Page 1

IN THIS ISSUE VPH-DARE@IT - This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no FP7-ICT-2011-9-601055.

Editorial

Project Focus • Lifestyle Factors

Newsletter Issue VI November 2017

Partner Profiles • EBIR

Research Focus • LIDO Study Impact

People

• Micaela Mitolo (IRCCS/FOSC)

Project Outputs: Platform • MULTIX: A Multi-domain Research-As-A-Service Platform • The Patient Care Platform Clinical Decision Support Tool • The Citizen Portal – a webbased portal for assessing cognition

Project Outputs: Tools • Novel image computing tools deployed within Multix • Multiple-Network Poroelastic Theory (MPET)

Article

• A personal journey through dementia

Researchers Experience • Nishant Ravikumar • Liwei Guo

Project coordinator: The University of Sheffield Contact person: Professor Alejandro Frangi Tel: +44 114 222 6071 Email: contact@vph-dare.eu

w ww.vph-da re .eu


editorial

Editorial

Newsletter Issue VI

Welcome to the six and final issue to the VPH-DARE@IT newsletter! Since the last issue the project has continued to focus on the implementation of various clinical scenarios. In Year 4, the work concentrated on developing a coherent approach to modelling/accounting for lifestyle and environmental factors (LEFs) across all WPs of the project and to illustrate how complementary perspectives can be bought together through various clinical scenarios. All scenarios focused on Alzheimer’s Disease (AD) and Vascular Dementia (VaD), and their relationship to a selected number of LEFs (smoking, alcohol consumption and obesity). Much progress has also been made into the sustainability and exploitation of both platforms and in the route to market. Thank you for all of your contributors to this issue of the newsletter! It is great to hear about the research being undertaken by our partners and the progress toward product development and exploitation. Dementia is a major socio-economic challenge, and research that aims to shorten the current average 20-month time lapse between the onset of cognitive and memory deficits and its specific clinical diagnosis is worth shooting about! In this issue of the newsletter, we hear from • • •

The work undertaken by UCL (WP5) on Multiple-Network Poroelasticity Theory (MPET) The impact of the LIDO study The main outputs from the project

Dr Corinne Howse

Project Manager Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB) Department of Mechanical Engineering The University of Sheffield Mappin Street, Sheffield S1 3JD United Kingdom T: +44 (0) 114 222 0166 E: c.howse@sheffield.ac.uk

w

w

w .

v

p

h

-

d

a

r e

.

e

u


November - 2017

Lifestyle Factors The focus of UCL within WP5 is to develop a simulation platform capable of representing the transport and interplay of blood and cerebrospinal (CSF)/interstitial fluid (ISF) with the parenchyma – the neuronal and astrocyte tissue that constitutes the functioning brain. The work that we do within WP5 revolves around an extended 3D model of poroelasticity (namely Multiple-Network Poroelasticity Theory, or MPET) to represent the parenchymal

tissue being percolated by blood and CSF/ ISF. In the model developed thus far, the parenchyma is perforated by arterial blood, arteriole/capillary blood, venous blood and CSF/ISF, i.e. four interconnected networks. In order to make such a platform applicable to the study of dementia, we had to focus on a specific compartment (arterial) of the 3D-MPET system, and link this to a well-grounded hypothesis in that the cardiovascular system appears to mediate

Consolidated poroelastic pipeline

N ovem ber 2017

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

3


VPH-DARE@ IT

a plausible and quantifiable pathway associating lifestyle and dementia.

Newsletter Issue IV

Alzheimer’s and Vascular Dementia share vascular and cardiac risk factors such as smoking, obesity, hypertension, diabetes and coronary artery disease to name a few. Cardiovascular risk factors reflect genetic predisposition as well as environmental and modifiable lifestyle factors. At the University of Sheffield, a cardiovascular system model was developed by Dr. Toni Lassila and Dr. Luigi Di Marco, whereby we could model systemic distributed factors such as ageing “The developed cardiovascular system model is parameterised accounting for lifestyle and cardiovascular systemic factors.”

(since this is a major contributor to vascular cognitive impairment and the main risk factor for late-onset Alzheimer’s) as well as specific lifestyle-related effects, e.g. reduced arterial compliance in smokers. The developed cardiovascular system model is parameterised accounting for lifestyle and cardiovascular

systemic factors. In order to further assess the observation that vascular and cardiac risk factors for dementia are indirectly linked to lifestyle, a prospective data collection programme was established, namely the Lido study. This study was coordinated by Prof. Annalena Venneri, in WP1. It is based on a cohort of over 52 cognitively impaired subjects and an equal number of age and educationmatched healthy controls. For each subject, lifestyle information is collected by means of questionnaires stemming from the Cardiovascular Risk Factors, Ageing and Dementia (CAIDE) study – and complemented by lifestyle-related cardiovascular variables (electrocardiogram, blood pressure, cardiac function/ disease, carotid blood flow velocity, and wall thickness) and body motion. Following data collection and subject-based model parameterisation, the subjectspecific lifestyle-derived boundary conditions (waveforms of internal carotid and vertebral artery flow in both left and right sides of the cerebrum and cerebellum) that feed into the arterial compartment

ww ww ww . . v v p p h h - - d d a a r r e e . . e e u u


November - 2017

of the MPET model, are generated. The Lido dataset also provides the opportunity to obtain subject-specific representations of parenchymal tissue and the cerebral ventricles via the multi-label segmentations which are used directly in the MPET models (in the form of personalised volumetric tetrahedral meshes). In WP 2, Dr. Zeike Taylor and Dr. Leandro Beltrachini have been working on accurate, fullyautomated, and fast image-based modelling techniques that are able to extract the aforementioned anatomical representations, in addition to the personalised white matter permeability maps which are necessary when investigating accurate fluid transport in the brain. The distinct labels generated for the cerebral and cerebellar hemispheres were necessary in order to prescribe the subject-specific lifestyle-derived boundary conditions to the arterial compartment of the MPET model. The image-based modelling techniques, which are required to obtain the personalised representations of parenchymal tissue (which are needed to create the meshes

that the MPET model functions on) along with the feeding subject-specific lifestylederived boundary conditions prescribed on the left and right sides of the cerebrum and cerebellum make up the consolidated poroelastic pipeline. The output of this pipeline was focused on the computed “the observation that vascular and cardiac risk factors for dementia are indirectly linked to lifestyle”

transport maps (perfusion, clearance, intracranial pressure) between a set of control and MCI cases during a state of high and low activity. This interdisciplinary effort was recently outlined and published in Interface Focus, under the journal title: Subject-specific multiporoelastic model for exploring the risk factors associated with the early stages of Alzheimer’s Disease.

Dr. John Vardakis

Researcher Department of Mechanical Engineering University College London Torrington Place London, WC1E7JE United Kingdom T: +44 (0) 1865 283 454

N ovem ber 2017

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

5


VPH-DARE@ IT

LIDO Study Impact

Newsletter Issue VI

By Annalena Venneri A nervous system in good condition is extremely important for living well. Unfortunately, no flawless formula exists for maintaining a healthy and fullyfunctioning brain. Particularly, we do not know which and how many ingredients take part in this particular recipe. Epidemiological studies (i.e. studies carried out on large cohorts which search for general patterns of association between the presence of a disease and a certain risk factor) suggest that there are a large number of variables that contribute, to some extent, to neural health. These include genetic traits and environmental factors. Traditionally we tend to formulate a contrast between these two types of variable. In fact, a genetic trait is something we are unable to modify, while environmental features are very often the product of a conscious choice, and, as a consequence, define our lifestyle (e.g. being a smoker, or maintaining a healthy diet). At present, it is undetermined whether genetic variables exert a more powerful effect on the ageing processes than lifestyle choices.We know that, in a small percentage of cases, the presence of a genetic mutation leads to the inevitable onset of a neurodegenerative disease (e.g. Alzheimer’s disease) regardless of how healthy our lifestyle is. These are very rare occurrences, though, as in the

w

“Epidemiological studies.... suggest that there are a large number of variables that contribute, to some extent, to neural health” manner (e.g. as for smoking habits: current smokers vs. former smokers; heavy smokers vs. light smokers; regular smokers vs. intermittent smokers; cigarette smokers vs. cigar smokers…). A major goal of the FOSC-USFD cohort is to shed light on this research field and to try and disentangle the association between lifestyle variables and parameters of brain structure and brain function. The nature of this association will then be interpreted as a function of the current knowledge of ageing and disease processes, in order to hypothesise or speculate one or more potential biological mechanisms by which a certain lifestyle variable influences the nervous system. This pursuit will be complementary to that

“environmental features are very often the product of a conscious choice, and, as a consequence, define our lifestyle” w

majority of cases neurodegenerative diseases are not explicitly caused by any genes, but are believed to be the result of the interplay between genetic and environmental susceptibility. The study of genetic susceptibility is relatively simple in terms of methodology. Genetic variables, in fact, can be defined in a clear way, i.e. presence of the genetic trait vs. absence of the genetic trait. Environmental susceptibility, on the other hand, can be much more difficult to capture in a precise quantifiable

w .

v

p

h

-

d

a

r e

.

e

u


November - 2017

of epidemiological studies, because variables (i.e. both neural and lifestyle parameters) will be quantified with more accuracy, and more specific conclusions will be drawn. As an example, in a first study carried out on this cohort, “A major goal of the FOSC-USFD we explored the statistical association between parameters cohort is .... to try and disentangle of brain structure and one of the most influential risk the association between lifestyle factors which cause a burden on the vascular system, that is, obesity. Adopting multiple methodological approaches, we variables and parameters of brain structure and brain function.” found significant associations between a parameter of body weight (Body Mass Index, or BMI) and brain tissue density in a number of regions known to be susceptible to normal ageing and Alzheimer’s disease, suggesting that being overweight (a modifiable variable) can act as a detrimental risk factor.

The linear association between brain tissue density and Body Mass Index in two cerebral regions.

Pof. Annalena Venneri

Researcher Department of Neuroscience University of Sheffield Royal Hallamshire Hospital Sheffield South Yorkshire S10 2JF United Kingdom T: +44 (0) 114 271 3430

N ovem ber 2017

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

7 13


VPH-DARE@ IT

Micaela Mitolo IRCCS / Fondazione Ospedale San Camillo

Newsletter Issue VI

Background and Research I completed my doctoral studies in Neuroscience at the University of Padua in Italy, focusing my thesis on cognitive processes and neuroimaging correlates of neurodegenerative diseases. During my PhD I took the opportunity to develop part of my project abroad. I worked as a Visiting PhD student at the University College London (UCL), then I moved to the Alzheimer’s Disease Research Center (ADRC) at the

“We believe that this project may have a big impact on the understanding of neurodegenerative diseases.”

