Neurogenesis Fall 2015

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The Undergraduate Journal of Neuroscience

Volume 5 Issue 1 Fall 2015

Copyright © 2015

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Editorial Board Editors-In-Chief Parth Chodavadia Class of 2016 parth.chodavadia@duke.edu Lefko Charalambous Class of 2016 lefko.charalambous@duke.edu

Publishing Editors Syed Adil Class of 2016 syed.adil@duke.edu Sagar Patel Class of 2016 sagar.patel@duke.edu Jennie Xu Class of 2016 jennie.xu@duke.edu Tannya Cai Class of 2016 tannya.cai@duke.edu Shreya Ahuja Class of 2018 shreya.ahuja@duke.edu

Managing Editors Katrina Vokt Class of 2017 katrina.vokt@duke.edu

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Danielle Scarano Class of 2017 danielle.scarano@duke.edu Devon DiPalma Class of 2016 devon.dipalma@duke.edu Audra York Class of 2016 audra.york@duke.edu

Craig Roberts, Ph.D. Assistant Director of Education Duke Institute for Brain Sciences craig.roberts@duke.edu Ann Motten, Ph.D. Department of Chemistry ann.motten@duke.edu

Shangnon Fei Class of 2016 shangnon.fei@duke.edu

Design Team Gehua Tong Class of 2018

gehua.tong@duke.edu

Shivee Gilja Class of 2017 shivee.gilja@duke.edu

Faculty Advisors Leonard White, Ph.D. Duke University School of Medicine Director of Education Duke Institute for Brain Sciences len.white@duke.edu Christina Williams, Ph.D. Professor Director of Undergraduate Studies Duke Institute for Brain Sciences williams@psych.duke.edu

The Undergraduate Journal of Neuroscience

Letter from the Editors For centuries, the brain has remained an enigma. Somehow our brain manages to convert what we see, hear, and feel into decisions and actions – all of this within a framework of values that draw upon a lifetime of memories and experience. How can a single organ bridge the physical and the imagined, translate objective reality into subjective representations, so seamlessly?

With the advent of recent neuro-technologies, these missing pieces are slowly being answered. Functional magnetic resonance imaging allows us to examine cognitive processes in the brain. Optogenetics allows us to activate specific cell populations to evaluate neural correlates of behavior. These new technologies are pioneering the emergence of new interdisciplinary fields with the brain at central focus. Neuroeconomics, neuropolitics, neuromarketing, neurotheology, neuroethics – just about any field that involves the use of the human mind has been or will be examined through the lens of neuroscience.

Such an interdisciplinary focus has come to define neuroscience. And it is this interdisciplinary focus that we strive to highlight in the pages before you. As you navigate through this issue, you will find yourself traversing through the realms of cognitive, social, and cellular neuroscience – learning about the effects of social context on reward in autism, examining the role of sleep in memory consolidation, discovering how bilingualism and music are encoded in the brain, and navigating through the politics of socioeconomic inequality, addiction, and drug treatment programs in prison systems. Just as the human brain traverses time and space, we have built this issue to reflect the universality and centrality of the study of neuroscience to understanding the world around us and the decisions we make on a daily basis.

Last, we would like to especially thank Dr. Miguel Nicolelis for an exclusive interview regarding his work with paralyzed patients during the 2014 FIFA World Cup and his recent projects to develop a shared technological interface for multiple brains. Pioneers like Nicolelis remind us that as much as neuroscience is an inquiry into the human mind, neuroscience is also an inquiry into the future of medicine, which, at the moment, is on the precipice of major breakthroughs in manipulating our most complex organ to improve human health. Recent investment by governments in projects such as the BRAIN Initiative in the U.S. and the Human Brain Project in the E.U. highlights the urgency and promise of understanding the three-pound mass of jelly commanding our mind in both health and disease. We sincerely hope that this issue of Neurogenesis inspires you to continue studying the brain, as we have been inspired by leaders in the field today.

