRI: precision, accuracy and flow effects. NMR Biomed. 8(7):307-32; 1995.

Conturo, T. E.; McKinstry, R. C.; Akbudak, E.; Robinson, B. H. Encoding of anisotropic diffusion with tetrahedral gradients: a general mathematical diffusion formalism and experimental results. Magn.

Reson. Med. 35(3):399-412; 1996.

Da Mota, B.; Tudoran, R.; Costan, A.; Varoquaux, G.; Brasche, G.; Conrod, P.; Lemaitre, H.; Paus, T.; Rietschel, M.; Frouin, V.; Poline, J. B. Machine learning patterns for neuroimaging-genetic studies in the cloud.

Front. Neuroinform. 8:31; 2014.

CSF SUPPRESSION METHODS FOR DTI

21. De Coene, B.; Hajnal, J. V.; Gatehouse, P.; Longmore, D. B.; White, S. J., Oatridge, A.; Pennock, J. M.; Young, I. R.; Bydder, G. M. MR of the brain using fluid-attenuated inversion recovery (FLAIR) pulse sequences.

Am. J. Neuroradiol. 13(6):1555-64; 1992.

22. Dietrich, O.; Biffar, A.; Baur-Melnyk, A.; Reiser, M. F. Technical aspects of MR diffusion imaging of the body. Eur. J. Radiol. 76(3):314-322; 2010.

23. Dyrba, M.; Barkhof, F.; Fellgiebel, A.; Filippi, M.; Hausner, L.; Hauenstein, K.; Kirste, T.; Teipel, S. J. Predicting prodromal Alzheimer’s disease in subjects with mild cognitive impairment using machine learning classification of multimodal multicenter diffusion‐tensor and magnetic resonance imaging data.

J. Neuroimaging 5(25):738-747; 2015.

24. Gould, I. C.; Shepherd, A. M.; Laurens, K. R.; Cairns, M. J.; Carr, V. J.; Green, M. J. Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: a support vector machine learning approach.

Neuroimage Clin. 6:229-36; 2014.

25. Greicius, M. D.; Supekar, K.; Menon, V.; Dougherty, R. F. Resting-state functional connectivity reflects structural connectivity in the default mode network.

Cereb. Cortex. 19(1):72-8; 2009.

26. Hagmann, P.; Jonasson, L.; Maeder, P.; Thiran, J. P.; Wedeen, V. J.; Meuli, R. Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond.

Radiographics 26(1): S205-223; 2006.

27. Hajnal, J. V.; Bryant, D. J.; Kasuboski, L.; Pattany, P. M.; De Coene, B.; Lewis, P. D.; Pennock, J. M.; Oatridge, A.; Young, I. R.; Bydder, G. M. Use of fluid attenuated inversion recovery (FLAIR) pulse sequences in MRI of the brain. J. Comput. Assist. Tomo. 16(6):841-4; 1992.

28. Hansen, M. B.; Jespersen, S. N.; Leigland, L. A.; Kroenke, C. D. Using diffusion anisotropy to characterize neuronal morphology in gray matter: the orientation distribution of axons and dendrites in the NeuroMorpho.org database. Front. Integr. Neurosci.

7(31):1-13; 2013.

29. Hashemi, R. H.; Bradley, W. G.; Lisanti, C. J. MRI: the basics. Philadelphia, PA: Lippincott Williams & Wilkins; 2012.

30. He, Y.; Wang, L.; Zang, Y.; Tian, L.; Zhang, X.; Li, K.; Jiang, T. Regional coherence changes in the early stages of Alzheimer’s disease: a combined structural and resting-state functional MRI study. Neuroimage 35(2):488-500; 2007.

31. Hecke, W. V.; Nagels, G.; Leemans, A.; Vandervliet, E.; Sijbers, J.; Parizel, P. M. Correlation of cognitive dysfunction and diffusion tensor MRI measures in patients with mild and moderate multiple sclerosis.

J. Magn. Reson. Im. 31(6):1492-1498; 2010.

17

32. Honey, C. J.; Sporns, O.; Cammoun, L.; Gigandet, X.; Thiran, J. P.; Meuli, R.; Hagmann, P. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. U.S.A. 106(6):2035-2040; 2009.

33. Hui, E. S.; Cheung, M. M.; Chan, K. C.; Wu, E. X. B-value dependence of DTI quantitation and sensitivity in detecting neural tissue changes. Neuroimage 49(3):2366-2374; 2010.

34. Jensen, J. H.; Helpern, J. A.; Ramani, A.; Lu, H.; Kaczynski, K. Diffusional kurtosis imaging: the quantification of non‐Gaussian water diffusion by means of magnetic resonance imaging. Magn. Reson. Med. 53(6):1432-1440; 2005.

35. Jones, D. K.; Horsfield, M. A.; Simmons, A. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn. Reson.

Med. 42(3):515-525; 1999.

36. Kalaria, R. N. Small vessel disease and Alzheimer’s dementia: pathological considerations. Cerebro-vasc.

Dis. 13(2):48-52; 2002.

37. Kalender, W. A. X-ray computed tomography. Phys.

Med. Biol. 51(13):R29; 2006.

38. Klöppel, S.; Stonnington, C. M.; Barnes, J.; Chen, F.; Chu, C.; Good, C. D.; Mader, I.; Mitchell, L. A.; Patel A. C.; Roberts, C. C.; Fox, N. C. Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method. Brain 131(11):2969-74; 2008.

39. Klöppel, S.; Chu, C.; Tan, G. C.; Draganski, B.; Johnson, H.; Paulsen, J. S.; Kienzle, W.; Tabrizi, S. J.; Ashburner, J.; Frackowiak, R. S. Automatic detection of preclinical neurodegeneration; presymptomatic Huntington disease. Neurology 72(5):426-31; 2009.

40. Klöppel, S.; Abdulkadir, A.; Jack, C. R.; Koutsouleris, N.; Mourão-Miranda, J.; Vemuri, P. Diagnostic neuroimaging across diseases. Neuroimage 61(2):457-63; 2012.

41. Koenig, S. H. Classes of hydration sites at protein-water interfaces: the source of contrast in magnetic resonance imaging. Biophys. J. 69(2):593-603; 1995.

42. Koo, B. B.; Hua, N.; Choi, C. H.; Ronen, I.; Lee, J. M.; Kim, D. S. A framework to analyze partial volume effect on gray matter mean diffusivity measurements.

Neuroimage 44(1):136-44; 2009.

43. Kubicki, M.; Park, H.; Westin, C. F.; Nestor, P. G.; Mulkern, R. V.; Maier, S. E.; Niznikiewicz, M.; Connor, E. E.; Levitt, J. J.; Frumin, M. M.; Kikinis, R. DTI and MTR abnormalities in schizophrenia: analysis of white matter integrity. Neuroimage 26(4):1109-1118; 2005.

44. Latour, L. L.; Warach, S. Cerebral spinal fluid contamination of the measurement of the apparent diffusion coefficient of water in acute stroke. Magn. Reson. Med.

18

SALMINEN ET AL.

48(3):478-486; 2002.

45. Lauterbur, P. C. Image formation by induced local interactions: examples employing nuclear magnetic resonance. Nature 242(5394):190-191; 1973.

46. Le Bihan, D.; Breton, E.; Lallemand, D.; Grenier, P.; Cabanis, E.; Laval-Jeantet, M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161(2):401-407; 1986.

47. Lee, J. E.; Chung, M. K.; Lazar, M.; DuBray, M. B.; Kim, J.; Bigler, E. D.; Lainhart, J. E.; Alexander, A. L. A study of diffusion tensor imaging by tissue-specific, smoothing-compensated voxel-based analysis.

Neuroimage 44(3):870-83; 2009.

48. Lemm, S.; Blankertz, B.; Dickhaus, T.; Müller, K. R. Introduction to machine learning for brain imaging.

Neuroimage 56(2):387-99; 2011.

49. Linninger, A. A.; Tsakiris, C.; Zhu, D. C.; Xenos, M.; Roycewicz, P.; Danziger, Z.; Penn, R. Pulsatile cerebrospinal fluid dynamics in the human brain. IEEE Trans. Biomed. Eng. 52(4):557-565; 2005.

50. Luchsinger, J. A.; Reitz, C.; Honig, L. S.; Tang, M. X.; Shea, S.; Mayeux, R. Aggregation of vascular risk factors and risk of incident Alzheimer disease. Neurology 65(4):545-551; 2005.

51. Maier, S. E.; Vajapeyam, S.; Mamata, H.; Westin, C. F.; Jolesz, F. A.; Mulkern, R. V. Biexponential diffusion tensor analysis of human brain diffusion data. Magn.

Reson. Med. (2):321-330; 2004.

52. Maier, S. E.; Mulkern, R. V. Biexponential analysis of diffusion-related signal decay in normal human cortical and deep gray matter. J. Magn. Reson. Im. 26(7):897-904; 2008.

53. Metzler-Baddeley, C.; O’Sullivan, M. J.; Bells, S.; Pasternak, O.; Jones, D. K. How and how not to correct for CSF-contamination in diffusion MRI. Neuroimage 59(2):1394-1403; 2012.

54. Mitchell, M. R.; Conturo, T. E.; Gruber, T. J.; Jones, J. P. Two computer models for selection of optimal magnetic resonance imaging (MRI) pulse sequence timing. Invest. Radiol. 19(5):350-60; 1984.

55. Mori, S.; Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research.

Neuron. 51(5):527-39; 2006.

56. Moseley, M. Diffusion tensor imaging and aging–a review. NBM 15(7‐8):553-560; 2002.

57. Mulkern, R. V.; Haker, S. J.; Maier, S. E. On high b diffusion imaging in the human brain: ruminations and experimental insights. J. Magn. Reson. Im. 27(8):11511162; 2009.

58. Nakada, T. Clinical application of high and ultra highfield MRI. Brain Dev. 29(6):325-335; 2007.

59. Ni, J. M.; Chen, S.; Liu, J. J.; Huang, G.; Shen, T. Z.;

60.

61.

62.

63.

64.

65.

66.

67.

68.

69.

70.

71.

Chen, X. R. Regional diffusion changes of cerebral grey matter during normal aging—a fluid-inversion prepared diffusion imaging study. Eur. J. Radiol.

75(2):134-8; 2010.

Nir, T. M.; Jahanshad, N.; Villalon-Reina, J. E.; Toga, A. W.; Jack, C. R.; Weiner, M. W.; Thompson, P. M. Effectiveness of regional DTI measures indistinguishing Alzheimer’s disease, MCI, and normal aging.

Neuroimage Clin. 3:180-195; 2013.

O’Brien J. T.; Erkinjuntti, T.; Reisberg, B.; Roman, G.; Sawada, T.; Pantoni, L.; Bowler, J. V.; Ballard, C.; DeCarli, C.; Gorelick, P. B.; Rockwood, K. Vascular cognitive impairment. Lancet Neurol. 2(2):89-98; 2003.

Orrù, G.; Pettersson-Yeo, W.; Marquand, A. F.; Sartori, G.; Mechelli, A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobeh. Rev. 36(4):1140-1152; 2012.

Papadakis, N. G.; Murrills, C. D.; Hall, L. D.; Huang, C. L.; Carpenter, T. A. Minimal gradient encoding for robust estimation of diffusion anisotropy. J. Magn.

Reson. Im. 18(6):671-679; 2000.

Papadakis, N. G.; Martin, K. M.; Mustafa, M. H.; Wilkinson, I. D.; Griffiths, P. D.; Huang, C. L.; Woodruff, P. W. Study of the effect of CSF suppression on white matter diffusion anisotropy mapping of healthy human brain. Magn. Reson. Med. 48(2):394398; 2002.

Pasternak, O.; Sochen, N.; Gur, Y.; Intrator, N.; Assaf, Y. Free water elimination and mapping from diffusion MRI. Magn. Reson. Med. 62(3):717-730; 2009.

Pasternak, O.; Shenton, M. E.; Westin, C. F. Estimation of extracellular volume from regularized multi-shell diffusion MRI. Med. Image Comput. Comput. Assist.

Interv. 15(2):305-312; 2012.

Pereira, F.; Mitchell, T.; Botvinick, M. Machine learning classifiers and fMRI: a tutorial overview.

Neuroimage 45(1):S199-209; 2009.

Pfefferbaum, A.; Adalsteinsson, E.; Rohlfing, T.; Sullivan, E. V. Diffusion tensor imaging of deep gray matter brain structures: effects of age and iron concentration. Neurobiol. Aging 31(3):482-93; 2010.

Pierpaoli, C.; Jones, D. K. Removing CSF contamination in brain DT-MRIs by using a two-compartment tensor model. Proc. Int. Soc. Magn. Reson. Med. 11:1215; 2004.

Provenzale, J. M.; Engelter, S. T.; Petrella, J. R.; Smith, J. S.; MacFall, J. R. Use of MR exponential diffusion-weighted images to eradicate T2 “shine-through” effect. American J. Roentgenol. 172(2):537-9; 1999.

Ragin, A. B.; Wu, Y.; Ochs, R.; Scheidegger, R.; Cohen, B. A.; Edelman, R. R.; Epstein, L. G.; McArthur, J.

72.

73.

74.

75.

76.

77.

78.

79.

80.

CSF SUPPRESSION METHODS FOR DTI Biomarkers of neurological status in HIV infection: a 3‐year study. Proteomics Clin. Appl. 4(3):295-303; 2010.

Rashid, W.; Hadjiprocopis, A.; Griffin, C. M.; Chard, D. T.; Davies, G. R.; Barker, G. J.; Tofts, P. S.; Thompson, A. J.; Miller, D. H. Diffusion tensor imaging of early relapsing-remitting multiple sclerosis with histogram analysis using automated segmentation and brain volume correction. Mult. Scler. 10(1):9-15; 2004.

Rick-Jackson, A.; Stoffers, D.; Sheldon, S.; Kuperman, J.; Dale, A.; Goldstein, J.; Corey-Bloom, J.; Poldrack, R. A.; Aron, A. R. Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington’s disease using machine learning techniques. Neuroimage 56(2):788-96; 2011.

Roosendaal, S. D.; Geurts, J. J.; Vrenken, H.; Hulst, H. E.; Cover, K. S.; Castelijns, J. A.; Pouwels, P. J.; Barkhof, F. Regional DTI differences in multiple sclerosis patients. Neuroimage 44(4):1397-1403; 2009.

Salminen, L. E.; Conturo, T. E.; Laidlaw, D. H.; Cabeen, R. P.; Akbudak, E.; Lane, E. M.; Heaps, J. M.; Bolzenius, J. D.; Baker, L. M.; Cooley, S.; Scott, S.; Cagle, L. M.; Phillips, S.; Paul, R. H. Regional age differences in gray matter diffusivity among healthy older adults. Brain Imag. Behav. 10(1):203-211; 2016.

Salminen, L. E.; Schofield, P. R.; Pierce, K. D.; Luo, X.; Zhao, Y.; Laidlaw, D. H.; Cabeen, R. P.; Conturo, T. E.; Lane, E. M.; Heaps, J. M.; Bolzenius, J. D., Baker, L. M.; Cooley, S. A.; Scott, S.; Cagle, L. M.; Paul, R. H. Genetic markers of cholesterol transport and gray matter diffusion: a preliminary study of the CETP I405V polymorphism. J. Neural Transm. 122(11):1581-92; 2015.

Schrouff, J.; Cremers, J.; Garraux, G.; Baldassarre, L.; Mourão-Miranda, J.; Phillips, C. Localizing and comparing weight maps generated from linear kernel machine learning models. In: 3rd Workshop on Pattern Recognition in Neuroimaging (PRNI 2013).

Philadelphia, PA: IEEE Computer Society Conference Publishing Services; 2013:124-127.

Shimony, J. S.; McKinstry, R. C.; Akbudak, E.; Aronovitz, J. A.; Snyder, A. Z.; Lori, N. F.; Cull, T. S.; Conturo, T. E. Quantitative diffusion-tensor anisotropy brain MR imaging: normative human data and anatomic analysis. Radiology 212(3):770-84; 1999.

Skudlarski, P.; Jagannathan, K.; Anderson, K.; Stevens, M. C.; Calhoun, V. D.; Skudlarska, B. A.; Pearlson, G. Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol. Psychiatry 68(1):61-69; 2010.

Stanisz, G. J.; Odrobina, E. E.; Pun, J.; Escaravage, M.; Graham, S. J.; Bronskill, M. J.; Henkelman, R. M. T1, T2 relaxation and magnetization transfer in tissue at 3T. Magn. Reson. Med. 54(3):507-512; 2005.

19

81. Steel, R. M.; Bastin, M. E.; McConnell, S.; Marshall, I.; Cunningham-Owens, D. G.; Lawrie, S. M.; Johnstone, E. C.; Best, J. J. Diffusion tensor imaging (DTI) and proton magnetic resonance spectroscopy (1 H MRS) in schizophrenic subjects and normal controls. Psychiat.

Research: Neuroimag. 106(3):161-70; 2001.

82. Teipel, S. J.; Meindl, T.; Wagner, M.; Stieltjes, B.; Reuter, S.; Hauenstein, K. H.; Filippi, M.; Ernemann, U.; Reiser, M. F.; Hampel, H. Longitudinal changes in fiber tract integrity in healthy aging and mild cognitive impairment: a DTI follow-up study. J. Alzheimers Dis. 22(2):507-522; 2009.

83. Teipel, S. J.; Reuter, S.; Stieltjes, B.; Acosta-Cabronero, J.; Ernemann, U.; Fellgiebel, A.; Filippi, M.; Frisoni, G.; Hentschel, F.; Jessen, F.; Klöppel, S. Multicenter stability of diffusion tensor imaging measures: a European clinical and physical phantom study. Psychiatry Res. Neuroimaging 94(3):363-371; 2011.

84. Turner, R.; Le Bihan, D.; Maier, J.; Vavrek, R.; Hedges, L. K.; Pekar, J. Echo-planar imaging of intravoxel incoherent motion. Radiology 177(2):407-414; 1990.

85. Vapnik, V. N. The nature of statistical learning theory.

New York, NY: Springer; 2000.

86. Vaughan, J. T.; Adriany, G.; Snyder, C. J.; Tian, J.; Thiel, T.; Bolinger, L.; Liu, H.; DelaBarre, L.; Ugurbil, K. Efficient high‐frequency body coil for high‐field MRI. Magn. Reson. Med. 52(4):851-859; 2004.

87. Vos, S. B.; Viergever, M.A.; Leemans, A. The anisotropic bias of fractional anisotropy in anisotropically acquired DTI data. Proc. Int. Soc. Mag. Reson. Med. 19:1945; 2011.

88. Vos, S. B.; Jones, D. K.; Viergever, M. A.; Leemans, A. Partial volume effect as a hidden covariate in DTI analyses. Neuroimage 55(4):1566-1576; 2011.

89. Wang, H.; Nie, F.; Huang, H.; Risacher, S. L.; Saykin, A. J.; Shen, L. Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics 28(12):i12736; 2012.

90. Wedeen, V. J.; Hagmann, P.; Tseng, W. Y.; Reese, T. G.; Weisskoff, R. M. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging.

Magn. Reson. Med. 54(6):1377-1386; 2005.

91. Weishaupt, D.; Köchli, V. D.; Marincek, B. How does MRI work?: an introduction to the physics and function of magnetic resonance imaging. Heidelberg, Germany: Springer Science & Business Media; 2008.

92. Wesbey, G. E.; Moseley, M. E.; Ehman, R. L. Translational molecular self-diffusion in magnetic resonance imaging: II. measurement of the self-diffusion coefficient. Invest. Radiol. 19(6):491-8; 1984.

93. Yang, H.; Liu, J.; Sui, J.; Pearlson, G.; Calhoun, V. D. A

20

SALMINEN ET AL.

hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia. Front. Hum. Neurosci. 4:192; 2010.

94. Ystad, M.; Hodneland, E.; Adolfsdottir, S.; Haász, J.; Lundervold, A. J.; Eichele, T.; Lundervold, A. Corticostriatal connectivity and cognition in normal aging: a combined DTI and resting state fMRI study.

Neuroimage 55(1):24-31; 2011.

95. Zacharopoulos, N. G.; Narayana, P. A. Selective measurement of white matter and gray matter diffusion trace values in normal human brain. Medical physics 25(11):2237-41; 1998.

96. Zhang, Y.; Schuff, N.; Du, A. T.; Rosen, H. J.; Kramer, J. H.; Gorno-Tempini, M. L.; Miller, B. L.; Weiner, M. W. White matter damage in frontotemporal dementia and Alzheimer’s disease measured by diffusion MRI. Brain 132(9):2579-2592; 2009.

97. Zhu, Y. C.; Dufouil, C.; Mazoyer, B.; Soumaré, A.; Ricolfi, F.; Tzourio, C.; Chabriat, H. Frequency and location of dilated Virchow-Robin spaces in elderly people: a population-based 3D MR imaging study.

Am. J. Neuroradiol. 32(4):709-713; 2011.

Technology and Innovation, Vol. 18, pp. 21-29, 2016 Printed in the USA. All rights reserved.

Copyright © 2016 National Academy of Inventors.

ISSN 1949-8241 • E-ISSN 1949-825X http://dx.doi.org/10.21300/18.1.2016.21 www.technologyandinnovation.org

APPLICATION OF A NOVEL QUANTITATIVE TRACTOGRAPHY-BASED ANALYSIS OF DIFFUSION TENSOR IMAGING TO EXAMINE FIBER BUNDLE LENGTH IN HUMAN CEREBRAL WHITE MATTER Laurie M. Baker1, Ryan P. Cabeen2, Sarah Cooley1, David H. Laidlaw2, Robert H. Paul1,3 1

Department of Psychology, University of Missouri – Saint Louis, St. Louis, MO, USA 2 Computer Science Department, Brown University, Providence, RI, USA 3 Missouri Institute of Mental Health, St. Louis, MO, USA

This paper reviews basic methods and recent applications of length-based fiber bundle analysis of cerebral white matter using diffusion magnetic resonance imaging (dMRI). Diffusion weighted imaging (DWI) is a dMRI technique that uses the random motion of water to probe tissue microstructure in the brain. Diffusion tensor imaging (DTI) is an extension of DWI that measures the magnitude and direction of water diffusion in cerebral white matter, using either voxel-based scalar metrics or tractography-based analyses. More recently, quantitative tractography based on diffusion tensor imaging (qtDTI) technology has been developed to help quantify aggregate structural anatomical properties of white matter fiber bundles, including both scalar metrics of bundle diffusion and more complex morphometric properties, such as fiber bundle length (FBL). Unlike traditional scalar diffusion metrics, FBL reflects the direction and curvature of white matter pathways coursing through the brain and is sensitive to changes within the entire tractography model. In this paper, we discuss applications of this approach to date that have provided new insights into brain organization and function. We also discuss opportunities for improving the methodology through more complex anatomical models and potential areas of new application for qtDTI. Key words: Diffusion tensor imaging; White matter; Quantitative tractography; Aging

INTRODUCTION Advances in diffusion weighted imaging (DWI) technology have allowed researchers to characterize the structural integrity of white matter tissue.

Diffusion tensor imaging (DTI) is an extension of DWI utilized to non-invasively examine neuronal tracts to quantitatively measure white matter integrity (1,22,34,37,46). Highly advanced DTI methods have

been developed in recent years and have significantly improved the utility of diffusion tensor measurements to detect subtle white matter changes in both healthy and diseased populations (13-14,17,39-41).

One example includes the integration of quantitative tractography based on diffusion tensor imaging (qtDTI) technology that has enhanced our ability to examine specific detail about the direction and

_____________________

Accepted December 10, 2015.

Address correspondence to Laurie M. Baker, Department of Psychology, University of Missouri – Saint Louis, One University Boulevard, Stadler Hall, Saint Louis, MO 63121, USA. Tel: +1 (314) 566-3761; E-mail: lauriebaker@umsl.edu

21

22

BAKER ET AL.

curvature of white matter pathways using in vivo imaging (17). This method is highly sensitive to white matter changes within entire tracts and, therefore, may be more advantageous than methods that involve placing regions of interest on two-dimensional scalar DTI parameter maps (17). In this review, we describe the fundamentals of the diffusion tensor model and qtDTI technology. We then review the existing literature on length-based metrics using qtDTI, followed by a discussion of the strengths and limitations of qtDTI. Finally, a brief review of future applications is provided.

DIFFUSION MR TECHNIQUES DTI Physical Basis DTI is a noninvasive magnetic resonance imaging (MRI) technology that measures water diffusion at each voxel in the brain. Water molecules diffuse differently along tissues depending on tissue microstructure and the presence of anatomical barriers.

One simple and useful way to characterize diffusion at a location in the brain is along a spectrum between isotropic and anisotropic. Diffusion that is highly similar in all directions (i.e., isotropic diffusion) is typically observed in grey matter and cerebrospinal fluid. By contrast, directionally dependent diffusion (i.e., anisotropic diffusion) is observed in white matter due to the linear organization of the fiber tracts. Water within these tracts preferentially diffuses in one direction because physical barriers such as axonal walls and myelin restrict water movement in other directions (5,24,47,48). Neuropathological mechanisms associated with multiple conditions, including subcortical ischemia, neurodegeneration, and traumatic brain injury, cause reductions in the linear organization of white matter pathways with corresponding reductions in linear anisotropy (5,19,48,52). DTI is sensitive to these changes in linear anisotropy even when white matter integrity appears healthy based on structural neuroimaging methods (referenced as normal appearing white matter) (4,30), making DTI a powerful in vivo imaging method for the examination of the microstructural integrity of white matter.

DTI Scalar Metrics A symmetric 3x3 diffusion tensor characterizes water diffusion in brain tissues. This model represents the diffusion pattern with a second-order tensor that can be decomposed into three non-negative eigenvalues and three eigenvectors that describe the magnitude and orientation of water diffusion in each voxel (Figure 1). Eigenvalues describe the shape and size of the tensor, independent of orientation, while eigenvectors describe the orientation of the tensor, independent of shape and size. The tensor model parameterizes the diffusion in each voxel with an ellipsoid whose diameter in any direction estimates the diffusivity in that direction and whose major principle axis is oriented in the direction of maximum diffusivity. The major axis of the ellipsoid (v1) points in the direction of the maximum diffusivity (λ1) of a voxel. The direction of the maximum diffusion is oriented in the direction of the major fiber tract in the voxel. The directions perpendicular to the main fiber orientation along the medium (v2) and minor axes (v3) of the diffusion ellipsoid are also computed (λ2, λ3) in the tensor analysis. DTI scalar metrics are functions of three diffusion eigenvalues (λ1, λ2, λ3).

Axial diffusivity (AD = λ1) is the maximum diffusivity in the voxel and decreases with greater axonal injury (15,29). Radial diffusivity is the average of the diffusivity perpendicular to the major axis and increases with abnormal myelination (1). Mean diffusivity is the average of the diffusivity values of the three axes of the diffusion ellipsoid and is sensitive to cellularity, edema, and necrosis (46) (Figure 2). Lastly, fractional anisotropy

parameterizes the degree to which the diffusion ellipsoid deviates from spherical. FA is a normalized measure ranging from zero to one that decreases with axonal degeneration, abnormal myelination, and fiber orientation dispersion (27, 35-36. 47) (Figure 2). These scalar metrics describe microstructural

FIBER BUNDLE LENGTH IN CEREBRAL WHITE MATTER

23

Figure 1. Visual depiction of tensor-based modeling and diffusion tensor images: an illustration of tensor-based modeling and diffusion tensor imaging. The top panel shows a single tensor model, which can be decomposed into eigenvectors and eigenvalues. The eigenvectors (v1, v2, v3) represent the major, medium, and minor principle axes of the ellipsoid, and the eigenvalues (位1, 位2, 位3) represent the diffusivities in these three directions, respectively. The eigenvalues can be used to describe the shape with fractional anisotropy (FA) and mean diffusivity (MD). The bottom panel shows diffusion tensor images, which are composed of glyphs representing the tensor models in each image voxel. The glyphs are ellipsoid shaped and can be colored based on fiber orientation, FA, MD, etc

properties of white matter; however, inclusion of the full structure of the tensor model assists in determining subtle changes related to tract directionality (17,25).

DTI Tractography DTI tractography is a technique for creating geometric models that reflect the large-scale structure of fiber bundles. These models are created based on voxel-wise estimates of local fiber orientation using the primary tensor eigenvector, which is indicative of the direction of the dominant fiber bundle. The primary advantage of tractography compared with regional analysis of scalar metrics is the integration of data across an entire white matter tract (17). This process can be repeated to provide both a curve

representing the three-dimensional (3D) path as well as diffusivity properties sampled along the fiber bundle. The trajectories are then graphically depicted using 3D rendering of lines, tubes, or surfaces (31,54).

Tractography can be performed using both deterministic and probabilistic approaches. In deterministic tractography, white matter tracts are reconstructed by selecting a seed region and performing streamline integration based on the preferred direction of the diffusion ellipsoids until one of several stopping criteria is reached. Stopping criteria include decreased anisotropy, sharp curvature, and reaching tissue boundaries (33). However, deterministic tractography is limited by the accumulation of errors during tracking and sensitivity to seeding conditions (26). Probabilistic tractography is an alternative approach that more

24

BAKER ET AL.

Figure 2. Visual depiction of whole brain and tract-specific fiber bundles in cerebral white matter: an illustration of tractography modeling of white matter. The left panel shows whole brain tractography models, which consist of a complex pattern of connections. The right panel shows specific bundles extracted from whole brain tractography, allowing anatomically specific metrics to be computed.

completely samples the space of possible tracks and accounts for uncertainty during tracking (7). This technique estimates the most likely fiber orientations at each voxel along with the probability distribution that a fiber would run along those directions (46).

These probability distributions are then used to sample from a large population of probable paths based on complex diffusion models (7). While probabilistic tracking technology helps to overcome complex anatomy and uncertainty, it is limited in the morphometric properties that can be measured, such as fiber bundle length (FBL).