University of California, San Diego (UCSD). I completed my doctoral studies in 2013 and I started my first Post-Doc in January 2014 at the University of Sheffield as part of the Translational Neuropsychology Group (TNG) led by Professor Annalena Venneri. While at the University of Sheffield I had a chance to improve my neuroimaging data

w w

w w

analysis skills. I attended weekly lab meetings, neuropathology brain-cutting sessions and I also participated in several research conferences. In 2015, under the supervision of my manager, I moved to FOSC in Venice Lido and I started working on the VPH-DARE@ IT project, maintaining the collaboration with all the other members of the TNG group. In May 2017 I moved to Bologna and I am currently working at the Functional MR Unit of S.Orsola-Malpighi Hospital, Bologna (Italy).

What is your specific role in VPHDARE@IT? I was in charge of the overall logistic management of the Lido data collection, managing recruitment of participants, organizing the weekly schedule with all assessments for each participant (including MRI and neuropsychological assessment), managing relations (in person and on the phone) with caregivers of people who were cognitively impaired, and collecting demographic features and measures of height and weight for each participant. Furthermore, I administered a Lifestyle Questionnaire, tested for orthostatic hypotension, collected

w w ..

vv

pp

hh

--

d d

aa

rr ee

..

ee

uu


“VPH-DARE@IT has offered our institute the opportunity to collaborate with several research specialists, expanding our network on a worldwide scale.” data from the Actigraph for 5 days and data from the “diary of events” during the 24-Hours ECG Holter and during the 24-Hours BP Holter. I was also in charge of all data management, ensuring that data collected from the various measurements (ultrasound, Holter, MRI, etc) were properly downloaded and organized for transfer to the VPHDARE@IT platform and for implementation of the behavioural databases.

Impact of the project institution (FOSC)

on

your

I was enthusiastic about working as part of this multicenter project. VPH-DARE@IT was offering our institute the opportunity to collaborate with several research specialists, expanding our network on a worldwide scale. The main challenging and

N ovem ber 2017

motivating aspects of the VPH-DARE@ITis certainly the synergy and collaboration between people with different professional expertise and different backgrounds (e.g. software engineers, physicians, neuropsychologists, mathematicians, physicists, etc). This kind of collaboration helps researchers, stimulates creative thinking and leads us to address complex research questions. Due to the VPH-DARE@IT project, FOSC has also had a better chance to be known by locals, to divulge awareness of dementia and to make first class facilities accessible to a wide range of people, including the less able ones. We believe that this project may have a big impact on the understanding of neurodegenerative diseases.

What is next? The next step will be the management of all data analyses and the dissemination of results. These results would stimulate the writing of new funding applications and the development of future collaborations with all other VPH-DARE@IT partners.

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

9 19


partners

VPH-DARE@ IT The European Institute for Biomedical Imaging Research (EIBIR) By Michael Crean

Newsletter Issue VI

About EIBIR The European Institute for Biomedical Imaging Research (EIBIR) was founded in 2006 to coordinate and support biomedical imaging research in Europe. With its head office at the headquarters of its main shareholder the European Society of Radiology (ESR) in Vienna, Austria, EIBIR serves as network of scientific, research and clinical institutions as well as industry. The main aim of the EIBIR network is to strengthen biomedical imaging research throughout Europe by bringing international research expertise together. To achieve this, it supports networking activities in research which are also vital to spreading good practice, promoting common initiatives and interoperability in the field of biomedical imaging research. These activities are further complemented by the work of the EIBIR Office, which lends practical support to researchers applying for EU-funding, manages and coordinates

“The main aim of the EIBIR network is to strengthen biomedical imaging research throughout Europe by bringing international research expertise together.”

projects, and ensures the effective dissemination of scientific results. The EIBIR Office has been successfully preparing proposals and managing projects since the EU’s Framework 6 funding programme. A multidisciplinary organisation, EIBIR is an umbrella for all disciplines related to biomedical imaging and has 11 other shareholder organisations which represent a wide range of medical professionals with an interest in promoting research in biomedical imaging.the EU’s Framework 6 funding programme. A multidisciplinary organisation, EIBIR is an umbrella for all disciplines related to biomedical imaging and has 11 other shareholder organisations which represent a wide range of medical professionals with an interest in promoting research in biomedical imaging.

Role in the VPH-DARE@IT Project For any research project, making sure the results are disseminated to the scientific community is integral to its success. With its strong links to medical societies within the field of biomedical imaging, EIBIR is uniquely positioned to help effectively disseminate results to this scientific community.Through its main shareholder, the European Society of

w w w . v p h - d a r e . e u w w w . v p h - d a r e . e u


November November- -20177 2017

Partners

“EIBIR is uniquely positioned to help effectively disseminate results to this scientific community.” Radiology (ESR), it can reach out to a community of more than 65,000 members around the world and give a spotlight to EU-funded projects at many major events. As a partner in the VPH-DARE@IT project, EIBIR facilitated a dedicated session for the project at the European Congress of Radiology 2016 (ECR), the ESR’s annual meeting, which attracts upwards of 20,000 people each year. The session proved popular and attendees also had the opportunity to find out more about the project at the EIBIR booth throughout the congress. Due to its success in 2016, another VPH-DARE@IT session was added to the ECR 2017 programme to give attendees yet another opportunity to learn how imaging data plays an indispensable role in the research of the VPH-DARE@IT project. Both sessions were also recorded and are available to watch online via ECR Online. EIBIR has been promoting these videos online since then via its website, newsletter and social media channels. However, scientists are not the only stakeholders in a project like VPH-DARE@IT, and informing patients and the wider public of the project’s objectives and goals is equally important. EIBIR has helped the VPH-DARE@IT project to achieve this by utilising its close ties with the ESR Patient Advisory

Group (ESR-PAG).The Group includes a number of patient organisations including the European Federation for Neurological Associations (EFNA), which has so far contributed to VPH-DARE@IT’s newsletter and has provided invaluable feedback on how to improve patient outreach. EFNA has also used its own communication channels to bring the project and its objectives to the attention of patients suffering from neurological disorders, their carers and other patient advocates; all of whom are key stakeholders of this project. Working closely with the coordinator, the University of Sheffield, EIBIR has helped the project reach out to medical professionals within the medical community as well as key patient stakeholder groups. This has led to the VPH-DARE@IT receiving strongly positive feedback from the European Commission’s reviewers in previous periods, and EIBIR, along with all the project partners, intends to continue and further enhance the project’s already highly successful dissemination strategy in its final period.

“Working closely with the coordinator, the University of Sheffield, EIBIR has helped the project reach out to medical professionals within the medical community as well as key patient stakeholder groups.”

Michael Crean

Project Manager European Institute for Biomedical Imaging Research (EIBIR) Neutorgasse 9 1010 Vienna, Austria T: +43-1-533-4064-321 E: mcrean@eibir.org

N ovem ber 2017

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

11


VPH-DARE@ IT MULTIX: A Multi-domain Research-As-A-Service Platform By Milton Hoz de Vila The MULTIX Platform is the final version of the Clinical Research Platform developed as part of the VPH-DARE@IT project in WP7. MULTIX has integrated the efforts of all the work packages, providing a single framework where new generation workflows were created focusing on multiscale patient-specific treatment for dementia. The goal has been to develop a “Research as a Service” platform to enable clinical research scientists to perform ad hoc and large-scale analysis and feature extraction on heterogeneous dementia-related datasets and to identify biomarkers for early and differential diagnosis of dementia.

Newsletter Issue VI

Background

During more than a decade different European Commission projects have contributed to the conceptualization, deployment, and testing of the current platform (Figure 1). Its development started within the VPH-Share project1 , implementing concepts of the Virtual Physiological Human (VPH) European initiative2 .

Figure 1 Background of the development of the platform and European projects involved

VPH-DARE@IT took over the deployment of the platform on 2015. In this last phase of the project, the whole architecture has been updated focusing on three aspects: usability, scalability, and reproducibility. (Figure 2).

Cross-domain: flexible, modular and scalable, it can be used in a wide range of research areas • Accessible: targeting both, highly specialised and non-expert users, supporting multiple programming languages and providing userfriendly interfaces • Collaborative: designed for secure and controlled sharing, connecting people and organizations, making efforts available to the research community • Reproducible: fosters reproducible science, enabling the reuse of data, applications, and workflows MULTIX has been constructed as the engine that enables clinical researchers to collaborate and translate research ideas into products, platforms, and services for the community.

Key capabilities

The development of the MULTIX platform is based on the following capacities and differentiating factors: Improves scientific productivity: automates the execution of computational tasks • Modular: facilitates the addition and mixture of multiple applications and datasets to achieve tailored results • Integrative: addresses key issues for the integration of highly heterogeneous data, cohorts, and scientific applications • Simple and powerful: hides the complexity of accessing high-performance computational and data services • Workflow-oriented: easy composition of heterogeneous tools and data into large-scale parallel executions • Flexible and scalable: based on a serviceoriented cloud ecosystem and cutting-edge infrastructure technologies

Main areas

Figure 2 MULTIX updated systems architecture

What is MULTIX?

MULTIX is a cross-domain research-oriented platform for accessible, collaborative and reproducible computational and data-intensive analysis.

w

w

MULTIX was designed to foster integration and testing of existing and new software tools across the research community, facilitating their connection with large-scale federated data repositories and providing analysis tools to get immediate insights about the results.To this end, it comprises five main areas (Figure 3): 1. DATA: a flexible repository of data collections for structured (databases) and non-structured data (files)

w .

v

p

h

-

d

a

r e

.

e

u


November - 2017

Project Outputs: Platforms 2. TOOLS: a catalog of multi-domain scientific tools, fed by a flexible development environment, and based on containers and virtual machines 3. PIPELINES:a set of processing services for orchestrating and performing processing on heterogeneous data 4. ANALYSIS: a modular dashboard with powerful analysis and data visualization tools 5. SHARE: a web-based user interface with sharing capabilities containing all the tools to streamline interaction with the various system components

Figure 5 Catalogue of VPH-DARE@IT Datasets

MULTIX is populated with data (selected retrospective/ prospective cohorts) and image-processing workflows and tools, accessible via a user-friendly web-portal (Figure 6). It represents a single access point for all the different multidisciplinary biomedical imaging and simulation tools, which extract knowledge from the data collections, in terms of image features and biomarkers.