Sincerely, Parth Chodavadia & Lefko Charalambous http://www.neurogenesisjournal.org

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The Undergraduate Journal of Neuroscience

Neurogenesis

TABLE OF CONTENTS ARTICLES ARTICLES 8

The effects of social contexts on reward motivation in autism spectrum disorders Payal Chakraborty

REVIEWS REVIEWS 15

Want to learn something new? Take a quick nap: Sleep and memory consolidation Akash Patel

18

Bilingualism and executive control function Rachel Gallegos

22

Socioeconomic status and addiction: A review of patterns of addiction across the socioeconomic spectrum Leah Hershberger

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Essential elements of drug treatment programs in the California Prison System Ella Moberg

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The brain on music Gabriela Gomez

INTERVIEW INTERVIEW 39

Helping paralyzed patients walk again: An Interview with Miguel Nicolelis Katrina Vokt

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Neurogenesis

ARTICLE

The effects of social contexts on reward motivation in autism spectrum disorders Payal Chakraborty1 Duke University, Durham, NC 27708 Correspondence should be addressed to Payal Chakraborty (payal.chakraborty@duke.edu) 1

Although social deficits are readily apparent in Autism Spectrum Disorders (ASDs), it is not clear whether the origin of these social impairments lies in impaired capabilities in the understanding of and coping with the social environment, or in reduced motivation to engage with social environments and associations. While evidence from false-belief ‘theory of mind’ tasks suggest that the social deficits of autism may originate from capabilities of processing social information, attentional tasks studying circumscribed interests have shown that these social deficits may emerge due to atypical attention towards social stimuli from a very young age, implicating missed early experiences necessary for normal social development (Baron-Cohen, Leslie, & Frith, 1985; Dawson, Meltzoff, Osterling, Rinaldi, & Brown, 1998; Perner, Frith, Leslie, & Leekam, 1989; Sasson, Turner-Brown, Holtzclaw, Lam, & Bodfish, 2008). The resulting capability versus motivation argument has raised important questions about the roots of the social deficits observed in ASDs. This is a grant proposal for a project that seeks to expand on the body of research involving the capability-motivation argument by exploring the role of the ventral medial prefrontal cortex (vmPFC) in monetary reward processing in social and nonsocial contexts. The vmPFC is known to encode the subjective value of reward (Kouneiher, Charron, & Koechlin, 2009; Smith et al., 2010), and may give valuable insight on the motivation of individuals with ASD to seek rewards in social versus nonsocial environments, a phenomenon that has not been studied extensively in ASD. This study will use a simplified poker task under a functional magnetic resonance imaging scanner to detect differential activation of the vmPFC in these different contexts and participant groups. By tracking the origin of the social deficits of autism in either capabilities or motivation, this study has the potential to have therapeutic implications for the treatment of autism spectrum disorders.

SPECIFIC AIMS

Autism spectrum disorders (ASDs) are characterized by social dysfunction, communication and language problems, as well as repetitive behaviors and interests. Given that humans are social by nature, our social environments heavily influence our interactions and behaviors. Therefore, individuals with autism face a significant burden when presented with social situations, and consequently, autistic individuals have shown differential responses to social stimuli compared to individuals without autism. Despite evidence of these disparate responses, it is not clear where the origin of these social impairments lies.

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Evidence from false-belief ‘theory of mind’ tasks has suggested that the social deficits of autism may originate from reduced capabilities of processing social information. On the other hand, attentional tasks that have studied circumscribed interests have shown that these social deficits may emerge due to atypical attention towards social stimuli, resulting in missed early experiences that are important for social development. It is therefore not clear whether the social deficits in autism are the result of social processing deficits or motivational deficits.