Quantitative Tractography Based on Diffusion Tensor Imaging (qtDTI) qtDTI technology combines scalar metrics with tractography to estimate bundle-specific properties that characterize the structural properties of fiber

bundles, including both simple statistical summaries of bundle propertiesâ&#x20AC;&#x201D;for example, average FBL, total length, and average scalar metrics (AD, RD, MD, and FA). These simpler metrics can also be combined to form more complex composite metrics, such as intracranial volume (ICV)-normalized length and anisotropy-weighted FBL (17). Alterations in microstructure result in lowered anisotropy or sharp changes in fiber orientation, which lead to fiber termination during tractography (36). These outcomes are potentially useful for detecting white matter injuries, such as those associated with inflammation in multiple sclerosis, which can cause such microstructural changes and associated changes in bundle metrics (42). Compared with traditional DTI scalar metrics such as FA, qtDTI technology is effective in detecting tract specific alterations that may be distributed anywhere along the tractography

FIBER BUNDLE LENGTH IN CEREBRAL WHITE MATTER

25

model (17). This novel approach has high potential to advance our current understanding of white matter structure and function in healthy and diseased populations.

temporal and frontal lobes and increased age (6,9,43).

These results suggest that volumetric reductions in white matter among older adults are likely due to shortened FBL in major white matter tracts (10).

WHITE MATTER FIBER LENGTHS DECREASE WITH AGE

The Relationship between qtDTI In Vivo and Cognition in Healthy Older Adults

Postmortem Evidence of Reduced White Matter Fiber Length Postmortem studies of human tissue have revealed significantly shorter total neuronal fiber lengths among older adults compared with younger adults (32,38,49). Specifically, Marner and colleagues (32) quantified total nerve fiber lengths in adults between the ages of 18 to 93 by using stereologic methods in myelinated nerve fibers. Results revealed a 10% decrease in myelinated nerve fibers per decade of life, with a total decrease of 45% from age 20 to 80 (32). Tang and colleagues (49) also used stereologic methods to examine potential age-related shortening of white matter fiber lengths and demonstrated that the total length of the cerebral fibers was significantly longer in younger individuals (118,000 km) compared with older individuals (86,000 km) (49). This loss of total nerve fiber length with age was accompanied by a decline in the number of small-diameter myelinated fibers.

Associations between fiber length and age-related cognitive decline could not be examined prior to the development of in vivo qtDTI technology. Our group recently reported that performance on tests of executive functioning was associated with shorter FBL in the frontal, occipital, and parietal lobes in older adults (6). Additionally, lower performance on tests of processing speed was associated with shorter FBL across all four lobes of the brain. The findings of shorter mean lobar FBL as a correlate of poorer cognitive performance provides a functional outcome related to reduced FBL and cognitive aging. Results suggest a possible role for qtDTI in identifying older adults at risk for clinically relevant cognitive dysfunction, including prodromal dementia.

Application of qtDTI in the Context of AgeRelated White Matter Atrophy in Healthy Older Adults The postmortem studies described above provide evidence that the total length of white matter fibers represents a biomarker of age-related white matter degradation (31,49), yet this could not be confirmed in vivo prior to the development of qtDTI. Our group recently reported a significant negative relationship between FBL and age, specific to the anterior thalamic radiation (3) and uncinate fasciculus (44). The anterior thalamic radiation is formed by fibers interconnecting the anterior and medial thalamic nuclei and the frontal lobe via the anterior limb of the internal capsule. The uncinate fasciculus connects the hippocampus and amygdala in the temporal lobe with the frontal lobe. Additional publications reported significant correlations between shortened FBL in the

Utilizing qtDTI In Vivo to Examine Risk Factors for Reduced White Matter Integrity Our group has extended this research to determine the capacity of qtDTI to identify risk variables for suboptimal brain health in a healthy older adult population (9,43-44). The epsilon 4 (e4) isoform of apolipoprotein E (ApoE) and the angiotensin (AGT) M268T polymorphism (rs699; historically referred to as M235T) are two genetic risk factors for reduced brain health in older adults, with the ApoE e4 (ApoE4) allele also representing a known risk factor for developing Alzheimerâ&#x20AC;&#x2122;s disease (AD) (16,45). Members of our group utilized qtDTI to investigate differences in FBL among individuals with an e4 allele compared with those with e3 alleles (43) and with the MetMet (MM) genotype of the AGT M268T polymorphism compared with the ThrThr (TT) genotype (44). Both studies revealed an effect of genotype group on FBL in specific white matter tracts. FBL in the left UF were significantly shorter in e4 carriers compared to non-carriers (43). Similarly, healthy older adults with the TT genotype exhibited shorter FBL in the left superior longitudinal fasciculus and cingulate gyrus

26

BAKER ET AL.

segment of the cingulum compared with their MM counterparts (44). Our group has also revealed that higher body mass index (BMI) was independently and significantly associated with shorter white matter FBL in the temporal lobe (9).

While age may represent a salient factor in white matter integrity of a healthy older adult population, additional factors such as BMI and genetic polymorphisms can contribute to subtle, yet identifiable, alterations to white matter fiber bundles that are detectable with qtDTI. Collectively, these three studies lend support to the utility of qtDTI in the assessment of cerebral white matter changes associated with common risk factors that often lead to suboptimal brain health in older adults.

APPLICATION OF qtDTI TECHNOLOGY TO DETERMINE WHITE MATTER INTEGRITY IN A CLINICAL POPULATION qtDTI technology has also been used to examine white matter fiber lengths in individuals with subcortical ischemic vascular disease (SIVD) compared with healthy controls to determine the sensitivity of qtDTI to detect white matter changes in a clinical population. SIVD is a condition that is associated with significant white matter microstructural damage in the brain (12,20-21,28). Prior studies have demonstrated the sensitivity of traditional DTI indices to the white matter alterations associated with SIVD in specifically defined regions of interest, such as the corpus callosum and the corona radiate, in addition to the presence of white matter hyperintensities (WMH) that are characteristic of the disease (2021). Members of our group investigated the utility of qtDTI metrics, particularly those associated with length (e.g., average fiber length, FA-weighted fiber length, normalized fiber length), to examine white matter tract integrity and its relation to cognitive performance in a group of individuals with SIVD (17). Average FA was included to investigate the relative performance of traditional DTI compared to qtDTI in identifying white matter alterations. Both average FA and qtDTI metrics, particularly those associated with fiber length, revealed poorer white matter integrity of transcallosal fibers in individuals with SIVD compared with healthy controls. Reduced

fiber length was additionally associated with worse performance on tests of executive functioning and processing speed in the SIVD group. Effect sizes were consistently smaller for average FA values compared with qtDTI metrics, suggesting that qtDTI may be a more robust indicator of white matter tract damage in SIVD compared with traditional DTI scalar metrics.

STRENGTHS AND LIMITATIONS OF qtDTI TECHNOLOGY The first major strength is that qtDTI provides multi-faceted measurements of fiber bundles that provide complementary measures, such as FBL, volume, FA, and MD, that can detect and gauge the magnitude of various aspects of white matter anatomy. As discussed earlier, FBL can provide a valuable signal that goes beyond diffusivity measurements alone.

Furthermore, composite measures such as anisotropy-weighted FBL are potentially more sensitive to pathology than any one alone, as they can reflect both fiber termination and overall changes in anisotropy (17). The second major strength is the anatomical specificity of qtDTI, which is valuable for localizing pathological effects that would be undetectable when simpler whole-brain analysis is performed. Together, these aspects make qtDTI a unique and powerful tool for understanding structural aspects of fiber bundles.

Several limitations of qtDTI technology warrant discussion. First, qtDTI is sensitive to imaging artifacts such as partial-volume averaging of fiber bundle populations with varying degrees of myelination, fiber orientation, and/or axon caliber. Partial-volume confounds can be somewhat managed by decreasing the voxel size and increasing the gradient strengths and number of directions. However, these adjustments reduce signal-to-noise ratios and increase scan time and post-processing complexity. Additional artifacts include subject motion, magnetic susceptibility, and echo planar imaging distortion. While these artifacts are often difficult to avoid, they can be readily detected by inspection, and negative effects on bundle reconstruction can often be avoided by quality control (51).

FIBER BUNDLE LENGTH IN CEREBRAL WHITE MATTER

FUTURE APPLICATIONS AND NEW INNOVATIONS OF qtDTI TECHNOLOGY As a novel method, qtDTI remains in the early stages of development, and research is needed to examine reproducibility and reliability. Studies including histological techniques and known markers of white matter pathology (e.g., WMH) will support the validity of the approach. Furthermore, qtDTI technology has the potential to broaden our understanding of the associations between white matter integrity and cognitive development in children. It is also possible that these metrics (i.e., FBL) can be used to improve sensitivity to injury and abnormal development. However, studies in newborns and children are necessary to determine the applicability of qtDTI metrics in this population, particularly in relation to work on myelin water fraction mapping (18). There is also much knowledge to gain by going beyond the relatively simple single tensor model to explore more complex diffusion models. Such methods can potentially improve the reconstruction of crossing bundles and enable the quantitation of features such as fiber dispersion and free water contamination. Current efforts to address these limitations include tractography methods that utilize more complex models (11), such as constrained spherical convolution (50), ball-and-sticks diffusion model (8), and neurite orientation dispersion and density imaging (53). Finally, there is a great deal to learn about the relationship between qtDTI and other imaging modalities. In particular, the combination of qtDTI and functional MRI has the potential to provide a much more complete model of brain integrity, as it would provide a parallel view of both structural and functional brain integrity.

SUMMARY qtDTI is a relatively novel imaging approach that exhibits high potential to advance our current understanding of the organization and function of the human brain. Although traditional DTI metrics provide important information about white matter integrity within a single voxel, qtDTI technology has facilitated the examination of specific detail about the direction and curvature of white matter pathways in vivo. While this method is currently in the early

27

stages of technological advancement, research to date has provided novel insights into cerebral white matter integrity in adult populations. Overall, qtDTI represents a potentially useful tool in future investigations of white matter fiber bundles in healthy and clinical populations.

ACKNOWLEDGMENTS Study Funding. Supported by National Institutes of Health/National Institute of Neurological Disorders and Stroke grant number R01 NS052470 and R01 NS039538 and National Institutes of Health/National Institute of Mental Health grant R21 MH090494.

Recruitment database searches were supported in part by National Institutes of Health/National Center for Research Resources grant UL1 TR000448. The authors declare that they have no conflict of interest.

REFERENCES 1.

2.

3.

4.

5.

6.

7.

Alexander, A.L.; Lee, J.E.; Lazar, M.; Field, A.S. Diffusion tensor imaging of the brain. Neurotherapeutics 4(3):316-329; 2007.

Allen, J.S.; Bruss, J.; Brown, C.K.; Damasio, H. Normal neuroanatomical variation due to age: the major lobes and a parcellation of the temporal region. Neurobiol.

Aging 26:1245-1260; 2005.

Baker, L. M.; Laidlaw, D. H.; Conturo, T. E.; Hogan, J.; Zhao, Y.; Luo, X.; Correia, S.; Cabeen, R.; Lane, E.M.; Heaps, J.M.; Bolzenius, J; Salminen, L.E.; Akbudak, E.; McMichael, A.R.; Usher, C.; Behrman, A.; Paul, R.H. White matter changes with age utilizing quantitative diffusion MRI. Neurology 83(3):247-252; 2014.

Basser, P. J.; Pajevic, S.; Pierpaoli, C.; Duda, J.; Aldroubi, A. In vivo fiber tractography using DT‐MRI data.

Magn. Reson. Med. 44(4):625-632; 2000.

Beaulieu, C. The basis of anisotropic water diffusion in the nervous system–a technical review. NMR Biomed.

15(7‐8):435-455; 2002.

Behrman-Lay, A. M.; Usher, C.; Conturo, T. E.; Correia, S.; Laidlaw, D. H.; Lane, E. M.; Bolenzius, J.; Heaps, J.M.; Salminen, L.E.; Baker, L.M.; Cabeen, R.; Akbudak, E.; Luo, X.; Yan, P.; Paul, R.H. Fiber bundle length and cognition: a length-based tractography MRI study.

Brain Imaging Behav. 9(4):765-775; 2015.

Behrens, T. E.J; Woolrich, M. W.; Jenkinson, M.; Johansen‐Berg, H.; Nunes, R. G.; Clare, S.; Matthews, P.M.; Brady, J.M.; Smith, S. M. Characterization and propagation of uncertainty in diffusion‐weighted MR

28

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

BAKER ET AL. imaging. Magn. Reson. Med. 50(5):1077-1088; 2003.

Behrens, T. E. J.; Berg, H. J.; Jbabdi, S.; Rushworth, M. F. S.; Woolrich, M. W. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34(1):144-155; 2007.

Bolzenius, J. D.; Laidlaw, D. H.; Cabeen, R. P.; Conturo, T. E.; McMichael, A. R.; Lane, E. M.; Heaps, J.M.; Salminen, L.E.; Baker, L.M.; Gunstad, J.; Paul, R. H. Impact of body mass index on neuronal fiber bundle lengths among healthy older adults. Brain Imaging Behav. 7(3):300-306; 2013.

Brickman, A. M.; Zimmerman, M. E.; Paul, R. H.; Grieve, S. M.; Tate, D. F.; Cohen, R. A.; Williams, L.M.; Clark, C.R.; Gordon, E. Regional white matter and neuropsychological functioning across the adult lifespan. Biol. Psychiat. 60(5):444-453; 2006.

Cabeen, R. P.; Bastin, M. E.; Laidlaw, D. H. Kernel regression estimation of fiber orientation mixtures in diffusion MRI. Neuroimage 127:158-172; 2016.

Chui, H. C. Subcortical ischemic vascular dementia.

Neurol. Clin. 25(3):717-740; 2007.

Ciccarelli, O.; Parker, G. J. M.; Toosy, A. T.; WheelerKingshott, C. A. M.; Barker, G. J.; Boulby, P. A.; Miller, D.H.; Thompson, A. J. From diffusion tractography to quantitative white matter tract measures: a reproducibility study. Neuroimage 18(2):348-359; 2003.

Ciccarelli, O.; Toosy, A. T.; Parker, G. J. M.; WheelerKingshott, C. A. M.; Barker, G. J.; Miller, D. H.; Thompson, A. J. Diffusion tractography based group mapping of major white-matter pathways in the human brain. Neuroimage 19(4):1545-1555; 2003.

Concha, L.; Gross, D. W.; Wheatley, B. M.; Beaulieu, C. Diffusion tensor imaging of time-dependent axonal and myelin degradation after corpus callosotomy in epilepsy patients. Neuroimage 32(3):1090-1099; 2006.

Corder, E.H.; Saunders, A.M.; Strittmatter, W.J.; Schmechel, D.E.; Gaskell, P.C.; Small, G.W.; Roses, A.D.; Haines, J.L.; Pericak-Vance, M.A. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261(5123):921-923; 1993.

Correia, S.; Lee, S. Y.; Voorn, T.; Tate, D. F.; Paul, R. H.; Zhang, S.; Salloway, S.P.; Malloy, P.F.; Laidlaw, D. H. Quantitative tractography metrics of white matter integrity in diffusion-tensor MRI. Neuroimage 42(2):568-581; 2008.

Deoni, S. C.; Dean, D. C.; O’Muircheartaigh, J.; Dirks, H.; Jerskey, B. A. Investigating white matter development in infancy and early childhood using myelin water faction and relaxation time mapping.

Neuroimage 63(3):1038-1053; 2012.

Edlow, B.L.; Copen, W.A.; Izzy, S.; Bakhadirov, K.;

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

van der Kouwe, A.; Glenn, M.B.; Greenberg, S.M.; Greer, D.M.; Wu, O. Diffusion tensor imaging in acuteto-subacute traumatic brain injury: a longitudinal analysis. BMC Neurol. 16(1):1; 2016.

Erkinjuntti, T. Subcortical ischemic vascular disease and dementia. Int. Pscyhogeriatr. 15:23-26; 2003.

Fu, J.L.; Zhang, T.; Chang, C.; Zhang, Y.Z.; Li, W.B. The value of diffusion tensor imaging in the differential diagnosis of subcortical ischemic vascular dementia and Alzheimer’s disease in patients with only mild white matter alterations on T2-weighted images. Acta Radiol. 53(3):312-317; 2012.

Gunning-Dixon, F. M.; Brickman, A. M.; Cheng, J. C.; Alexopoulos, G. S. Aging of cerebral white matter: a review of MRI findings. Int. J. Geriatr. Psych.

24(2):109–117; 2009.

Guttman, C.R.; Jolesz, F.A.; Kikinis, R.; Killiany, R.J.; Moss, M.B.; Sandor, T.; Albert, M.S. White matter changes with normal aging. Neurology 50:972-978; 1998.

Hagman, P.; Jonasson, L.; Maeder, P.; Thiran, J. P.; Wedeen, V. J.; Meuli, R. Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond.

Radiographics 26(Suppl. 1):S205-S223; 2006.

Jellison, B.J.; Field, A.S.; Medow, J.; Lazar, M.; Salamat, M.S.; Alexander, A.L. Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. Am. J. Neuroradiol. 25:356-369; 2004.

Jiang, S.; Zhang, P.; Han,T.; Liu, W.; Liu, M. Tri-linear interpolation-based cerebral white matter fiber imaging. Neural Regener. Res. 8(23):2155-2164; 2013.

Jones, D. K. Studying connections in the living human brain with diffusion MRI. Cortex 44(8):936-952; 2008.

Lin, L.; Xue, Y.; Duan, Q.; Sun, B.; Lin, H.; Chen, X.; Wei, X.; Zhang, Z. Microstructural white matter abnormalities and cognitive dysfunction in subcortical ischemic vascular disease: an atlas-based diffusion tensor analysis study. J. Mol. Neurosci. 56(2):363370; 2015.

Madden, D. J.; Bennett, I. J.; Burzynska, A.; Potter, G. G.; Chen, N. K.; Song, A. W. Diffusion tensor imaging of cerebral white matter integrity in cognitive aging.

BBA-Mol. Basis Dis. 1822(3):386-400; 2012.

Malloy, P.; Correia, S.; Stebbins, G.; Laidlaw, D. H. Neuroimaging of white matter in aging and dementia. Clin. Neuropsychol. 21(1):73-109; 2007.

Margulies, D.S.; Böttger, J.; Watanabe, A.; Gorgolewski, K.J. Visualizing the human connectome. Neuroimage 80:445-461; 2013.

Marner, L.; Nyengaard, J. R.; Tang, Y.; Pakkenberg, B.

33.

34.

35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

FIBER BUNDLE LENGTH IN CEREBRAL WHITE MATTER Marked loss of myelinated nerve fibers in the human brain with age. J. Comp. Neurol. 462(2):144-152; 2003.

Mori, S.; Crain, B. J.; Chacko, V. P.; Van Zijl, P. Three‐ dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol.

45(2):265-269; 1999.

Mori, S.; Zhang, J. Principles of diffusion tensor imaging and its applications to basic neuroscience research.

Neuron 51:527-539; 2006.

Mukherjee, P.; Berman, J. I.; Chung, S. W.; Hess, C. P.; Henry, R. G. Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. Am. J. Neuroradiol. 29(4):632-641; 2008.

Nucifora, P. G.; Verma, R.; Lee, S. K.; Melhem, E. R. Diffusion-tensor MR imaging and tractography: exploring brain microstructure and connectivity.

Radiology 245(2):367-384; 2007.

O’Sullivan, M.; Summers, P. E.; Jones, D. K.; Jarosz, J. M.; Williams, S. C. R.; Markus, H. S. Normalappearing white matter in ischemic leukoaraiosis: a diffusion tensor MRI study. Neurology 57(12):23072310; 2001.

Pakkenberg, B.; Pelvig, D.; Marner, L.; Bundgaard, M. J.; Gundersen, H. J. G.; Nyengaard, J. R.; Regeur, L. Aging and the human neocortex. Exp. Gerontol.

38(1):95-99; 2003.

Parker, G. J. M. Tracing fiber tracts using fast marching. P. Int. Soc. Magn. Reson. 85; 2000.

Parker, G. J.; Stephan, K. E.; Barker, G. J.; Rowe, J. B.; MacManus, D. G.; Wheeler-Kingshott, C. A.; Ciccarelli, O.; Passingham, R.E.; Sprinks, R.L.; Lemon, R.N.; Turner, R. Initial demonstration of in vivo tracing of axonal projections in the macaque brain and comparison with the human brain using diffusion tensor imaging and fast marching tractography. Neuroimage 15(4):797-809; 2002.

Parker, G. J.; Wheeler-Kingshott, C. A.; Barker, G. J. Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging. IEEE Trans. Med. Imaging 21(5):505-512; 2002.

Roosendaal, S. D.; Geurts, J. J. G.; Vrenken, H.; Hulst, H. E.; Cover, K. S.; Castelijns, J. A.; Pouwels, P.J.; Barkhof, F. Regional DTI differences in multiple sclerosis patients. Neuroimage 44(4):1397-1403; 2009.

Salminen, L. E.; Schofield, P. R.; Lane, E. M.; Heaps, J. M.; Pierce, K. D.; Cabeen, R.; Laidlaw, D.H.; Akbudak, E.; Conturo, T.E.; Correia, S.; Paul, R. H. Neuronal fiber bundle lengths in healthy adult carriers of the ApoE4 allele: a quantitative tractography DTI study.

Brain Imaging Behav. 7(3):274-281; 2013.

Salminen, L. E.; Schofield, P. R.; Pierce, K. D.; Zhao, Y.; Luo, X.; Wang, Y.; Laidlaw, D.H.; Cabeen, R.P.;

45.

46.

47.

48.

49.

50.

51.

52.

53.

54.

29

Conturo, T.E.; Tate, D.F.; Akbudak, E; Lane, E.M.; Heaps, J.M.; Bolzenius, J.D.; Baker, L.M.; Cagle, L.M.; Paul, R.H. Neuromarkers of the common angiotensinogen polymorphism in healthy older adults: a comprehensive assessment of white matter integrity and cognition. Behav. Brain Res. 296:85-93; 2016.

Schmidt, R.; Schmidt, H.; Fazekas, F.; Launer, L.J.; Niederkorn, K.; Kapeller, P.; Lechner, A.; Kostner, G.M. Angiotensinogen polymorphism M235T, carotid atherosclerosis, and small-vessel disease-related cerebral abnormalities. Hypertension 38(1):110-115; 2001.

Sherbondy, A. J.; Dougherty, R. F.; Ben-Shachar, M.; Napel, S.; Wandell, B. A. ConTrack: finding the most likely pathways between brain regions using diffusion tractography. J. Vis. 8(9):15; 2008.

Soares, J.M.; Marques, P.; Alves, V.; Sousa, N. A hitchhiker’s guide to diffusion tensor imaging. Front.

Neurosci. 7(31):1-14; 2013.

Sun, S. W.; Neil, J. J.; Liang, H. F.; He, Y. Y.; Schmidt, R. E.; Hsu, C. Y.; Song, S. K. Formalin fixation alters water diffusion coefficient magnitude but not anisotropy in infarcted brain. Magn. Reson. Med. 53(6):14471451; 2005.

Tang, Y.; Nyengaard, J. R.; Pakkenberg, B.; Gundersen, H. J. G. Age-induced white matter changes in the human brain: a stereological investigation. Neurobiol.

Aging 18(6):609-615; 1997.

Tournier, J. D.; Calamante, F.; Connelly, A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35(4):1459-1472; 2007.

Vilanova, A.; Zhang, S.; Kindlmann, G.; Laidlaw, D. An introduction to visualization of diffusion tensor imaging and its applications. In: Weickert, J.; Hagen, H., eds. Visualization and processing of tensor fields. The Netherlands: Springer Berlin Heidelberg; 2006:121153.

Zhang, S.; Demiralp, C.; Laidlaw, D. H. Visualizing diffusion tensor MR images using streamtubes and streamsurfaces. IEEE Trans. Vis. Comput. Graphics 9(4):454-462; 2003.

Wu, X.P.; Gao, Y.J.; Yang, J.L.; Xu, M.; Sun, D.H. Quantitative measurement to evaluate morphological changes of the corpus callosum in patients with subcortical ischemic vascular dementia. Acta Radiol.

56(2):214-218; 2015.

Zhang, H.; Schneider, T.; Wheeler-Kingshott, C. A.; Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4):1000-1016; 2012.

Technology and Innovation, Vol. 18, pp. 31-37, 2016 Printed in the USA. All rights reserved.

Copyright Š 2016 National Academy of Inventors.

ISSN 1949-8241 â&#x20AC;˘ E-ISSN 1949-825X http://dx.doi.org/10.21300/18.1.2016.31 www.technologyandinnovation.org

DIFFUSION IMAGING FIBER BUNDLES Song Zhang Computer Science and Engineering Department Mississippi State University, Mississippi State, MS, USA Diffusion imaging fiber bundles reconstructed from diffusion-weighted images reveal the shape and size of neural fiber bundles in brain white matter. Further, the global connectivity information in the diffusion imaging fiber bundles helps researchers examine the integrity of the neural bundles and injury from diseases. In this paper, we review the background, the methods, and the applications of diffusion imaging fibers.

Key words: Diffusion imaging; Diffusion tensor; HARDI; Fiber bundles

can be done qualitatively with visualization or quantitatively with metrics on the fiber bundles. Figure 1 shows the pipeline for diffusion imaging fiber bundle analysis. In this paper, we review the key steps in the generation and analysis of diffusion imaging fiber bundles with applications in brain white matter.

INTRODUCTION Diffusion imaging (DI) is a type of magnetic resonance imaging (MRI) that is widely used to probe the three dimensional structure of fibrous tissues, such as brain, heart, and muscle, in vivo (20). The method for reconstructing the fibrous structures from diffusion weighted images is called tractography (3), which tracks integral curves following the principal direction of diffusion. The resulting diffusion fibers follow biological tissue fibers, such as the white matter pathways. These fibers can be further analyzed to understand anatomical structures, check structural integrity, and study the effect of diseases on brain, heart, and muscle. An important step in analyzing diffusion fibers is to identify groups of fibers that are relevant to the study from all the fibers. These are called tracts-of-interest (9). Manually selecting these tracts is both time-consuming and error-prone.

Hence, automatic fiber bundling methods have been proposed to group the diffusion fiber into fiber bundles. Once the fiber bundles are generated, analysis

DIFFUSION TENSOR IMAGING Diffusion imaging records the diffusion process of water molecules in biological tissues in vivo. Water molecules in biological tissues engage in random Brownian motion from collision with other atoms and molecules. The motion is restricted by microstructures in the tissues. For example, in white matter, water diffuses faster along the axons but slower perpendicular to axons because the motion is restricted by the tightly packed multiple myelin membranes encompassing axons (21). This phenomenon of different diffusion rate in different directions is called anisotropy, which can be captured by diffusion MRI to explore the white matter structures in vivo.

_____________________ Accepted December 10, 2015.

Address correspondence to Song Zhang, Computer Science and Engineering Department, Mississippi State University, Box 9637, Mississippi State, MS 39762, USA. Tel: +1 (662) 325-7510; E-mail: szhang@cse.msstate.edu

31

32 ZHANG

Tractography Diffusion â&#x20AC;&#x201C; Weighted Images

Clustering Diffusion Fibers

Visualization Fiber Bundles

Metrics

Figure 1. The work flow for diffusion imaging fiber bundle analysis.

The resolution of diffusion imaging is usually above 1mm, while a single axon can be as thin as a few microns. However, neural fibers form large coherent bundles in the brain that are well above the resolution of diffusion imaging. Diffusion anisotropy has been verified to correlate with the highly structured nerve fibers in brain white matter (13,16).

The raw signals from the diffusion weighted images are often fit to second-order tensors called diffusion tensors that give the diffusion rate along all directions when the diffusion is Gaussian (2). A diffusion tensor field contains diffusion tensors at all points on a regular grid of the data volume. The relation between the raw diffusion signals from diffusion imaging and diffusion tensors can be written as follows:

integral curves from diffusion imaging data. The most common approach is to integrate these curves along the fastest direction of diffusion. In a diffusion tensor field, this amounts to integration in the vector field corresponding to the largest eigenvalue of the diffusion tensor, or first eigenvector field. The tangent vector at any point on the curve points to the fastest direction of diffusion. If the diffusion is Gaussian and faster along the biological fibers than other directions, then these integral curves from tractography will follow the biological fibers. Mathematically, tractography is based on the following equation:

đ?&#x203A;Şđ?&#x203A;Ş(đ?&#x2018;Ľđ?&#x2018;Ľ, đ?&#x2018;Śđ?&#x2018;Ś) = đ?&#x203A;Şđ?&#x203A;Şâ&#x201A;&#x20AC;(đ?&#x2018;Ľđ?&#x2018;Ľ, đ?&#x2018;Śđ?&#x2018;Ś)đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;(đ?&#x2018;?đ?&#x2018;?đ??ˇđ??ˇ)

where pâ&#x20AC;&#x2030;(t) is the integral curve and is the first eigenvector field. pâ&#x20AC;&#x2030;(0)is the seed point of the integral curve.

Tractography has three main components: the seed point, the integration process, and the stopping criteria. Seed points can either be selected automatically or manually. Regions-of-interest can be defined in the data for seed point placement. To cover all important fibers, seed points need to be placed densely in the data volume. However, a balance needs to be reached between covering important features and limiting the computational cost. Similarly, even spacing of the fibers is important. Integration can be implemented with the Euler method, second-order RungaKutta, or the more accurate but slower fourth-order Runge-Kutta. Stopping criteria avoid calculation of the fibers where the first eigenvector field is not robustly defined. The user can usually set a threshold based on the anisotropy indicesâ&#x20AC;&#x201D;e.g., fractional anisotropy to mark the areas where the first eigenvector field is well defined. The value of this threshold depends on the data-acquisition protocol and the

~

where I0â&#x20AC;&#x2030;(x,â&#x20AC;&#x2030;y)represents the signal intensity in the absence of diffusion weighting, b is a 3Ă&#x2014;3 matrix characterizing the diffusion-encoding gradient pulses (timing, amplitude, shape) used in the MRI sequence, and D is the 3Ă&#x2014;3 diffusion tensor. Scalar metrics from diffusion tensors are often used for analysis because of their simplicity and interpretability. For example, mean diffusivity indicates the overall velocity of diffusion, and fractional anisotropy indicates the difference in diffusivity along different directions (4).

TRACTOGRAPHY While diffusion tensors and the derived metrics can be used to evaluate the diffusion profile and change in diffusion across subjects at a single location, they characterize local properties. To capture global properties on the winding biological fibers, these tensors need to be connected to a fibrous model.

Tractography (3) refers to the method of tracing 3D

đ?&#x2018;?đ?&#x2018;?(đ?&#x2018;Ąđ?&#x2018;Ą) =

đ?&#x2018;Ąđ?&#x2018;Ą

ďż˝â&#x201A;&#x20AC; đ?&#x2018;Łđ?&#x2018;Łďż˝đ?&#x2018;?đ?&#x2018;?(đ?&#x2018; đ?&#x2018; )ďż˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018; đ?&#x2018;

DIFFUSION IMAGING FIBER BUNDLES

nature of the object being scanned. Other criteria can also be used, such as the curvature or length of the curve, or the signal-to-noise ratio.