Figure 3 Diagram and key features of the new MULTIX platform

Deployment of the VPH-DARE@IT project in MULTIX

The MULTIX platform infrastructure is largely developed at the University of Sheffield, with content (data, tools and workflows) provided by our partners in WP1 (UEF, FOSC), WP2 (Philips Research), WP3 (UCL), WP4 (ASD), WP5 (UCL-ENG), WP6 (ICL, VTT) and WP7 (USFD & STH). The platform has provided the fundamental technological underpinning to bring together such a disperse and diverse cadre of technologists, scientists, domain experts and clinicians to help tackle the problem of dementia.

Figure 6 MULTIX User Interface

Conclusions

The VPH-DARE@IT consortium has built a comprehensive platform to provide clinical “Research As A Service” (RaaS) enabling open innovation and enhancing multidisciplinary collaboration. Data providers, scientific software developers, clinical researchers and clinicians have all benefited with the development of a community of practice around an advanced research ecosystem that enables all parties to grow, develop and sustain research and translate ideas into products, services and vertical platforms for the research community. VPH-Share (FP7-ICT-269978 – 2012-2015) http://cordis. europa.eu/project/rcn/97442_en.html 2 http://www.vph-institute.org/history.html 1

Figure 4 Catalogue of VPH-DARE@IT Applications

Mr. Milton Hoz de Vila

Technical Development and Support Officer Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB) Department of Electrical & Electronic Engineering The University of Sheffield United Kingdom

N ovem ber 2017

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

13 13


VPH-DARE@ The Patient Care Platform

Newsletter Issue VI

Platforms - Platforms

Clinical Decision Support Tool By Jyrky Lotjonen Diagnosing cognitive disorders is not easy. In addition to multiple dementing diseases, there are many other conditions that can affect our cognition. On the other hand, more and more data are acquired from patients for diagnostic purposes, such as data from clinical and neuropsychological tests, imaging studies and lab results from analysis of body fluids. When interpreting all these complex data, a specialist must also consider multiple background factors of the patient. For example, the decreasing size of the hippocampus, measured from magnetic resonance images, is

known to be a hallmark in Alzheimer’s disease - but its size decreases also due to normal aging. This means that a value which is normal for a 90-year old person is abnormal for a 60-year old person. Finally, economic constraints or availability of specific diagnostic tools define partly which tests can be performed.Today, clinicians make all this complicated reasoning and decisions in their minds and the outcome depends on the expertise of the clinician in question. In VPH-DARE@IT, we developed computer-based tools for helping to

Figure 1: The Patient Care Platform

w

w

w .

v

p

h

-

d

a

r e

.

e

u


November - 2017

Project Outputs: Platforms make clinical reasoning more systematic and the module for differential diagnostics, the PCP objective. The patient care platform (PCP) contains different prediction models. One model developed in the project compares the data can be used to predict progression to dementia from a patient being studied to data from a for the cases with mild cognitive impairment. high number of previously diagnosed patients. Another, recently developed model, enables It computes an index which measures the predicting pathology biomarkers present similarity of the patient’s data in Alzheimer’s disease “ In VPH-DARE@IT, we to data of earlier patients who and vascular dementia developed computer-based tools had a certain disease. The key using demographics, lifefor helping to make clinical innovation of the PCP is that it style factors and simple reasoning more systematic and provides tools to understand neuropsychological test objective ” why the index is pointing to a results. The latter model specific disease. Although the can be used to assess the PCP is based on machine learning, it is not a risk of dementia and to find patients for more black-box artificial intelligence (AI) system that expensive diagnostic tests, e.g., in clinical drug merely suggests the most likely diagnosis to a trials. specialist but it also helps explain where this VPH-DARE@IT has already taken its first steps suggestion stems from . The PCP is a joint effort in bringing its innovations to clinical practice. The of multiple EU FP7 projects (PredictAD, TBIcare project partner Combinostics Ltd. has recently and PredictND, in addition to VPH-DARE@IT). launched a product family cNeuro® exploiting VPH-DARE@IT’s major contributions related to the technologies developed in the PCP research PCP were the extension to differential diagnostics prototype including the clinical decision support and the inclusion of novel image quantification for differential diagnostics and advanced image tools, especially for measuring vascular burden quantification tools. from magnetic resonance images. In addition to

Dr. Jyrki Lötjönen

Principal Investigator Teknologian Tutkimuskeskus VTT Technical Research Centre of Finland P.O. Box 1000 FI-02044 Finland T: +358 20 722 3378 E: jyrki.lotjonen@vtt.fi

N ovem ber 2017

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

15 13


Newsletter Issue VI

Platforms - Platforms - Plat-

VPH-DARE@ IT The Citizen Portal

a web-based portal for assessing cognition

By Jyrky Lotjonen Early diagnostics of memory problems is important because possible treatments should be started as early as possible to be efficient. This is especially important in neurodegenerative diseases which injure and finally destroy brain cells. Early diagnosis, however, is not a trivial task. Additionally, making the diagnosis immediately after e.g. memory-related symptoms appear is not

enough in practice, because we know that pathological processes start even decades before the symptoms in Alzheimer’s disease show themselves. Thus, our ultimate goal is to detect the disease already before such symptoms occur. There are early indicators (biomarkers) available that can help diagnosing dementias earlier. These include biomarkers of amyloid-

Figure 1: The Citizen Portal Platform

w

w

w .

v

p

h

-

d

a

r e

.

e

u


November - 2017

Project Outputs: Platforms beta (Ab) accumulation, and biomarkers of neuronal web-based service that provides tools to assess the degeneration or injury. The disadvantage of these brain health. The focus of use is on the primary care measures is that they require expensive and invasive setting or even in empowering citizens to evaluate measurement techniques (PET or MRI scans, or their brain health independently. It thus works in cerebrospinal fluid (CSF) analysis), and thus are not combination with the Patient Care Platform which is suitable for screening purposes on a large scale. more targeted towards the specialised clinical setting. Being able to screen populations A key objective of VPH-DARE@IT has on a large scale is essential for been to develop the citizen platform first detecting cases at high risk concept and validate it in research “our ultimate goal is to of developing dementia and then settings. detect the disease already The validation of the platform showed starting effective life-style and/ before such symptoms or pharmaceutical interventions, very interesting and promising results when available in the future. For (Paajanen et al. AAIC 2017, Mahdiani occur” this to succeed, we need low-cost, et al. AE 2017). The portal has great easy to acquire biomarkers that potential in providing tools for the can be measured effortlessly at early detection of memory disorders. It could be a relatively young ages already. Such biomarkers include, cost-efficient tool for initial assessment of the patient e.g., computerized tests or games, and changes in the in primary care, used even independently by citizens walking (gait) pattern. at home for assessing their brain health, and be a tool in clinical trials for enriching populations for costIn VPH-DARE@IT, we have developed a Citizen efficient patient selection. The potential of the tool Platform that helps address these needs. It is a step is already shown in several hospitals indicating their towards the detection of memory diseases already at interest to use especially the web-based cognitive test the pre-symptomatic phase. The Citizen Platform is a both in research and clinical practice. References Paajanen T, Mahdiani S, Bruun M, Baroni M, Rhodius-Meester H, Lemstra A, Herukka S-K, Pikkarainen M, Hänninen T, Ngandu T, Kivipelto M, van Gils M, Hasselbalch S, Mecocci P, van der Flier W, Remes A, Soininen H, Lötjönen J. Detecting cognitive disorders using Muistikko web-based cognitive test battery - validation in three cohorts. Alzheimer’s association international conference AAIC-17, July 16-20, London, UK, abstract 19005, 2017. Mahdiani S, Paajanen T, Bruun M, Baroni M, Rhodius-Meester H, Lemstra A, Herukka S-K, Pikkarainen M, Hänninen T, Ngandu T, Kivipelto M, van Gils M, Hasselbalch S, Mecocci P, van der Flier W, Remes A, Soininen H, Lötjönen J. Detection of cognitive disorders using web-based cognitive test battery - validation against traditional methods. 27th Alzheimer Europe Conference, October 2-4, Berlin, Germany, 2017.

Dr. Jyrki Lötjönen

Principal Investigator Teknologian Tutkimuskeskus VTT Technical Research Centre of Finland P.O. Box 1000 FI-02044 Finland T: +358 20 722 3378 E: jyrki.lotjonen@vtt.fi

N ovem ber 2017

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

17 13


Newsletter Issue VI

Tools - Tools - Tools - Tools

VPH-DARE@ IT Novel image computing tools deployed within Multix

By Zeike Taylor

One of the key roles of Work Package 2 has been to develop new algorithms and tools for neuroimage analysis in dementia studies, and for personalising biophysical models of brain fluid transport developed in WP5. Over the course of the project, several of these tools have been deployed in the Multix platform, to enable their use on the various prospective and retrospective imaging cohorts. Some highlights of these developments are described below.

the quantification of atrophy in terms of loss of volume of brain structures, e.g. for the hippocampus. Many approaches for fully automated segmentation of clinically relevant brain structures have been proposed in recent years. Nevertheless, very few of these algorithms are used in clinical routine, partly due to unmet practical requirements like processing time, or the difficulty in efficiently correcting segmentation errors in case of severely abnormal shapes of individual brain structures.

Figure 1: Sub-cortical brain region delineations in T1 MRI (left), and corresponding anatomical brain model depictions (right).

Anatomical brain models for segmentation of cortical and sub-cortical brain structures Much research is aimed at establishing image-based biomarkers that support diagnostic workflows with quantitative information. Moreover, many imaging biomarkers require extraction of anatomical brain regions in a scan, allowing derivation of region-based assessment of image-based parameters. One of the clinically most accepted biomarkers is

w

w

w .

Task 2.4, led by PRH, dealt with a novel approach for automated segmentation of sub-cortical and cortical brain structures in T1-weighted MRI by utilizing a shape-constrained deformable surface model. In contrast to other related approaches, its design allows for parallel segmentation of individual brain structures within a flexible and robust hierarchical framework such that accurate adaptation can be achieved within seconds. Three different models

v

p

h

-

d

a

r e

.

e

u


November - 2017

Project Outputs: Tools for segmenting brain structures have been finalized in this task, and integrated into Multix: 1) a model for segmenting 27 different sub-cortical brain regions (Fig 1), 2) a model for segmenting the left and right hemisphere, and 3) a model for segmenting cortical regions. Segmentation accuracy has been assessed quantitatively in various experiments, e.g. by comparing to publicly available, independent, manually labelled ground truth data.