One way to study motivation towards social environments in individuals with ASD is to analyze the The Undergraduate Journal of Neuroscience

Chakraborty | ARTICLE

subjective values they give to social rewards. There are many studies that have established a behavioral linkage between motivation and reward, in which motivation reflects a drive to obtain a reward (Miller, Shankar, Knutson, & McClure, 2014; Niv, Daw, Joel, & Dayan, 2007). The prefrontal cortex in particular has been implicated in the integration of motivation in cognitive control and the formation of a decision (Kouneiher et al., 2009). Furthermore, the ventral medial prefrontal cortex (vmPFC) has been shown to not only encode reward outcomes, but also the relative decision value between reward categories. (Smith et al., 2010) These trends include decision-making with social rewards (Smith, Clithero, Boltuck, & Huettel, 2014). To dissociate and analyze the effects of motivation in the social deficits of ASDs, we are studying the way that the human brain, particularly the vmPFC computes the subjective value of reward in ASD in social and nonsocial environments.

Based on the aforementioned findings about the role of the vmPFC in reward related motivation and decision value, we expect to see differential activation in response to social environments and nonsocial environments in individuals with ASD, if their social deficits are rooted in motivation and reward.

Hence, the overall purpose of this study is to analyze the following relationships: (1) the differences in vmPFC activity between stimuli presented with a nonsocial context (a computer opponent) and a social context (a human opponent) in subjects with ASDs, and (2) a comparison of the activation patterns of the vmPFC in people without ASDs (control group) and in people with ASDs (experimental group).

This study has implications both in a clinical setting as well as in our understanding of cognition in social decision-making. This project may lead to additional insight into some of the neural mechanisms that underlie autism, which may influence treatment of this disorder. It also has the potential to enhance our understanding of the implications of the role of the vmPFC on the influence of social contexts in decision-making, and its relationship to reward.

Principle Aim: Determining the role of the vmPFC in social vs. nonsocial contexts in ASD The paradigm of this study consists of a simplified poker game under an fMRI scanner. Each trial exhibits either a social condition or a nonsocial condition. The social condition consists of a human http://www.neurogenesisjournal.org

opponent, while the nonsocial condition consists of a computer opponent, whose responses are generated by a computer algorithm. If the social deficits in ASD are caused by deficits in motivation, the participants in the ASD group will exhibit greater response in the vmPFC to reward in the nonsocial context, and a lower response to reward in the social context. This will reflect a higher subjective reward value assigned to the monetary reward in a nonsocial environment because of the higher motivation induced by circumscribed objects (i.e. the computer player) will be encoded by the vmPFC. However, if the social deficits are caused by deficits in capabilities, there will be no significant difference in vmPFC activation between social and nonsocial contexts in the ASD group. If the task motivation is the same for both contexts, the participant should display the same subjective values to rewards in win trials no matter what the social context is. Based on a similar experiment conducted by a previous study in our laboratory in typically developed individuals (Carter, Bowling, Reeck, & Huettel, 2012), we expect no differences in subjective value assigned to reward between the social and nonsocial contexts.

RESEARCH STRATEGY

Significance Autism spectrum disorders (ASDs) are very prevalent in the United States: about one in 68 children have been identified with ASD, and it is present in all racial, ethnic, and socioeconomic groups (Baio, 2014). ASDs range from mild to severe, and consist of a range of complex neurodevelopment disorders characterized by social dysfunction, communication and language problems, and repetitive behaviors and interests (Baron-Cohen et al., 1985; Dichter et al., 2012; Kaiser et al., 2010; Pelphrey, Adolphs, & Morris, 2004).

There has been an ongoing debate in the autism literature about the root of the social deficits in the disorder: whether these deficits rooted in capabilities of processing social stimuli or motivation towards social environments. Many researchers have suggested that childhood autism is a disorder in the understanding of and coping with the social environment, supporting the capability argument.

However, others have noted atypical social attention in children with autism, and have suggested a more developmental cause of the social deficits observed in ASD. Children typically develop a ‘theory of mind,’ which refers to the ability to attribute mental states to oneself and to outers (Premack & Woodruff, 1978), at about the ages four to six (Wimmer & Per Fall 2015 | Volume 5 | Issue 1 | 9

ARTICLE | Effects of Social Contexts on Reward Motivation in Autism

ner, 1983). However, children with autism exhibit difficulty in representing the mental states of others, and have performed poorly on ‘theory of mind’ false belief tasks (Perner et al., 1989). The apparent lack or delayed development of ‘theory of mind’ in these children represent the notion that the social environment is unpredictable and incomprehensible for these children (Baron-Cohen et al., 1985).