Integral curves from the first eigenvector field can be augmented by additional information from the tensor field. For example, streamtubes (23) use cross section shape and color to map the second and third eigenvectors and the anisotropy values along the integral curve.

FIBER CLUSTERING Diffusion fibers can be densely generated over the entire data volume. These fibers often need to be selected based on their locations and anatomical features for further analysis. Individually selected diffusion fibers are sensitive to small changes in seed point location and difficult to match across subjects.

Individual neural axons tend to group into large and coherent bundles that have a natural boundary.

Diffusion fiber bundles can be selected to emulate these anatomical fiber bundles by setting a regionof-interest to catch all fibers passing through the targeted region (9). Multiple regions-of-interest and Boolean logic can be used to select more complicated fiber bundles (18). However, this approach needs considerable expert knowledge of white matter anatomy, is prone to rater error from misidentification of tracts or improper decisions about whether to include anatomically ambiguous fibers in a specific tract, and is susceptible to experimenter bias. Hence, automatic algorithms for clustering diffusion fibers into fiber bundles have been proposed.

Most fiber bundling algorithms work by grouping fibers while trying to minimize in-group distances and maximize between-group distances. There are two key components: a similarity measure between fibers and a clustering algorithm. There are a number of similarity measures between two curves that can be roughly grouped into several categories. The first category is the Euclidean distance between two selected points on each curve, such as the closest point measure, the Hausdorff distance (17), or the Fréchet distance (1). The second category is the mean Euclidean distance along the run lengths of the curves, such as the mean distance of closest distances (8) or the mean thresholded closest distances

33

(23). The third category is the distance between two Euclidean space embeddings of the curves, such as the Gaussian kernel distance (5). Of all these proximity measures, only Hausdorff distance, Fréchet distance, and the distance between two Euclidean space curve embeddings are metrics that satisfy triangle inequality and symmetry. To get rid of the bias from the order of variables, asymmetric distance measure d (x,y) can be made symmetric by averaging two distances d' (x,y) = (d (x, y) + d (y, x))/2.

Many clustering algorithms can be applied to diffusion fiber bundling—e.g., agglomerative hierarchical clustering method, nearest neighbor, spectral clustering method, etc. (11). As an example, agglomerative hierarchical clustering starts from putting each fiber into a single element cluster and progressively merges the two closest clusters until there is only one cluster of all fibers. If the distance between two clusters is defined as the minimum distance between any two fibers from the two clusters, the agglomerative hierarchical clustering algorithm is also called the single-linkage algorithm. It was suggested in previous studies (14) that the single linkage algorithm performs well in clustering fiber bundles in the brain white matter. The tree structure built from the cluster merging process of the agglomerative hierarchical clustering is called a dendrogram and can be used to select the number of clusters.

TRACTOGRAPHY-BASED METRICS An important application of diffusion imaging is to assess the integrity of the white matter fiber bundles. There are two categories of methods for the integrity assessment with diffusion imaging.

Voxel-based methods calculate metrics on individual tensor or group of tensors in a region-of-interest.

Metrics on individual tensor include mean diffusivity, fractional anisotropy, linear anisotropy, planar anisotropy, etc. They measure the velocity, anisotropy, or shape of the diffusion. Tractography-based methods complement and extend voxel-based methods by providing detailed information about the orientation and curvature of white matter pathways as they course through the brain. Tractography-based metrics can be designed to describe the shape and diffusion

34 ZHANG

Figure 2. Visualization of diffusion fiber bundles. (a) Diffusion fibers (left) and a fiber bundle selected by regions-of-interest (right). (b) Automatically clustered fiber bundles. (c) Geometric hulls wrap around the fiber bundles for better shape illustration. (d) Diffusion fibers are projected as 2D points for an uncluttered visualization.

DIFFUSION IMAGING FIBER BUNDLES

characteristics of a single fiber (e.g., the length, the average curvature, the average fractional anisotropy, etc.). Metrics can also be applied to fiber bundles which contain a group of fibersâ&#x20AC;&#x201D;e.g., the number of fibers, the total length of all fibers, the total summed length of all fibers in the bundle after weighting each fiber by its average fractional anisotropy, etc. (9). The fiber bundle metrics are likely influenced by brain size and, thus, may require further correction. The metrics can be normalized by the size of the brain, or the intracranial volume, which may provide a better index of brain size prior to the impact of age and pathology.

VISUALIZATION Complementary to the quantitative metrics of diffusion fiber bundles, visualization provides an intuitive and direct means to explore these fiber bundles.

Diffusion fibers form complicated shapes in brain white matter echoing the shapes of the neural fiber bundles. The challenge in visualization includes the amount of fibers, the complexity of the fiber shapes, and the multivariate nature of the tensor data. The 3D diffusion fibers generated from the tractography can be visualized with 3D curves, or streamtubes (23), that use cross section shape and color to map additional tensor properties like the eigenvectors and anisotropy (Figure 2a left). Fiber bundles can be manually selected by setting regions-of-interests

35

(Figure 2a right) or automatically selected with clustering algorithms (Figure 2b). Geometric hulls (7) can be wrapped around the boundary of the fiber bundles to better illustrate the shape and size of the fiber bundles (Figure 2c). Alternative methods have been proposed, such as projecting the 3D curves to 2D points while preserving the similarities among the fibers (Figure 2d) (6).

HARDI FIBER BUNDLES As a model for diffusion imaging, diffusion tensor has limitations. More than one fiber orientation (e.g., in crossing or kissing fibers) may exist within a single imaging voxel, and simple diffusion tensor methods are limited in the recovery of structures in areas with complex intra-voxel heterogeneity. To address this problem, high angular resolution diffusion imaging (HARDI) techniques were developed to resolve local crossing fibers within a voxel. Using HARDI, the orientation distribution functions (ODF) for describing the diffusion profile allow multiple maxima and, thus, capture complex fiber structures, such as crossing, kissing, merging, curving, and fanning fibers.

HARDI-based tractography algorithms can be classified as deterministic or probabilistic. Deterministic tracking methods such as streamlines (3) or variations of streamlines (15) in 3D are often used because of their efficient computation.

Figure 3. Applications of diffusion fiber bundles. (a) shows the fibers-at-risk in a multiple sclerosis patient. (b) and (c) show consistency in matched diffusion fiber bundles across subjects.

36 ZHANG Deterministic tracking methods are limited by their sensitivity to the estimated principal direction and their dependency on the choice of initialization (10).

Therefore, HARDI-based probabilistic algorithms were developed to overcome these drawbacks (12).

However, since errors can accumulate with long distance fiber tracking, the probabilistic tractography might produce spurious connections. The evaluation and visualization of these potential fiber errors remain a challenge.

APPLICATIONS Analysis of diffusion fiber bundles has been used in disease studies on brain white matter, heart, and muscle.

Simon et al. (19) studied the connections between focal demyelinating lesions and intersecting fibers to analyze the pathology of secondary fiber injury in multiple sclerosis. Fibers-at-risk were defined by diffusion fiber bundles seeded in the lesion regions on corpus callosum at mid brain. Figure 3a shows the fibers-at-risk with red streamtubes, yellow lesions, and blue ventricles. The streamtubes model helps examine the extent and shape of the fibers-at-risk.

Another example of tracking methods was provided

by Zhang et al. (22) in healthy adults. Figures 3b and 3c show several matched fiber bundles across subjects. It was shown that automatically clustered diffusion fiber bundles correlate with anatomical neural fiber bundles, and they can be matched across subjects for comparative studies.

CONCLUSION Diffusion imaging allows the analysis of tissue microstructures in vivo. Analysis of diffusion imaging includes tensor-based local analysis, tractography-based analysis, and fiber-bundle-based analysis. Diffusion fiber bundles reveal the shape and size of the anatomical neural fiber bundles as well as the global connectivity information. Application of these fiber tracking methods has been applied across multiple organ systems and patient populations.

REFERENCES 1. Alt, H.; Godau, M. Computing the FrĂŠchet distance between two polygonal curves. Int. J. Comput. Geom. Ap. 5(1-2):75-91; 1995.

2. Basser, P.J.; Mattiello, J.; LeBihan, D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. Ser. B 103(3):247-54; 1994.

3. Basser, P.J.; Pajevic, S.; Pierpaoli, C.; Duda, J.; Aldroubi, A. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44(4):62532; 2000.

4. Basser, P.J.; Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn.

Reson. Ser. B 111(3):209-19; 1996.

5. Brun, A.; Knutsson, H.; Park, H.-J.; Shenton, M.E.; Westin, C.-F. Clustering fiber traces using normalized cuts. Med. Image Comput. Comput.

Assist. Interv. 3216:368-375; 2004.

6. Chen, W.; Ding, Z.; Zhang, S.; Brandt, A.M.; Correia, S.; Qu, H.; Crow, J.A.; Tate, D.F.; Yan, Z.; Peng, Q. A novel interface for interactive exploration of DTI Fibers. IEEE Trans. Vis. Comput.

Graph. 15(6):1449-1456; 2009.

7. Chen, W.; Zhang, S.; Correia, S.; Ebert, D.S. Abstractive representation and exploration of hierarchically clustered diffusion tensor fiber tracts. Comput. Graph. Forum 27(3):1071-1078; 2008.

8. Corouge, I.; Gouttard, S.; Gerig, G. Towards a shape model of white matter fiber bundles using diffusion tensor MRI. in Biomedical Imaging: Nano to Macro, 2004. IEEE Int. Symp. Biomed.

Imag. 344-347; 2004.

9. Correia, S.; Lee, S.; Voorn, T.; Tate, D.; Paul, R.; Zhang, S.; Salloway, S.; Malloy, P.; Laidlaw, D.H. Quantitative tract-of-interest metrics for white matter integrity based on diffusion tensor MRI data. Neuroimage 42(2):568-581; 2008.

10. Descoteaux, M.; Deriche, R.; KnĂśsche, T.R.; Anwander, A. Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imag. 28(2):269286; 2009.

DIFFUSION IMAGING FIBER BUNDLES

11. Duda, R.O.; Hart, P.E.; Stork, D.G. Pattern classification (2nd ed.). New York, NY: John Wiley & Sons; 2012.

12. Friman, O.; Farnebäck, G.; Westin, C.-F. A Bayesian approach for stochastic white matter tractography. IEEE Trans. Med. Imag. 25(8):965978; 2006.

13. Hajnal, J.V.; Doran, M.; Hall, A.S.; Collins, A.G.; Oatridge, A.; Pennock, J.M.; Bydder, G.M. MR imaging of anisotropically restricted diffusion of water in the nervous system: technical, anatomic, and pathologic considerations. J. Comput. Assist.

Tomo. 15(1):1-18; 1991.

14. Moberts, B.; Vilanova, A.; van Wijk, J.J. Evaluation of fiber clustering methods for diffusion tensor imaging. IEEE Trans. Vis. Comput.

Graph. 9; 2005.

15. Mori, S.; Crain, B.J.; Chacko, V.; Van Zijl, P. Threedimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45(2):265-269; 1999.

16. Moseley, M.E.; Kucharczyk, J.; Asgari, H.S.; Norman, D. Anisotropy in diffusion-weighted MRI. Magn. Reson. Med. 19(2):321-326; 1991.

17. Rockafellar, R.T.; Wets, R.J.-B. Variational analysis. Heidelberg, Germany: Springer Berlin Heidelberg; 2009.

18. Sherbondy, A.; Akers, D.; Mackenzie, R.; Dougherty, R.; Wandell, B. Exploring connec-

19.

20.

21.

22.

23.

37

tivity of the brainâ&#x20AC;&#x2122;s white matter with dynamic queries. IEEE Trans. Vis. Comput. Graph.

11(4):419-430; 2005.

Simon, J.H.; Zhang, S.; Laidlaw, D.H.; Miller, D.E.; Brown, M.; Corboy, J.; Bennett, J. Identification of fibers at risk for degeneration by diffusion tractography in patients at high risk for MS after a clinically isolated syndrome. J. Magn. Reson.

Imag. 24(5):983-988; 2006.

Taylor, D.G.; Bushell, M.C. The spatial mapping of translational diffusion coefficients by the NMR imaging technique. Phys. Med. Biol. 30(4):345; 1985.

Westin, C.F.; Maier, S.E.; Khidhir, B.; Everett, P.; Jolesz, F.A.; Kikinis, R. Image processing for diffusion tensor magnetic resonance imaging.

Proc. 2nd international conference medical image computing and computer-assisted intervention. Heidelberg, Germany: Springer Berlin Heidelnerg; 1999: 441-452.

Zhang, S.; Correia, S.; Laidlaw, D.H. Identifying white-matter fiber bundles in DTI data using an automated proximity-based fiber-clustering method. IEEE Trans. Vis. Comput. Graph.

14(5):1044-1053; 2008.

Zhang, S.; Demiralp, C.; Laidlaw, D.H. Visualizing diffusion tensor MR images using streamtubes and streamsurfaces. IEEE Trans. Vis. Comput.

Graph. 9(4):454-462; 2003.

Technology and Innovation, Vol. 18, pp. 39-50, 2016 Printed in the USA. All rights reserved.

Copyright © 2016 National Academy of Inventors.

ISSN 1949-8241 • E-ISSN 1949-825X http://dx.doi.org/10.21300/18.1.2016.39 www.technologyandinnovation.org

ASSESSING THE STRUCTURAL AND FUNCTIONAL EFFECTS OF NEUROMODULATION USING MAGNETIC RESONANCE IMAGING David F. Tate1,2, Jacob D. Bolzenius1, Carmen S. Velez1, Elisabeth A. Wilde2,3,4, Sylvain Bouix5, Carlos A. Jaramillo6, Jeffrey D. Lewis7, and Michael Weisend8 1 Missouri Institute of Mental Health, University of Missouri – Saint Louis, St.Louis, MO, USA Department of Physical Medicine and Rehabiliatation, Baylor College of Medicine, Houston, TX, USA 3 Department of Neurology, Baylor College of Medicine, Houston, TX, USA 4 Michael E. DeBakey VA Medical Center, Houston, TX, USA 5 Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA 6 Polytrauma Rehabilitation Center, South Texas Veterans Health Care System, San Antonio, TX, USA 7 Department of Neurology, Uniformed Services University of the Health Sciences School of Medicine, Bethseda, MD, USA 8 Rio Grande Neurosciences, Dayton, OH, USA 2

Neuromodulation is a growing industry that promises to treat many disabling psychiatric (e.g., mood disorders) and other neurologic disorders (e.g., stroke). Given these claims, it is important to advocate for research to examine these assertions so that the best interests of patients and the general public are protected. With this in mind, this review examines the current literature regarding three commonly used neuromodulation methods (cranial stimulation therapy (CES), transcranial direct current stimulation (tDCS), and transcranial magnetic therapy (TMS)), focusing on magnetic resonance imaging (MRI) methods of assessing any therapeutic effects. Currently, the effort to validate these methods using state-of-the-art MRI methods is in its infancy though there are a growing number of studies that demonstrate objective MRI findings that illustrate therapeutic effects. The possible benefits of using MRI to study the biological underpinnings of any neuromodulation effects, to improve delivery of treatment, and to further the science of neuromodulation are described along with suggestions for future research directions.

Key words: Neuromodulation; Direct current stimulation; Cranial electrotherapy stimulation; MRI; Transcranial magnetic stimulation; DTI

INTRODUCTION Neuromodulation as a means of treatment for various disorders and/or of augmenting brain activity to enhance learning or therapeutic effects has recently garnered a substantial amount of clinical and research interest. There are now a number of indications and

applications for which this technology can be used (e.g., insomnia, pain management, major depression, and anxiety) with very few side effects or safety concerns. For these reasons, there is a growing interest in these technologies that warrants research consideration to further establish efficacy and elucidate the

_____________________ Accepted December 10, 2015.

Address correspondence to David F. Tate, Ph.D., Associate Professor, Research, Missouri Institute of Mental Health (MIMH), University of Missouri – Saint Louis, 4633 World Parkway Circle, St.Louis, MO 63134-3115, USA. Tel: +1 (314) 516-8409; E-mail: David.Tate@mimh.edu

39

40 TATE biological underpinnings of therapeutic effects. With proper evidence guiding the use of neuromodulation therapies, these methods could be used to treat a number of complex disorders or serve as a valuable addition to established treatment paradigms.

As these technologies are being recognized and optimized, the research regarding the clinical potential has evolved. Thus, the purpose of this manuscript is to review the current magnetic resonance imaging (MRI) literature for more objective evidence of treatment efficacy or biological insights into the brain changes associated with three common neuromodulation techniques (cranial electrotherapy stimulation (CES), transcranial direct current stimulation (tDCS), and transcranial magnetic stimulation (TMS)). Though there are a number of different ways to measure or image the effects of these neuromodulatory methods on the brain, the main focus of this review is on the use of various MRI sequences. Each neuromodulation technique is described briefly below, and the current imaging literature supporting/disregarding these treatments is then summarized.

Cranial Electrotherapy Stimulation (CES) In the United States, CES technology is classified by the Food and Drug Administration (FDA) as a Class III medical device and is approved for the treatment of insomnia, depression, anxiety, and pain. CES is a non-invasive device that uses transcutaneous pulsed microcurrents of less than 1000 ÂľA to the head, usually with electrodes applied to the ear lobes or scalp. Dosage can be managed by the individual, and a therapy session usually ranges from 20 to 60 minutes. Reports of adverse effects (e.g., slight tingling sensation at the electrode site, lightheadedness, dizziness) from CES therapy are rare, with CES users generally reporting more positive experiences.

CES is thought to generate its effects by direct action on the brain, but the exact mechanism by which the electrical current from CES alters brain function remains somewhat unclear. Early electroencephalographic (EEG) studies found changes during and after CES treatment, including alpha EEG wave changes similar to trained meditators (18,73).

There are also a number of controlled studies that show benefits of CES treatments for patients suffer-

ing from anxiety (16), depression (3,34), headaches, drug withdrawal symptoms (72), fibromyalgia (24), post-traumatic stress disorder (PTSD) (35,57), and even bipolar depression (43). Most of these studies demonstrate positive effects of CES on these disorders and include symptom reduction with minimal side effects. There have even been a few studies that have demonstrated positive effects on learning paradigms that are interpreted as influencing neuroplasticity (32). However, there is a need for additional research before the biological mechanisms of action are clearly understood (58).

MRI as a technological means of assessing structural and functional change in the brain before, during, and after treatment presents a plausible method of examining the effects of CES. Structural and functional differences in the brain are often observed in a number of the disorders for which CES is indicated (33,45,50), and regularization/minimization of these differences could be used as a way of understanding the biological or neuroplastic changes that occur in the context of treatment. Currently, there are no structural MRI studies of therapeutic effects following CES. Structural and more advanced MRI methods such as diffusion weighted or diffusion tensor imaging (DWI/DTI) might prove useful in demonstrating structural connectivity. The physics behind DWI/DTI acquisition, the growing number of ways to examine these data, and the studies that demonstrate changes in connectivity combined with the positive results of CES seen in the controlled studies provide fertile ground for investigating the effects of CES with MRI. Though there are currently no significant structural MRI findings supporting CES, functional MRI methods have been used. One advanced MRI method that has been used is resting state functional MRI (rsfMRI). rsfMRI allows researchers to characterize the large-scale organization of the neural networks by looking at the temporal relationship of the changes (how one signal changes in time relative to another signal in time) of the blood-oxygenated-level-dependent (BOLD) signals from throughout the brain.

Examination of these signals when the person is not actively engaged by a task in the scanner (at rest) has documented the default mode network (DMN) (66), the functional connectivity pattern the brain reverts

EVALUATION OF NEUROMODULATION USING MRI

to when at rest. Importantly, there are a number of studies that have shown that unique alterations in the DMN occur in patients with different disorders, including anxiety, depression, and acute/chronic pain (11,26,27). A preliminary study of healthy control participants indicated a significant change in the DMN and cortical deactivation, especially at the higher frequency (100-Hz) CES stimulation (19).

These specific changes in the DMN and cortical activation patterns could lead to the therapeutic effects seen in clinical studies by altering thought patterns (e.g., worry or rumination) and promoting attention to other stimuli (e.g., surrounding environment).

However, these potential therapeutic effects remain speculative currently and require additional studies.

From this review of the literature, it is clear that CES has positive results in a number of different clinical settings. The literature certainly could benefit from clinical trial methodologies (e.g., randomization, placebo, cross-over designs, etc.) and more direct observation of brain changes using advanced technologies like MRI. Future studies will be important in not only helping to characterize the functional effects of CES and elucidate the biological underpinnings of CES efficacy, but may very well lead to improved methods CES treatment (e.g., better delivery, dosage requirements, improved target accuracy).

Transcranial Magnetic Stimulation (TMS) TMS is a noninvasive method of targeting and stimulating brain areas via the induction of a strong electromagnetic field. This magnetic field is created using a coil placed near the head that produces ion movement within brain tissue orthogonal to the magnetic coil. Spatial acuity and penetrability of the magnetic pulse are determined by variations in coil size and design. TMS is generally safe and well-tolerated, has received FDA approval for clinical use in treatment-resistant depression (59), and is generally associated with only a few mild adverse effects (e.g., minor pain, scalp discomfort) when appropriate protocols are followed (40,88). It also represents an improved method of cortical stimulation over electric stimulation due to its improved ability to permeate bone, comparatively reduced signal loss across distance, and the lack of a need for physical

41

contact between the apparatus and the subject (74).

Of note, the properties of cerebrospinal fluid and surface properties of the brain likely produce a wider area of effect than the target location for stimulation (89).

TMS can be delivered through different techniques (single, paired, and repeated) to produce either a disruptive, excitatory, or inhibitory effect on cortical function (74,89). The excitatory, or facilitative, effect on motor cortex was the rationale for repetitive TMS (rTMS) treatment of depression. The facilitative effect of TMS has been demonstrated to persist for days to weeks following a series of rTMS treatments (87).

The inhibitory effect of TMS delivered to a specific location has been extensively utilized in cognitive neuroscience to understand structural-functional relationships in the brain (74). In part because of the ability to produce facilitative or inhibitory effects,

Figure 1. A typical TMS equipment configuration showing the capacitor/switch and â&#x20AC;&#x153;figure 8â&#x20AC;? stimulator coil (end of blue cable).

42 TATE research has focused on the potential of rTMS to alleviate symptoms associated with a wide range of neurological and psychiatric conditions, including schizophrenic hallucinations, tinnitus, anxiety, neurodegenerative disorders, and chronic pain (89) The use of MRI in combination with TMS has been the subject of significant interest (87). TMS can be delivered before, during, or after MRI. This flexibility can be used to establish causality or correlations between imaging changes and behavior previously noted on MRI. Frameless stereotactic systems use structural MRI data to precisely deliver TMS in a specific location (75). While these multimodal studies increase specificity regarding focal brain targets for treatment, they do not explain the full effect of TMS within the brain and are significantly limited by the interconnected nature of brain networks and the diffuse activation that occurs with TMS (61,89).

Recently, functional neuroimaging techniques using positron emission tomography (PET) and functional MRI (fMRI) have been employed to study the effects of TMS. These studies support the efficacy of TMS methods in eliciting noticeable functional brain changes. For example, rTMS has been linked to subcortical dopamine release with connections to cortical projection fibers using PET (79). Simultaneous TMSfMRI studies also yield valuable information that has both high temporal and spatial resolution. These data demonstrate the ability of the brain to adapt to inhibitory TMS effects in a specific region and highlight compensatory neural connections outside of the region of TMS stimulation (4).

Resting state fMRI shows promise in demonstrating functional connectivity changes induced by TMS (86). This may allow the use of resting state connectivity as a surrogate marker for TMS effectiveness in diseases such as chronic pain, where the clinical effect size of treatment is small (58).

While a useful research and clinical modality, TMS suffers from several limitations. In modeling structural-functional relationships, one notable drawback in TMS studies is the fact that TMS stimulates neural tissue in an amplified and perhaps artificial manner, which may not accurately represent conventional neuronal network firing patterns (22). TMS also suffers from a lack of animal data describing

Figure 2. Participant working on training task while wearing tDCS electrodes.

its mechanisms of action due to technical problems with coil miniaturization. In addition, the fact that TMS has already received FDA approval reduces the incentive for further animal work (89). To address these limitations, human studies combining neuroimaging with TMS represent an important avenue for additional research.

Transcranial Direct Current Stimulation (tDCS) tDCS is another noninvasive method of modulating neural activity via increases or decreases in excitability using the application of weak electrical currents (0.5â&#x20AC;&#x201C;2 mA) to the brain with two or more electrodes. The current enters the head from the anode(s), travels through the tissue, and flows back to the cathode(s). As the current flows between the electrodes, it is believed to modulate neural activity beneath the electrodes, and the effects are dependent on the direction, strength, and duration of the current (48). At moderate levels of current intensity (e.g., 1 mA), neurons influenced by the anodal (+) stimulation appear to increase neuronal excitability via slight depolarization. In contrast, neurons that are influenced by the cathode (-) stimulation are inhibited by hyperpolarization (52,67). However, higher current strengths (e.g., 2 mA) have been shown to cause increases in excitability in brain tissues influenced by both anode and cathode. Thus, cortical effects of different anode and cathode placements are not

EVALUATION OF NEUROMODULATION USING MRI

straightforwardly predictable in terms of the effects that are conventionally associated with increases or decreases in excitability.

Unlike TMS, which directly elicits action potentials, tDCS appears to affect cortical excitability through multiple mechanisms during stimulation and for a short period of time after stimulation. Inhibitory neurotransmitters such as gamma-aminobutyric acid (GABA) may be reduced by anodal tDCS while excitatory neurotransmitters such as glutamate may not be affected (76). This increases the propensity for affected neurons to fire in response to additional inputs (5). tDCS also affects ionic shifts that enhance sodium and calcium conductance in affected neurons as well as alter the resting membrane potential such that neurons are more likely to fire in response to their natural inputs (53). Increased regional cerebral blood flow and oxyhemoglobin are also noted in cortical and subcortical areas near the anode (36).

The mechanisms of cathodal inhibition are less well studied though cathodal stimulation results in both glutamate and GABA decreases (76). The sustained effects of tDCS on cortical excitability after stimulation ends are dependent on the N-methyl-D-aspartate glutamate receptor subtype (54). Animal and human studies indicate that tDCS may also induce long-term potentiation and depression-like processes (LTP and LTD) that strengthen or weaken synaptic communication in affected neuronal pathways depending on how they are applied (77). Following LTP induction, a pre-synaptic stimulation induces a “potentiated” post-synaptic response whereby a lower stimulus intensity may activate the set of synapses or the same stimulus intensity may activate a larger set of synapses.

Increased excitability may promote improvements in task performance by facilitating LTP-like processes between the neurons involved in the task. Therefore, increasing neuronal excitability with brain stimulation may provide a means of inducing a physiological state supporting improved task performance or the acquisition of novel skills (21,64). In contrast, cathodal stimulation may induce LTD-related reduction in neuronal synapse efficacy and be important in suppressing responses (20).

The application of tDCS has attracted some attention for multiple reasons. tDCS is relatively

43

inexpensive, easy to use, safe, and well tolerated.

Electrodes can be placed anywhere on the scalp to influence a brain region(s) or network(s) thought to subserve a particular function that could be altered by changing brain excitation or inhibition. Large electrodes can be held in place with an elastic headband, while multiple small electrodes can be held in place with caps much like EEG. In general, one session lasts 10–30 minutes. To minimize reactions at the electrode-skin interface, tDCS should be performed with non-metallic, conductive rubber electrodes, covered by saline soaked sponges or with electrodes made of metal or rubber that are separated from the skin and electrical contact established with conductive gel. Extensive data support the safety of tDCS with only mild and transient adverse effects (13,56). In a systematic review, Brunoni et al. (20) concluded that 56% of the investigations reported adverse effects, but these were generally limited to itching or tingling under the electrodes, headache, and discomfort. Moreover, the adverse effects reported were also present in participants receiving sham tDCS, suggesting that side effects may not be attributable wholly to the current itself. Recent studies utilizing imaging (EEG and fMRI) showed that tDCS does not induce elevations of a neuronal damage marker (neuron-specific enolase) and is not associated with evidence of edema or other maladaptive functional or structural changes (30,55,56). However, plasticity in neurophysiology and in white matter integrity has been demonstrated (62). tDCS may play an important role in neuroscience research to provide causal evidence for the involvement of a specific brain region in a particular task.

Importantly, tDCS has been observed to enhance performance in cognitive tasks in healthy individuals (15,17,28,44,81) and to delay, suspend, or reverse the effects of aging in cognition and motor skill (80). It has also been examined as a treatment for neurological and psychiatric disorders, including addiction, Alzheimer’s disease (9), chronic pain (1), depression (7), developmental disorders (10), eating disorders (70), epilepsy (37), migraine headaches (2), mild cognitive impairment (47), multiple sclerosis (51), Parkinson’s disease (6), schizophrenia (12,14,49,60), substance use disorders (8), and stroke (31).

44 TATE One of the most frequently cited limitations of tDCS is its limited spatial precision in terms of the brain areas that are stimulated. tDCS spreads current over a wide area of the brain, and this creates difficulties with relating the effects of tDCS to a specific brain region or network (64). tDCS not only affects the brain regions near the electrodes but may also modulate functional connectivity between remote but functionally associated brain areas (63) or influence within-network connectivity (46). The degree to which the effects of tDCS applied to frequently-investigated regions such as the primary motor cortex (M1) extrapolate to additional areas of the cortex is also unclear, particularly those involved in complex, higher-order cognitive processes. Although promising short-term results have been reported in several populations, evidence from larger randomized controlled trials (RCTs) remains limited. Finally, optimal stimulation protocols (in terms of intensity, duration, and repetition of stimulation) that lead to sustained improvements in outcome measures relevant to activities of daily life have not yet been established and should be investigated in future studies (41,64,69).

There is a growing MRI literature examining the structural and functional effects of tDCS. However, there are a number of caveats to consider when examining this literature. Though the motor cortex has been a primary target for stimulation using tDCS, location or placement of the electrodes does vary from study to study. In addition, the placement of anodal and cathodal electrodes and the strength of the current used may or may not be consistent between the studies. Regardless, MRI has proven effective in elucidating structural and functional changes during and after tDCS stimulation.