Biomarker models for Disease Progression estimation of Alzheimer’s disease The estimation of disease progression in Alzheimer’s disease (AD) based on quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In Task

2.9, led by ICL and supported by USFD, quantile regression was employed to learn statistical models describing the evolution of such biomarkers. Two separate models were constructed using 1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and 2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models were then automatically combined to develop a multi-stage disease progression model for the whole disease course (Fig 2). A probabilistic approach was derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and

Figure 2: Construction of the Disease Progression model: the overall model (right) is a composition of individual models of conversion from CN to MCI (left), and from MCI to AD (middle). Disease scores are derived from ensembles of imaging- and non-imaging

image-based biomarkers, the method was used to estimate DP and DPR for subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks was demonstrated: for example, accuracy of 64% was reached for CN vs. MCI vs. AD classification. The complete pipeline was deployed within Multix in Y3, allowing it to be used with markers derived from any of the incorporated cohorts.

N ovem ber 2017

Construction of personalised brain models for fluid transport simulations The development of mechanistic models for obtaining simulation-based biomarkers supporting differential diagnosis and progress monitoring of dementia has been one of the main goals of VPHDARE@IT. The quality of any resulting biomarkers is tightly coupled to the quality of the computational model used in the in silico experiments, and

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

19 13


Newsletter Issue VI

Tools - Tools - Tools - Tools

VPH-DARE@ IT

similarly their specificity relies on use of personalised models. In T2.6 & T2.10, USFD, supported by UCL and PRH, have developed a pipeline for constructing personalised models of brain fluid transport (namely, the multiplenetwork poroelastic models developed in WP5), comprising the following components: 1) creation of a discretised model (i.e. a computational mesh) of the brain volume; 2) estimation of spatial maps of interstitial CSF anisotropic permeability; and 3) generation of subject-specific model boundary conditions (BCs). Volumetric meshes are derived from T1 MR images, segmented

using the above-mentioned tools developed by PRH. Label propagation is used to ensure biomechanically important structures like the falx cerebri and interthalamic adhesion in the third ventricle are well captured. Permeability maps are produced from diffusion-weighted images by estimating a dominant permeability vector direction, and mapping these onto the volumetric mesh. Finally, BCs are generated from a statistical model of cerebral blood flow that is personalised using cardiac and carotid US and blood pressure measurements. Examples of the resulting meshes and permeability maps are shown in Fig 3.

Figure 3: Example outputs from the MPET model personalisation pipeline (left-to-right): cortical surface model; ventricle surface model; cross-section of a computational mesh; spatial map of anisotropic permeability, colour-mapped according to strength of the anisotropy.

Robust analytical pipelines for the extraction of imaging biomarkers Clinical research studies and clinical trials are gradually evolving away from a design where only a few imaging modalities are acquired at a single site and with limited longitudinal follow-up. As imaging measures become more integral to the characterisation of the disease progression and potential modifications by drugs, modern studies are now relying on a more complex, involving more sites,

w

w

w .

more imaging modalities to characterise the brain (including structure, microstructure, activity, perfusion, protein load, and function), and monitoring participants for a longer period. These changes in study design result in several methodological challenges and they must be taken into account when developing dedicated image processing tools if the biomarkers are going to provide robust, precise results that are sensitive to changes in the disease process. For example, the extraction of

v

p

h

-

d

a

r e

.

e

u


November - 2017

Project Outputs: Tools imaging biomarkers from such data requires a multi-disciplinary team with associated expertise, including clinicians, computer scientists, medical physicists and statisticians amongst others. The developed tools must also cope with the large variability of the acquired images, due for example, to differences in the acquisition device. As there are now more trials enrolling more participants, the processing pipelines need to be scalable so that large amount of information can be analysed in an efficient manner. To address these challenges, we have been integrating well established analytical pipelines into the Nipype framework. Nipype is an opensource project that integrates tools from popular neuro-imaging software packages into common pipelines, saving the relevant provenance information to ensure reproducibility. This

approach enabled us to take advantage of the greater community expertise and fuse it with the specific neuroimaging strenghts of the project partners. Results from the pipelines incorporated into the VPH-DARE@IT project have already been contributed back to some of the clinical questions that reseachers are asking around the effects of lifestyle factors on Alzheimer’s disease and other dementias. Nipype is also able to seamlessly work with high performance computing system by parallelising any process and thus ensures that a large amount of data can be processed in a reasonable time frame. We implemented several pipelines with increasing complexity from correcting the bias field on a single MR image up to extracting spatio-temporal atlases from a whole population in order to automatically assess the influence of a disease over time.

Dr. Zeike Taylor

Senior Lecturer Department of Mechanical Engineering The University of Sheffield Sir Frederick Mappin Building Mappin Street Sheffield United Kingdom

N ovem ber 2017

∙

VPH-DA RE @I T Pro j e c t

∙

New sl e tte r I ssu e V I

21 13


Newsletter Issue VI

Tools - Tools - Tools - Tools

VPH-DARE@ IT Multiple-Network Poroelastic Theory (MPET) By Yiannis Ventikos Movement of fluids in the brain (blood, interstitial fluid, cerebrospinal fluid), plays an important role in many types of dementia. Vascular Dementia is linked to chronic reduction of blood flow in certain parts of the brain, whereas Alzheimer’s Disease, the most common form of dementia, involves the chronic deposition of accumulation of metabolic products in parts of the brain, leading in local neurodegeneration. There is actually emerging evidence suggesting that even Alzheimer’s Disease often has a significant vascular component, further emphasizing the significance of transport and fluid movement in the brain. In order to help decipher some of the underlying mechanisms of such a hypothesis, it is essential to model fluid transport within the brain in a personalised manner and from first principles. Unfortunately, there were no such computational tools available when we started the VPD-DARE@IT project, either from commercial software packages or from open-source research codes. Therefore, we have developed our own in-

house numerical suite based on MultipleNetwork Poroelastic Theory (MPET). This novel computational paradigm can be used to conduct mechanistic modelling of fluid transport through the perfused brain tissue. Summarily, in a porous medium representing the cerebral environment, the solid matrix represents the various brain cells and structures (neurons, astrocytes, vasculature, membranes etc). The gaps between or within these structures are filled with blood (in the case of vasculature) or with a clear water-like filtrate of blood – we use the general term cerebrospinal fluid to describe that medium. The various fluid media communicate amongst themselves. Within the VPD-DARE@IT project an MPET model with 4 compartments was used: it involved an arterial network, an arteriole/capillary network, a CSF/ISF network and a venous network. This model allows for the simultaneous solutions of continuity and momentum conservation equations, in four interconnected fluid networks (directional flow is defined

Figure 1 Section of an MCI subject, with CSF/ ISF microscale velocity (clearance) maps computed using the MPET paradigm

between fluid compartments). Importantly, as the pressure of the various compartments may change locally, this pressure variation may cause deformation and change of shape of the solid matrix. Conversely, deformation of the solid matrix may cause flow of the liquid media. These two features signify the term “poroelastic” when such a model is concerned.

The formulation is applicable for any arbitrary 3D domain; therefore it is capable of modelling realistic cerebral geometries, which can be extracted from structural MR images.

The numerical code is modular in structure. This feature is reflected on the latest subject-specific modelling by replacing the original constant permeability formulation with spatial maps of heterogeneous and anisotropic

The main features of this MPET model can be summarised as follows:

w

w

w .

v

p

h

-

d

a

r e

.

e

u


November - 2017

Project Outputs: Tools permeability for the CSF/ISF compartment (as generated by diffusion tensor imaging), and using real measurements of blood flow as personalised boundary conditions for the arterial compartment. The MPET model has been validated by classic benchmark tests of the consolidation theory, e.g. Terzaghi’s and Mandel’s problems. Moreover, it has also been validated using experimental data of infusion tests on mice collected by the VPD-DARE@ IT project partners at the University of Oslo. The MPET system gives rise to a generic model that simulates biomechanical behaviour of perfused tissue. Within the VPH-DARE@IT project, the MPET model has been coupled with separate workflows concerning subject-specific meshes, permeability tensor maps and cerebral blood flow (CBF) variability. The consolidated pipeline has been implemented

on the Multix platform, which provides a software infrastructure for integration and harmonisation of disparate data inputs from multiple collections, and for orchestrating large-scale analyses of the same using cloud, and other, computing resources. It correspondingly streamlines the deployment of complex computational toolchains, such as described here, on data from large cohorts of subjects. The pipeline has been successfully used for the mechanistic modelling of subject-specific datasets (Lido study), which include mild cognitive impairment (MCI) patients and control subjects. Typical results can be seen in Figure 1, where maps of the microscale velocity for the interstitial fluid (a surrogate measure for clearance) are shown. Processing multiple subjects through the Multix environment allows us to generate comprehensive correlation maps, a sample of which can be seen in Figure 2.

Figure 2. Collection and organisation of LIDO data and MPET simulation results, as generated and codified by the Multix Platform.

To the best of our knowledge, this is the first attempt worldwide of modelling dementia based on biomechanical first principles. Modelling perfused parenchymal tissue may enhance our

understanding of the influence of modifiable lifestyle factors such as smoking, dietary habits and leisure activities in addition to environmental risk factors in dementia.

Prof.Yiannis Ventikos

Kennedy Chair and Head of Department Department of Mechanical Engineering Faculty of Engineering Science University College London Gower Street London

N ovem ber 2017

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

23 13


VPH-DARE@ IT

Gabriella and Dario: A personal journey through dementia

By Gabriela Mariotti

Newsletter Issue VI

It never came across to me that one day I would have had to deal with dementia. At the beginning my husband and I we were joking about his “short memory” we did not pay too much attention to frequent questions like “where are my keys?” Or “did you see my phone? “. My grandson even called him “nonno scordone” literally “big forgetting grandpa”. But, helas, after a while we all in the family realized that the joke was over and the matter

was becoming increasingly more serious. One day he got lost in the street incapable to remember where to go and where was the store where he was supposed to shop. He called me and with patience, step by step, we found the way to get back home. From that day on, Dario did not use the car anymore or go on a bicycle. We bought a

w w

At this point, we had to do something. Our family doctor suggested getting an appointment with a specialist, a neurologist, who could give us an evaluation and some advice. Yes, Dario had some brain “ischemia“ and needed to do some tests to determine the vastity of the damage. Luckily, in our Venice Lido island, we have a great hospital located between the lagoon and the Adriatic Sea.