Circumscribed interests, a type of repetitive behavior, is another core feature in autism spectrum disorders. This behavior is characterized by an interest in a narrow range of subjects, and conducting activities that are associated with these interests (Sasson et al., 2008). An eye-tracking study with a passive viewing task has shown that the ASD group explored fewer images than the typically developed group, attended more to the images they did explore, and remembered more details about the images they explored (Sasson et al., 2008). Additionally, children with autism are impaired in orienting to visual social stimuli (Dawson et al., 1998). It is not clear whether this observed lack of attention is

due to the difficulty that autistic children may have in processing and representing social stimuli, and therefore deterring their attention away, or because these children lack motivation in attending to such stimuli. Nevertheless, because attention in autistic children is not drawn to social stimuli, they miss the early experiences that are required for social development (Dawson et al., 1998). These results suggest that there may be a significant role of motivation behavior affecting the development of the social deficits associated with ASDs. Overall, ‘theory of mind’ studies support the capability argument, while attentional tasks that have studied circumscribed interests support the motivation argument. From the results and implications in both types of studies, it is not clear where the origin of the social deficits in ASDs lie. Further research to dissociate these effects is needed to understand ways to approach treatment of this disorder.

The present study aims to contribute to this debate by analyzing the ventromedial prefrontal cortex (vmPFC) response in autistic individuals to gain insight on how these individuals assign value to reward in a social versus nonsocial context during social gameplay under an fMRI. The vmPFC is known for encoding ‘the common neural currency,’ or value based decision models, and has not been extensively studied in individuals with ASD. The present study has the potential to have therapeutic implications on the treatment of autism spectrum disorders. In other words, depending on if the social deficits in autism are rooted in motivation or capabilities, compensatory therapy would have to be targeted in different ways to promote social learning.

APPROACH

Figure 1: Sally Anne Task: The Sally Anne Task is one of the first false belief tasks applied in studying ‘theory of mind.’ Typically developing children generally produce the right answer: realizing that Sally will falsely believe that the ball is in the basket. Children with autism have great difficulty with these tasks. (Frith & Frith, 1999)

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Participants Participants from the ASD group will be recruited through advertisements and registries such as Duke Center for Autism Diagnosis and Treatment, Autism Society of North Carolina, and Autism Speaks.

Control subjects will be recruited from advertisements on Craigslist and DukeList.

Two subjects will be required for each scanning session, Player 1 and Player 2. Player 1 will either be from the ASD group or the control group. Player 2 will be a non-fMRI participant and will be recruited separately.

All participants will undergo a series of rigorous inclusion criteria. According to our IRB protocol, we The Undergraduate Journal of Neuroscience

Chakraborty | ARTICLE

Figure 2: Passive Viewing Task: (A) These are examples of visual scan paths of a typically developing child (left) and a child with ASD (right) on a single array that was presented for 10 seconds. The blue circles indicate fixations, while the lines indicate saccades. A bigger circle indicates a longer fixation. Arrays with social and nonsocial images were also used in this study. Visual scan data was collected using an eye tracker. (B) This shows group differences between typically developing children (black bars) and children with ASD (gray bars) in exploration (left), preservation (middle), and detail orientation (right). Compared to the control group, visual attention in the ASD group is more circumscribed (exploration of fewer images overall), more preservative (longer fixation times per image explored), and more detail oriented (greater number of discrete fixations on explored images). (Sasson et al., 2008)