To date, there do not appear to be any studies utilizing standard structural MRI (i.e., volumetric or shape analyses) to examine the effects of tDCS. More advanced, experimental structural imaging methods like DTI have demonstrated a number of positive changes in diffusion metrics (i.e., decreased apparent diffusion coefficient (ADC) and increased fractional anisotropy (FA)) in the areas targeted with direct current (90). The improvements observed in these metrics appeared only in the region and tracks ipsilateral to the stimulated hemisphere. Functionally,

the improvements in DTI metrics also correlated with measures of functional ability (i.e., motor) and task acquisition. Though these metrics can be influenced by a number of biological factors, including membrane and myelin integrity, fiber density, and even inflammatory processes (39,83), the combination of prospective changes noted after treatment and important functional correlations with these changes emphasizes the value of utilizing DTI as a non-invasive marker of tDCS therapeutic effects.

Early task related functional MRI studies using tDCS therapeutic technologies reinforce and extend the findings of the DTI studies. In healthy controls, anodal tDCS applied to the motor cortex results in increased BOLD activation both during and/or for a short period of time after stimulation (38,68,78).

Another interesting observation of these studies includes increased/decreased activation in adjacent areas known to be functionally and structurally connected to the area being directly stimulated. The signal changes are consistent with changes in oxygen usage and indicate functional connectivity or co-active networks. Importantly, it appears that neuromodulation occurs on a network level rather than simply at the site of stimulation. This has significant implications for rehabilitation efforts that have not been examined thus far. As such, structural and functional MRI represents an interesting technology for evaluating the therapeutic effects of tDCS and promise to elucidate a number of important underlying biological processes of neuromodulation technologies.

DISCUSSION AND FUTURE DIRECTIONS Neuromodulation is a rapidly growing industry that promises a number of positive therapeutic and educational enhancement results. Given the intense interest, there are a number of neuromodulation technologies that have already obtained FDA approval for treatment of various neurologic conditions. Studies demonstrate a good safety record and report very few side effects. Thus, these types of interventions may be particularly valuable in patients sensitive or vulnerable to the side effects of central nervous system (CNS) acting medications or where polypharmacy is already a concern, including pediatric, geriatric, and veteran populations. These technologies might also

EVALUATION OF NEUROMODULATION USING MRI

be useful as an adjunct for individuals with polypharmacy, where physicians are trying to wean down medications. This may be easier to employ for PRN medications such as anxiolytics than long-term medications. Beyond treatment for specific psychiatric or neurologic conditions, these types of technologies may also be used to help improve performance or enhance quality of life. However, future studies will need to address these question more directly.

MRI technologies have been successfully deployed in the research setting and demonstrate a number of significant findings supporting the efficacy of neuromodulation and are likely to be key to understanding the biological effects of neuromodulation.

Advanced structural (i.e., diffusion imaging) and functional imaging have been used to document the effects of neuromodulation. These studies demonstrate improved white matter integrity measures and increased functional activation in the areas stimulated. Volumetric and/or shape studies using MRI technologies have not been utilized to examine the effects of neuromodulation, but such studies may represent an area of new research that can be explored.

Regardless, it is clear that research using volumetric, diffusion, and functional imaging methods has demonstrated the ability of MRI technology to capture changes in the cortical and subcortical white/ gray matter areas of the brain in injury and disease that are undergoing repair (29,42,65,71,84). Even healthy aging participants demonstrated structural and shape changes in response to training paradigms (23,25). This technology has also been useful in predicting functional improvements or outcomes across a number of patient populations (82,85). Thus, MRI analyses may yet prove helpful in documenting the therapeutic impact of neuromodulation techniques.

REFERENCES 1. Antal, A.; Kriener, N.; Lang, N.; Boros, K.; Paulus, W. Cathodal transcranial direct current stimulation of the visual cortex in the prophylactic treatment of migraine. Cephalalgia 31(7):820828; 2011.

2. Antal, A.; Terney, D.; Kühnl, S.; Paulus, W. Anodal transcranial direct current stimulation of the

3.

4.

5.

6.

7.

8.

9.

10.

45

motor cortex ameliorates chronic pain and reduces short intracortical inhibition. J. Pain Symptom Manage. 39(5):890-903; 2010.

Barclay, T. H.; Barclay, R. D. A clinical trial of cranial electrotherapy stimulation for anxiety and comorbid depression. J. Affect. Disord.

164:171-177; 2014.

Bestmann, S; Ruff, C. C.; Blankenburg, F.; Weiskopf, N.; Driver, J.; Rothwell, J. C. Mapping causal interregional influences with concurrent TMS-fMRI. Exp. Brain Res. 191(4):383-402; 2008.

Bikson, M.; Inoue, M.; Akiyama, H.; Deans, J. K.; Fox, J. E.; Miyakawa, H.; Jefferys, J. G. Effects of uniform extracellular DC electric fields on excitability in rat hippocampal slices in vitro. J. Physiol. 557(Pt 1):175-190; 2004.

Boggio, P. S.; Ferrucci, R.; Rigonatti, S. P.; Covre, P.; Nitsche, M.; Pascual-Leone, A.; Fregni, F. Effects of transcranial direct current stimulation on working memory in patients with Parkinson’s disease. J. Neurol. Sci. 249(1):31-38; 2006.

Boggio, P. S.; Rigonatti, S. P.; Ribeiro, R. B.; Myczkowski, M. L.; Nitsche, M. A.; PascualLeone, A.; Fregni, F. A randomized, double-blind clinical trial on the efficacy of cortical direct current stimulation for the treatment of major depression. Int. J. Neuropsychopharmacol.

11(2):249-254; 2008.

Boggio, P. S.; Sultani, N.; Fecteau, S.; Merabet, L.; Mecca, T.; Pascual-Leone, A.; Basaglia, A.; Fregni, F. Prefrontal cortex modulation using transcranial DC stimulation reduces alcohol craving: a double-blind, sham-controlled study. Drug Alcohol Depend. 92(1-3):55-60; 2008.

Boggio, P. S.; Khoury, L. P.; Martins, D. C.; Martins, O. E.; de Macedo, E. C.; Fregni, F. Temporal cortex direct current stimulation enhances performance on a visual recognition memory task in Alzheimer disease. J. Neurol.

Neurosurg. Psychiatry. 80(4):444-447; 2009.

Boggio, P. S.; Asthana, M. K.; Costa, T. L.; Valasek, C. A.; Osório, A. A. Promoting social plasticity in developmental disorders with non-invasive brain stimulation techniques. Front. Neurosci.

9:294; 2015.

46 TATE 11. Broyd, S. J.; Demanuele, C.; Debener, S.; Helps, S. K.; James, C. J.; Sonuga-Barke, E. J. Defaultmode brain dysfunction in mental disorders: a systematic review. Neurosci. Biobeh. Rev. 33(3):279-296; 2009.

12. Brunelin, J.; Mondino, M.; Gassab, L.; Haesebaert, F.; Gaha, L.; Suaud-Chagny, M. F.; Saoud, M.; Mechri, A.; Poulet, E. Examining transcranial direct-current stimulation (tDCS) as a treatment for hallucinations in schizophrenia. Am. J. Psychiatry 169(7):719-724; 2012.

13. Brunoni, A. R.; Amadera, J.; Berbel, B.; Volz, M. S.; Rizzerio, B. G.; Fregni, F. A systematic review on reporting and assessment of adverse effects associated with transcranial direct current stimulation. Int. J. Neuropsychopharmacol.

14(8):1133-1145; 2011.

14. Brunoni, A. R.; Shiozawa, P.; Truong, D.; Javitt, D. C.; Elkis, H.; Fregni, F.; Bikson, M. Understanding tDCS effects in schizophrenia: a systematic review of clinical data and an integrated computation modeling analysis. Expert Rev. Med. Devices 11(4):383-394; 2014.

15. Bullard, L. M.; Browning, E. S.; Clark, V. P.; Coffman, B. A.; Garcia, C. M.; Jung, R. E.; van der Merwe, A. J.; Paulson, K. M.; Vakhtin, A. A.; Wootton, C. L.; Weisend, M. P. Transcranial direct current stimulation’s effect on novice versus experienced learning. Exp. Brain Res. 213(1):9-14; 2011.

16. Bystritsky, A.; Kerwin, L.; Feusner, J. A pilot study of cranial electrotherapy stimulation for generalized anxiety disorder. J. Clin. Psychiatry 69(3):412-417; 2008.

17. Coffman, B. A.; Trumbo, M. C.; Clark, V. P. Enhancement of object detection with transcranial direct current stimulation is associated with increased attention. BMC Neurosci. 13:108; 2012.

18. Cvetkovic, D.; Cosic, I. Alterations of human electroencephalographic activity caused by multiple extremely low frequency magnetic field exposures. Med. Biol. Eng. Comput. 47(10):10631073; 2009.

19. Feusner, J. D.; Madsen, S.; Moody, T.D.; Bohon, C.; Hembacher, E.; Bookheimer, S. Y.; Bystritsky,

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

A. Effects of cranial electrotherapy stimulation on resting state brain activity. Brain. Behav.

2(3):211-220; 2012.

Filmer, H. L.; Dux, P. E.; Mattingley, J.B. Applications of transcranial direct current stimulation for understanding brain function. Trends Neurosci. 37(12):742-753; 2014.

Floel, A.; Cohen, L. G. Recovery of function in humans: cortical stimulation and pharmacological treatments after stroke. Neurobiol. Dis. 37(2):243-251; 2010.

Fox, M. D.; Halko, M. A.; Eldaief, M. C.; PascualLeone, A. Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS). Neuroimage 62(4):2232-2243; 2012.

Fraser, M. A.; Shaw, M. E.; Cherbuin, N. A systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing.

Neuroimage 112:364-374; 2015.

Gilula, M. F. Cranial electrotherapy stimulation and fibromyalgia. Expert Rev. Med. Devices.

4(4):489-495; 2007.

Goldenberg, D.; Galván, A. The use of functional and effective connectivity techniques to understand the developing brain. Dev. Cogn. Neurosci.

12:155-164; 2015.

Gudayol-Ferré, E.; Peró-Cebollero, M.; GonzálezGarrido, A. A.; Guàrdia-Olmos, J. Changes in brain connectivity related to the treatment of depression measured through fMRI: a systematic review. Front. Hum. Neurosci. 9; 2015.

Hemington, K. S.; Wu, Q.; Kucyi, A.; Inman, R. D.; Davis, K. D. Abnormal cross-network functional connectivity in chronic pain and its association with clinical symptoms. Brain Struct.

Func. 1-7; 2015.

Hill, A. T.; Fitzgerald, P. B.; Hoy, K. E. Effects of anodal transcranial direct current stimulation on working memory: a systematic review and meta-analysis of findings from healthy and neuropsychiatric populations. Brain Stimul. 2015.

Irimia, A.; Van Horn, J. D. Functional neuroimaging of traumatic brain injury: advances and clinical utility. Neuropsychiatr. Dis. Treat.

EVALUATION OF NEUROMODULATION USING MRI

11:2355; 2015.

30. Iyer, M. B.; Mattu, U.; Grafman, J.; Lomarev, M.; Sato, S.; Wassermann, E. M. Safety and cognitive effect of frontal DC brain polarization in healthy individuals. Neurology 64(5):872-875; 2005.

31. Jo, J. M.; Kim, Y. H.; Ko, M. H.; Ohn, S. H.; Joen, B.; Lee, K. H. Enhancing the working memory of stroke patients using tDCS. Am. J. Phys. Med. Rehabil. 88(5):404-409; 2009.

32. Kadosh, R. C. The stimulated brain. London, UK: Elsevier; 2014.

33. Kairys, A. E.; Schmidt-Wilcke, T.; Puiu, T.; Ichesco, E.; Labus, J. S.; Martucci, K.; Farmer, M. A.; Ness, T.J.; Deutsch, G.; Mayer, E. A.; Mackey, S. Increased brain gray matter in the primary somatosensory cortex is associated with increased pain and mood disturbance in patients with interstitial cystitis/painful bladder syndrome. J. Urol. 193(1):131-137; 2015.

34. Kirsch, D. L.; Nichols, F. Cranial electrotherapy stimulation for treatment of anxiety, depression, and insomnia. Psychiatr. Clin. North Am. 36(1):169-176; 2013.

35. Kirsch, D. L.; Price, L. R.; Nichols, F.; Marksberry, J. A.; Platoni, K. T. Military service member and veteran self reports of efficacy of cranial electrotherapy stimulation for anxiety, posttraumatic stress disorder, insomnia, and depression. US Army Med. Dep. J. Oct-Dec:46-54; 2014.

36. Lang, N.; Siebner, H. R.; Ward, N. S.; Lee, L.; Nitsche, M. A.; Paulus, W.; Rothwell, J. C.; Lemon, R. N.; Frackowiak, R. S. How does transcranial DC stimulation of the primary motor cortex alter regional neuronal activity in the human brain?

Eur. J. Neurosci. 22(2):495-504; 2005.

37. Liebetanz, D.; Klinker, F.; Hering, D.; Koch, R.; Nitsche, M. A.; Potschka, H.; Löscher, W.; Paulus, W.; Tergau, F. Anticonvulsant effects of transcranial direct-current stimulation (tDCS) in the rat cortical ramp model of focal epilepsy. Epilepsia 47(7):1216-1224; 2006.

38. Lindenberg, R.; Nachtigall, L.; Meinzer, M.; Sieg, M. M.; Flöel, A. Differential effects of dual and unihemispheric motor cortex stimulation in older adults. J. Neurosci. 33(21):9176-9183; 2013.

39. Lockhart, S. N.; DeCarli, C. Structural imaging

40.

41.

42.

43.

44.

45.

46.

47.

48.

47

measures of brain aging. Neuropsychol. Rev. 24(3):271-289; 2014.

López-Ibor, J. J.; López-Ibor, M. I.; Pastrana, J. I. Transcranial magnetic stimulation. Curr. Opin.

Psychiatry 21(6):640-644; 2008.

Malavera, A.; Vasquez, A.; Fregni, F. Novel methods to optimize the effects of transcranial direct current stimulation: a systematic review of transcranial direct current stimulation patents. Expert Rev. Med. Devices. 12(6):679-688; 2015.

Mallik, S.; Samson, R. S.; Wheeler-Kingshott, C. A.; Miller, D. H. Imaging outcomes for trials of remyelination in multiple sclerosis. J. Neurol.

Neurosurg. Psychiatry 85(12):1396-1404; 2014.

McClure, D.; Greenman, S. C.; Koppolu, S. S.; Varvara, M.; Yaseen, Z. S.; Galynker, I. I. A pilot study of safety and efficacy of cranial electrotherapy stimulation in treatment of bipolar II depression. J. Nerv. Ment. Dis. 203(11):827-835; 2015.

McKinley, R. A.; McIntire, L.; Bridges, N.; Goodyear, C.; Bangera, N. B,; Weisend, M. P. Acceleration of image analyst training with transcranial direct current stimulation. Behav.

Neurosci. 127(6):936-946; 2013.

McKinnon, M. C.; Yucel, K.; Nazarov, A.; MacQueen, G. M. A meta-analysis examining clinical predictors of hippocampal volume in patients with major depressive disorder. J. Psychiatry Neurosci. 34(1):41; 2009.

Meinzer, M.; Antonenko, D.; Lindenberg, R.; Hetzer, S.; Ulm, L.; Avirame, K.; Flaisch, T.; Flöel, A. Electrical brain stimulation improves cognitive performance by modulating functional connectivity and task-specific activation. J. Neurosci. 32(5):1859-1866; 2012.

Meinzer, M.; Lindenberg, R.; Phan, M. T.; Ulm. L.; Volk, C.; Flöel, A. Transcranial direct current stimulation in mild cognitive impairment: behavioral effects and neural mechanisms.

Alzheimers Dement. 11(9):1032-1040; 2015.

Miranda, P. C.; Lomarev, M.; Hallett, M. Modeling the current distribution during transcranial direct current stimulation. Clin. Neurophysiol.

48 TATE 117(7):1623-1629; 2006.

49. Mondino, M.; Haesebaert, F.; Poulet, E.; SuaudChagny, M. F.; Brunelin, J. Fronto-temporal transcranial direct current stimulation (tDCS) reduces source-monitoring deficits and auditory hallucinations in patients with schizophrenia.

Schizophr. Res. 161(2-3):515-516; 2015.

50. Moon, C. M.; Jeong, G. W. Brain morphological alterations and cellular metabolic changes in patients with generalized anxiety disorder: a combined DARTEL-based VBM and (1)H-MRS study. J. Magn. Reson. Im. 34(4):429-436; 2016.

51. Mori, F.; Codecà, C.; Kusayanagi, H.; Monteleone, F.; Buttari, F.; Fiore, S.; Bernardi, G.; Koch, G.; Centonze, D. Effects of anodal transcranial direct current stimulation on chronic neuropathic pain in patients with multiple sclerosis. Pain 11(5):436-442; 2010.

52. Nitsche, M. A.; Paulus, W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J. Physiol. 527(3):633-639; 2000.

53. Nitsche, M. A.; Fricke, K.; Henschke, U.; Schlitterlau, A.; Liebetanz, D.; Lang, N.; Henning, S.; Tergau, F.; Paulus, W. Pharmacological modulation of cortical excitability shifts induced by transcranial direct current stimulation in humans. J. Physiol. 553(Pt 1):293-301; 2003.

54. Nitsche, M. A.; Jaussi, W.; Liebetanz, D.; Lang, N.; Tergau, F.; Paulus, W. Consolidation of human motor cortical neuroplasticity by D-cycloserin e. Neuropsychopharmacology 29(8):1573-1578; 2004.

55. Nitsche, M. A.; Niehaus, L.; Hoffmann, K. T.; Hengst, S.; Liebetanz, D.; Paulus, W.; Meyer, B. U. MRI study of human brain exposed to weak direct current stimulation of the frontal cortex. Clin. Neurophysiol. 115(10):2419-2423; 2004.

56. Nitsche, M. A.; Cohen, L. G.; Wassermann, E. M.; Priori, A.; Lang, N.; Antal, A.; Paulus, W.; Hummel, F.; Boggio, P. S.; Fregni, F.; PascualLeone, A. Transcranial direct current stimulation: state of the art 2008. Brain Stimul. 1(3):206-223; 2008.

57. Novakovic, V.; Sher, L.; Lapidus, K. A.; Mindes, J.

A.; Golier, J.; Yehuda, R. Brain stimulation in posttraumatic stress disorder. Eur. J. Psychotraumatol.

2; 2011.

58. O’Connell, N. E.; Wand, B. M.; Marston, L.; Spencer, S.; Desouza, L. H. Non-invasive brain stimulation techniques for chronic pain.

Cochrane Database Syst. Rev. 4:CD008208; 2014.

59. O’Reardon, J. P.; Solvason, H. B.; Janicak, P. G.; Sampson, S.; Isenberg, K. E.; Nahas, Z.; McDonald, W. M.; Avery, D.; Fitzgerald, P. B.; Loo, C.; Demitrack, M. A.; George, M. S.; Sackeim, H. A. Efficacy and safety of transcranial magnetic stimulation in the acute treatment of major depression: a multisite randomized controlled trial. Biol. Psychiatry 62(11):12081216; 2007.

60. Palm, U.; Keeser, D.; Blautzik, J.; Pogarell, O.; Ertl-Wagner, B.; Kupka, M. J.; Reiser, M.; Padberg, F. Prefrontal transcranial direct current stimulation (tDCS) changes negative symptoms and functional connectivity MRI (fcMRI) in a single case of treatment-resistant schizophrenia. Schizophr. Res. 150(2-3):583-585; 2013.

61. Paus, T. Imaging the brain before, during, and after transcranial magnetic stimulation. Neuropsychologia 37(2):219-224; 1999.

62. Plis, S. M.; Weisend, M. P.; Damaraju, E.; Eichele, T.; Mayer, A.; Clark, V. P.; Lane, T.; Calhoun, V. D. Effective connectivity analysis of fMRI and MEG data collected under identical paradigms. Comput. Biol. Med. 41(12):1156-1165; 2011.

63. Polanía, R.; Nitsche, M. A.; Paulus, W. Modulating functional connectivity patterns and topological functional organization of the human brain with transcranial direct current stimulation. Hum. Brain Mapp. 32(8):1236-1249; 2011.

64. Prehn, K.; Flöel, A. Potentials and limits to enhance cognitive functions in healthy and pathological aging by tDCS. Front. Cell. Neurosci.

9:355; 2015.

65. Quarantelli, M. MRI/MRS in neuroinflammation: methodology and applications. Clin. Transl.

Imaging 3(6):475-489; 2015.

66. Raichle, M. E.; MacLeod, A. M.; Snyder, A. Z.; Powers, W. J.; Gusnard, D. A.; Shulman, G. L. A

EVALUATION OF NEUROMODULATION USING MRI

default mode of brain function. Proc. Natl. Acad.

Sci. U.S.A. 98(2):676-682; 2001.

67. Reis, J.; Robertson, E.; Krakauer, J. W.; Rothwell, J.; Marshall, L.; Gerloff, C.; Wassermann, E.; Pascual-Leone, A.; Hummel, F.; Celnik, P. A.; Classen, J.; Floel, A.; Ziemann, U.; Paulus, W.; Siebner, H. R.; Born, J.; Cohen, L. G. Consensus: “can tDCS and TMS enhance motor learning and memory formation?” Brain Stimul. 1(4):363369; 2008.

68. Saiote, C.; Turi, Z.; Paulus, W.; Antal, A. Combining functional magnetic resonance imaging with transcranial electrical stimulation. Front.

Hum. Neurosci. 7; 2013.

69. Sale, M. V.; Mattingley, J. B.; Zalesky, A.; Cocchi, L. Imaging human brain networks to improve the clinical efficacy of non-invasive brain stimulation. Neurosci. Biobehav. Rev. 57:187-198; 2015.

70. Sauvaget, A.; Trojak, B.; Bulteau, S.; JiménezMurcia, S.; Fernández-Aranda, F.; Wolz, I.; Menchón, J. M.; Achab, S.; Vanelle, J. M.; Grall-Bronnec, M. Transcranial direct current stimulation (tDCS) in behavioral and food addiction: a systematic review of efficacy, technical, and methodological issues. Front. Neurosci.

9:349; 2015.

71. Schmidt, R.; Seiler, S.; Loitfelder, M. Longitudinal change of small-vessel disease–related brain abnormalities. J. Cereb. Blood Flow Metab. 36(1): 26-39; 2016.

72. Schmitt, R.; Capo, T.; Boyd, E. Cranial electrotherapy stimulation as a treatment for anxiety in chemically dependent persons. Alcohol Clin.

Exp. Res. 10(2):158-160; 1986.

73. Schroeder, M. J.; Barr, R. E. Quantitative analysis of the electroencephalogram during cranial electrotherapy stimulation. Clin. Neurophysiol.

112(11):2075-2083; 2001.

74. Siebner, H. R.; Rothwell, J. Transcranial magnetic stimulation: new insights into representational cortical plasticity. Exp. Brain Res. 148(1):1-16; 2003.

75. Siebner, H. R.; Bergmann, T. O.; Bestmann, S.; Massimini, M.; Johansen-Berg, H.; Mochizuki, H.; Bohning, D. E.; Boorman, E. D.; Groppa.

S.; Miniussi, C.; Pascual-Leone, A.; Huber, R.;

49

Taylor, P. C.; Ilmoniemi, R. J.; De Gennaro, L.; Strafella, A. P.; Kähkönen, S.; Klöppel, S.; Frisoni, G. B.; George, M. S.; Hallett, M.; Brandt, S. A.; Rushworth, M. F.; Ziemann, U.; Rothwell, J. C.; Ward, N.; Cohen, L. G.; Baudewig, J.; Paus, T.; Ugawa, Y.; Rossini, P. M. Consensus paper: combining transcranial stimulation with neuroimaging. Brain Stimul. 2(2):58-80; 2009.

76. Stagg, C. J.; Best, J. G.; Stephenson, M. C.; O’Shea, J.; Wylezinska, M.; Kincses, Z. T.; Morris, P. G.; Matthews, P. M.; Johansen-Berg, H. Polarity-sensitive modulation of cortical neurotransmitters by transcranial stimulation. J. Neurosci. 29(16):5202-5206; 2009.

77. Stagg, C. J.; Nitsche, M. A. Physiological basis of transcranial direct current stimulation. Neuroscientist 17(1):37-53; 2011.

78. Stagg, C. J.; Johansen-Berg, H. Studying the effects of transcranial direct-current stimulation in stroke recovery using magnetic resonance imaging. Front. Hum. Neurosci. 7; 2013.

79. Strafella, A. P.; Paus, T.; Barrett, J.; Dagher, A. Repetitive transcranial magnetic stimulation of the human prefrontal cortex induces dopamine release in the caudate nucleus. J. Neurosci.

21(15):RC157; 2001.

80. Summers, J. J.; Kang, N.; Cauraugh, J. H. Does transcranial direct current stimulation enhance cognitive and motor functions in the ageing brain? A systematic review and meta- analysis. Ageing Res. Rev. 25:42-54; 2016.

81. Tayeb, Y.; Lavidor, M. Enhancing switching abilities: Improving practice effect by stimulating the dorsolateral pre frontal cortex. Neuroscience.

313:92-98; 2016.

82. Teipel, S. J.; Grothe, M.; Lista, S.; Toschi, N.; Garaci, F. G.; Hampel, H. Relevance of magnetic resonance imaging for early detection and diagnosis of Alzheimer disease. Med. Clin. North Am. 97(3):399-424; 2013.

83. Truong, T. K.; Guidon, A.; Song, A. W. Cortical depth dependence of the diffusion anisotropy in the human cortical gray matter in vivo. PLoS One 9(3); 2014.

84. Vértes, P. E.; Bullmore, E. T. Annual research review: growth connectomics–the organization

50 TATE and reorganization of brain networks during normal and abnormal development. J. Child Psychol. Psychiatry 56(3):299-320; 2015.

85. Walhovd, K. B.; Fjell, A. M.; Espeseth, T. Cognitive decline and brain pathology in aging– need for a dimensional, lifespan and systems vulnerability view. Scand. J. Psychol. 55(3):244254; 2014.

86. Wang, J. X.; Rogers, L. M.; Gross, E. Z.; Ryals, A. J.; Dokucu, M. E.; Brandstatt, K. L.; Hermiller, M. S.; Voss, J. L. Targeted enhancement of cortical-hippocampal brain networks and associative memory. Science 345(6200):1054-1057; 2014.

87. Wang, J. X.; Voss, J. L. Long-lasting enhancements of memory and hippocampal-cortical functional connectivity following multiple-day targeted noninvasive stimulation. Hippocampus

25(8):877-883; 2015.

88. Wassermann, E. M.; Zimmermann, T. Transcranial magnetic brain stimulation: therapeutic promises and scientific gaps. Pharmacol.

Ther. 133(1):98-107; 2012.

89. Wassermann, E. M. Inter-and intra-individual variation in the response to TMS. The Oxford handbook of transcranial magnetic stimulation.

Oxford, UK: Oxford UP; 2008:401-408.

90. Zheng, X.; Schlaug, G. Structural white matter changes in descending motor tracts correlate with improvements in motor impairment after undergoing a treatment course of tDCS and physical therapy. Front. Hum. Neurosci. 9; 2015.

Technology and Innovation, Vol. 18, pp. 51-61, 2016 Printed in the USA. All rights reserved.

Copyright © 2016 National Academy of Inventors.

ISSN 1949-8241 • E-ISSN 1949-825X http://dx.doi.org/10.21300/18.1.2016.51 www.technologyandinnovation.org

USING PITTSBURGH COMPOUND B FOR PET IMAGING ACROSS THE ALZHEIMER’S DISEASE SPECTRUM Ann D. Cohen Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA Use of biomarkers in the detection of early and preclinical Alzheimer’s disease (AD) has become of central importance for the diagnosis of AD, mild cognitive impairment (MCI), and preclinical AD following publication of the NIA-Alzheimer’s Association revised criteria for diagnosis across the spectrum of AD pathogenesis. The use of in vivo PET amyloid imaging agents, such as Pittsburgh Compound-B, allows early detection of AD pathological processes and subsequent neurodegeneration. Imaging with PiB provides early, or perhaps even preclinical, detection of disease and accurately distinguishes AD from dementias of other etiologies in which the diagnostic distinction is difficult to make clinically. From a research perspective, utilizing amyloid imaging agents allows us to study relationships between amyloid pathology and changes in cognition, brain structure, and function across the continuum from normal aging to MCI to AD. The present review focuses on use of PiB-PET across the spectrum of AD pathogenesis.

Key words: Amyloid; Alzheimer’s disease; Pittsburgh Compound B; Neuroimaging

Alzheimer’s disease (AD) is the most common cause of dementia and is pathologically characterized by the presence of amyloid plaques containing amyloid-beta (Aβ) and neurofibrillary tangles containing hyperphosphorylated tau, as well as significant loss of neurons and deficits in neurotransmitter systems. The “amyloid cascade hypothesis” points to deposition of Aβ plaques as a central event in the pathogenesis of AD (16,18). This states that overproduction of Aβ, or failure to clear this peptide, leads to AD primarily through amyloid deposition, by way of the production of NFT, cell death, and, ultimately, the clinical symptoms such as memory loss and other domains of cognitive impairment (17).

Pittsburgh Compound-B (PiB), also known as [11C]6-OH-BTA-1 or [N-methyl-11C]2-(4’-methyl-

aminophenyl)-6-hydroxybenzothiazole (33)), is a thioflavin-T (ThT) derivative, a small molecule known to bind amyloid proteins aggregated into a beta-pleated sheet structure (31). The development of amyloid imaging PET tracers, such as PiB, have made the in vivo imaging of amyloid possible, with striking differences in PiB retention observed between control and AD subjects in brain areas known to contain significant amyloid deposits in AD (e.g., frontal cortex and parietal cortex).