“I joined my husband in this study, so I was able to be near him, and so we could share this experience together hoping for the best”

w w

three wheels’ bike which is more stable and safe, and I drive him everywhere he wants to go.

w w ..

It was a discovery for us, nothing to do with the classic typical sad hospitals. We arrived and saw lots of trees and flowers in the therapeutic garden. As we entered the San Camillo hospital, we could smell the freshly made coffee from the great cafeteria down the hall. Many young people walked towards us with a smile. We met Dr Francesca Meneghello and we talked very openly and sincerely about Dario’s problem and how to deal

vv

pp

hh

--

d d

aa

rr ee

..

ee

uu


with it. At the interview, Dario unfortunately didn’t recall the year or the current date. She invited us to participate in the therapeutic research project that was going to start in her ward at San Camillo. So we met the entire team, Professor Annalena Venneri, Dr Elena Cosentino, Dr Vincenzo Iaia, Dr Giorgio Levedianos, Dr Micaela Mitolo, Dr Camilla dell Pieta and Dr Ana Kostivic. They were all kind and friendly to us and at this point we both agreed with enthusiasm to be part of it. I joined my husband in this study, so I was able to be near him, and so we could share this experience together hoping for the best

“We remain hopeful that one day, not too far in the future, the cure for dementia will be found” final results. Dr Meneghello called us to let us know that she had all the documents ready to make an evaluation. Dario and I while holding hands in her office listened very carefully, and felt like the time we left Italy for the great American adventure years ago: Serene but determined to succeed.

We started with the mnemonic tests: I concentrated page after page of drawings and figures. I was very tired and I even doubted of my mental capacity. I went home and started doing cross words hoping to improve my brain skills. While Dario was amazed by the tests and even smiled, I was very concerned about the results. But Dr Ana Kostivic said that it was a normal reaction and that reassured me. Every week or so, we had a test, blood test, MRI scan, cardiac ultrasound, carotid ultrasound, all the tests necessary for the project. Every time we became more familiar with the team and felt like a part of a great project. So two months passed by quickly, while driving to “Alberoni” was also a good excuse to stop by for a little venetian “Cicchetto” - a small fish appetizer typical of our island. At the end of all the tests and screenings we almost felt sorry it was over but we couldn’t wait for the

N ovem ber 2017

Now that we know, with the help of the neurologist and the patch that every night Dario applies on his body, we are very confident that we, in our small way, have contributed to ongoing research on ‘dementia’. We remain hopeful that one day, not too far in the future, the cure for dementia will be found, or at least, there will be some treatment available so that its progression can be halted. We thank the entire team and we are ready and willing to participate in any other new project that may help research to defeat dementia.

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

25 19


VPH-DARE@IT IT news VPH-DARE@

Researchers Experience Nishant Ravikumar The University of Sheffield

Background and Research My name is Nishant Ravikumar and I recently completed my doctoral degree at CISTIB,Centre for Computational Imaging and Simulation Technologies in Biomedicine, The University of Sheffield. My work was primarily concerned with developing a probabilistic framework for the construction of statistical shape models and atlases of neuroanatomical structures. During my time at CISTIB I also worked part time as a research assistant in computational imaging and modelling, for the VPH-DARE@IT project.

Newsletter Issue VI

What is your specific role in VPHDARE@IT?

My responsibilities consisted of assisting in the development of automated pipelines for the Clinical Research Platform (CRP), to generate personalised 3D computational meshes of the brain, for use in in silico studies investigating fluid transport and its potential implications in the onset and progression of Alzheimer’s This project gave me the opportunity to work with industrial and academic partners across Europe and learn from leading scientists in the field.

and clinical data, as well as life style factors, to identify potential biomarkers and improve our understanding of dementia-related processes and disease progression.

What do you as a researcher find most challenging about working in VPHDARE@IT? The experience of working on such a largescale, international project was invaluable for my professional growth and gave me critical insight into the complexity of collaborating with numerous partners from varied professional

backgrounds.The tight deadline and deliverabledriven nature of such projects is challenging but helped me cultivate a pragmatic approach to problem solving. The promise it shows for improving patient care and contributing positively to society is highly encouraging for those looking to pursue a career in developing IT-driven solutions to clinical problems.

What do you find most interesting about the VPH-DARE@IT project? For an early career research scientist such as myself, projects like these are inspiring. They bring to attention the vast potential that computational imaging and modelling have for supporting and improving clinical decision making in the future.The high volume and quality of patient data available within the consortium provided a rare and exciting chance to analyse a wide variety of variables in the form of imaging

w

w

w .

v

p

h

-

d

a

r e

.

e

u


Researchers Experience Liwei Guo University College London Background and Research

I am a research associate in Work Package 5 at University College London. Before joining this project in February 2015 I was a postgraduate research student. I got my PhD degree in computational physics from Imperial College London and an MSc degree in engineering mechanics from the Chinese Academy of Sciences. My previous research was mainly on the development of new numerical methods and in-house numerical codes and their applications to engineering problems, such as plastic models for soft solids, absorbing boundary conditions for dynamic analyses, multi-body dynamics and advanced numerical models for three-dimensional fracture simulations of quasi-brittle materials. Now I have extended my research interests into brain biomechanics and poroelastic models.

What is your specific role inVPH-DARE@IT?

My current work in the VPH-DARE@IT project is to develop a multi-compartmental poroelastic model for the simulation of fluid transport in the brain.As we know, a breakdown in the cerebral environment is a common cause of many diseases of old age, such as dementia. However, there is still a lack of understanding of the basic mechanics of this environment. We hope that by using numerical tools we can learn more about cerebral dynamics and provide new biomarkers for more accurate and earlier differential diagnostics of cognitive diseases at the early symptomatic phase.

What do you find most interesting about the VPH-DARE@IT project?

The most interesting thing I find about this project is that there is always fascinating knowledge emerging from the research, both from my own work and from the work of our partners. It makes me realise how limited our understanding of the brain and dementia is.We know we cannot solve all the problems with only one project but it is still a great sense of achievement.

What do you as a researcher find most challenging about working in VPH-DARE@ IT?

As a researcher, the most challenging part about working in VPH-DARE@IT is that we need to constantly push

N ovem ber 2017

ourselves out of our comfort zones.The brain is the most complex of all the human organs and the problems we are investigating are extremely complicated. Very often our current tools and knowledge are not sufficient to answer our questions, so this has become an iterative process of developing new ideas and methods. It is challenging, but very rewarding too, as we are able to see the progress we are making.

How do you find working as a part of a large collaborative project?

I have worked on collaborative projects before, but the scale of collaboration of the VPH-DARE@IT project is much bigger and the relationships between partners much closer. The whole project works like machinery, where each of us is a cog, small but essential for the system to work efficiently. I really enjoy the process of combining our efforts to achieve the same goals.

Have you attended any of the VPH-DARE@ IT project meetings and if so, what benefits did you get from attending these events?

I have attended several general assembly project meetings. I learn something new every time I attend and the new knowledge helps me build a coherent understanding of the whole project. These meetings also create great opportunities for us to have face-to-face talks with our collaborators, which I consider a very efficient way of communication. I am also inspired by the presentations given by the invited guests.The content may not be linked directly with my own work, but it certainly allows me to see the same problem from a different perspective.

How has working on VPH-DARE helped to develop your career?

This is my first full-time job since my PhD degree and it’s a big step from a full-time student to an employed scientist. We have a strong group here at UCL and I can count on the support I need to tackle any problems I encounter. Extensive collaboration with partners also helps me build my knowledge and enrich my skill set very quickly, which I consider to be of great value to my career.

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

27


VPH-DARE@ IT

PROJECT PUBLICATIONS

1. Wolters FJ, Zonneveld HI, Hofman A, van der Lugt A, Koudstaal PJ, Vernooij MW, et al. Cerebral Perfusion and the Risk of Dementia A Population-Based Study. Circulation. 2017;136(8):719-+. 2. Winter F, Bludszuweit-Philipp C, Wolkenhauer O. Mathematical analysis of the influence of brain metabolism on the BOLD signal in Alzheimer’s disease. J Cereb Blood Flow Metab. 2017:271678X17693024. 3. Vosa SJB, Vos SJB, Soininen H, Lotjonen J, Koikkalainen J, Pikkarainen M, et al. Association Between Later Life Lifestyle Factors and Alzheimer’s Disease Biomarkers in Non-Demented Individuals: A Longitudinal Descriptive Cohort Study. Journal of Alzheimers Disease. 2017;60(4):1387-95. 4. Venneri A, Mitolo M, De Marco M. The network substrate of confabulatory tendencies in. Alzheimer’s disease. Cortex. 2017;87:6979. 5. Suhonen NM, Hallikainen I, Hanninen T, Jokelainen J, Kruger J, Hall A, et al. The Modified Frontal Behavioral Inventory (FBI-mod) for Patients with Frontotemporal Lobar Degeneration, Alzheimer’s Disease, and Mild Cognitive Impairment. Journal of Alzheimers Disease. 2017;56(4):1241-51. 6. Sudre CH, Cardoso MJ, Ourselin S. Longitudinal segmentation of age-related white matter hyperintensities. Medical Image Analysis. 2017;38:50--64. 7. Sudre CH, Cardoso MJ, Frost C, Barnes J, Barkhof F, Fox N, et al. APOE epsilon 4 status is associated with white matter hyperintensities volume accumulation rate independent of AD diagnosis. Neurobiology of Aging. 2017;53:67-75. 8. Sudre CH, Bocchetta M, Cash D, Thomas DL, Woollacott I, Dick KM, et al. White matter hyperintensities are seen only in GRN mutation carriers in the GENFI cohort. Neuroimage Clin. 2017;15:171-80. 9. Stephen R, Liu Y, Ngandu T, Rinne JO, Kemppainen N, Parkkola R, et al. Associations of CAIDE Dementia Risk Score with MRI, PIB-PET measures, and cognition. Journal of Alzheimers Disease. 2017;59(2):695-705. 10. Slattery CF, Zhang JY, Paterson RW, Foulkes AJM, Carton A, Macpherson K, et al. ApoE influences regional white-matter axonal density loss in Alzheimer’s disease. Neurobiology of Aging. 2017;57:8-17. 11. Russell CL, Mitra V, Hansson K, Blennow K, Gobom J, Zetterberg H, et al. Comprehensive Quantitative Profiling of Tau and Phosphorylated Tau Peptides in Cerebrospinal Fluid by Mass Spectrometry Provides New Biomarker Candidates. Journal of Alzheimers Disease. 2017;55(1):303-13. 12. Pekkala T, Hall A, Lotjonen J, Mattila J, Soininen H, Ngandu T, et al. Development of a late-life dementia prediction index with supervised machine learning in the population-based CAIDE study. Journal of Alzheimer’s Disease. 2017;55(3):1055--67. 13. Paajanen T, Mahdiani S, Bruun M, Baroni M, Rhodius- Meester HFM, Lemstra AW, et al. Detecting Cognitive Disorders Using Muistikko Web-Based Cognitive Test Battery: Validation in Three Cohorts. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2017;13(7):P380. 14. Fiford CM, Manning EN, Bartlett JW, Cash DM, Malone IB, Ridgway GR, et al. White Matter Hyperintensities are Associated with Disproportionate Progressive Hippocampal Atrophy. Hippocampus. 2017;27(3):249-62. 15. Mozumder M, Beltrachini L, Collier Q, Pozo JM, Frangi AF. Simultaneous magnetic resonance diffusion and pseudo-diffusion tensor imaging. Magnetic Resonance in Medicine. 2017;00:1--12. 16. Mendelson AF, Zuluaga MA, Lorenzi M, Hutton BF, Ourselin S, Alzheimers Dis Neuroimaging I. Selection bias in the reported performances of AD classification pipelines. Neuroimage-Clinical. 2017;14:400-16. 17. McGrath DM, Ravikumar N, Beltrachini L, Wilkinson ID, Frangi AF, Taylor ZA. Evaluation of wave delivery methodology for brain MRE: Insights from computational simulations. Magnetic Resonan-