will recruit male or female participants of any race or ethnicity, and all participants will be between 18 and 55 years of age at the time of consent. Their estimated IQ scores must be greater than or equal to 80, and they must be capable of making informed decisions based on assessment of their understanding and judgment. We will exclude any participants with medical injuries that may interfere with participation in the study, or any participants with a history of a neurological injury. Because our task involves an fMRI scan, participants who are claustrophobic, and participants with implanted metal devices will be excluded. For control participants, anyone with a reported DSM-IV axis one disorders, a score of 6 or more on the AQ-10, a score of greater than 15 on the BDI, a score of 15 or more on the SCQ, and a score of greater than 120 on VIQ and PIQ will be excluded. For ASD participants, a clinical diagnosis of ASD and results of the Autism Diagnostic Observation Schedule (ADOS) will determine the screening criteria.

Control Group: Behavioral Tests The phone screening will include the North American Adult Reading Test (NAART) and AQ-10 http://www.neurogenesisjournal.org

questionnaires. During their study visits, they will complete the WASI, HOQ, and other behavioral questionnaires.

The NAART is a quickly administered verbal intelligence test (Uttl, 2002), while the AQ-10 (a ten question autism spectrum quotient questionnaire) is a brief autism screening test (Booth et al., 2013).

The purpose of both of these tests during the phone screening is to have an idea if a new participant will be eligible for the study before the first visit.

During the participant visits, more comprehensive tests will be used, such as the Wechsler Abbreviated Scale of Intelligence (WASI) for measuring intelligence, and the HOQ as a personality test.

ASD Group: Behavioral Tests The phone screening will also include the NAART and AQ-10 questionnaires. During their study visits, they will complete the WASI, ADOS, and other behavioral questionnaires.

The Autism Diagnostic Observation Schedule (ADOS) will be used to diagnose and assess autism.

Task: Simplified Poker Game The paradigm of this study, which models that of a

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ARTICLE | Effects of Social Contexts on Reward Motivation in Autism

Figure 3: Task Sequence: Each trial begins with a picture of the opponent. Then the participant views his or her card, and can decide whether to bet or to fold. If the participant folds, then the trial ends. If he or she bets, then control goes to the opponent, who can choose to call or fold. Credit: Huettel Lab.

previously conducted study by Carter et al. in the Huettel laboratory, consists of a simplified poker game under an fMRI scanner (Carter et al., 2012).

Each trial exhibits either a social condition or a nonsocial condition. The social condition consists of a human opponent (who plays the poker game from a computer outside the scanner), while the nonsocial condition consists of a computer opponent, whose responses are generated by a computer algorithm.

In each trial, the participant views a picture of his or her opponent, and then is presented with either a high card or a low card. The participant can then choose to bet (add money) or fold (stop playing the hand). If the participant bets, control of the task goes to the opponent, who can either call (add more money) or fold (relinquish the pot to the participant). The subject wins trials following a bet if the opponent bets while the subject is presented with a high card, or if the opponent folds, while the participant is presented with a low card. The latter situation is called bluffing, a strategy that may be used to maximize gains.

Below are the six detailed steps in each trial (as proposed in a BIAC proposal form): 1. A picture of either Player 2 or the computer player will appear on the screen for 1 second. The pot will also appear at this time and will read “2 Dukes” (an arbitrary currency).

2. A picture of a card appears (Low will be signified as L and High will be signified as an H) for 3 seconds.

The pot remains on the screen.

3. Two decision buttons appear on the screen, one

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reading B for bet, and the other reading F for fold.

Player 1 will have 2 seconds to select an option. The picture of the card and the pot remain on the screen.

4. If Player one folds, the pot disappears, the word “Fold” appears for 1 second, and the trial is over. If Player 1 bets, the pot changes to read “4 Dukes,” and a picture of the current opponent appears along with a message indicating that they are deciding. This screen lasts for 3 seconds.

5. Player 2’s decision and the amount won or lost

by Player 1 are displayed for 1 second. Wins are displayed in green, and loses are displayed in red.

6. The screen goes blank except for a fixation cross that remains on the screen at all times. This screen persists between 1.25 and 1.75 seconds until the next trial begins.