Imaging AD pathology, using amyloid PET imaging agents such as PiB, has several potential clinical benefits, including preclinical detection of disease and accurately distinguishing AD from non-AD dementia in patients with mild or atypical symptoms or confounding comorbidities (in which the

_____________________ Accepted December 10, 2015.

Ann D. Cohen, Ph.D., 1406 Western Psychiatric Institute and Clinic, 3811 O’Hara Street, Pittsburgh, PA 15213, USA. Tel: +1 (412) 246-6251; Fax: +1 (412) 246-6466; E-mail: cohenad@upmc.edu

51

52 COHEN distinction is difficult to make clinically). From a research perspective, these imaging techniques allow us to study relationships among amyloid, cognitive function, and neurodegenerative processes across the continuum from normal aging to AD and to monitor the biological effects of anti-Aβ drugs and relate them to effects on neurodegeneration and cognition. In particular, understanding biomarkers such as PiB in relation to normal aging has become critical given that we have entered the era of “prevention” trials in AD with two studies targeting autosomal dominant AD (DIAN and API), one study targeting homozygous APOE*4 carriers (API), and one study targeting typical late-onset disease (A4). All of these studies rely heavily on biomarkers in general and on Aβ biomarkers in particular. A key concept underlying these trials is the NIA-Alzheimer’s Association research criteria for preclinical AD, which argues that Aβ deposition in individuals without cognitive impairments is, in fact, a preclinical stage of AD (55).

These criteria have been operationalized by Jack et al. (19) and suggest amyloid biomarkers, including PiB-PET, become abnormal first and are followed by biomarkers of neuronal injury and degeneration, including FDG-PET, closer to the time when cognitive symptoms appear (19). The present review focuses on use of PiB-PET across the spectrum of AD. EARLY PiB STUDIES The earliest studies with PiB in AD patients showed marked increases in PiB retention in brain areas known to contain high levels of amyloid plaques when compared to cognitively normal subjects. PiB retention in AD patients was generally most prominent in cortical areas and lower in white matter areas, consistent with post-mortem studies of Aβ plaques in the AD brain (58). PiB retention was observed at high levels in frontal cortex in AD but also was observed in precuneus/posterior cingulate, temporal, and parietal cortices. The occipital cortex and lateral temporal cortex were also significantly affected with a relative sparing of the mesial temporal areas. Significant striatal PiB retention also was observed, consistent with previous reports of extensive Aβ deposition in the striatum of AD patients (6,7,57,66). These

original studies provided a landmark description of the natural history of Aβ deposition in living subjects and were later confirmed by additional studies using PiB in AD patients and cognitively normal subjects (4,8,10-12,24,32,34,39,43,44,52,69). A recent meta-analysis among participants with dementia demonstrated that amyloid positivity was associated with clinical diagnosis (i.e., AD), age, and APOE genotype. Additionally, similar associations of age and APOE ε4 with amyloid positivity were observed in participants with AD dementia at autopsy (41) (Figure 1).

AMYLOID IMAGING IN AUTOSOMAL DOMINANT AD Roughly 1% of all AD cases are caused by single gene mutations that are transmitted in an autosomal dominant pattern with nearly 100% penetrance.

Familial AD has been linked to mutations in presenilin-1 (PS1, chromosome 14, the most commonly involved gene), amyloid precursor protein (APP, chromosome 21), or presenilin-2 (PS2, chromosome 1). All these mutations are thought to cause early-onset familial AD (eoFAD) by promoting the cleavage of APP to the pro-aggregatory Aβ1-42 peptide (17).

In order to explore the natural history of preclinical amyloid deposition in people at high risk for AD, individuals with eoFAD have been evaluated in several studies. In the first PiB-PET study, subjects with two different PS1 mutations were explored (27).

The PS1 mutation carriers, independent of cognitive status, showed a strikingly similar focal amyloid deposition that appeared to begin in the striatum, in contrast to early deposition of amyloid in non-mutation carriers, typically in the frontal cortex and the precuneus/posterior cingulate region but not in striatum (34).

These data have been extended to autosomal dominant dementia and frequent cerebral amyloid angiopathy and intracerebral hemorrhages due to an APP locus duplication (47,50). Similar to previous findings, PiB retention was highest in the striatum (up to 280% of the control mean), and the overall pattern of increased PiB retention was different from that seen in sporadic AD (48). Theuns et al. (59) reported widespread retention of PiB, typical of that observed

USE OF PiB FOR PET IMAGING OF AD

53

Figure 1. Prevalence of Amyloid Positivity According to Age for the Different Dementia Diagnostic Groups. Reprinted from Ossenkoppele et al. (41) with permission from the American Medical Association: JAMA, copyright 2015.

in sporadic AD, in a 57 y/o patient (MMSE of 18) with a novel K724N mutation in the C-terminal intracytosolic fragment of APP. The subject showed no disproportionate PiB retention in the striatum. However, Villemagne et al. (62) has demonstrated increased striatal PiB deposition in PS1 and APP mutation carriers. Recently, autosomal dominant Alzheimerâ&#x20AC;&#x2122;s disease mutation carriers have been shown to have elevated PiB levels in nearly every cortical region 15 years before the estimated age of disease onset that predates changes in cortical glucose metabolism

and cortical thinning (5) (Figure 2). Further, several groups have shown a similar striatal PiB retention pattern in older non-demented subjects with Downâ&#x20AC;&#x2122;s syndrome (3,15,29,30). These early-onset forms of AD all share overproduction of Ab (particularly the 42 amino acid form) as a proposed mechanism of Ab deposition (68), whereas decreased clearance might be more important in late-onset AD (65). It may be that the cellular milieu of the striatum is particularly prone to amyloid deposition under conditions of overproduction.

54 COHEN It has been reported that two genetic forms of AD, the Arctic APP mutation and the Osaka APP mutation, were found to have little PiB retention in the brains of mutation carriers—in contrast to subjects with late-onset AD. Interestingly, these mutations have been associated with enhanced formation of Ab oligomers without Ab fibril formation (40,60). The lack of PiB-PET signal in both the Arctic and Osaka mutations suggests that oligomeric Ab, rather than fibrillar Ab, plays a significant role in the cause of dementia symptoms observed in patients carrying these genetic mutations (53,54,60).

AMYLOID IMAGING IN MCI In early studies of mild cognitive impairment (MCI), PiB appeared to show a bimodal distribution, with 60%-75% of subjects showing an AD-like pattern of PiB retention, while the remaining subjects showed levels typical of PiB-negative [PiB(-)] controls (21,32,45,52). Variations in PiB retention have also been explored when examining MCI subjects based on MCI subtype; subjects with non-amnestic MCI were much less likely to be PiB-positive [PiB(+)] than subjects with amnestic MCI (20,24,43,44,52)

although other studies also found significant PiB retention in non-amnestic MCI (67). These studies have suggested that the non-amnestic MCI subtype may include depression or incipient dementia where Aβ deposition is not a feature (e.g., frontotemporal or vascular dementia), or they may prove to be part of the 5-10% who have stable MCI or the 20% who revert to apparent normality (9,14).

Longitudinal studies have also suggested that MCI subjects with high PiB retention are much more likely to convert to AD than subjects with low PiB retention.

Forsberg and colleagues (13) demonstrated that all seven MCI-to-AD converters were amyloid-positive at baseline, and nine of the 14 non-converters were amyloid-negative. In addition, none of the baseline PiB(-) MCI subjects converted to AD. This effect has also been observed in several subsequent studies, with MCI subjects with increased PiB retention showing much more frequent conversion to AD (25,28,63).

Therefore, amyloid PET is likely to have a prognostic role in the clinical evaluation of MCI by identifying subjects who have underlying AD pathophysiology and are therefore at high risk for further clinical decline (2).

Figure 2. P value maps showing differences between carriers and noncarriers in PiB (A), FDG (B), and cortical thickness (C) at −15, −10, −5, and 0 y before predicted symptom onset. (P < 0.01 after correction for multiple comparisons) increases are shown in shades of red and decreases in shades of blue. Reprinted from Benzinger et al. (5) with permission from the National Academy of Sciences: PNAS, copyright 2013.

USE OF PiB FOR PET IMAGING OF AD

AMYLOID IMAGING IN NORMAL COGNITION Several studies have now demonstrated PiB retention in cognitively normal controls. Depending on the site, reports have ranged from a proportion of 10-30% of normal elderly subjects with significant PiB retention (i.e., PiB(+))(1,20,23,26,34-36,43,46,51,64).

PiB-PET studies have also demonstrated that ApoE4 genotype is associated with higher PiB retention in cognitively normal elderly in a dose-dependent manner (37,46,51), and ApoE4 carriers are more than twice as likely to convert from PiB(-) to PiB(+) over time (46). Conversely, ApoE2 has been associated with lower PiB retention in normal elderly (37). This wide range likely depends on such factors as the age of the cohort, proportion of subjects carrying the ApoE4 allele, definition of “cognitively normal,” and the threshold for defining amyloid-positivity.

The relationship between increased PiB retention and cognition in the normal elderly has been difficult to define, as significant plaque load is not related to broad differences in cognitive function (1,20,34,38,51). However, in other studies, an increase in PiB retention has been associated with poorer performance on episodic memory tests (23,35,36,43,64).

Additionally, a recent study of a community-based sample demonstrated that elevated amyloid levels at baseline were associated with worse cognition and AD-like imaging biomarkers at baseline and with greater clinical decline and neurodegeneration.

Further, this study demonstrated the increased amyloid was associated with clinical conversion to MCI (42). In support of this finding, longitudinal studies have found that cognitively normal individuals with elevated PiB are at much higher risk for longitudinal cognitive decline and the emergence of clinically significant cognitive impairment than PiB(-) age and education matched subjects (37,49,56,63,64). Further, recent theoretical models suggest that the period of time from the first detection of Aβ deposition to levels typically seen in MCI is ~15 years, providing further evidence for an extended preclinical phase of AD (61). A recent meta-analysis of amyloid PET imaging in non-demented individuals demonstrated that the age at which 15% of cognitively normal participants were amyloid positive was approximately 40 years for APOE ε4ε4 carriers, 50 years for ε2ε4

55

carriers, 55 years for ε3ε4 carriers, 65 years for ε3ε3 carriers, and 95 years for ε2ε3 carriers, suggesting a 20- to 30-year interval between first development of amyloid positivity and onset of dementia (22).

CONCLUSIONS The use of amyloid imaging, alone or in conjunction with other biomarkers, will likely be critical to the identification of subjects at risk for AD and future decline, as has been suggested in the new diagnostic criteria for preclinical, prodromal, and clinical AD (55). One aspect of Ab pathology that has become clear from amyloid imaging studies is how early in the spectrum of AD the full burden of amyloid plaques begins to develop. Therefore, major challenges for amyloid imaging will be: 1) how to determine the earliest signs of amyloid accumulation; 2) the associations of amyloid accumulation with cognitive impairments; and, ultimately, 3) whether or not this early amyloid deposition will invariably lead to clinical dementia in a high percentage of individuals. This will likely require the field to continue to focus on cognitively normal elderly and detection of the earliest signs of amyloid deposition, along with markers of neurodegeneration such as MRI and FDG-PET, in order to determine the clinical significance of pre-symptomatic pathology. Further, as anti-amyloid clinical trials begin in asymptomatic individuals, it will be critical to effectively identify the earliest changes in amyloid deposition and the significance of such changes on downstream neurodegenerative processes.

REFERENCES 1. Aizenstein, H. J.; Nebes, R. D.; Saxton, J. A.; Price, J. C.; Mathis, C. A.; Tsopelas, N. D.; Ziolko, S. K.; James, J. A.; Snitz, B. E.; Houck, P. R.; Bi, W.: Cohen, A.D.; Lopresti, B.J.; DeKosky, S.T.; Halligan, E.M.; Klunk, W.E. Frequent amyloid deposition without significant cognitive impairment among the elderly. Arch. Neurol.

65(11):1509-1517; 2008.

2. Albert, M. S.; DeKosky, S. T.; Dickson, D.; Dubois, B.; Feldman, H. H.; Fox, N. C.; Gamst, A.; Holtzman, D. M.; Jagust, W. J.; Petersen, R. C.; Snyder, P.J.; Carrillo, M.C.; Thies, B.; Phelps, C.H.

56 COHEN

3.

4.

5.

6.

7.

8.

The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement.

7(3):270-279; 2011.

Annus, T.; Wilson, L. R.; Hong, Y. T.; AcostaCabronero, J.; Fryer, T. D.; Cardenas-Blanco, A.; Smith, R.; Boros, I.; Coles, J. P.; Aigbirhio, F. I.; Menon, D.K.; Zaman, S.H.; Nestor, P.J.; Holland, A.J. The pattern of amyloid accumulation in the brains of adults with Down syndrome.

Alzheimers Dement., in press..

Archer, H. A.; Edison, P.; Brooks, D. J.; Barnes, J.; Frost, C.; Yeatman, T.; Fox, N. C.; Rossor, M. N. Amyloid load and cerebral atrophy in Alzheimer’s disease: an 11C-PIB positron emission tomography study. Ann. Neurol. 60(1):145-147; 2006.

Benzinger, T. L.; Blazey, T.; Jack, C. R., Jr.; Koeppe, R. A.; Su, Y.; Xiong, C.; Raichle, M. E.; Snyder, A. Z.; Ances, B. M.; Bateman, R. J.; Cairns, N.J.; Fagan, A.M.; Goate, A.; Marcus, D.D.; Aisen, P.S.; Christensen, J.J.; Ercole, L.; Hornbeck, R.C.; Farrar, A.M.; Aldea, P.; Jasielec, M.S.; Owen, C.J.; Xie, X.; Mayeux, R.; Brickman, A.; McDade, E.; Klunk, W.; Mathis, C.A.; Ringman, J.; Thompson, P.M.; Ghetti, B.; Saykin, A.J.; Sperling, R.A.; Johnson, K.A.; Salloway, S.; Correia, S.; Schofield, P.R.; Masters, C.L.; Rowe, C.; Villemagne, V.L.; Martins, R.; Ourselin, S.; Rossor, M.N.; Foz, N.C.; Cash, D.M.; Weiner, M.W.; Holtzman, D.M.; Buckles, V.D.; Moulder, K.; Morris, J.C. Regional variability of imaging biomarkers in autosomal dominant Alzheimer’s disease. Proc. Natl. Acad.

Sci. U.S.A. 110(47):E4502-4509; 2013.

Braak, H.; Braak, E. Alzheimer’s disease: striatal amyloid deposits and neurofibrillary changes.

J. Neuropathol. Exp. Neurol. 49:215-224; 1990.

Brilliant, M. J.; Elble, R. J.; Ghobrial, M.; Struble, R. G. The distribution of amyloid beta protein deposition in the corpus striatum of patients with Alzheimer’s disease. Neuropathol. Appl.

Neurobiol. 23(4):322-325; 1997.

Buckner, R. L.; Snyder, A. Z.; Shannon, B. J.; LaRossa, G.; Sachs, R.; Fotenos, A. F.; Sheline, Y. I.; Klunk, W. E.; Mathis, C. A.; Morris, J. C.;

9.

10.

11.

12.

13.

14.

15.

Mintun, M.A. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J. Neurosci.

25(34):7709-7717; 2005.

Busse, A.; Hensel, A.; Guhne, U.; Angermeyer, M. C.; Riedel-Heller, S. G. Mild cognitive impairment: long-term course of four clinical subtypes.

Neurology 67(12):2176-2185; 2006.

Edison, P.; Archer, H. A.; Hinz, R.; Hammers, A.; Pavese, N.; Tai, Y. F.; Hotton, G.; Cutler, D.; Fox, N.; Kennedy, A.; Rossor, M.; Brooks, D.J. Amyloid, hypometabolism, and cognition in Alzheimer disease: an [11C]PIB and [18F]FDG PET study. Neurology 68:501-508; 2006.

Fagan, A. M.; Mintun, M. A.; Mach, R. H.; Lee, S. Y.; Dence, C. S.; Shah, A. R.; Larossa, G. N.; Spinner, M. L.; Klunk, W. E.; Mathis, C. A.; DeKosky, S.T.; Morris, J.C.; Holtzman, D.M. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta(42) in humans. Ann. Neurol. 59(3):512-519; 2006.

Fagan, A. M.; Roe, C. M.; Xiong, C.; Mintun, M. A.; Morris, J. C.; Holtzman, D. M. Cerebrospinal fluid tau/beta-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults.

Arch. Neurol. 64(3):343-349; 2007.

Forsberg, A.; Engler, H.; Almkvist, O.; Blomquist, G.; Hagman, G.; Wall, A.; Ringheim, A.; Langstrom, B.; Nordberg, A. PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol. Aging 29(10):14561465; 2007.

Gauthier, S.; Reisberg, B.; Zaudig, M.; Petersen, R. C.; Ritchie, K.; Broich, K.; Belleville, S.; Brodaty, H.; Bennett, D.; Chertkow, H.; Cummings, J.L.; de Leon, M.; Feldman, H.; Ganguli, M.; Hampel, H.; Scheltens, P.; Tierney, M.C.; Whitehouse, P.; Winblad, B. Mild cognitive impairment. Lancet 367(9518):1262-1270; 2006.

Handen, B. L.; Cohen, A. D.; Channamalappa, U.; Bulova, P.; Cannon, S. A.; Cohen, W. I.; Mathis, C. A.; Price, J. C.; Klunk, W. E. Imaging brain amyloid in nondemented young adults with Down syndrome using Pittsburgh compound B. Alzheimers Dement. 8(6):496-501; 2012.

USE OF PiB FOR PET IMAGING OF AD

16. Hardy, J.; Allsop, D. Amyloid deposition as the central event in the aetiology of Alzheimer’s disease. Trends Pharmacol. Sci. 12:383-388; 1991.

17. Hardy, J.; Duff, K.; Hardy, K. G.; Perez-Tur, J.; Hutton, M. Genetic dissection of Alzheimer’s disease and related dementias: amyloid and its relationship to tau. Nature Neuroscience 1(5):355-358; 1998.

18. Hardy, J. A.; Higgins, G. A. Alzheimer’s disease: the amyloid cascade hypothesis. Science 256(5054):184-185; 1992.

19. Jack, C. R., Jr.; Knopman, D. S.; Weigand, S. D.; Wiste, H. J.; Vemuri, P.; Lowe, V.; Kantarci, K.; Gunter, J. L.; Senjem, M. L.; Ivnik, R. J.; Roberts, R.O.; Rocca, W.A.; Boeve, B.F.; Petersen, R.C. An operational approach to National Institute on Aging-Alzheimer’s Association criteria for preclinical Alzheimer disease. Ann. Neurol.

71(6):765-775; 2012.

20. Jack, C. R., Jr.; Lowe, V. J.; Senjem, M. L.; Weigand, S. D.; Kemp, B. J.; Shiung, M. M.; Knopman, D. S.; Boeve, B. F.; Klunk, W. E.; Mathis, C. A.; Petersen, R.C. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain 131(Pt 3):665-680; 2008.

21. Jack, C. R., Jr.; Lowe, V. J.; Weigand, S. D.; Wiste, H. J.; Senjem, M. L.; Knopman, D. S.; Shiung, M. M.; Gunter, J. L.; Boeve, B. F.; Kemp, B. J.; Weiner, M.; Petersen, R.C.; Alzheimer’s Disease Neuroimaging Initiative. Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer’s disease: implications for sequence of pathological events in Alzheimer’s disease.

Brain 132(Pt 5):1355-1365; 2009.

22. Jansen, W. J.; Ossenkoppele, R.; Knol, D. L.; Tijms, B. M.; Scheltens, P.; Verhey, F. R.; Visser, P. J.; Amyloid Biomarker Study Group; Aalten, P.; Aarsland, D.; Alcolea, D.; Alexander, M.; Almdahl, I.S.; Arnold, S.E.; Baldeiras, I.; Barthel, H.; van Berckel, B.N.; Bibeau, K.; Blennow, K.; Brooks, D.J.; van Buchem, M.A.; Camus, V.; Cavedo, E.; Chen, K.; Chetelat, G.; Cohen, A.D.; Drzezga, A.; Engelborghs, s.; Fagan, A.M.; Fladby, T.; Fleisher, A.S.; van der Flier, W.M.; Ford, L.; Förster, S.; Fortea, J.; Foskett, N.; Fredriksen, K.S.; Freund-

57

Levi, Y.; Frisoni, G.B.; Froelich, L.; Gabryelewicz, T.; Gill, K.D.; Gkatzima, O.; Gómez-Tortosa, E.; Gordon, M.F.; Grimmer, T.; Hampel, H.; Hausner, L.; Hellwig, S.; Herukka, S.K.; Hildebrandt, H.; Ishihara, L.; Ivanoiu, A.; Jaqust, W.J.; Johannsen, P.; Kandimalla, R.; Kapaki, E.; KlimkowiczMrowiec, A.; Klunk, W.E.; Köhler, S.; Koglin, N.; Kornhuber, J.; Kramberger, M.G.; Van Laere, K.; Landau, S.M.; Lee, D.Y.; de Leon, M.; Lisetti, V.; Lleó, A.; Madsen, K.; Maier, W.; Marcusson, J.; Mattsson, N.; de Mendonça, A.; Meulenbroek, O.; Meyer, P.T.; Mintun, M.A.; Mok, V.; Molinuevo, J.L.; Møllergård, H.M.; Morris, J.C.; Mroczko, B.; Van der Mussele, S.; Na, D.L.; Newberg, A.; Nordberg, A.; Nordlund, A.; Novak, G.P.; Paraskevas, G.P.; Parnetti, L.; Perera, G.; Peters, O.; Popp, J.; Prabhakar, S.; Rabinovici, G.D.; Ramakers, I.H.; Rami, L.; Resende de Oliveira, C.; Rinne, J.O.; Rodrigue, K.M.; RodríguezRodríguez, E.; Roe, C.M.; Rot, U.; Rowe, C.C.; Rüther, E.; Sabri, O.; Sanchez-Juan, P.; Santana, I.; Sarazin, M.; Schröder, J.; Schütte, C.; Seo, S.W.; Soeteway, F.; Soininen, H.; Spiru, L.; Strufys, H.; Teunissen, C.E.; Tsolaki, M.; Vandenberghe, R.; Verbeek, M.M.; Villemagne, V.L.; Vos, S.J.; van Waalwijk van Doorn, L.J.; Waldemar, G.; Wallin, A.; Wallin, Å.K.; Wiltfang, J.; Wolk, D.A.; Zboch, M.; Zetterberg, H. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA 313(19):1924-1938; 2015.

23. Kantarci, K.; Lowe, V.; Przybelski, S. A.; Weigand, S. D.; Senjem, M. L.; Ivnik, R. J.; Preboske, G. M.; Roberts, R.; Geda, Y. E.; Boeve, B. F.; Knopman, D.S.; Petersen, R.C.; Jack, C.R., Jr. APOE modifies the association between Abeta load and cognition in cognitively normal older adults.

Neurology 78(4):232-240; 2012.

24. Kemppainen, N. M.; Aalto, S.; Wilson, I. A.; Nagren, K.; Helin, S.; Bruck, A.; Oikonen, V.; Kailajarvi, M.; Scheinin, M.; Viitanen, M.; Parkkola, R.; Rinne, J.O. Voxel-based analysis of PET amyloid ligand [11C]PIB uptake in Alzheimer disease. Neurology 67(9):1575-1580; 2006.

25. Kemppainen, N. M.; Scheinin, N. M.; Koivunen, J.; Johansson, J.; Toivonen, J. T.; Nagren, K.;

58 COHEN

26.

27.

28.

29.

30.

Rokka, J.; Karrasch, M.; Parkkola, R.; Rinne, J. O. Five-year follow-up of 11C-PIB uptake in Alzheimer’s disease and MCI. Eur. J. Nucl. Med. Mol. Imaging 41(2):283-289; 2014.

Klunk, W. E.; Engler, H.; Nordberg, A.; Wang, Y.; Blomqvist, G.; Holt, D. P.; Bergstrom, M.; Savitcheva, I.; Huang, G. F.; Estrada, S.; Ausén, B.; Debnath, M.L.; Barletta, J.; Price, J.C.; Sandell, J.; Lopresti, B.J.; Wall, A.; Koivisto, P.; Antoni, G.; Mathis, C.A.; Långström, B. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann. Neurol. 55(3):306-319; 2004.

Klunk, W. E.; Lopresti, B. J.; Ikonomovic, M. D.; Lefterov, I. M.; Koldamova, R. P.; Abrahamson, E. E.; Debnath, M. L.; Holt, D. P.; Huang, G. F.; Shao, L.; deKosky, S.T.; Price, J.C.; Mathis, C.A. Binding of the positron emission tomography tracer Pittsburgh compound-B reflects the amount of amyloid-beta in Alzheimer’s disease brain but not in transgenic mouse brain. J. Neurosci.

25(46):10598-10606; 2005.

Koivunen, J.; Scheinin, N.; Virta, J. R.; Aalto, S.; Vahlberg, T.; Nagren, K.; Helin, S.; Parkkola, R.; Viitanen, M.; Rinne, J. O. Amyloid PET imaging in patients with mild cognitive impairment: a 2-year follow-up study. Neurology 76(12):10851090; 2011.

Landt, J.; D’Abrera, J. C.; Holland, A. J.; Aigbirhio, F. I.; Fryer, T. D.; Canales, R.; Hong, Y. T.; Menon, D. K.; Baron, J. C.; Zaman, S. H. Using positron emission tomography and Carbon 11-labeled Pittsburgh Compound B to image Brain Fibrillar beta-amyloid in adults with down syndrome: safety, acceptability, and feasibility. Arch. Neurol.

68(7):890-896; 2011.

Lao, P. J.; Betthauser, T. J.; Hillmer, A. T.; Price, J. C.; Klunk, W. E.; Mihaila, I.; Higgins, A. T.; Bulova, P. D.; Hartley, S. L.; Hardison, R.; Tumuluru, R.V.; Murali, D.; Mathis, C.A.; Cohen, A.D.; Barnhart, T.E.; Devenny, D.A.; Mailick, M.R.; Johnson, S.C.; Handen, B.L.; Christian, B.T. The effects of normal aging on amyloid-beta deposition in nondemented adults with Down syndrome as imaged by carbon 11-labeled Pittsburgh compound B. Alzheimers Dement.

1-11; 2015.

31. Levine, H., 3rd. Thioflavin T interaction with amyloid b-sheet structures. Amyloi. 2:1-6; 1995.

32. Lopresti, B. J.; Klunk, W. E.; Mathis, C. A.; Hoge, J. A.; Ziolko, S. K.; Lu, X.; Meltzer, C. C.; Schimmel, K.; Tsopelas, N. D.; DeKosky, S. T.; Price, J.C. Simplified quantification of Pittsburgh Compound B amyloid imaging PET studies: a comparative analysis. J. Nucl. Med. 46(12):19591972; 2005.

33. Mathis, C. A.; Wang, Y.; Holt, D. P.; Huang, G. F.; Debnath, M. L.; Klunk, W. E. Synthesis and evaluation of 11C-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. J. Med. Chem. 46(13):2740-2754; 2003.

34. Mintun, M. A.; Larossa, G. N.; Sheline, Y. I.; Dence, C. S.; Lee, S. Y.; Mach, R. H.; Klunk, W. E.; Mathis, C. A.; DeKosky, S. T.; Morris, J. C. [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease.

Neurology 67(3):446-452; 2006.

35. Mormino, E. C.; Kluth, J. T.; Madison, C. M.; Rabinovici, G. D.; Baker, S. L.; Miller, B. L.; Koeppe, R. A.; Mathis, C. A.; Weiner, M. W.; Jagust, W. J. Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain 132(Pt 5):1310-1323; 2009.

36. Mormino, E. C.; Smiljic, A.; Hayenga, A. O.; S, H. O.; Greicius, M. D.; Rabinovici, G. D.; Janabi, M.; Baker, S. L.; I, V. Y.; Madison, C. M.; Miller, B.L.; Jagust, W.J. Relationships between β-amyloid and functional connectivity in different components of the default mode network in aging. Cereb.

Cortex 21(10):2399-2407; 2011.

37. Morris, J. C.; Roe, C. M.; Xiong, C.; Fagan, A. M.; Goate, A. M.; Holtzman, D. M.; Mintun, M. A. APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging.

Ann. Neurol. 67(1):122-131; 2010.

38. Nebes, R. D.; Snitz, B. E.; Cohen, A. D.; Aizenstein, H. J.; Saxton, J. A.; Halligan, E. M.; Mathis, C. A.; Price, J. C.; Kamboh, M. I.; Weissfeld, L. A.; Klunk, W.E. Cognitive aging in persons with minimal amyloid-beta and white matter hyperintensities. Neuropsychologia 51(11):2202-2209; 2013.

USE OF PiB FOR PET IMAGING OF AD

39. Nelissen, N.; Vandenbulcke, M.; Fannes, K.; Verbruggen, A.; Peeters, R.; Dupont, P.; Van Laere, K.; Bormans, G.; Vandenberghe, R. Abeta amyloid deposition in the language system and how the brain responds. Brain 130(Pt 8):20552069; 2007.

40. Nilsberth, C.; Westlind-Danielsson, A.; Eckman, C. B.; Condron, M. M.; Axelman, K.; Forsell, C.; Stenh, C.; Luthman, J.; Teplow, D. B.; Younkin, S. G.; Näslund, J.; Lannfelt, L. The ‘Arctic’ APP mutation (E693G) causes Alzheimer’s disease by enhanced Abeta protofibril formation. Nat. Neurosci. 4(9):887-893; 2001.

41. Ossenkoppele, R.; Jansen, W. J.; Rabinovici, G. D.; Knol, D. L.; van der Flier, W. M.; van Berckel, B. N.; Scheltens, P.; Visser, P. J.; Amyloid, P. E. T. S. G.; Verfaillie, S. C.; Zwan, M.D.; Adriaanse, S.M.; Lammertsma, A.A.; Barkhof, F.; Jagust, W.J.; Miller, B.L.; Rosen, H.J.; Landau, S.M.; Villemagne, V.L.; Rowe, C.C.; Lee, D.Y.; Na, D.L.; Seo, S.W.; Sarazin, M.; Roe, C.M.; Sabri, O.; Barthel, H.; Koglin, N.; Hodges, J.; Leyton, C.E.; Vandenberghe, R.; van Laere, K.; Drzezga, A.; Forster, S.; Grimmer, T.; Sánchez-Juan, P.; Carril, J.M.; Mok, V.; Camus, V.; Klunk, W.E.; Cohen, A.D.; Meyer, P.T.; Hellwig, S.; Newberg, A.; Frderiksen, K.S.; Fleisher, A.S.; Mintun, M.A.; Wolk, D.A.; Nordberg, A.; Rinne, J.O.; Chételat, G.; Lleo, A.; Blesa, R.; Fortea, J.; Madsen, K.; Rodrigue, K.M.; Brooks, D.J. Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. JAMA 313(19):1939-1949; 2015.