w

ce in Medicine. 2017;78(1):341-56. 18. Holmes HE, Powell NM, Ma D, Ismail O, Harrison IF, Wells JA, et al. Comparison of In Vivo and Ex Vivo MRI for the Detection of Structural Abnormalities in a Mouse Model of Tauopathy. Front Neuroinformatics. 2017;11:15. 19. Martiskainen H, Paldanius KMA, Natunen T,Takalo M, Marttinen M, Leskel S, et al. DHCR24 exerts neuroprotection upon inflammation-induced neuronal death. Journal of Neuroinflammation. 2017;14:16. 20. Martiskainen H, Herukka SK, Stancakova A, Paananen J, Soininen H, Kuusisto J, et al. Decreased plasma beta-amyloid in the Alzheimer’s disease APP A673T variant carriers. Ann Neurol. 2017;82(1):128-32. 21. Lotjonen J, Koikkalainen J, Rhodius- Meester HFM, van der Flier WM, Scheltens P, Barkhof F, et al. Computed Rating Scales for Cognitive Disorders from Mri. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2017;13(7):P1108. 22. Lassila T, Di Marco LY, Mitolo M, Iaia V, Levedianos G, Venneri A, et al. Screening for Cognitive Impairment by Model Assisted Cerebral Blood Flow Estimation. IEEE Trans Biomed Eng. 2017. 23. Laitera T, Paananen J, Helisalmi S, Sarajarvi T, Huovinen J, Laitinen M, et al. Effects of Alzheimer’s Disease-Associated Risk Loci on Amyloid-beta Accumulation in the Brain of Idiopathic Normal Pressure Hydrocephalus Patients. Journal of Alzheimers Disease. 2017;55(3):995-1003. 24. Kasztelnik M, Coto E, Bubak M, Malawski M, Nowakowski P, Arenas J, et al. Support for Taverna workflows in the VPHShare cloud platform. Computer Methods and Programs in Biomedicine. 2017;146:37--46. 25. Irntiaz B, Taipale H, Tanskanen A, Tiihonen M, Kivipelto M, Heikkinen AM, et al. Risk of Alzheimer’s disease among users of postmenopausal hormone therapy: A nationwide case-control study. Maturitas. 2017;98:7-13. 26. Imtiaz B, Tolppanen AM, Solomon A, Soininen H, Kivipelto M. Estradiol and Cognition in the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) Cohort Study. Journal of Alzheimers Disease. 2017;56(2):453-8. 27. Huovinen J, Helisalmi S, Paananen J, Laitera T, Kojoukhova M, Sutela A, et al. Alzheimer’s Disease-Related Polymorphisms in Shunt-Responsive Idiopathic Normal Pressure Hydrocephalus. Journal of Alzheimers Disease. 2017;60(3):1077-85. 28. Holter KE, Kehlet B, Devor A, Sejnowski TJ, Dale AM, Omholt SW, et al. Interstitial solute transport in 3D reconstructed neuropil occurs by diffusion rather than bulk flow. Proceedings of the National Academy of Sciences of the United States of America. 2017;114(37):9894-9. 29. Enger R, Dukefoss DB, Tang WN, Pettersen KH, Bjornstad DM, Helm PJ, et al. Deletion of Aquaporin-4 Curtails Extracellular Glutamate Elevation in Cortical Spreading Depression in Awake Mice. Cerebral Cortex. 2017;27(1):24-33. 30. Dourlen P, Fernandez-Gomez FJ, Dupont C, Grenier-Bo-ley B, Bellenguez C, Obriot H, et al. Functional screening of Alzheimer risk loci identifies PTK2B as an in vivo modulator and early marker of Tau pathology. Molecular Psychiatry. 2017;22(6):874--83. 31. De Marco M, Manca R, Mitolo M, Venneri A. White Matter Hyperintensity Load Modulates Brain Morphometry and Brain Connectivity in Healthy Adults: A Neuroplastic Mechanism? Neural Plasticity. 2017:10. 32. De Marco M, Duzzi D, Meneghello F, Venneri A. Cognitive Efficiency in Alzheimer’s Disease is Associated with Increased Occipital Connectivity. Journal of Alzheimers Disease. 2017;57(2):541-56. 33. Bron EE, Smits M, Papma JM, Steketee RME, Meijboom R, de Groot M, et al. Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI. European Radiology.

w

w .

v

p

h

-

d

a

r e

.

e

u


2017;27(8):3372-82. 34. Bos I,Vos SJ, Frolich L, Kornhuber J,Wiltfang J, Maier W, et al.The frequency and influence of dementia risk factors in prodromal Alzheimer’s disease. Neurobiol Aging. 2017;56:33-40. 35. Weston PSJ, Nicholas JM, Lehmann M, Ryan NS, Liang Y, Macpherson K, et al. Presymptomatic cortical thinning in familial Alzheimer disease A longitudinal MRI study. Neurology. 2016;87(19):2050-7. 36. Vindedal GF, Thoren AE, Jensen V, Klungland A, Zhang Y, Holtzman MJ, et al. Removal of aquaporin-4 from glial and ependymal membranes causes brain water accumulation. Mol Cell Neurosci. 2016;77:47--52. 37. Rohrer J, Bocchetta M, Gordon E, Cash D, Dick K, Thomas D, et al. Patterns of longitudinal neuroanatomical change in genetic FTD: results from the Genetic FTD Initiative (GENFI). Journal of Neurochemistry. 2016;138:397-. 38. Reijs BLR,Vos SJB, Jansen WJ,Verhey FRJ,Visser PJ. Relation between Lifestyle Factors and Alzheimer’s Disease Biomarkers in Subjects with Sci or Mci. Alzheimer’s & Dementia:The Journal of the Alzheimer’s Association. 2016;12(7):P294-P5. 39. Pardini M, Sudre CH, Prados F, Yaldizli O, Sethi V, Muhlert N, et al. Relationship of grey and white matter abnormalities with distance from the surface of the brain in multiple sclerosis. Journal of Neurology Neurosurgery and Psychiatry. 2016;87(11):12127. 40. Oxtoby NP, Young AL, Lorenzi M, Cash DM, Weston PSJ, Ourselin S, et al. Model-Based Comparison of Autosomal-Dominant and Late-Onset Alzheimer’s Disease Progression in the Dian and Adni Studies. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2016;12(7):P668-P9. 41. Munoz-Ruiz MA, Hall A, Mattila J, Koikkalainen J, Herukka SK, Husso M, et al. Using the Disease State Fingerprint Tool for Differential Diagnosis of Frontotemporal Dementia and Alzheimer’s Disease. Dementia and Geriatric Cognitive Disorders Extra. 2016;6(2):313-29. 42. Markiewicz PJ, Thielemans K, Schott JM, Atkinson D, Arridge SR, Hutton BF, et al. Rapid processing of PET list-mode data for efficient uncertainty estimation and data analysis. Physics in Medicine and Biology. 2016;61(13):N322-N36. 43. Luikku AJ, Hall A, Nerg O, Koivisto AM, Hiltunen M, Helisalmi S, et al. Multimodal analysis to predict shunt surgery outcome of 284 patients with suspected idiopathic normal pressure hydrocephalus. Acta Neurochirurgica. 2016;158(12):2311-9. 44. Lötjönen J, Tolonen A, Rhodius- Meester HFM, Bruun M, Koikkalainen J, Barkhof F, et al. Towards Data-Driven Medicine in Differential Diagnostics of Neurodegenerative Diseases. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2016;12(7):P355. 45. Lorenzi M, Simpson IJ, Mendelson AF,Vos SB, Cardoso MJ, Modat M, et al. Multimodal Image Analysis in Alzheimer’s Disease via Statistical Modelling of Non-local Intensity Correlations. Scientific Reports. 2016;6. 46. Lorenzi M, Gutman BA, Altmann A, Hibar DP, Jahanshad N, Alexander DC, et al. Linking Gene Pathways and Brain Atrophy in Alzheimer’s Disease. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2016;12(7):P6-P7. 47. Lorenzi M, Gutman B, Hibar DP, Altmann A, Jahanshad N, Thompson PM, et al. Partial Least Squares Modelling for Imaging-Genetics in Alzheimer’s Disease: Plausibility and Generalization. 2016 Ieee 13th International Symposium on Biomedical Imaging (Isbi). 2016:838-41. 48. Lorenzi M, Gutman B, Altmann, Hibar D, Jahanshad N, Alexander D, et al. Linking gene pathways and brain atrophy in alzheimer’s disease. Alzheimer’s & Dementia. 2016;12(7, Supplement):P6-P7. 49. Kurkinen KM, Marttinen M,Turner L, Natunen T, Makinen P, Haapalinna F, et al. SEPT8 modulates $\beta$-amyloidogenic processing of APP by affecting the sorting and accumulation of BACE1. Journal of Cell Science. 2016;129(11):2224--38. 50. Huovinen J, Kastinen S, Komulainen S, Oinas M, Avellan C, Frantzen J, et al. Familial idiopathic normal pressure hydrocephalus.