Although an arbitrary currency will be used for the paradigm, the participant will be informed that the amount of “Dukes” won during the task will determine the amount of money he or she will earn.

Functional Magnetic Resonance Imaging We will use functional magnetic resonance imaging (fMRI) to measure brain activation during the reward outcome phase of our simplified poker game paradigm. fMRI measures brain activity indirectly by utilizing BOLD (blood oxygen-level dependent) responses to detect changes in blood oxygenation of the brain (DeYoe, Bandettini, Neitz, Miller, & Winans, 1994). The advantage to using fMRI is that it provides non-invasive in vivo recordings that allow experimenters to observe brain-behavior The Undergraduate Journal of Neuroscience

Chakraborty | ARTICLE

associations during cognitive tasks. Because it is very low-risk (i.e. non-invasive, and it does not involve ionizing radiation unlike PET imaging), this imaging method can be used to sample many individuals. This is especially useful in studying autism because of the wide range of levels of severity of the disorder (Pelphrey et al., 2004).

filtering (Gaussian-weighted least-squares straight line fitting, with sigma = 50.0 s). Functional images will be normalized to the MNI standard space at a resolution of 2 x 2 x 2 mm and smoothed using a Gaussian kernel of FWHM 6 mm. First, second, and third level time-series analysis of the raw 4D fMRI data will also be conducted using FEAT.

fMRI Data Analysis Functional images will be first preprocessed using FEAT (version 5.98) from Oxford’s FMRIB software library (FSL). Pre- processing will include: motion correction using MCFLIRT; slice-timing correction using Fourier-space time-series phase-shifting; non-brain removal using BET; grand-mean intensity normalization of the entire 4D dataset by a single multiplicative factor; and high-pass temporal

Evidence from false-belief ‘theory of mind’ tasks has suggested that the social deficits of autism may originate from capabilities of processing social information. On the other hand, attentional tasks that have studied circumscribed interests have shown that these social deficits may emerge due to atypical attention towards social stimuli, resulting in missed early experiences that are important for social development. From the results and implications in both types of studies, it is not clear whether the social deficits in autism are the result of social processing deficits or motivational deficits.

The present study attempts to further analyze reward motivation in social contexts by exploring the role of the ventral medial prefrontal cortex (vmPFC) in monetary reward processing in social and nonsocial contexts in participants with ASDs versus typically developed participants. This study will use a simplified one-card poker fMRI task to detect differential activation of the vmPFC in these different contexts and participant groups. By tracking the origin of the social deficits of autism in either capabilities or motivation, this study has the potential to have therapeutic implications on the treatment of autism spectrum disorders.

Baio, J. (2014). Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2010 Morbidity and Mortality Weekly Report (Vol. 63): Center for Disease Control and Prevention.

Baron-Cohen, S., Leslie, A. M., & Frith, U. (1985). Does the autistic child have a “theory of mind”? Cognition: International Journal of Cognitive Science, 21(1), 37-46.

Booth, T., Murray, A. L., McKenzie, K., Kuenssberg, R., O’Donnell, M., & Burnett, H. (2013). Brief report: an evaluation of the AQ-10 as a brief screening instrument for ASD in adults. J Autism Dev Disord, 43(12), 2997-3000. doi: 10.1007/s10803-013-1844-5 Carter, R. M., Bowling, D. L., Reeck, C., & Huettel, S. A. (2012). A distinct role of the temporal-parietal junction in predicting socially guided decisions. Science, 337(6090), 109-111. doi: 10.1126/science.1219681 Dawson, G., Meltzoff, A. N., Osterling, J., Rinaldi, J., & Brown, E. (1998).

Children with autism fail to orient to naturally occurring social stimuli. J Autism Dev Disord, 28(6), 479-485.

DeYoe, E. A., Bandettini, P., Neitz, J., Miller, D., & Winans, P. (1994). Functional magnetic resonance imaging (FMRI) of the human brain. Journal of Neuroscience Methods, 54(2), 171-187.