42. Petersen, R. C.; Wiste, H. J.; Weigand, S. D.; Rocca, W. A.; Roberts, R. O.; Mielke, M. M.; Lowe, V. J.; Knopman, D. S.; Pankratz, V. S.; Machulda, M. M.. Association of elevated amyloid levels with cognition and biomarkers in cognitively normal people from the community. JAMA Neurol.

73(1):85-92; 2016.

43. Pike, K. E.; Savage, G.; Villemagne, V. L.; Ng, S.; Moss, S. A.; Maruff, P.; Mathis, C. A.; Klunk, W. E.; Masters, C. L.; Rowe, C. C. Beta-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer’s disease.

Brain 130(Pt 11):2837-2844; 2007.

44. Price, J. C.; Klunk, W. E.; Lopresti, B. J.; Lu, X.;

59

Hoge, J. A.; Ziolko, S. K.; Holt, D. P.; Meltzer, C. C.; Dekosky, S. T.; Mathis, C. A. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J. Cereb.

Blood Flow Metab. 25:1528–1547; 2005.

45. Price, J. C.; Ziolko, S. K.; Weissfeld, L. A.; Klunk, W. E.; Lopresti, B. J.; Hoge, J. A.; Lu, X. L.; Bi, W. Z.; Meltzer, C. C.; Tsopelas, N; DeKosky, S.T.; Mathis, C.A. FDG and PIBPET imaging in Alzheimer’s disease and mild cognitive impairment. Neuropsychopharmacology 30:S225-S225; 2005.

46. Reiman, E. M.; Chen, K.; Liu, X.; Bandy, D.; Yu, M.; Lee, W.; Ayutyanont, N.; Keppler, J.; Reeder, S. A.; Langbaum, J. B. S.; Alexander, G.E.; Klunk, W.E.; Mathis, C.A.; Price, J.C.; Aizenstein, H.J.; DeKosky, S.T.; Caselli, R.J. Fibrillar amyloid-beta burden in cognitively normal people at 3 levels of genetic risk for Alzheimer’s disease. Proc. Natl.

Acad. Sci. U.S.A. 106(16):6820-6825; 2009.

47. Remes, A. M.; Finnila, S.; Mononen, H.; Tuominen, H.; Takalo, R.; Herva, R.; Majamaa, K. Hereditary dementia with intracerebral hemorrhages and cerebral amyloid angiopathy.

Neurology 63(2):234-240; 2004.

48. Remes, A. M.; Laru, L.; Tuominen, H.; Aalto, S.; Kemppainen, N.; Mononen, H.; Någen, K.; Parkkola, R.; Rinne, J. O. 11C-PIB-PET amyloid imaging in patients with APP locus duplication.

Arch. Neurol. 65(4):540-544; 2007.

49. Resnick, S. M.; Sojkova, J. Amyloid imaging and memory change for prediction of cognitive impairment. Alzheimers Res. Ther. 3(1):3; 2011.

50. Rovelet-Lecrux, A.; Frebourg, T.; Tuominen, H.; Majamaa, K.; Campion, D.; Remes, A. M. APP locus duplication in a Finnish family with dementia and intracerebral haemorrhage. J. Neurol. Neurosurg. Psychiatry 78(10):11581159; 2007.

51. Rowe, C. C.; Ellis, K. A.; Rimajova, M.; Bourgeat, P.; Pike, K. E.; Jones, G.; Fripp, J.; Tochon-Danguy, H.; Morandeau, L.; O’Keefe, G.; Price, R.; Raniga, P.; Robins, P.; Acosta, O.; Lenzo, N.; Szoeke, C.; Salvado, O.; Head, R.; Martins, R.; Masters, C.L.; Ames, D.; Villemagne, V.L. Amyloid imaging results from the Australian Imaging, Biomarkers

60 COHEN and Lifestyle (AIBL) study of aging. Neurobiol.

Aging 31(8):1275-1283; 2010.

52. Rowe, C. C.; Ng, S.; Ackermann, U.; Gong, S. J.; Pike, K.; Savage, G.; Cowie, T. F.; Dickinson, K. L.; Maruff, P.; Darby, D.; Smith, C.; Woodward, M.; Merory, J.; Tochon-Danguy, H.; O’Keefe, G.; Klunk, W.E.; Mathis, C.A.; Price, J.C.; Masters, C.L.; Villemagne, V.L. Imaging beta-amyloid burden in aging and dementia. Neurology 68(20):1718-1725; 2007.

53. Scholl, M.; Wall, A.; Thordardottir, S.; Ferreira, D.; Bogdanovic, N.; Langstrom, B.; Almkvist, O.; Graff, C.; Nordberg, A. Low PiB PET retention in presence of pathologic CSF biomarkers in Arctic APP mutation carriers. Neurology 79(3):229236; 2012.

54. Shimada, H.; Ataka, S.; Tomiyama, T.; Takechi, H.; Mori, H.; Miki, T. Clinical course of patients with familial early-onset Alzheimer’s disease potentially lacking senile plaques bearing the E693Delta mutation in amyloid precursor protein. Dement. Geriatr. Cogn. Disord. 32(1):4554; 2011.

55. Sperling, R. A.; Aisen, P. S.; Beckett, L. A.; Bennett, D. A.; Craft, S.; Fagan, A. M.; Iwatsubo, T.; Jack, C. R., Jr.; Kaye, J.; Montine, T. J.; Paark, D.C.; Reiman, E.M.; Rowe, C.C.; Siemers, E.; Stern, Y.; Yaffe, K.; Carrillo, M.C.; Thies, B.; Morrison-Bogorad, M.; Wagster, M.V.; Phelps, C.H. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement.

7(3):280-292; 2011.

56. Storandt, M.; Mintun, M. A.; Head, D.; Morris, J. C. Cognitive decline and brain volume loss as signatures of cerebral amyloid-beta peptide deposition identified with Pittsburgh compound B: cognitive decline associated with Abeta deposition. Arch. Neurol. 66(12):1476-1481; 2009.

57. Suenaga, T.; Hirano, A.; Llena, J. F.; Yen, S. H.; Dickson, D. W. Modified Bielschowsky stain and immunohistochemical studies on striatal plaques in Alzheimer’s disease. Acta Neuropathol.

80(3):280-286; 1990.

58. Thal, D. R.; Rub, U.; Orantes, M.; Braak, H. Phases of Ab-deposition in the human brain and its relevance for the development of AD. Neurology 58(12):1791-1800; 2002.

59. Theuns, J.; Marjaux, E.; Vandenbulcke, M.; Van Laere, K.; Kumar-Singh, S.; Bormans, G.; Brouwers, N.; Van den Broeck, M.; Vennekens, K.; Corsmit, E.; Cruts, M.; De Strooper, B.; Van Broeckhoven, C.; Vandenberghe, R. Alzheimer dementia caused by a novel mutation located in the APP C-terminal intracytosolic fragment.

Hum. Mutat. 27(9):888-896; 2006.

60. Tomiyama, T.; Nagata, T.; Shimada, H.; Teraoka, R.; Fukushima, A.; Kanemitsu, H.; Takuma, H.; Kuwano, R.; Imagawa, M.; Ataka, S.; Wada, Y.; Yoshioka, E.; Nishizaki, T.; Watanabe, Y.; Mori, H. A new amyloid beta variant favoring oligomerization in Alzheimer’s-type dementia. Ann. Neurol. 63(3):377-387; 2008.

61. Villemagne, V. L.; Burnham, S.; Bourgeat, P.; Brown, B.; Ellis, K. A.; Salvado, O.; Szoeke, C.; Macaulay, S. L.; Martins, R.; Maruff, P.; Ames. D.; Rowe, C.C.; Masters, C.L.; Australian Imaging Biomarkers and Lifestyle (AIBL) Research Group.

Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurol. 12(4):357-367; 2013.

62. Villemagne, V. L.; Ong, K.; Mulligan, R. S.; Holl, G.; Pejoska, S.; Jones, G.; O’Keefe, G.; Ackerman, U.; Tochon-Danguy, H.; Chan, J. G.; Reininger, C.B.; Fels, L.; Putz, B.; Rohde, B.; Masters, C.L.; Rowe, C.C. Amyloid imaging with (18) F-florbetaben in Alzheimer disease and other dementias. J. Nucl. Med. 52(8):1210-1217; 2011.

63. Villemagne, V. L.; Pike, K. E.; Chetelat, G.; Ellis, K. A.; Mulligan, R. S.; Bourgeat, P.; Ackermann, U.; Jones, G.; Szoeke, C.; Salvado, O.; Martins, R.; O’Keefe, G.; Mathis, C.A.; Klunk, W.E.; Ames, D.; Masters, C.L.; Rowe, C.C. Longitudinal assessment of Abeta and cognition in aging and Alzheimer disease. Ann. Neurol. 69(1):181-192; 2011.

64. Villemagne, V. L.; Pike, K. E.; Darby, D.; Maruff, P.; Savage, G.; Ng, S.; Ackermann, U.; Cowie, T. F.; Currie, J.; Chan, S. G.; Jones, G.; Tochon-Danguy,

USE OF PiB FOR PET IMAGING OF AD

H.; O’Keefe, G.; Masters, C.L.; Rowe, C.C. Abeta deposits in older non-demented individuals with cognitive decline are indicative of preclinical Alzheimer’s disease. Neuropsychologia 46(6):16881697; 2008.

65. Whitaker, C.; Eckman, C.; Almeida, C.; Feinstein, D.; Atwood, C.; Eckman, E.; Crutcher, K.; Hersh, L.; Leissring, M.; LaVoie, M.; Ertekin-Taner, N.; Shapiro, P.; Takahashi, R.; Yamin, R.; Mansourian, S.; Estus, S.; Lesné, S.; Turner, T.; Farris, W.; Stroebel, G. Live discussion: amyloid-beta degradation: the forgotten half of Alzheimer’s disease. 12 September 2002. J. Alzheimers Dis. 5(6):491-497; 2003.

66. Wolf, D. S.; Gearing, M.; Snowdon, D. A.; Mori, H.; Markesbery, W. R.; Mirra, S. S. Progression of regional neuropathology in Alzheimer disease and normal elderly: findings from the Nun study.

Alzheimer Dis. Assoc. Disord. 13(4):226-231;

61

1999.

67. Wolk, D. A.; Price, J. C.; Saxton, J. A.; Snitz, B. E.; James, J. A.; Lopez, O. L.; Aizenstein, H. J.; Cohen, A. D.; Weissfeld, L. A.; Mathis, C. A.; Klunk, W.E.; DeKosky, S.T. Amyloid imaging in mild cognitive impairment subtypes. Ann. Neurol. 65(5):557-568; 2009.

68. Younkin, S. G. The APP and PS1/2 mutations linked to early onset familial Alzheimer’s disease increase the extracellular concentration and A beta 1-42 (43). Rinsho Shinkeigak. 37(12):1099; 1997.

69. Ziolko, S. K.; Weissfeld, L. A.; Klunk, W. E.; Mathis, C. A.; Hoge, J. A.; Lopresti, B. J.; Dekosky, S. T.; Price, J. C. Evaluation of voxel-based methods for the statistical analysis of PIB PET amyloid imaging studies in Alzheimer’s disease.

Neuroimage 33(1):94-102; 2006.

Technology and Innovation, Vol. 18, pp. 63-74, 2016 Printed in the USA. All rights reserved.

Copyright © 2016 National Academy of Inventors.

ISSN 1949-8241 • E-ISSN 1949-825X http://dx.doi.org/10.21300/18.1.2016.63 www.technologyandinnovation.org

THE EMERGING FIELD OF PERIVASCULAR FLOW DYNAMICS: BIOLOGICAL RELEVANCE AND CLINICAL APPLICATIONS Jacob Huffman1, Sarah Phillips1, George T. Taylor1,3, and Robert Paul1,2 1

Behavioral Neuroscience Program, Department of Psychological Sciences, University of Missouri – St. Louis, MO, USA 2 Missouri Institute of Mental Health (MIMH), St. Louis, MO, USA 3 Interfakultäre Biomedizinische Forschungseinrichtung (IBF) der Universität Heidelberg, Heidelberg, Germany Brain-wide pathways of perivascular flow help clear the brain of proteins and metabolic waste linked to the onset and progression of neurodegenerative diseases. Recent studies on the glymphatic system and novel lymphatic vessels of the meninges have prompted new insight into the clinical significance of perivascular flow. Current techniques in both humans and animals are unable to fully reveal the complex functional and anatomical features of these clearance pathways. While much research has stemmed from fluorescence microscopy and MR imaging, clinical and experimental investigations are hindered by the lack of more advanced and precise technology. In this review, we discuss the biological relevance of the glymphatic and perivascular clearance systems, the innovative technology that has defined these pathways, and the potential for new studies to advance our understanding of degenerative brain diseases using similar technology.

Key words: Perivascular space; Glymphatic; Imaging; Fluorescence; Contrast-enhance; MRI

differences among neurodegenerative disorders, imaging modalities have been employed in both animal and human research to uncover the etiological mechanisms associated with neurodegenerative diseases.

INTRODUCTION Maintaining homeostatic function of the central nervous system (CNS) relies not only on the proper regulation and composition of cerebrospinal fluid (CSF) but its distribution and drainage from the brain.

The dynamic flow of CSF and interstitial fluid (ISF) along brain-wide networks of fluid motion permit the transport of molecules and substrates throughout multiple brain regions (68). Dysfunction of these delivery and clearance systems may render the brain vulnerable to the accumulation of metabolic waste and aggregation of proteins, such as amyloid β (Aβ), possibly leading to the onset and/or progression of diseases such as Alzheimer’s Disease (AD) and Cerebral Amyloid Angiopathy (CAA) (36,53,72).

Due to the inherent physiological and behavioral

CSF AND PERIVASCULAR FLOW Clinical evaluations of CSF production, flow, and reabsorption in humans rely primarily on phase contrast magnetic resonance imaging (PC-MRI) (7,27,40,69). However, CSF moves with relatively low velocity and is confined to small anatomical areas; therefore, a number of limitations arise with image quality and accuracy. As a result, the vast majority of studies focused on mechanisms of CSF physiology rely on animal models. A brief review of these

_____________________

Accepted December 10, 2015.

Address correspondence to Jacob Huffman, Behavioral Neuroscience/Psychology, University of Missouri – Saint Louis, One University Boulevard, St. Louis, MO 63121, USA, Tel: +1 (314) 486-3407; Fax: (314) 516-5392, E-mail: jnhryb@mail.umsl.edu

63

64

HUFFMAN ET. AL

mechanisms is provided below, followed by discussion of advanced technological approaches to enhance these models.

CSF is primarily produced in choroid plexuses of the lateral, third, and fourth ventricles and is modestly regulated by the blood-CSF barrier (62). Functionally similar to the blood brain barrier, the blood-CSF barrier controls the production of CSF and its ionic and molecular composition while also serving as a pathway for solute and waste removal (18,39). CSF travels through the ventricular system from the lateral ventricles to the third and fourth ventricles, passing through the foramen of Magendie and Luschka where it mixes with existing fluid in the subarachnoid space (SAS) (54). The movement of CSF is then derived from multiple mechanisms, including hydrostatic pressure gradients between CSF compartments and sites of reabsorption (52), the pulsatility of the cardiac cycle and vascular smooth muscle (2,23,37,43,74), the respiratory cycle (13,16,43), and body posture (47).

The gap between the arachnoid membrane and pia mater gives way to the SAS where both arteries and veins along the pial surface of the brain are bathed in CSF. Surface arteries penetrate the pia and project into the parenchyma, carrying the pial membrane for a short distance. Astrocyte endfeet wrap around these penetrating vessels, presumably covering the pial membrane, to form a canal or perivascular space (also known as the Virchow-Robin space). There is a distinct gap between the basement membrane of the vascular smooth muscle and the astrocyte endfeet (38). Studies utilizing fluorescent tracers injected into the cisterna magna have identified CSF traveling from the SAS into deeper periarterial pathways toward brain capillary beds (36,37,45,79). As reviewed by Jessen et al. (38), these periarterial spaces become tighter as smooth muscle dissipates and eventually joins with the basal lamina surrounding the capillary endothelium. From there, CSF can either move across the astrocyte endfeet and into the interstitial space or continue into perivenous pathways of draining venules and veins. Because the bulk flow of CSF through the perivascular space is driven in part by the pulsatile activity of the vascular smooth muscle (23,37,74), the low resistance basal lamina facilitates the exchange of CSF and interstitial fluid (ISF) at deeper capillary beds (38).

Earlier studies initially identified this perivascular space where tracers move along periarterial spaces (33,63,64,71,83). More recently, Iliff and colleagues used in vivo two-photon microscopy to show that ISF moves by bulk flow toward perivenous pathways (36). This movement is made possible by the influx of CSF and subsequent CSF-ISF exchange in the interstitium (Figure 1). The authors refer to this as the ‘glymphatic’ pathway due to the involvement of glial processes in fluid transport and its similarity to peripheral lymphatic function (36).

There is also evidence of a different mechanism for ISF clearance in which bulk flow transports solutes from the interstitium into periarterial pathways, where they are subsequently transported to cervical lymphatics along arteries in the opposite direction of blood flow (1,4,10,11) (Figure 1). The concept of periarterial ISF efflux from the parenchyma and CSF influx via glymphatic flow can be viewed as two mechanisms acting in opposition to one another.

Described in more detail below, it is important to note that the precise anatomical differences between these pathways are not yet fully understood. For the purpose of this review, ‘perivascular space’ will broadly denote a single space between vascular endothelium and astrocyte endfeet that permits the movement of CSF to, and ISF from, the parenchyma. ‘Glymphatic flow’ will represent the movement of subarachnoid CSF along periarterial pathways, into the interstitium to exchange with ISF, and then drained along perivenous pathways.

CLEARING THE BRAIN INTERSTITIUM CSF Clearance Subarachnoid CSF can be transported to peripheral blood and lymphatics along four main routes of reabsorption: 1) arachnoid villi present in the dural sinuses, 2) drainage pathways at the cranial nerves, 3) nerve sheaths along spinal roots, and 4) the more recently discovered lymphatic vessels in the meninges (5,50,57). These drainage pathways are necessary for the removal of large solutes and metabolic waste dumped into ventricular and subarachnoid CSF. Clearance of the interstitial space relies in part on either the bulk flow of ISF from the parenchyma to CSF compartments or the exchange of CSF and ISF

IMAGING THE PERIVASCULAR SPACE

of the glymphatic flow. Nevertheless, both systems require proficient passage along perivascular spaces.

Perivascular and Glymphatic Clearance As mentioned above, previous studies derived mostly from a single group of researchers have shown that ISF can move along the perivascular spaces of arteries in the opposite direction of blood

65

flow, which has been implicated in the removal of Aβ (4,10,11,28,29,53), a protein well-known for its role in AD (72). ISF bulk flow pathways are thought to originate in the walls of cerebral vessels, running along the basement membrane of capillaries and arteries that drive ISF toward CSF compartments.

While the removal of ISF along periarterial pathways provides one mechanism for solute and waste

Figure 1. Perivascular and Glymphatic Flow Perivascular clearance comprises perivascular drainage and glymphatic pathways. The perivascular drainage pathway (white arrows) moves waste into the periarterial space (located along smooth muscle cells and the capillary basement membrane) and towards the subarachnoid space in the direction opposite to blood flow. The glymphatic pathway (black arrows) clears waste from the ISF through the brain parenchyma, and comprises three functional components. (1) CSF influx, unidirectionally with blood flow, into the periarterial space (between the basement membrane of smooth muscle cells and pia mater), where the water component of CSF crosses astrocytic AQP4 channels to enter the brain parenchyma. CSF solutes can be cleared with astroglial transporters or channels, or can pass through the astrocytic endfeet clefts. (2) CSF–ISF exchange within the brain parenchyma. (3) CSF–ISF movement into the perivenous space of deep‑draining veins. Effluxed waste can then recirculate with the CSF, or eventually be absorbed into the lymphatic system. Arrows indicate direction of flow. Abbreviations: AQP4, aquaporin‑4; CSF, cerebrospinal fluid; ISF, interstitial fluid. Adapted with permission from AAAS: Science (Nedergaard, M. Garbage truck of the brain. Science. 2013;340:1529-1530). Reprinted by permission from Macmillan Publishers Ltd: Nature Reviews Neurology (Tarasoff-Conway, J.; Carare, R.; Osorio, R.; Glodzik, L.; Butler, T.; Fieremans, E.; Axel, L.; Rusinek, H.; Nicholson, C.; Zlokovic, B.; Frangione, B; Blennow, K.; Ménard, J.; Zetterberg, H.; Wisniewski, T.; Leon, M. Clearance systems in the brain—implications for Alzheimer disease. Nature Reviews Neurology. 2015;11:457-470), copyright 2015.

66

HUFFMAN ET. AL

clearance from the interstitium, the glymphatic flow hypothesis offers an alternative explanation.

Expanding prior research on perivascular flow (33,63,64,71,83), Iliff and colleagues employed in vivo two-photon microscopy to visualize the flow of CSF from the SAS into the parenchyma via periarterial spaces in real time (36). Their findings revealed a pathway of bulk flow allowing CSF to directly interact with ISF in the interstitium (36).

The exchange of CSF-ISF is a core aspect of the glymphatic hypothesis and is grounded by the flow of subarachnoid CSF into the parenchyma along periarterial pathways, across astrocyte endfeet, and into the interstitium (Figure 1). As CSF interacts with ISF, bulk flow facilitates the movement of solutes and metabolic waste toward perivenous pathways surrounding large draining veins and white matter tracks (36). Fluids entering these perivenous pathways drain from the parenchyma to the SAS and eventually to the cervical lymphatics.

CSF-ISF exchange in the brain interstitium is heavily reliant on the protein aquaporin-4 (AQP4), a water channel present on astrocyte endfeet that aids the movement of fluid in and out of perivascular spaces (36). Earlier studies have shown that basic diffusion is not sufficient for moving large molecular weight tracers over distances suitable for clearing the interstitial space, thus a system of ISF bulk flow must exist (1). According to the glymphatic hypothesis, the convective flow of CSF into the interstitial space is a key mechanism involved in the removal of solutes and metabolic waste from the interstitium. When Iliff and colleagues utilized AQP4 knockout mice to examine the relation between this water channel and solute clearance, their observations revealed that the removal of soluble Aβ was reduced by ~65% (36).

Thus, the AQP4 water channel is a main facilitator of CSF-ISF exchange.

Although the glymphatic hypothesis may simply be an extension to the retrograde flow of ISF, it is again important to note that scenarios permitting the efflux of ISF along periarterial pathways over glymphatic flow, or vice versa, remain to be determined. Figure 1 represents both mechanisms and how these different models may be associated with fluid movement and waste clearance. Pathways of ISF efflux

retrograde to blood flow occur within the vessel walls of arteries and capillaries, enabling the movement of solutes and waste along the basement membrane of the vascular smooth muscle (4,10,11,53). On the other hand, glymphatic flow utilizes the space between the basement membrane of the vascular smooth muscle and astrocyte endfeet and is involved with CSF-ISF exchange (36). Such models may help explain these seemingly opposing mechanisms; however, more research is required to understand whether inconsistent results (4,8,36) stem from anatomical differences (31,53) or unknown physiological conditions (31,66).

Meningeal Lymphatic Vessels The anatomical pathways by which CSF-ISF efflux from glymphatic flow reaches the cervical lymphatics remain controversial but are thought to drain into and spread from the SAS (66). Recent research has revealed novel meningeal lymphatic vessels associated with the dural sinuses of mice that may redefine the immunological basis of CNS function (5,50).

Fluorescent tracers injected into the cisterna magna and parenchyma drain along the lymphatic vessels of the meninges to the deep cervical lymph nodes (5,50). Of these lymphatic vessels identified so far, all have been shown to exit the base of the skull along draining veins, cranial nerves, and the pterygopalatine artery connected to the internal carotid artery (5). Others have also used the technique of in vivo hyperspectral imaging to show that quantum dot fluorescent nanoparticles injected into the cisterna magna drain to the cervical lymph nodes (51).

Although unable to identify specific efflux pathways, hyperspectral imaging with quantum dots may prove to be a uniquely noninvasive technique for gauging lymphatic drainage of the brain. Taken together, these findings provide convincing evidence that the CNS is far more connected to the peripheral environment than previously considered.

The meningeal lymphatic vessels have been recognized as a main route for CSF-ISF efflux from the SAS (35). As reviewed in Simon and Iliff, glymphatic flow drains parenchymal fluid along perivenous tracks to cisterns and may enable the communication between these lymphatic vessels and perivascular drainage (66). Such models have claimed that dural lymphatic

IMAGING THE PERIVASCULAR SPACE

vessels may be critically important in the removal and transport of macromolecules from CSF to peripheral lymph nodes (49). As such, the meningeal lymphatic vessels are a key target of future research.

IMAGING FLUID PATHWAYS IN ANIMALS Fluorescence Microscopy Fluorescence microscopy is the most common method for imaging fluid movement in brain slices and is subject to a number of excellent reviews (48,75,77). Standard procedures examining the movement of CSF/ISF use fluorescence microscopy to quantify light emitted from brain slices stained with dyes containing fluorophores. Fluorescent dyes are commonly administered to CSF by injection into the ventricles (intraventricular), cisterns (intracisternal), or spinal cord (intrathecal), while ISF is usually accompanied by injection directly into the brain (intracerebral). Both intraventricular and intracisternal injections are standard practice for studying the distribution of tracers throughout brain-wide networks of perivascular flow. Intracerebral injections are utilized when the activity and movement of solutes and waste of the ISF must be examined directly. While intrathecal injections are also used to examine global fluid movement, this application is perhaps transferable into clinical settings and is further explained in the next section (8).

Multiphoton microscopy is a powerful imaging technique capable of detailing the three-dimensional morphology of biological structures tagged with fluorescent stains (14,70). One major advantage of this imaging technique is the in vivo application to examine perivascular pathways in animals. Iliff et al. utilized in vivo two-photon microscopy to visualize the real-time movement of tracers along perivascular spaces (36). Their findings revealed that tracers injected into the CSF of the cisterna magna traveled along the exterior of arteries projecting into the brain from the SAS (36). Others have also used multiphoton microscopy to examine ISF clearance in vivo, showing that tracers injected intracerebrally accumulate around arteries and capillaries (4).

Despite the strengths, multiphoton microscopy is limited to cortical brain areas with imaging depths

67

restricted to ~250Âľm below the cortical surface in recent studies (36,37,45,79). This depth does not permit the measurement of fluid influx and efflux routes along subcortical brain regions in vivo, and thus requires the use of fluorescence microscopy for ex vivo analysis. While progress has been made to increase the imaging depth of multiphoton microscopy (26,32,44), present technology still limits the examination of CSF and ISF movement in real time to cortical areas.

Bedussi and colleagues have recently used serial brain slices to create computer-generated 3D models of the mouse vasculature (8). This innovative imaging technique has allowed the authors to visualize the distribution of tracers along brain-wide networks of perivascular flow. Their findings, specifically regarding ISF efflux, show that intracerebral tracers moved by bulk flow toward the ventricles (8) rather than perivenous pathways as proposed by the glymphatic hypothesis (36). This comparison helps frame the importance of utilizing more advanced imaging techniques to measure perivascular processes and demonstrates why further procedural and technical development is necessary to clarify inconsistent results.

Contrast-Enhanced MRI MRI allows for the noninvasive visualization of healthy or pathological brain tissue, which has been widely used for both clinical research and diagnostic purposes. Recent animal studies using MRI have utilized lumbar intrathecal injections of gadolinium-based contrasts and were successfully able to examine CSF-ISF exchange in vivo (20,34,81).

Additionally, the dispersion rates of contrasts with different molecular weights were analyzed. Regardless of molecular weight, contrasts were transported at similar rates, indicating that bulk flow, aside from basic diffusion, can be examined using contrast enhanced MRI (34). These results are consistent with the basic principles of the glymphatic hypothesis and show that bulk flow, which is the main mechanism driving interstitial clearance, can be examined via contrast-enhanced MRI (34,81). Unfortunately, contrast-enhanced MRI cannot yet identify specific biological variations that alter glymphatic flow.

68

HUFFMAN ET. AL

Recent efforts to overcome this limitation have shown promise utilizing mathematical models to define fluid shifts (61), yet additional work is needed for clinical application.

CLINICAL SIGNIFICANCE OF THE PERIVASCULAR SPACE Previous studies in animals have reported dysfunction within perivascular pathways in advanced aging though the etiology of this pathological marker remains unclear (45). Dilated perivascular spaces (DPS) can be present in healthy adults of all ages (22,46) and have traditionally been considered a benign variation in brain anatomy. Recent studies have established links between pathology of the perivascular space and clinical disorders such as AD, small vessel disease, and multiple sclerosis (15,19,56,60,78) though the causal direction of the association remains unclear. On MRI, DPS are commonly observed in three primary anatomical locations: 1) along the lenticulostriate arteries as they penetrate the basil ganglia, 2) the centrum semiovale, along the medullary arteries as they project into the cortical gray matter and extend into the white matter, and 3) the midbrain (30,58). DPS evident in the basal ganglia are more commonly attributed to vascular dementia (25), while DPS in white matter may be more indicative of AD (60).

Perivascular pathways are responsible for the clearance of proteins and metabolic waste from the interstitium, and their dilation has been implicated in dysregulated CSF flow, possibly due to the buildup of amyloid deposits (21). The accumulation of Aβ in the cerebral arteries, a condition referred to as cerebral amyloid angiopathy (CAA), is frequently observed in the aging brain. Buildup of Aβ leads to plaque formation within the perivascular space and is likely caused by the impaired drainage of Aβ (65,76). One leading hypothesis for the development of CAA is based on gradual hardening of cerebral vessels, resulting in a significant decrease in the rates of arterial pulsation (29,45). Decreases in arterial pulsation are thought to slow the drainage of solutes from the perivascular pathways, leading to Aβ deposition and protein aggregation within the leptomeningeal and cortical arteries (29,36,41,59).