N ovem ber 2017

Journal of the Neurological Sciences. 2016;368:11--8. 51. Huizinga W, Poot DHJ, Guyader JM, Klaassen R, Coolen BF, van Kranenburg M, et al. PCA-based groupwise image registration for quantitative MRI. Medical Image Analysis. 2016;29:65-78. 52. Gordon E, Bocchetta M, Cardoso MJ, Ourselin S, Warren JD, Rohrer JD. Clinical, genetic and pathological stratification in frontotemporal dementia: implications for clinical trial design. Journal of Neurochemistry. 2016;138:398-. 53. Frangi AF, Taylor ZA, Gooya A. Precision Imaging: more descriptive, predictive and integrative imaging. Medical Image Analysis. 2016;33:27--32. 54. Cremers LGM, de Groot M, Hofman A, Krestin GP, van der Lugt A, Niessen WJ, et al. Altered tract-specific white matter microstructure is related to poorer cognitive performance: The Rotterdam Study. Neurobiology of Aging. 2016;39:108-17. 55. Cash DM, Ridgway GR, Kinnunen KM, Benzinger TLS, Wallon D, Jack CR, et al. A Longitudinal Morphometric Study of Familial Alzheimer’s Disease: Results from Dian. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2016;12(7):P187-P9. 56. Cash DM, Kinnunen KM, Weston PSJ, Ryan NS, Modat M, Bateman R, et al. Longitudinal Atrophy in Autosomal Dominant Ad and Sporadic Ad: Lessons from Dian. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2016;12(7):P368-P9. 57. Bos I, Vos SJB, Frölich L, Kornhuber J, Wiltfang J, Maier W, et al. Prevalence of Vascular Risk Factors in Different Stages of Prodromal Alzheimer’s Disease and Its Influence on Cognitive Decline. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2016;12(7):P1059-P61. 58. Bocchetta M, Toussaint N, Hutel M, Modat M, Cardoso MJ, Gordon E, et al. Multimodal imaging analysis of C9orf72-associated FTD in the Genetic Frontotemporal dementia Initiative (GENFI) study. Journal of Neurochemistry. 2016;138:245-. 59. Bocchetta M, Gordon E, Marshall CR, Slattery CF, Cardoso MJ, Cash DM, et al.The habenula: an under-recognised area of importance in frontotemporal dementia? Journal of Neurology Neurosurgery and Psychiatry. 2016;87(8):910-U135. 60. Bocchetta M, Cardoso MJ, Cash DM, Ourselin S, Warren JD, Rohrer JD. Patterns of regional cerebellar atrophy in genetic frontotemporal dementia. Neuroimage-Clinical. 2016;11:287-90. 61. Andrews KA, Frost C, Modat M, Cardoso MJ, Rowe CC, Villemagne V, et al. Acceleration of hippocampal atrophy rates in asymptomatic amyloidosis. Neurobiology of Aging. 2016;39:99-107. 62. Altmann A, Modat M, Ourselin S. Genome-Wide Polygenic Risk for Alzheimer’s Disease Is Associated with Rate of Metabolic Decline but Not with Rate of Amyloid Deposition. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2016;12(7):P717. 63. Tang W, Szokol K, Jensen V, Enger R, Trivedi CA, Hvalby O, et al. Stimulation-Evoked Ca2+ Signals in Astrocytic Processes at Hippocampal CA3-CA1 Synapses of Adult Mice Are Modulated by Glutamate and ATP. Journal of Neuroscience. 2015;35(7):3016--21. 64. Haj-Yasein NN, Bugge CE, Jensen V, Ostby I, Ottersen OP, Hvalby O, et al. Deletion of aquaporin-4 increases extracellular K+ concentration during synaptic stimulation in mouse hippocampus. Brain Structure and Function. 2015;220(4):2469-74. 65. Enger R, Tang W, Vindedal GF, Jensen V, Johannes Helm P, Sprengel R, et al. Dynamics of ionic shifts in cortical spreading depression. Cerebral Cortex. 2015;25(11):4469--76. 66. Beltrachini L, De Marco M, Taylor ZA, Lotjonen J, Frangi AF, Venneri A. Integration of Cognitive Tests and Resting State fMRI for the Individual Identification of Mild Cognitive Impairment. Curr Alzheimer Res. 2015;12(6):592-603. 67. Beltrachini L, Taylor ZA, Frangi AF. A parametric finite ele-

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

29


VPH-DARE@ IT 68.

69.

70.

71. 72.

73.

74.

75. 76. 77.

78.

79.

80.

81.

82.

83.

84.

ment solution of the generalised Bloch-Torrey equation for arbitrary domains. J Magn Reson. 2015;259:126-34. Bocchetta M, Gordon E, Manning E, Barnes J, Cash DM, Espak M, et al. Detailed volumetric analysis of the hypothalamus in behavioral variant frontotemporal dementia. J Neurol. 2015;262(12):2635-42. Cardoso MJ, Modat M, Wolz R, Melbourne A, Cash D, Rueckert D, et al. Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion. IEEE Trans Med Imaging. 2015;34(9):1976-88. Cash DM, Frost C, Iheme LO, Ünay D, Kandemir M, Fripp J, et al. Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge. Neuroimage. 2015;123:149-64. Chou D, Vardakis JC, Guo L, Tully BJ, Ventikos Y. A fully dynamic multi-compartmental poroelastic system: Application to aqueductal stenosis. J Biomech. 2015. de Groot M, Cremers LG, Ikram MA, Hofman A, Krestin GP, van der Lugt A, et al. White Matter Degeneration with Aging: Longitudinal Diffusion MR Imaging Analysis. Radiology. 2015:150103. de Groot M, Ikram M, Akoudad S, Krestin G, Hofman A, van der Lugt A, et al. Tract-specific white matter degeneration in aging: The Rotterdam Study. Alzheimers & Dementia. 2015;11(3):321-30. De Marco M, Meneghello F, Duzzi D, Rigon J, Pilosio C, Venneri A. Cognitive stimulation of the default-mode network modulates functional connectivity in healthy aging. Brain Res Bull. 2016;121:26-41. De Marco M, Venneri A. ‘O’ blood type is associated with larger grey-matter volumes in the cerebellum. Brain Res Bull. 2015;116:1-6. Haapasalo A, Pikkarainen M, Soininen H. Alzheimer’s disease: a report from the 7th Kuopio Alzheimer symposium. Neurodegener Dis Manag. 2015;5(5):379-82. Koikkalainen J, Rhodius-Meester H, Tolonen A, Barkhof F, Ti-jms B, Lemstra AW, et al. Differential diagnosis of neurodegenerative diseases using structural MRI data. NeuroImage: Clinical. 2016;11:435-49. Laiterä T, Kurki MI, Pursiheimo ,JPZetterberg H, Helisal-mi S, Rauramaa T, et al. The Expression ofransthyretin T and Amyloid-β Protein Precursor is Altered in the Brain of Idiopathic Normal Pressure Hydrocephalus Patients. J Alzheimers Dis. 2015;48(4):959-68. Lambert SA, Näsholm SP, Nordsletten D, Michler C, Juge L, Serfaty JM, et al. Bridging Three Orders of Magnitude: Multiple Scattered Waves Sense Fractal Microscopic Structures via Dispersion. Phys Rev Lett. 2015;115(9):094301. Lupton MK, Strike L, Hansell NK, Wen W, Mather KA, Armstrong NJ, et al. The effect of increased genetic risk for Alzheimer’s disease on hippocampal and amygdala volume. Neurobiol Aging. 2016;40:68-77. McGrath DM, Ravikumar N, Wilkinson ID, Frangi AF, Taylor ZA. Magnetic resonance elastography of the brain: An in silico study to determine the influence of cranial anatomy. Magn Reson Med. 2015. Natunen T,Takalo M, Kemppainen S, Leskelä S, Marttinen M, Kurkinen KM, et al. Relationship between ubiquilin-1 and BACE1 in human Alzheimer’s disease and APdE9 transgenic mouse brain and cell-based models. Neurobiol Dis. 2016;85:187-205. Rhodius-Meester HF, Koikkalainen J, Mattila J, Teunissen CE, Barkhof F, Lemstra AW, et al. Integrating Biomarkers for Underlying Alzheimer’s Disease in Mild Cognitive Impairment in Daily Practice: Comparison of a Clinical Decision Support System with Individual Biomarkers. J Alzheimers Dis. 2015;50(1):261-70. Rog T, Koivuniemi A.The biophysical properties of ethanolamine