Dichter, G. S., Felder, J. N., Green, S. R., Rittenberg, A. M., Sasson, N. J., & Bodfish, J. W. (2012). Reward circuitry function in autism spectrum disorders. Soc Cogn Affect Neurosci, 7(2), 160-172. doi: 10.1093/ scan/nsq095 Frith, C. D., & Frith, U. (1999). Interacting Minds--A Biological Basis. Science, 286(5445), 1692-1695. doi: 10.1126/science.286.5445.1692 Kaiser, M. D., Hudac, C. M., Shultz, S., Lee, S. M., Cheung, C., Berken, A. M., . . . Pelphrey, K. A. (2010). Neural signatures of autism. Proc Natl Acad Sci U S A, 107(49), 21223-21228. doi: 10.1073/pnas.1010412107 Kouneiher, F., Charron, S., & Koechlin, E. (2009). Motivation and cognitive control in the human prefrontal cortex. Nature Neuroscience, 12(7), 939-945. doi: 10.1038/nn.2321 Miller, E. M., Shankar, M. U., Knutson, B., & McClure, S. M. (2014). Dissociating motivation from reward in human striatal activity. J Cogn Neurosci, 26(5), 1075-1084. doi: 10.1162/jocn_a_00535 Niv, Y., Daw, N. D., Joel, D., & Dayan, P. (2007). Tonic dopamine: opportunity costs and the control of response vigor. Psychopharmacology (Berl), 191(3), 507-520. doi: 10.1007/s00213-006-0502-4 Pelphrey, K., Adolphs, R., & Morris, J. P. (2004). Neuroanatomical substrates of social cognition dysfunction in autism. Ment Retard Dev

fMRI Data Collection A 4T scanner at the Duke-UNC Brain Imaging and Analysis Center in Durham, NC, USA will be used to acquire functional MRI data. An initial T1-weighted whole-brain structural image will be acquired with and FSPGR sequence at a spatial resolution of 1 x 1 x 1.9 mm. Eight fMRI will be acquired using an inverse-spiral pulse sequence. Each run will consist of either 213 or 214 volumes, and each volume will consist of 34 interleaved axial slices oriented parallel to the axial plane connecting the anterior and posterior commissures [repetition time (TR) = 2000 ms; echo time (TE) = 27 ms; matrix = 64 x 64; field of view (FOV) = 240 mm; voxel size = 3.75 x 3.75 x 3.8 mm; saturation buffer = 6 volumes] (Carter et al., 2012).

REFERENCES

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SUMMARY

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ARTICLE | Effects of Social Contexts on Reward Motivation in Autism Disabil Res Rev, 10(4), 259-271. doi: 10.1002/mrdd.20040 Perner, J., Frith, U., Leslie, A. M., & Leekam, S. R. (1989). Exploration of the autistic child’s theory of mind: knowledge, belief, and communication. Child Development, 60(3), 688-700.

Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind? Behavioral and Brain Sciences, 1(04), 515-526.

Sasson, N. J., Turner-Brown, L. M., Holtzclaw, T. N., Lam, K. S. L., & Bodfish, J. W. (2008). Children with autism demonstrate circumscribed attention during passive viewing of complex social and nonsocial picture arrays. Autism Research, 1(1), 31-42. doi: 10.1002/aur.4 Smith, D. V., Clithero, J. A., Boltuck, S. E., & Huettel, S. A. (2014). Functional connectivity with ventromedial prefrontal cortex reflects subjective value for social rewards. Soc Cogn Affect Neurosci. doi: 10.1093/

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scan/nsu005 Smith, D. V., Hayden, B. Y., Truong, T. K., Song, A. W., Platt, M. L., & Huettel, S. A. (2010). Distinct value signals in anterior and posterior ventromedial prefrontal cortex. Journal of Neuroscience, 30(7), 2490-2495. doi: 10.1523/jneurosci.3319-09.2010 Uttl, B. (2002). North American Adult Reading Test: age norms, reliability, and validity. J Clin Exp Neuropsychol, 24(8), 1123-1137. doi: 10.1076/jcen.24.8.1123.8375 Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition: International Journal of Cognitive Science, 13(1), 103-128.