While the exact cause of DPS is not fully known, some suggest that perivascular spaces become dilated when ISF fluid is retained as a result of impaired drainage (12,65). This impaired fluid movement could further exacerbate the buildup of proteins along pathways and intensify the reduction in arterial pulses, creating a feed-forward cycle and increasing the amount of Aβ deposits (4,12). Increased deposits of Aβ may exacerbate the dilation of perivascular spaces, causing constriction of the surrounding cerebral arteries and thus impairing overall neurovascular circulation and fluid movement (21). Correspondingly, animal models have exhibited significant decreases in ISF flow following the buildup of Aβ (4). Previous studies have linked CAA to the development of AD (24,73), with disruption of perivascular flow possibly present in both conditions. Further, Aβ and tau proteins are present in the interstitial space and are thought to be cleared along perivascular pathways (59,76,80). Collectively, these results suggest that the impact of aging on perivascular function may be a critical mechanism associated with AD pathology.

Kress and colleagues have observed a sizable decrease in Aβ clearance in aging mice (45). These older mice also displayed significantly reduced concentrations of AQP4 in astrocyte endfeet (45).

The redistribution of AQP4 has been linked to the pathology of AD (82), as it is required for CSF-ISF interchanges and the clearance of Aβ. As mentioned previously, the AQP4 knockout mice exhibit an ~65% reduction in the clearance of Aβ (36), suggesting that the decreased expression within these channels is involved in protein aggregation and plaque formation.

Dilated perivascular spaces, protein accumulation, and mislocalization of AQP4 are all pathological features observed in AD though it is likely they are less specific to AD and broadly applicable as a consequence of advanced aging.

Recent advances in imaging techniques may allow further insight into the functional implications of perivascular dysfunction. Given the small size of perivascular spaces, the advent of 7T MRI has shown the potential in enhancing detection and visibility of DPS (9,42,84). Clearer imaging may allow for quantitative measuring of DPS, which would aid in our understanding of the pathology of DPS in illnesses such as

IMAGING THE PERIVASCULAR SPACE

AD (Figure 2). Furthermore, as animal studies have successfully utilized contrast-enhanced MRI to examine global fluid motion in rodents, these MR-based imaging techniques may have an important clinical application in the future. Current clinical practice utilizes intrathecal injections of gadolinium-based contrasts to examine CSF leakage in disorders such as intracranial hypotension syndrome and CSF rhinor-

69

rhea (3,6). A recent clinical case study conducted by Eide and Ringstad also examined the distribution of intrathecal injected gadolinium-based contrast agent throughout the brain, which the authors attribute to glymphatic transport (17). As these results have yet to be further substantiated (17), conclusions cannot be fully drawn regarding future application. With that said, using gadolinium-based contrast materials

Figure 2. 7T MRI DPS The observation of brain perivascular spaces. A representative slice of T2- weighted high resolution brain MR image at 7 T from a healthy subject (a) and an AD patient (b). (c) and (d) are magnified images from the regions of interest marked by yellow squares in(a) and (b) respectively. Red arrows show examples of PVSs with hyperintensity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article: http://dx.doi.org/10.1016/j.jneumeth.2015.09.001). Reprinted by permission from Elsevier: The Journal of Neuroscience Methods (Cai, K.; Tain, R.; Das, S.; Damen, F.C.; Sui, Y.; Valyi-Nagy, T.; Elliot, M.A.; Zhou, X.J. The feasibility of quantitative MRI of perivascular spaces at 7T. Journal of Neuroscience Methods. 2015;256:151-156), copyright 2015.

70

HUFFMAN ET. AL

in MRI may have future potential in gaining a better understanding of the perivascular flow in humans.

Successful visualization of CSF and ISF flow in clinically relevant brain regions may help uncover the neuropathology of AD and related disorders.

SUMMARY CSF-ISF movement throughout the brain is a central mechanism to the removal of metabolic waste associated with the pathology of neurodegenerative disorders. Clearing the interstitial space is driven by either the bulk flow of ISF along periarterial pathways (53) or the glymphatic flow of subarachnoid CSF into the parenchyma and subsequent CSF-ISF exchange (36). Fluid cleared from the interstitium then interacts with CSF absorption pathways (57) or drains along the meningeal lymphatic vessels (5,50). While these clearance routes shed light on a possible means of removing proteins such as Aβ, current technology is limited in its ability to image the movement of CSF and ISF along perivascular pathways while maintaining clinical significance.

Whereas advanced imaging in animal models, such as two-photon microscopy, has produced insights into the structural and functional characteristics of fluid movement, human studies are needed to similarly characterize these dynamics.

Numerous clinical studies have utilized MRI to examine dilated perivascular spaces, which may be a marker of impaired fluid movement in the perivascular system (12,15,22,30,53,56). While these spaces are implicated in a number of neurological disorders, imaging preexisting dilations does not permit insight into the underlying mechanisms. The potential for CSF flow dynamics to influence onset or progression of neuropathological disorders raises the importance of needed technical advances to examine these processes. Gadolinium-based contrast materials for MRI have potential for validating perivascular activity in humans and fluctuations in fluid movement that may lead to the onset and progression of neurodegenerative diseases.

ACKNOWLEDGMENTS The authors declare no conflict of interest.

REFERENCES 1. Abbott, N. Evidence for bulk flow of brain interstitial fluid: significance for physiology and pathology. Neurochem. Int. 45:545-552; 2004.

2. Abdullah, A.; Hirayama, A.; Yatsushiro, S.; Matsumae, M.; Kuroda, K. Cerebrospinal fluid pulsatile segmentation - a review. In 5th Biomed.

Engin. Int. Conf. Ubon Ratchathani, Thailand: 2012.

3. Akbar, J.J.; Luetmer, P.H.; Schwartz, K.M.; Hunt, C.H.; Diehn, F.E.; Eckel, L.J. The role of MR myelography with intrathecal gadolinium in localization of spinal CSF leaks in patients with spontaneous intracranial hypotension. Am. J. Neuroradiol. 33:535-540; 2012.

4. Arbel-Ornath, M.; Hudry, E.; Eikermann-Haerter, K.; Hou, S.; Gregory, J.; Zhao, L.; Betensky, R.; Frosch, M.; Greenber S.; Bacskai, B. Interstitial fluid drainage is impaired in ischemic stroke and Alzheimer’s disease mouse models. Acta.

Neuropathol. 126:353-364; 2013 5. Aspelund, A.; Antila, S.; Proulx, S.; Karlsen, T.; Karaman, S.; Detmar, M.; Wiig, H.; Alitalo, K. A dural lymphatic vascular system that drains brain interstitial fluid and macromolecules. J. Exp. Med. 212:991-999; 2015.

6. Aydin, K.; Terzibasioglu, E.; Sencer, S.; Sencer, A.; Suoglu, Y.; Karasu, A.; Kiris, T.; Turantan, M.I. Localization of cerebrospinal fluid leaks by gadolinium-enhanced magnetic resonance cisternography: a 5-year single center experience.

Neurosurgery 62:584-589; 2008.

7. Battal, B.; Kocaoglu, M.; Bulakbasi, N.; Husmen, G.; Sanal, H.; Tayfun, C. Cerebrospinal fluid flow imaging by using phase-contrast MR technique.

Brit. J. Radiol. 84:758-765; 2011.

8. Bedussi, B.; G. J. T. B. van Lier, M.; Bartstra, J.; Vos, J.; Siebes, M.; Vanbavel, E.; Bakker, E. Clearance from the mouse brain by convection of interstitial fluid towards the ventricular system.

Fluids Barriers CNS 12:1-13; 2015.

9. Cai, K.; Tain, R.; Das, S.; Damen, F.C.; Sui, Y.; Valyi-Nagy, T.; Elliot, M.A.; Zhou, X.J. The feasibility of quantitative MRI of perivascular spaces at 7T. J. Neurosci. Meth. 256:151-156; 2015.

IMAGING THE PERIVASCULAR SPACE

10. Carare, R.; Bernardes-Silva, M.; Newman, T.; Page, A.; Nicoll, J.; Perry, V.; Weller, R. Solutes, but not cells, drain from the brain parenchyma along basement membranes of capillaries and arteries: significance for cerebral amyloid angiopathy and neuroimmunology. Neuropathol.

Appl. Neurobiol. 34:131-144; 2008.

11. Carare, R.; Teeling, J.; Hawkes, C.; Püntener, U.; Weller, R.; Nicoll, J.; Perry, V. Immune complex formation impairs the elimination of solutes from the brain: Implications for immunotherapy in Alzheimer’s disease. Acta. Neuropathol.

Commun. 1:1-11; 2013.

12. Charidimou, A.; Jaunmuktane, Z.; Baron, J.; Burnell, M.; Varlet, P.; Peeters, A.; Xuereb, J.; Jäger, R.; Brandner, S.; Werring, D. White matter perivascular spaces: an MRI marker in pathology-proven cerebral amyloid angiopathy? Neurology 82:57–62; 2014.

13. Chen, L.; Beckett, A.; Verma, A.; Feinberg, D. Dynamics of respiratory and cardiac CSF motion revealed with real-time simultaneous multi-slice EPI velocity phase contrast imaging. Neuroimage 122:281-287; 2015.

14. Denk, W.; Strickler, J.; Webb, W. Two-photon laser scanning fluorescence microscopy. Science 248:73-76; 1990.

15. Doubal, F.; MacLullich, A.; Ferguson, K.; Dennis, M.; Wardlaw, J. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease.

Stroke 41:450–454; 2010.

16. Dreha-Kulaczewski, S.; Joseph, A.; Merboldt, K.; Ludwig, H.; Gartner, J.; Frahm, J. Inspiration is the major regulator of human CSF flow. J. Neurosci. 35:2485-2491; 2015.

17. Eide, P.K.; Ringstad, G. MRI with intrathecal MRI gadolinium contrast medium administration: a possible method to assess glymphatic function in human brain. Acta.

Radiol. 4(11):2058460115609635; 2015.

18. Engelhardt, B.; Sorokin, L. The blood–brain and the blood–cerebrospinal fluid barriers: function and dysfunction. Semin. Immunopathol. 31:497511; 2009.

19. Etemadifar, M.; Hekmatnia, A.; Tayari, N.; Kazemi, M.; Ghazevi, A.; Akbari, M.; Maghzi,

71

A.H. Features of Virchow-Robin spaces in newly diagnosed multiple sclerosis patients. Eur. J. Radiol.80(2):e104-108; 2010.

20. Gaberel, T.; Gakuba, C.; Goulay, R.; Lizarrondo, S.; Hanouz, J.; Emery, E.; Touze, E.; Vivien, D.; Gauberti, M. Impaired glymphatic perfusion after strokes revealed by contrast-enhanced MRI: a new target for fibrinolysis? Stroke 45:30923096; 2014.

21. Gallina, P.; Scollato, A.; Conti, R.; Di Lorenzo, N.; Porfirio, B. Aβ clearance, “hub” of multiple deficiencies leading to Alzheimer disease. Front.

Aging Neurosci. 7; 2015.

22. Groeschel, S.; Chong, WK.; Surtees, R.; Hanefeld, F. Virchow–Robin spaces on magnetic resonance images: normative data, their dilatation, and a review of the literature. Neuroradiology 48: 745–754; 2006.

23. Hadaczek, P.; Yamashita, Y.; Mirek, H.; Tamas, L.; Bohn, M.; Noble, C.; Park, J; Bankiewicz, K. The “perivascular pump” driven by arterial pulsation is a powerful mechanism for the distribution of therapeutic molecules within the Brain. Mol. Ther. 14:69-78; 2006.

24. Haglund, M.; Kalaria R.; Slade, J.; Englund, E. Differential deposition of amyloid beta peptides in cerebral amyloid angiopathy associated with Alzheimer’s disease and vascular dementia. Acta.

Neuropathol. 111:430–435; 2006.

25. Hansen, T.; Cain, J.; Thomas, O.; Jackson, A. Dilated perivascular spaces in the basal ganglia are a biomarker of small-vessel disease in a very elderly population with dementia. Am. J. Neuroradiol. 36:893-898; 2015 26. Hama, H.; Kurokawa, H.; Kawano, H.; Ando, R.; Shimogori, T.; Noda, H.; Fukami, K.; SakaueSawano, A.; Miyawaki, A. Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain. Nat. Neurosci. 14:481-1488; 2011.

27. Hasiloglu, Z.; Albayram, S.; Gorucu, Y.; Selcuk, H.; Cagil, E.; Erdemli, H.; Adaletli, I. Assessment of CSF flow dynamics using PC-MRI in spontaneous intracranial hypotension. Headache 52:808-819; 2012.

28. Hawkes, C.; Gatherer, M.; Sharp, M.; Dorr,

72

29.

30.

31.

32.

33.

34.

35.

36.

37.

HUFFMAN ET. AL A.; Yuen, H.; Kalaria, R.; Weller, R.; Carare, R. Regional differences in the morphological and functional effects of aging on cerebral basement membranes and perivascular drainage of amyloid-β from the mouse brain. Aging Cell 12:224236; 2013.

Hawkes, C.; Härtig, W.; Kacza, J.; Schliebs, R.; Weller, R.; Nicoll, J.; Carare, R. Perivascular drainage of solutes is impaired in the ageing mouse brain and in the presence of cerebral amyloid angiopathy. Acta. Neuropathol. 121:431-443; 2011.

Heier L.; Bauer C.; Schwartz L.; Zimmerman R.; Morgello S.; Deck M. Large VirchowRobin spaces: MR-clinical correlation. Am. J. Neuroradiol. 10:929–936; 1989.

Hladky, S.; Barrand, M. Mechanisms of fluid movement into, through and out of the brain: evaluation of the evidence. Fluids Barriers CNS 11:1-32; 2014.

Horton, N.; Wang, K.; Kobat, D.; Wise, F.; Xu, C. In vivo three-photon microscopy of subcortical structures within an intact mouse brain.

Conference on Lasers and Electro-Optics 2012.

Munich, Germany: 2013.

Ichimura, T.; Fraser, P.; Cserr, H. Distribution of extracellular tracers in perivascular spaces of the rat brain. Brain Res. 545:103-113; 1991.

Iliff, J.; Lee, H.; Yu, M.; Feng, T.; Logan, J.; Nedergaard, M.; Benveniste, H. Brain-wide pathway for waste clearance captured by contrast-enhanced MRI. J. Clin. Invest. 123:1299-1309; 2013.

Iliff, J.; Goldman, S.; Nedergaard, M. Implications of the discovery of brain lymphatic pathways.

Lancet Neurol. 14:977-979; 2015.

Iliff, J.; Wang, M.; Liao, Y.; Plogg, B.; Peng, W.; Gundersen, G.; Benveniste, H.; Edward Vates, G.; Deane, R.; Goldman, S.; Nagelhus, E.; Nedergaard, M. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid. Sci. Transl. Med. 4:1-11; 2012.

Iliff, J.; Wang, M.; Zeppenfeld, D.; Venkataraman, A.; Plog, B.; Liao, Y.; Deane, R.; Nedergaard, M. Cerebral arterial pulsation drives paravascular CSF-interstitial fluid exchange in the murine brain. J. Neurosci. 33:18190-18199; 2013.

38. Jessen, N.; Munk, A.; Lundgaard, I.; Nedergaard, M. The glymphatic system: a beginner’s guide.

Neurochem. Res. 2583-2599; 2015.

39. Johanson, C.; Duncan, J.; Klinge, P.; Brinker, T.; Stopa, E.; Silverberg, G. Multiplicity of cerebrospinal fluid functions: new challenges in health and disease. Cerebrospinal Fluid Res. 5:1-32; 2008.

40. Kartal, M.; Algin, O. Evaluation of hydrocephalus and other cerebrospinal fluid disorders with MRI: an update. Insights Imaging 5:531-541; 2014.

41. Keable, A.; Fenna, K.; Yuen, H.M.; Johnston, D.A.; Smyth, N.R.; Smith, C.; Salman, R.A.; Samarasekera, N.; Nicoll, J.A.R.; Attems, J.; Kalaria, R.N.; Weller, R.O.; Carare, R.O. Deposition of amyloid β in the walls of human leptomeningeal arteries in relation to perivascular drainage pathways in cerebral amyloid angiopathy. Biochim. Biophys. Acta 2015.

42. Kilsdonk, I.D.; Steenwijk, M.D.; Pouwels, P.J.W.; Zwanenburg, J.J.M.; Visser, F.; Luijten, P.R.; Geurts, J.J.G.; Barkhof, F.; Wattjes, M.P. Perivascular spaces in MS patients at 7 Tesla MRI: a marker of neurodegeneration? Mult.

Scler. 21:155-162; 2015.

43. Kiviniemi, V.; Wang, X.; Korhonen, V.; Keinanen, T.; Tuovinen, T.; Autio, J.; LeVan, P.; Keilholz, S.; Zang, Y-F.; Hennig, J.; Nedergaard, M. Ultra-fast magnetic resonance encephalography of physiological brain activity - glymphatic pulsation mechanisms? J. Cereb. Blood Flow Metab. 1-13; 2015.

44. Kobat, D.; Horton, N.; Xu, C. In vivo two-photon microscopy to 1.6-mm depth in mouse cortex. J. Biomed. Opt. 16:106014-4; 2011.

45. Kress, B.; Iliff, J.; Xia, M.; Wang, M.; Wei, H.; Zeppenfeld, D.; Xie, L.; Kang, H.; Xu, Q.; Liew, J.; Plog, B.; Ding, F.; Deane, R.; Nedergaard, M. Impairment of paravascular clearance pathways in the aging brain. Ann. Neurol. 76:845-861; 2014.

46. Kwee, R.; Kwee, T. Virchow-Robin spaces at MR imaging. Radiographics 27:1071-1086; 2007.

47. Lee, H.; Xie, L.; Yu, M.; Kang, H.; Feng, T.; Deane, R.; Logan, J.; Nedergaard, M.; Benveniste, H. The effect of body posture on brain glymphatic

IMAGING THE PERIVASCULAR SPACE

transport. J. Neurosci. 35:11034-11044; 2015.

48. Lichtman, J.; Conchello, J. Fluorescence microscopy. Nature Methods 2:910-919; 2005.

49. Louveau, A.; Harris, T.; Kipnis, J. Revisiting the mechanisms of CNS immune privilege. Trends Immun. 36:569-577; 2015.

50. Louveau, A.; Smirnov, I.; Keyes, T.; Eccles, J.; Rouhani, S.; Peske, J.; Dereckil, N.; Castle, D.; Mandell, J.; Lee1, K.; Harris, T.; Kipnis, J. Structural and functional features of central nervous system lymphatic vessels. Nature 523: 337-341; 2015.

51. Mathieu, E.; Gupta, N.; Macdonald, R.; Ai, J.; Yücel, Y. In vivo imaging of lymphatic drainage of cerebrospinal fluid in mouse. Fluids Barriers CNS 10:1-5; 2013.

52. Matsumae, M.; Hirayama, A.; Atsumi, H.; Yatsushiro, S.; Kuroda, K. Velocity and pressure gradients of cerebrospinal fluid assessed with magnetic resonance imaging. J. Neurosurg.

120:218-227; 2014.

53. Morris, A.; Carare, R.; Schreiber, S.; Hawkes, C. The cerebrovascular basement membrane: role in the clearance of β-amyloid and cerebral amyloid angiopathy. Front. Aging Neurosci. 6:1-9; 2014.

54. Mortazavi, M.; Adeeb, N.; Griessenauer, C.; Sheikh, H.; Shahidi, S.; Tubbs, R.; Tubbs, R. The ventricular system of the brain: a comprehensive review of its history, anatomy, histology, embryology, and surgical considerations. Childs Nerv.

Syst. 30:19-35; 2014.

55. Nedergaard, M. Garbage truck of the brain.

Science 340:1529-1530; 2013.

56. Patankar, T.F.; Mitra, D.; Varma A.; Snowden, J.; Neary, D.; Jackson, A. Dilatation of the VirchowRobin space is a sensitive indicator of cerebral microvascular disease: study in elderly patients with dementia. A. J. Neuroradiol. 26:1512–1520; 2005.

57. Pollay, M. The function and structure of the cerebrospinal fluid outflow system. Cerebrospinal Fluid Res. 7:1-20; 2010.

58. Potter, G.M.; Chappell, F.M.; Morris, Z.; Wardlaw, J.M. Cerebral perivascular spaces visible on magnetic resonance imaging: development of a qualitative rating scale and its observer reliability. Cerebrovasc. Dis. 39:224-231; 2015.

73

59. Preston, S.D.; Steart, P.V.; Wilkinson, A.; Nicoll, J.A.R.; Weller, R. O. Capillary and arterial cerebral amyloid angiopathy in Alzheimer’s disease: defining the perivascular route for the elimination of amyloid β from the human brain. Neuropath. Appl. Neuro. 29:106-117; 2003.

60. Ramirez, J.; Berezuk, C.; McNeely, A.; Scott, C.; Gao, F.; Black, S. Visible Virchow-Robin spaces on magnetic resonance imaging of Alzheimer’s disease patients and normal elderly from the Sunnybrook dementia study. JAD 43:415-424; 2015.

61. Ratner, V.; Zhu, L.; Kolesov, I.; Nedergaard, M.; Benveniste, H.; Tannenbaum, A. Optimalmass-transfer-based estimation of glymphatic transport in living brain. Medical Imaging 2015: Image Processing. 9413; 2015.

62. Redzic, Z. Molecular biology of the blood-brain and the blood-cerebrospinal fluid barriers: similarities and differences. Fluids Barriers CNS 8:1-25; 2011.

63. Rennels, M.; Gregory, T.; Blaumanis, O.; Fujimoto, K.; Grady, P. Evidence for a ‘paravascular’ fluid circulation in the mammalian central nervous system, provided by the rapid distribution of tracer protein throughout the brain from the subarachnoid space. Brain Res. 326:47-63; 1985.

64. Rennels, M.; Blaumanis, O.; Grady, P. Rapid solute transport throughout the brain via paravascular fluid pathways. Adv. Neurol. 52:431-439; 1990.

65. Roher, A.E.; Kuo, Y.M.; Esh, C.; Knebel, C.; Weiss, N.; Kalback, W.; Luehrs, D.C.; Childress, J.L.; Beach, T.G.; Weller, R.O.; Kokjohn, T.A. Cortical and leptomeningeal cerebrovascular amyloid and white matter pathology in Alzheimer’s disease. Mol. Med. 9:112; 2003.

66. Simon, M.; Iliff, J. Regulation of cerebrospinal fluid (CSF) flow in neurodegenerative, neurovascular and neuroinflammatory disease. Biochim.

Biophys. Acta 30:234-239; 2015.

67. So, P.; Dong, C.; Masters, B.; Berland, K. Twophoton excitation fluorescence microscopy.

Annu. Rev. Biomed. Eng. 399-429; 2000.

68. Spector, R.; Snodgrass, S.; Johanson, C. A balanced view of the cerebrospinal fluid composition and functions: focus on adult humans. Exp.

74

HUFFMAN ET. AL

Neurol. 273:57-68; 2015.

69. Stivaros, S.; Jackson, A. Changing concepts of cerebrospinal fluid hydrodynamics: role of phase-contrast magnetic resonance imaging and implications for cerebral microvascular disease.

Neurotherapeutics 4:511-522; 2007.

70. Svoboda, K.; Yasuda, R. Principles of two-photon excitation microscopy and its applications to neuroscience. Neuron 50:823-839; 2006.

71. Szentistvanyi, I.; Patlak, C.; Ellis, R.; Cserr, H. Drainage of interstitial fluid from different regions of rat brain. Am. J. Physiol-Renal.

246:F835-F844; 1984 72. Tarasoff-Conway, J.; Carare, R.; Osorio, R.; Glodzik, L.; Butler, T.; Fieremans, E.; Axel, L.; Rusinek, H.; Nicholson, C.; Zlokovic, B.; Frangione, B; Blennow, K.; Ménard, J.; Zetterberg, H.; Wisniewski, T.; Leon, M. Clearance systems in the brain—implications for Alzheimer disease.

Nat. Rev. Neurol. 11:457-470; 2015.

73. Tian, J.; Shi, J.; Smallman, R.; Iwatsubo, T.; Mann, D.M. Relationships in Alzheimer’s disease between the extent of Abeta deposition in cerebral blood vessel walls, as cerebral amyloid angiopathy, and the amount of cerebrovascular smooth muscle cells and collagen. Neuropathol.

Appl. Neuro. 32:332–340; 2006.

74. Wang, P.; Olbricht, W. Fluid mechanics in the perivascular space. J. Theor. Biol. 274:52-57; 2011.

75. Webb, D.; Brown, C. Epi-fluorescence microscopy. Methods Mol. Bio. 931:29-59; 2013.

76. Weller, R.O.; Massey, A.; Kuo, Y.M.; Roher, A.E. Cerebral amyloid angiopathy: accumulation of Aβ in interstitial fluid drainage pathways in Alzheimer’s disease. Ann. NY. Acad. Sci. 903:110117; 2000.

77. Wolf, D. Fundamentals of fluorescence and fluorescence microscopy. Method Cell Biol. 81:6391; 2007.

78. Wuerfel, J.; Haertle, M.; Waiczies, H.; Tysiak, E.; Bechmann, I.; Wernecke, K.D.; Zipp., F.; Paul, F. Perivascular spaces—MRI marker of inflammatory activity in the brain? Brain 131:2332-2340; 2008.

79. Xie, L., Kang, H., Xu, Q., Chen, M., Liao, Y., Thiyagarajan, M.; O’Donnell, J.; Christensen, J.; Nicholson, C.; Iliff, J.; Takano, T.; Deane, R.; Nedergaard, M. Sleep drives metabolite clearance from the adult brain. Science 342:373-377; 2013.

80. Yamada, K.; Holth, J.; Liao, F.; Stewart, F.; Mahan, T.; Jiang, H.; Cirrito, J.; Patel, T.; Hochgrafe, K.; Mandelkow, E.; Holtzman, D. Neuronal activity regulates extracellular tau in vivo. J. Exp. Med. 211:387-393; 2014.

81. Yang, L.; Kress, B.; Weber, H.; Thiyagarajan, M.; Wang, B.; Deane, R.; Benveniste, H.; Iliff, J.; Nedergaard, M. Evaluating glymphatic pathway function utilizing clinically relevant intrathecal infusion of CSF tracer. J. Transl. Med. 1:1-9; 2013.

82. Yang, J.; Lundeb, L.K.; Nuntagijb, P.; Oguchib, T.; Camassab, L.M.; Nilssone, L.N.; Lannfelt, L.; Xu, Y.; Amiry-Moghaddam, M.; Otterson, O.P.; Torp, R. Loss of astrocyte polarization in the tg-ArcSwe mouse model of Alzheimer’s. J. Alzheimers Dis. 27:711-722; 2011.

83. Zhang, E.; Richards, H.; Kida, S.; Weller, R. Directional and compartmentalised drainage of interstitial fluid and cerebrospinal fluid from the rat brain. Acta Neuropathol. 83(3):233-239; 1984.

84. Zong, X.; Park, S.H.; Shen, D.; Lin, W. Visualization of perivascular spaces in the human brain at 7T: sequence optimization and morphology characterization. Neuroimage 125:895-902; 2016.

Technology and Innovation, Vol. 18, pp. 75-77, 2016 Printed in the USA. All rights reserved.

Copyright © 2016 National Academy of Inventors.

ISSN 1949-8241 • E-ISSN 1949-825X http://dx.doi.org/10.21300/18.1.2016.75 www.technologyandinnovation.org

THE PILLARS OF PATENT QUALITY Alex Camarota Office of Innovation Development, United States Patent and Trademark Office, Alexandria, VA, USA The Enhanced Patent Quality Initiative at the USPTO is an ongoing, comprehensive initiative to strengthen the quality of patents issued by the USPTO. Programs in the initiative focus the agency’s efforts on improving work products, measuring patent quality, and increasing customer service. In addition to the initiative, the USPTO is also working toward incorporating artificial intelligence and big data in its efforts to improve the quality of patent examination.

Key words: Patent quality; Innovation; USPTO; Patents; Enhanced Patent Quality Initiative

It wasn’t so long ago that the most valuable assets of companies were tangible: inventories, technical infrastructure, warehouses, and so on. Today, however, and particularly in the United States, the valuable assets of many of the most innovative companies are all intangible: inventions, algorithms, processes, designs, and brands—their intellectual property (IP).

But unlike their tangible counterparts, IP assets are far more vulnerable to theft and misappropriation.

Not only is this a threat to the livelihood of companies that operate on the basis of commercializing their IP assets, but it is also a threat to scientific and technological development in general.

IP rights, and specifically those embedded in patents—the ability to exclude others from making, using, selling, importing, or offering to sell an invention for a limited time—are not merely incentives for companies to increase their earnings potential.

On the contrary, patents in the United States have always existed, as the Constitution directs in Article 1, Section 8, “To promote the Progress of Science and Useful Arts.” Successfully balancing the transfer

of knowledge to the public in exchange for limited monopolies hinges on the integrity of the system— on its ability to discern which inventions are legally entitled to protection in the marketplace while also protecting the ability of others to invent around and improve upon them.

“Patent quality” does not have a standard definition. At a fundamental level, it describes the degree to which an issued patent fulfills statutory requirements to be granted, which are to be “novel” and “nonobvious.” Beyond that point, however, it can mean different things to different viewpoints based on different contexts. For example, an overly broad patent being used by a non-practicing entity to threaten lawsuits of dubious merit might be described as poor quality.

But a patent with claims that do not embody strong legal protection and thus places the holder at risk for infringement could also be categorized as poor quality.

The United States Patent and Trademark Office (USPTO) takes a holistic approach to addressing patent quality. Ultimately, every employee, function,

_____________________ Accepted December 10, 2015.

Address correspondence to Alex Camarota, Office of Innovation Development, U.S. Patent and Trademark Office, 600 Dulany Street, Alexandria, VA 22314; E-mail: Alexander.Camarota@uspto.gov

75

76 CAMAROTA and process in the Patents organization contributes to patent quality in some way or another, whether it is the specialist who reviews applications for errors prior to examination or the patent examiner who actually performs it, or the hundreds of personnel who provide technical, legal, training, research, and countless other types of support.

In February 2015, Under Secretary of Commerce for Intellectual Property and Director of the USPTO Michelle K. Lee (Director Lee was acting head of the USPTO at this time and confirmed by the Senate on March 9, 2015) launched the Enhanced Patent Quality Initiative (EPQI). This ongoing initiative is the most comprehensive effort the USPTO has ever undertaken to improve the quality of the patents and touches all business units within the Patents organization. It has also necessitated the creation of an entirely new business unit, headed by a Deputy Commissioner for Patent Quality and staffed by senior Patents personnel, whose focus is to analyze, improve, and ensure patent quality and oversee the implementation of EPQI-related programs. At every step, the USPTO has enlisted the help of the public to shape pilots and programing and offer input on ways to improve work processes, including forums, notices in the federal register, and a monthly patent quality chat webinar series that takes place on the second Tuesday of every month and is broadcasted live over the Internet.