w

plasmalogens revealed by atomistic molecular dynamics simulations. Biochim Biophys Acta. 2016;1858(1):97-103. 85. Salminen A, Haapasalo A, Kauppinen A, Kaarniranta K, Soininen H, Hiltunen M. Impaired mitochondrial energy metabolism in Alzheimer’s disease: Impact on pathogenesis via disturbed epigenetic regulation of chromatin landscape. Prog Neurobiol. 2015;131:1-20. 86. Salminen A, Jouhten P, Sarajarvi T, Haapasalo A, Hiltunen M. Hypoxia and GABA shunt activation in the pathogenesis of Alzheimer’s disease. Neurochemistry International. 2016;92:13-24. 87. Sarajärvi T, Marttinen M, Natunen T, Kauppinen T, Mäkinen P, Helisalmi S, et al. Genetic Variation in δ-Opioid Receptor Associates with Increased β- and γ-Secretase Activity in the Late Stages of Alzheimer’s Disease. J Alzheimers Dis. 2015;48(2):507-16. 88. Schmidt-Richberg A, Ledig C, Guerrero R, Molina-Abril H, Frangi A, Rueckert D, et al. Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease. PLoS One. 2016;11(4):e0153040. 89. Sedaghat S, Cremers LG, de Groot M, Hoorn EJ, Hofman A, van der Lugt A, et al. Kidney function and microstructural integrity of brain white matter. Neurology. 2015;85(2):154-61. 90. Sudre CH, Cardoso MJ, Bouvy WH, Biessels GJ, Barnes J, Ourselin S. Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation. IEEE Trans Med Imaging. 2015;34(10):2079-102. 91. Vardakis JC, Chou D, Tully BJ, Hung CC, Lee TH, Tsui PH, et al. Investigating cerebral oedema using poroelasticity. Med Eng Phys. 2016;38(1):48-57. 92. Weston PS, Paterson RW, Modat M, Burgos N, Cardoso MJ, Magdalinou N, et al. Using florbetapir positron emission tomography to explore cerebrospinal fluid cut points and gray zones in small sample sizes. Alzheimers Dement (Amst). 2015;1(4):440-6. 93. Weston PS, Simpson IJ, Ryan NS, Ourselin S, Fox NC. Diffusion imaging changes in grey matter in Alzheimer’s disease: a potential marker of early neurodegeneration. Alzheimers Res Ther. 2015;7(1):47. 94. Burgos N, Cardoso M, Thielemans K, Modat M, Pedemonte S, Dickson J, et al. Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies. Ieee Transactions on Medical Imaging. 2014;33(12):2332-41. 95. Cash DM, Rohrer JD, Ryan NS, Ourselin S, Fox NC. Imaging endpoints for clinical trials in Alzheimer’s disease. Alzheimer’s Research & Therapy. 2014;6(9):87. 96. Daga P, Pendse T, Modat M, White M, Mancini L, Winston G, et al. Susceptibility artefact correction using dynamic graph cuts: Application to neurosurgery. Medical Image Analysis. 2014;18(7):1132-42. 97. de Groot M, Ikram MA, Akoudad S, Krestin GP, Hofman A, van der Lugt A, et al. Tract-specific white matter degeneration in aging: The Rotterdam Study. Alzheimers Dement. 2015;11(3):321-30. 98. De Marco M, Clough PJ, Dyer CE, Vince RV, Waby JS, Midgley AW, et al. Apolipoprotein E Ɛ4 allele modulates the immediate impact of acute exercise on prefrontal function. Behavior Genetics. 2015;45:106-16. 99. Di Marco L, Marzo A, Munoz-Ruiz M, Ikram M, Kivipelto M, Ruefenacht D, et al. Modifiable Lifestyle Factors in Dementia: A Systematic Review of Longitudinal Observational Cohort Studies. Journal of Alzheimers Disease. 2014;42(1):119-35. 100. Di Marco LY, Venneri A, Farkas E, Evans PC, Marzo A, AF F. Vascular dysfunction in the pathogenesis of Alzheimer’s disease – A review of endothelium-mediated mechanisms and ensuing vicious circles. Neurobiology of Disease. 2015. 101. Di Marco LY, Farkas E, Martin C, Venneri A, Frangi AF. Is Vaso-

w

w .

v

p

h

-

d

a

r e

.

e

u


motion in Cerebral Arteries Impaired in Alzheimer’s Disease? J Alzheimers Dis. 2015. 102. Gundersen G, Vindedal G, Skare O, Nagelhus E. Evidence that pericytes regulate aquaporin-4 polarization in mouse cortical astrocytes. Brain Structure & Function. 2014;219(6):2181-6. 103. Hadjistassou C, Bejan A, Ventikos Y. Cerebral oxygenation and optimal vascular brain organization. J R Soc Interface. 2015;12: 20150245. 104. Jansen W, Ossenkoppele R, Knol D, Tijms B, Scheltens P, Verhey F, et al. Prevalence of Cerebral Amyloid Pathology in Persons Without Dementia. A Meta-analysis. JAMA Journal of the American Medical Association. 2015:1939. 105. Leung K, van der Lijn F, Vrooman H, Sturkenboom M, Niessen W. IT Infrastructure to Support the Secondary Use of Routinely Acquired Clinical Imaging Data for Research. Neuroinformatics. 2015;13(1):65-81. 106. Mahoney C, Simpson I, Nicholas J, Fletcher P, Downey L, Golden H, et al. Longitudinal Diffusion Tensor Imaging in Frontotemporal Dementia. Annals of Neurology. 2015;77(1):33-46. 107. Martiskainen H, Viswanathan J, Nykänen NP, Kurki M, Helisalmi S, Natunen T, et al. Transcriptomics and mechanistic elucidation of Alzheimer’s disease risk genes in the brain and in vitro models. Neurobiol Aging. 2015;36(2):1221.e15-28. 108. Marttinen M, Kurkinen KM, Soininen H, Haapasalo A, Hiltunen M. Synaptic dysfunction and septin protein family members in neurodegenerative diseases. Mol Neurodegener. 2015;10(1):16.

109. Parker C, Deligianni F, Cardoso M, Daga P, Modat M, Dayan M, et al. Consensus between Pipelines in Structural Brain Networks. Jama Neurology. 2014;9(10). 110. Rohrer J, Nicholas J, Cash D, van Swieten J, Dopper E, Jiskoot L, et al. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis. Lancet Neurology. 2015;14(3):253-62.

111. Solomon A, Soininen H. Dementia: Risk prediction models in dementia prevention. Nat Rev Neurol. 2015. 112. Steinberg S, Stefansson H, Jonsson T, Johannsdottir H, Ingason A, Helgason H, et al. Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nat Genet. 2015;47(5):445-7. 113. Takalo M, Haapasalo A, Martiskainen H, Kurkinen K, Koivisto H, Miettinen P, et al. High-fat diet increases tau expression in the brain of T2DM and AD mice independently of peripheral metabolic status. Journal of Nutritional Biochemistry. 2014;25(6):634-41. 114. Thomas JB, Brier MR, Bateman RJ, Snyder AZ, Benzinger TL, Xiong C, et al. Functional connectivity in autosomal dominant and late-onset Alzheimer disease. JAMA Neurol. 2014;71(9):1111-22. 115. Young A, Oxtoby N, Daga P, Cash D, Fox N, Ourselin S, et al. A data-driven model of biomarker changes in sporadic Alzheimer’s disease. Brain. 2014;137:2564-77.

Post Funding Opportunities The success of the VPH-DARE@IT project can be demonstrated, amongst other things, by • the patient care platform that has been further developed as a result of this project, and is now commercialised under the family cNeuro® by the partner Combinostics Ltd • the clinical research platform further developed by the University of Sheffield, Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB) currently under the name MULTIX • novel analysis tools and workflows were created focusing on multiscale patient specific treatment for dementia • journal and conference publications • further funding opportunities. The University of Sheffield and Erasmus MC have secured an EU H2020 grant “InSilC” with a further 11 partners. The total EC contribution iof €5,839,656.25 has been awarded with the aim of developing an insilico clinical trial (ISCT) platform for designing, developing and assessing drug-eluting bioresorbable vascular scaffolds (BVS). This will be achieved by building on the comprehensive biological and biomedical knowledge and advanced modelling approaches to simulate their implantation performance in the individual cardiovascular physiology. The University of Sheffield and Empirica have secured another EU H2020 grant “Back-UP” with a further 11 partners. This project will develop innovative approaches to create a prognostic model to underpin more effective and efficient management of neck and low back pain (NLBP), based on the digital representation of multidimensional clinical information and on simula-

N ovem ber 2017

tions of the outcomes of possible interventions. Patientspecific models will provide a personalised evaluation of the patient case, using multidimensional data sources: health, personal, psychological, behavioural, and socioeconomic factors, including biological patient characteristics, musculoskeletal structures and function, molecular data, and also workplace and lifestyle risk factors. As part of the same funding call (H2020 SC1-PM-17-2017 Personalised medicine) Empirica also applied for €5,978,245 funding for the “Precise4Q” grant with partners outside of theVPH-DARE@IT consortium, while the “MemoryCoach” grant (€3,741,727 in call H2020-SC1-2017-CNECT-1) in collaboration with VTT and Combinostics Ltd was rejected. VTT have secured various future funding, including an EU H2020 grant “Sniffphone” looking at development of a low cost, non-invasive, easily repeatable screening test, and a Finnish Technology Agency grant “MADDEC” worth €700,000 to develop an evidence based decision support tool that helps improve management of cardiac disease patients based on actual and cumulated patient data. University of Oslo have secured future funding, from The Research Council of Norway on imaging of glial endfoot(dys) function in awake behaving mice worth 21.2M NOK (~€2M) over 5 years, and from The Olav Thon Foundation on developing a novel experimental approach to identify subjects at risk for developing Alzheimer disease in cooperation with the University of Copenhagen worth 10M NOK (~€1M) over 4 years.

VPH-DA RE @I T Pro j e c t

New sl e tte r I ssu e V I

31


VPH-DARE@IT Partners USFD The University of Sheffield VTT VTT Technical Research Centre of Finland ESI ESI Group S.A ASD Advanced Simulation & Design GmbH EMP Empirica Gesellschaft für Kommunikations– und Technologieforschung mbH UIO Universitetet i Oslo EMC Erasmus Universitair Medisch Centrum Rotterdam HIRS Hirslanden Klinik PMS Philips Medical Systems Nederland BV ETHZ Eidgenössische Technische Hochschule Zürich KCL King’s College, London PRH Philips Technologie GmbH STH Sheffield Teaching Hospital NHS Foundation Trust UCL University College London UEF Itä-Suomen yliopisto UMA University of Maastricht TO Kinematix ICL Imperial College of Science, Technology and Medicine EIBIR EIBIR Gemeinnützige Gmbh zur Förderung der Erforschung der Biomedizinischen Bildgebung UOXF The Chancellor, Master and Scholars of the University of Oxford COMB Combinostic FOSC IRCCS Fondazione Ospedale San Camilo

This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no FP7-ICT-2011-9-601055 VPH-DARE@IT Virtual Physiological Human: DementiA Research Enabled by IT Project coordinator: Alejandro Frangi - The University of Sheffield Timetable: April 2013 to March 2017 VPH-DARE@IT Project University of Sheffield Sir Frederick Mappin Bldg, Mappin Street S1 3JD Sheffield, UK + 44 114 222 6071 contact@vph-dare.eu www.vph-dare.eu

VPH-DARE@IT Newsletter issue 6  
VPH-DARE@IT Newsletter issue 6  
Advertisement