Notably, the inaugural event of the EPQI was a patent quality summit hosted at the USPTO headquarters in Alexandria, Virginia, in which stakeholders from across the innovation community were invited to participate and offer their viewpoints on improving patent quality. As an example of how seriously the USPTO values the public’s opinion, all patent examiners were required to watch a recording of the summit as part of their mandatory training. The video, along with all components related to the EPQI, can be accessed by the general public on the USPTO website.

The EPQI has identified three “pillars” that contribute to overall patent quality: • Pillar 1: Excellence in Work Products • Pillar 2: Excellence in Measuring Patent Quality • Pillar 3: Excellence in Customer Service

As organizational units, each pillar helps the USPTO target its efforts to enhance patent quality. They also help to simplify an incredibly complex and expansive initiative and help frame and facilitate the conversation about patent quality for both experienced and novice audiences.

The first pillar includes many key EPQI programs that enhance the job of patent examiners. Some of the programs include the development of entirely new protocols and processes, while other programs raise awareness and provide better training and usage of resources already available to examiners, of which there are many.

The second pillar encompasses an extremely important aspect of patent quality enhancement: metrics. Developing metrics for patent quality has always been a challenge, not just for the USPTO but for all intellectual property offices around the world.

This difficulty is connected to the prior-mentioned problem regarding the amorphous nature of the term “patent quality.” While quality metrics already exist at the USPTO, Pillar 2 programs are taking advantage of lessons learned in recent years, retiring outdated or ineffective metrics and developing new ones that translate into higher accuracy and usefulness.

The third pillar focuses on the USPTO’s interaction with customers and includes re-evaluating current pilot programs for ways to heighten their effectiveness at facilitating patent prosecution. A new component falling under Pillar 3 involves enhancing examiner-applicant interview practice. Interviews are routinely viewed by both parties as effective and efficient ways to resolve issues in an examination.

Pillar 3 adds an Interview Specialist program that designates points of contact to act as resources for interview policy, assists remote examiners in interviews when an on-campus presence is required, and provides technical assistance to examiners and applicants during the interview.

These are just a small sampling of the ways in which the EPQI is addressing patent quality. The entire list may be reviewed on the USPTO’s website.

Outside the scope of the EPQI, the USPTO is working on exciting ways to enhance patent quality by harnessing the power of big data analysis and artificial intelligence.

THE PILLARS OF PATENT QUALITY

With over 8,300 patent examiners, one million pending cases at any given time, and 600,000 new applications submitted each year, an immense quantity of patent data is generated and stored on computer servers at the USPTO every single day. Each application submitted to the USPTO will eventually contain a prosecution history detailing the office actions, revisions, changes, interactions between applicant and examiner, and other details about the case. Along with such data, the USPTO holds, quite possibly, the largest repository of technical information known to mankind. With over nine million issued patents, millions more rejected applications, drawings, supporting documents, foreign patent literature, peer-reviewed articles, manuals, and any other piece of relevant information, the amount of prior art easily comprises billions of pages.

Such data presents a ripe opportunity. In 2015, Director Lee challenged a small internal team to develop innovative ways for utilizing this information.

It certainly helps that Director Lee not only grasps the concepts of big data and machine learning, but understands it at a practicing level, having received her master’s degree while working in the Artificial Intelligence Lab at MIT and then later serving as the head of patent strategy at Google during its formative years.

77

Already, the USPTO is developing algorithms to identify trends in patent examination and areas for improvement, which will result in further targeted training including training, potentially, by technology area. In addition, artificial intelligence offers the promise of supplementing an examiner’s manual searches of prior art with automated searches using artificial intelligence techniques.

Along with the ongoing development of big data and artificial intelligence to enhance patent quality, the USPTO is also inviting the public to join in the pursuit of data mining and making use of that data.

In fact, by the time this article publishes, the USPTO will have released application program interfaces (APIs) to its patent data for the first time. This will allow computer programmers to explore USPTO data according to their own interests, curiosity, or business needs.

The Enhanced Patent Quality Initiative, as well as the use of big data and artificial intelligence to assist in searching and data mining, promises to open a new age in patent examination practice at the USPTO and beyond. As “America’s Innovation Agency,” the USPTO is living up to that motto by not just protecting the ideas and inventions of the world, but also adding to them.

Technology and Innovation, Vol. 18, pp. 79-82, 2016 Printed in the USA. All rights reserved.

Copyright © 2016 National Academy of Inventors.

ISSN 1949-8241 • E-ISSN 1949-825X http://dx.doi.org/10.21300/18.1.2016.79 www.technologyandinnovation.org

THE NAI FELLOW PROFILE: AN INTERVIEW WITH DR. FRANCES ARNOLD Frances Arnold1, Kimberly A. Macuare2 Department of Chemical Engineering, California Institute of Technology, Pasadena, CA, USA 2 National Academy of Inventors, Tampa, FL, USA

1

In an interview with T&I, distinguished inventor and professor Dr. Frances Arnold discusses her most recent work and shares her thoughts on the benefits of her eclectic background, the divide between pure and applied science, and teaching students to innovate.

INTRODUCTION As a part of our continuing mission to honor academic invention and inventors, Technology and Innovation (T&I) is pleased to present Dr. Frances Arnold, renowned biochemist and chemical engineer, as the subject of this issue’s NAI Fellow Profile.

Arnold is the Dick and Barbara Dickinson Professor of Chemical Engineering, Bioengineering and Biochemistry and the director of the Donna and Benjamin M. Rosen Bioengineering Center at the California Institute of Technology.

Arnold holds a B.S. in mechanical and aerospace engineering from Princeton University and a Ph.D. in chemical engineering from the University of California, Berkeley. After completing postdoctoral work in chemistry at UC Berkeley and Caltech, she became a faculty member at Caltech, where she remains today. She is the author of over 200 peer-reviewed publications and numerous book chapters, commentaries, and reviews; the editor of three books; and inventor on 49 U.S. patents. She is the recipient of numerous prestigious awards, including the National Medal of Technology and Innovation and the Charles Stark Draper Prize, as well as one of the rare scholars who has achieved the distinction of being elected to

(photo courtesy of Frances Arnold)

_____________________ Accepted December 10, 2015.

Address correspondence to: Frances Arnold, Ph.D., California Insitute of Technology 210-41, 1200 East California Boulevard, Pasadena, California, 91125, USA Kimberly A. Macaure, Ph.D., Assistant Editor, Technology and Innovation, Journal of the National Academy of Inventors® at the USF Research Park, 3702 Spectrum Boulevard, Suite 165, Tampa, FL 33612, USA; Tel: +1 (813) 974-1347; E-mail: tijournal@academyofinventors.org

79

80

THE NAI PROFILE

the U.S. National Academies of Science, Engineering, and Medicine and elected a fellow of the National Academy of Inventors, the American Association for the Advancement of Science, and the American Academy of Arts and Sciences.

Arnold has been lauded for her pioneering work in directed evolution, which allows for the engineering of proteins through the recombination or mutation of genes, followed by a careful artificial selection process to choose the desired traits. The resulting engineered proteins have the potential to revolutionize how we solve major global issues in health care, agriculture, and sustainable energy, among many other areas. For example, her work with the P450 enzyme has opened new, sustainable routes to pharmaceutical intermediates, safer ways to protect crops, and enhanced neuroimaging. The Arnold research group is a breeding ground for some of the most innovative work in bioengineering being done today, and, a gifted teacher as well as researcher, Arnold is also fostering a new generation of innovators in her laboratory.

T&I was honored when Dr. Arnold agreed to discuss her most recent work and to share her thoughts on the benefits of her eclectic background to her success, the divide between pure and applied science, and strategies for teaching students to innovate.

you feel about having traversed such a wide intellectual terrain. In terms of invention and innovation, what advantages do your multiple perspectives offer.

Arnold: I have no doubt that my unusual training, and even my lack of formal training in biochemistry, contributed to the invention of directed enzyme evolution. I came at the protein engineering problem from an engineering perspective, free from the rigor that biochemical scientists feel the need to use. Rather than try to minimize the complexity of proteins by developing a ‘rational’ design approach, which would require that I understand the system in detail, I used what has worked well for billions of years: evolution.

I use technology to speed it up a bit.

T&I: When you first published work on protein engineering, the divide between pure and applied science was pronounced. From your perspective as a renowned scientist and a holder of 49 patents, to what extent has the relationship between pure and applied science changed?

Arnold: I will answer this big and interesting question only from the perspective of protein engineering and directed evolution. I always try to do both pure and applied science, solving an important problem while learning something deeper and more general along

INTERVIEW T&I: How would you describe your current work in a nutshell?

Arnold: I am interested in the evolution of chemical novelty—specifically, how do new enzyme catalysts arise from old ones. It happened a gazillion times in nature, but it’s hard to capture evolution in the act.

We can only see the results, usually well after the new and old functions diverged. I ‘breed’ enzymes in the laboratory, using mutation and recombination (a kind of ‘molecular sex’), to better understand how new catalytic activities appear and also to create catalysts that nature never made but are useful to humans. These experiments demonstrate the power of evolution as an innovation machine.

T&I: Your original training was in mechanical and aerospace engineering, your Ph.D. was in chemical engineering, and then you moved into biochemical engineering, biochemistry and chemistry. How do

(photo courtesy of Frances Arnold)

THE NAI PROFILE

the way. By making new enzymes in the laboratory, we have the opportunity to observe how molecular problems can be solved. We learn which problems can be solved by evolution—e.g., can a specific new catalytic activity appear through a series of single amino acid changes—and we can study the details of the solutions. With some reverse engineering (biochemistry), we learn new things about these very complicated catalytic systems and the amazing ability of nature to innovate.

T&I: In recent years, there has been a great deal of discussion concerning women and their underrepresentation in STEM fields. You have said that one of your greatest achievements has been your role in teaching and advising the many superb students who have studied under you at Caltech. Have your female students asked for advice on issues related to gender and science. If so, what advice have you offered them?

Arnold: Advice is cheap, and I try to avoid offering too much. Each young person has to navigate these issues on his or her own and make choices appropriate for them. Like evolution, many paths are possible, even if they are not direct!

T&I: Keeping with the topic of students and teaching, could you offer an outline of how you engage your students in the invention and innovation process.

What strategies or methods do you employ. Why is this an important learning process for them above and beyond their scientific training?

Arnold: I have a very simple strategy for promoting invention and innovation: collect really smart, interactive people together, give them space and resources, set the standard for excellence, and then largely leave them alone. Because my students and postdocs come from very different backgrounds in chemistry, engineering, and biology, they look to each other for help and a free exchange of ideas, not competition. This creates a supportive environment where everyone not only succeeds, but they can afford to take risks and do something really new. This is also how Caltech works as an institution—I try to emulate this process in my own laboratory.

T&I: You have said that you were interested in languages when you were a student. Although you have pursued a different career path, anyone who has seen you speak can see that you are a gifted communicator.

81

Because science is so dependent on communication for impact, I’d like to pursue that idea with you. What does being a good communicator mean for you in terms of advancing your scientific agenda. What role do more “popular” communication forums (e.g., TED talks)—as opposed to your scholarly publications and presentations—play in your work?

Arnold: I have benefited greatly from giving ‘popular’ talks to young audiences or broader audiences. These forums force one to think about the larger implications of the work and to identify effective ways to capture the audience’s attention and communicate ideas.

Because I am too lazy to generate many different talks, I started to use the same tools to communicate ideas in talks to experts as well. Then I learned that even the experts appreciate the emphasis on fundamental ideas and simple analogies—because no one can keep up with newest advances in all fields. Humor is also effective, and I wish I were better at that!

CONCLUSION Even with all of her accolades, in many ways, it seems as if her work is just beginning, as Arnold is constantly pushing limits, expanding boundaries, and seeking new opportunities for innovative science.

At the end of the interview, Arnold offered some thoughts on future directions. On the research side, she shared that her upcoming work “involves bringing new chemistry into the biological world to access whole new classes of chemicals and materials.. She also commented on her deep engagement on the tech transfer side with her newest venture, Provivi, Inc., where she sees her work as having two main goals: to “1) bring new enzymes to the market, so that they can replace dirtier, unsustainable chemical processes and 2) develop natural and non-toxic ways to combat pests in agriculture, so that we can feed growing populations without ecosystem destruction.. Whether hearing her speak or reading her research, it is clear that, in every aspect, Arnold’s work is characterized by a deep and sincere motivation to use innovation to better our lives and the state of our planet. In her final words, “I am always looking for ways to make the products we need in a cleaner, cheaper, and more sustainable fashion, and for where that ability opens new opportunities to combat chemical waste, pollution and harm to the planet.”

82

THE NAI PROFILE

FURTHER READING 1. Arnold, F.H. The Library of Maynard-Smith: my search for meaning in the protein universe.

Microbe 6(7):316-318; 2011.

2. Arnold, F.H. The nature of chemical innovation: new enzymes by evolution. Q. Rev. Biophys.

48(4)404-410; 2015 3. Flytzanis, N.C.; Bedbrook, C.N.; Chiu, H.; Engqvist, M.K.M.; Xiao, C.; Chan, K.Y.; Sternberg, P.W.; Arnold, F.H.; Gradinaru, V. Archaerhodopsin variants with enhanced voltage-sensitive fluorescence in mammalian and Caenorhabditis elegans neurons. Nat. Commun.

5:4894; 2014.

4. Prier, C.K.; Arnold, F.H. Chemomimetic biocatalysis: exploiting the synthetic potential of cofac-

tor-dependent enzymes to create new catalysts.

J. Am. Chem. Soc. 137(44):13992-14006; 2015.

5. Shapiro, M.G.; Westmeyer, G.G.; Romero, P.A.; Szablowski, J.O.; K端ster, B.; Shah, A.; Otey, C.R.; Langer, R.; Arnold, F.H.; Jasanoff, A. Directed evolution of a magnetic resonance imaging contrast agent for noninvasive imaging of dopamine.

Nat. Biotechnol. 28(3):264-272; 2010.

6. Wang, Z.J.; Renata, H.; Peck, N.E.; Farwell, C.C.; Coelho, P.S.; Arnold, F.H. Improved cyclopropanation activity of histidine-ligated cytochrome P450 enables the enantioselective formal synthesis of levomilnacipran. Agnew. Chem. Int. Ed. Engl. 53(26):6810-6813; 2014.

i

Aims and Scope The journal Technology and Innovation, Journal of the National Academy of Inventors (T&I) is a forum for presentation of information encompassing essentially the entire field of applied sciences, with a focus on transformative technology and academic innovation. Owing to the broad nature of the applied sciences, authors should be guided by the interest of the readers who are likely to be knowledgeable non-specialist scholars. Contributions containing the following information will be considered for publication: • Description of advances in transformative technology and translational science • Critical assessments of a segment of science, engi neering, medicine, or other technologies • Economics of a technology, governmental and policy action, and innovation as related to intellectual prop erty • Environmental (including human health) impact of various technologies • Articles on historical, societal, ethical, and related aspects of science,engineering, medicine, or technol ogy, provided they are written for the scientific com munity and in a style compatible with a scientific journal • Articles should have a discussion on the process of innovation and invention Because T&I serves a multidisciplinary audience, authors are urged to avoid writing for specialists. In particular, they are discouraged from using expressions that are understandable only to a select audience of specialists. For example, mathematical expressions should be explained in words to assure their appreciation by nonmathematicians. All contributions will be subjected to peer review and will be evaluated on the basis of their general usefulness for the readers, including scientific quality, originality, and compliance with the style and format of the journal.

The following categories of contributions will be considered for publication: Articles: Most articles will be review format with no minimum or maximum length. The journal subscribes to the concept that the length of an article is determined by its content. However, a preference will be given for articles that are between 7 and 15 published pages.

Commentaries and Discussions: (Letters to the editor, editorials, and similar contributions also fall into this category.) These are subjected to peer review and are required to follow T&I format and style and must be consistent with the requirements of a scholarly journal. The discussion of contested areas of science where a consensus is lacking is included in this category. Commentaries are shorter than regular manuscripts and must contain information that is likely to invoke scientific discussion with the objective of

promoting the development of a consensus.

Patent Reviews: New patents of interest to the readers of T&I are included in this category.

Book Reviews on Innovation and Technology: Solicited or unsolicited short reviews of relevant books and issued patents are considered for publication in this category.

Preparation of Manuscripts Submissions to Technology and Innovation must be in English, in an editable Microsoft Word-compatible electronic file, typed, 12-point font, double-spaced, formatted for 22 x 28 cm (8.5 x 11 in) with a margin of 2.5-3 cm (1 in) at the top, sides, and bottom of each page. Tables should be placed on separate page(s) sequentially at the end of the manuscript (after the ‘Reference’ section). Figures should be submitted separately from text.

Title page: Each paper should include a title page with the title of the paper, submission type, name(s) of author(s), and complete affiliation(s). Provide a short title to be used as running head. Indicate the author to whom correspondence and proofs should be addressed (i.e. ‘corresponding author’), and provide a complete physical mailing address, phone, fax, and email address.

Title: The title should be as short as possible but fully descriptive.

Submission Type: The author should indicate the type of submission that best describes their manuscript (Article, Commentary, Editorial, or Patent Review).

Abstract and key words: The abstract should contain a summary of the article, including its results in 250 words or less. Because many abstracting services use the abstract without reference to the content, the authors are urged to succinctly provide the essence of the paper in the (up to) 250 words allocated. Additionally, provide 3 to 6 key words after the abstract.

Tables and figures: Tables and figures should be understandable without excessive reference to the text; particularly, units and quantities should be clearly identified. In general, material should be presented in tables or figures but not in both. Avoid very wide or long figures and tables that would not fit on a printed page. By default, tables and figures will appear in B&W. If color figures are necessary or desired, there is a charge for their reproduction. Figures should be submitted at the highest resolution possible, preferably 300dpi at 7 inches (width or height). Low-resolution files that appear pixilated when printed will NOT be accepted for publication.

Tables: Present each table on a separate page at the end of the manuscript (i.e., not within the body of the text). Provide a short title for each table. Cite all tables sequentially

ii in the text and provide publishing staff with a cue for where they should approximately appear in the manuscript (e.g., ‘INSERT TABLE 1’) when published. Tables should be in an editable format.

time and carefully check all editorial changes within 48 hours of receipt. Corrections at this stage should be limited to printer’s errors and minor changes. No major changes or rewrites are allowed.

Figures: Figures should be submitted separately from the text. Cite all figures sequentially in the text and provide publishing staff with a cue for where they should approximately appear in the manuscript (e.g., ‘INSERT FIGURE 1’) when published. All figures must be high-quality art work in electronic format. Lettering should be large enough to be readable when reduced to fit page or column size. Avoid light lettering and gray shading. SPECIAL NOTE: Figures in accepted submissions are printed for free in black & white. If you wish to have your figures reproduced in color, there is an additional fee for this service. All legends for figures should be included on a separate page at the end of the manuscript.

Open Access: To help authors reach maximum exposure for manuscripts published in Technology and Innovation, T&I utilizes Open Access publishing. Fees are billed when a manuscript is accepted for publication.

Equations: All equations should be typewritten. Mathematical notations should be simple and suitable for a multidisciplinary audience. For example, fractions within fractions and subscripts within subscripts should be avoided. Where possible, incorporate equations into the text rather than as a separate figure.

Units, quantities, and abbreviations: Use SI (metric) units and international quantities and abbreviations. Equivalent values in other systems may be used, provided their metric equivalents are included in every case. Note that percent, ppm, and ppb are not metric units.

Footnotes: Avoid text footnotes. Footnote material should be incorporated into the text for the benefit of the readers, editors, and printers.

Financial Disclosure: The authors should indicate any financial or other relationships connected with the information in the article.

Acknowledgment: If an acknowledgment is included, it should not contain lengthy descriptions of the reason for the acknowledgement.

References: For references, please follow CSE citation-sequence style. Information about CSE citation format can be found at http://www.scientificstyleandformat.org/Tools/ SSF-Citation-Quick-Guide.html. If you have additional questions about references or other formatting issues, please contact T&I at tijournal@academyofinventors.org.

The designated corresponding author will receive a proof of their article in PDF format via email before publication.

The corresponding author should answer all queries at this

Open Access Fee Rates Standard Single Submission: ...................................$1,000 NAI Fellow Contributed Single Submission: ...........$800 If you are interested in publishing with T&I but believe you will be unable to meet the Open Access fees, please contact T&I at tijournal@academyofinventors.org or (813) 974-1347.

Copyright: If data from any source other than the authors is used in tables or figures, it is the responsibility of the authors to obtain permission to reproduce such material.

Editorial staff may ask authors to provide proof that permission has been granted from the original publisher and indicate the source when signing our copyright forms.

For any questions relating to the formatting or submitting of manuscripts, please contact T&I at tijournal@ academyofinventors.org or (813) 974-1347.

Ethics Statement The publishers and editorial board of Technology and Innovation have adopted the publication ethics and malpractice statements of the Committee on Publication Ethics (COPE) (http://publicationethics.org/resources/guidelines). These guidelines highlight what is expected of authors and what they can expect from the reviewers and editorial board in return. They also provide details of how problems will be handled. Briefly: Author Responsibilities: Authors listed on a manuscript must have made a significant contribution to the study and/ or writing of the manuscript. During revisions, authors cannot be removed without their permission and that of the other authors. All authors must also agree to the addition of new authors. It is the responsibility of the corresponding author to ensure that this occurs.

Financial support and conflicts of interest for all authors must be declared. Further information on this can be obtained from the International Committee for Medical Journal Editors (http://www.icmje.org/).

iii The reported research must be novel and authentic and the authors should confirm that the same data has not been and is not going to be submitted to another journal (unless already rejected). Statements made in the introduction and discussion should be supported by appropriate references, and sufficient experimental detail should be provided to allow for repetition of the study by another group. Plagiarism of the text/data will not be tolerated and could result in retraction of an accepted article. Any text or figures reproduced for another source require the permission of the original copyright holders (normally the publishers).

Any manipulation of figures should be equally applied and described in the text (including pseudocoloring) and must not change the meaning of the figure.

When humans, animals, or tissue derived from them have been used, then mention of the appropriate ethical approval must be included in the manuscript.

Reviewer responsibilities: Reviewers are expected to not possess any conflicts of interest with the authors and research. They should review the science objectively and provide recommendations for improvements where necessary. When aware of relevant published work not being cited, the reviewers should recommend inclusion of these references. If the reviewer feels that they would be unable to repeat the study as described, then additional methodological details should be requested. Any unpublished information read by a reviewer should be treated as confidential.

Editorial responsibilities: The editors will select an appropriate number of reviewers for the manuscript so that they can make an informed decision about whether to reject/ accept a manuscript. Their decision must be based only on the paperâ&#x20AC;&#x2122;s importance, originality, clarity, and suitability for the journal. They must not have a conflict of interest with the authors or work described. The anonymity of the reviewers must be maintained.

Should problems come to light after acceptance, then the editors agree to promote the publication of corrections and/or retractions as deemed necessary.

Publishing responsibilities: The publisher agrees to ensure that, to the best of their abilities, the information that they publish is genuine and ethically sound. If publishing ethics issues come to light, not limited to accusations of fraudulent data or plagiarism, during or after the publication process, they will be investigated by the editorial board, including contact with the authorâ&#x20AC;&#x2122;s institution if necessary

so that a decision on the appropriate corrections, clarifications, or retractions can be made. The publisher agrees to publish this as necessary so as to maintain the integrity of the academic record.

Protection for Research Participants These policies are in accordance with the recommendations of The International Committee of Medical Journal Editors (ICMJE) n

Humans

1. If experiments or research reported in the article in volve human subjects, the authors must indicate if their procedures were approved by an Institutional Review Board, ethics committee, or similar reg ulatory oversight committee. If a review board or committee is not available, the authors should indicate that their procedures are in accor dance with the Helsinki Declaration as revised in 2013.

3. Manuscripts must be accompanied by a statement that the informed consent of research participants was obtained prior to participation or that documen tation of informed consent was waived by the Insti tutional Review Board, ethics committee, or similar regulatory oversight committee.

If images or other identifying information is included in the manuscript, explicit written informed consent of the individual/patient must be obtained and included with your submission. Measures to protect the confidentiality of the individual(s) should also be employed. If consent cannot be obtained, you are encouraged to contact the editor for further guidance. n

Animals

If experiments or research reported in the article involve animals, the authors must indicate if their procedures were performed in accordance with the U.S. Public Health Serviceâ&#x20AC;&#x2122;s (PHS) Policy on Human Care and Use of Laboratory Animals and the Guide for the Care and Use of Laboratory Animals and were approved by appropriate institutional review committee(s).

Editors reserve the right to reject manuscripts if there is doubt that appropriate ethical standards have not been met in research involving human and animal subjects or if there is reason to suspect research misconduct.

Guest Editor: Rathindra DasGupta Technology and Innovation (T&I) is currently soliciting manuscripts for a special issue on innovation and entrepreneurship.

For this special issue, we are interested in receiving articles and reviews addressing topics associated with innovation and entrepreneurship. Relevant topics include but are not limited to: • • • • • •

Opportunities to accelerate innovation research Follow-on funding mechanisms after start-up creations Collaboration/Partnerships to accelerate innovation Developing entrepreneurial curricula Licensing activities Student involvement in innovation/entrepreneurship activities

Initial manuscripts should be submitted by September 1, 2016. Instructions for authors, including manuscript formatting information and author forms, can be found at: http://academyofinventors.org/ ti/resources.asp.

T&I is published by the National Academy of Inventors and presents information encompassing the entire field of applied sciences, with a focus on transformative technology and academic innovation, and welcomes manuscripts that meet the general criteria of significance and scientific excellence.

We publish original articles in basic and applied research, critical reviews, surveys, opinions, commentaries, essays, and patent and book reviews of interest to our readers.

If you have questions or would like to submit a manuscript, please contact the guest editor, Rathindra DasGupta, at babu.dasgupta@gmail.com or the assistant editor of T&I, Kimberly Macuare, at kmacuare@academyofinventors.org.

Contact Information 314-516-8400 evaluation@mimh.edu www.mimh.edu

For over 50 years, MIMH has partnered with governmental agencies and research teams in an effort to improve lives through the promotion of mental health via research, evaluation, professional training, program development, and community outreach.

PROGRAM EVALUATION SERVICES MIMH provides services and technical assistance to local, state and national agencies to improve the quality and increase the effectiveness of mental health programs and practices.

What is our expertise?

• Process and outcome evaluation

• Quantitative and qualitative data analysis

• Community needs assessment • Survey design & administration

• Dissemination of findings through reports, presentations & peer-reviewed journals

• Focus groups - design, facilitation & analysis

• Technical assistance

• Grant writing

◆

Program – design & implementation

• Development of data management systems & websites

◆

Evaluation – design & implementation

◆

Logic models / Theory of change

Who do we impact?

• Individuals with mental illness and their families

• Victims of trauma and abuse

• Individuals with substance use disorders

• Children who are at risk for substance use disorders, suicide, school dropout, pregnancy and violence

• Students • Individuals who are homeless

• Individuals with HIV/AIDS

Our clients include: • Substance Abuse and Mental Health Service Administration

• Hospitals

• Centers for Disease Control and Prevention

• Taxing authorities

• State mental health and health departments

• National Alliance for the Mentally Ill

• School districts

• Health Departments

MICHAEL BASS, University of Central Florida ISSA BATARSEH, University of Central Florida RAYMOND J. BERGERON, University of Florida SHEKHAR BHANSALI, Florida International University ROBERT H. BYRNE, University of South Florida SELIM A. CHACOUR, University of South Florida WILLIAM J. CLANCEY, Institute for Human & Machine Cognition ROY CURTISS III, University of Florida WILLIAM S. DALTON, H. Lee Moffitt Cancer & Research Institute PETER J. DELFYETT, University of Central Florida DONN M. DENNIS, University of Florida DAVID M. EDDY, University of South Florida GREGG B. FIELDS, Florida Atlantic University KENNETH M. FORD, Institute for Human & Machine Cognition MICHAEL W. FOUNTAIN, University of South Florida RICHARD D. GITLIN, University of South Florida LEONID B. GLEBOV, University of Central Florida D. YOGI GOSWAMI, University of South Florida CLIFFORD M. GROSS, University of South Florida BARBARA C. HANSEN, University of South Florida RICHARD A. HOUGHTEN, Torrey Pines Institute for Molecular Studies LONNIE O. INGRAM, University of Florida S. SITHARAMA IYENGAR, Florida International University RICHARD JOVE, Nova Southeastern University SAKHRAT KHIZROEV, Florida Internatitonal University DAVID C. LARBALESTIER, Florida State University C. DOUGLAS LETSON, H. Lee Moffitt Cancer & Research Institute GUIFANG LI, University of Central Florida STEPHEN B. LIGGETT, University of South Florida ALAN F. LIST, H. Lee Moffitt Cancer & Research Institute DEAN F. MARTIN, University of South Florida THOMAS O. MENSAH, Florida State University SHYAM MOHAPATRA, University of South Florida BRIJ M. MOUDGIL, University of Florida

INNOVATION CAN BE DIFFICULT TO CREATE and more difficult to sustain. For the past 6 years, the National Academy of Inventors has sustained and grown as an organization that recognizes and encourages invention.

CONGRATULATIONS TO THE NAI FOR 6 YEARS OF GROWTH and to these Florida inventors honored to be called NAI Fellows.

DAVID P. NORTON, University of Florida VICTOR L. POIRIER, University of South Florida ANN PROGULSKE-FOX, University of Florida ALAIN T. RAPPAPORT, Institute for Human & Machine Cognition PAUL R. SANBERG, University of South Florida W. GREGORY SAWYER, University of Florida ANDREW V. SCHALLY, University of Miami SUDIPTA SEAL, University of Central Florida SAID M. SEBTI, H. Lee Moffitt Cancer & Research Institute MARWAN A. SIMAAN, University of Central Florida FRANKY SO, University of Florida M. J. SOILEAU, University of Central Florida NAN-YAO SU, University of Florida HERBERT WEISSBACH, Florida Atlantic University SHIN-TSON WU, University of Central Florida

proud to partner with the

JAMES J. WYNNE, University of South Florida JANET K. YAMAMOTO, University of Florida JIANPING (JIM) P. ZHENG, Florida State University