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REVIEW

Want to learn something new?Take a quick nap: Sleep and memory consolidation Akash Patel1 Duke University, Durham, North Carolina 27708 Correspondence should be addressed to Akash Patel (akash.d.patel@duke.edu) 1

Sleep is known to improve memory consolidation; however, exactly how this occurs is an actively studied field. Previous studies have found that in addition to sleep, another mechanism during resting state also contributes to the entire memory consolidation mechanism. In one such study Gregory, Agam, Selvadurai, Nagy, Vangel, Tucker, … & Manoach, (2014) attempt to understand and analyze the resting state connectivity following motor learning. Using functional connectivity magnetic resonance imaging (fcMRI) this study finds that the resting state connectivity immediately following learning correlates with the succeeding improvement in motor task performance seen following sleep. Although these studies show that both resting state and sleep are integral to motor learning, practical implications of sleep research are still ongoing. Additionally, some medical studies and reviews have chosen to address how to effectively use resting state connectivity in treating medical ailments that affect motor tasks, such as stroke.

Although the exact function of sleep is still unknown, sleep is closely linked with memory consolidation. Quintilian noted in the 1st century that “it is a curious fact… that the interval of a single night will greatly increase the strength of a memory” (Sampley & Lampe, 2010). In the 21st century, Walker, Stickgold, Alsop, Gaab, & Schlaug (2005) tests this by conducting a study that shows motor task performance improving after sleep compared to wakeful rest. In the study two groups of participants learn a motor task, after which both groups rest for 12 hours: one awake and the other asleep.

Following the rest period each group’s performance is tested using functional magnetic resonance imaging (fMRI). The results indicate that there is greater memory consolidation in the group that sleeps, which is seen by the improved performance and increased brain activity in motor and memory regions (Walker et al., 2005).

Walker et al. (2005) shows that the mechanism for memory consolidation involve sleep; however, sleep is not the only aspect of memory consolidation. There is evidence that memory consolidation http://www.neurogenesisjournal.org

happens in resting states during both sleep and wake (Stickgold & Walker, 2007). In fact, the resting state activity in a region is equal in amplitude to when that region is specifically activated (Smith, Fox, Miller, Glahn, Fox, Mackay, ... & Beckmann, 2009). Using a database of functional imaging studies and by collecting their own data, Smith et al.

(2009) shows that the brain “is continuously and dynamically ‘active’ even when at ‘rest.’” To further understand the brain in a resting state, Ma, Narayana, Robin, Fox, & Xiong, (2011) show that resting state connectivity participates in the improved performance of motor skills. This relationship is seen by the strengthening of connectivity in the resting state that occurs throughout motor task learning.

Cordes et al. (2000) presents that functional connectivity magnetic resonance imaging (fcMRI) is a “useful method for evaluating the strength of neural connections;” however, the study continues to claim that fcMRI is not a replacement for fMRI but an additional tool used to specifically measure connectivity in the brain. These previous studies and new available measuring methods raise and Fall 2015 | Volume 5 | Issue 1 | 15

REVIEW | Sleep and Memory Consolidation

help answer the question: Are resting state connectivity following learning and the improved motor task performance seen following sleep correlated?

In the study conducted by Gregory, Agam, Selvadurai, Nagy, Vangel, Tucker, … & Manoach, (2014), the group hypothesizes that the connectivity seen after motor task learning would reflect the improved performance for motor tasks seen after sleep.

This correlational study involves 15 right-handed participants who undergo a behavioral training session and two fcMRI scan sessions. During the training session (which is used as the behavioral control) participants are randomly assigned “sleep” or “wake” and are measured for performance. Sleep participants learn the motor sequence task (MST) at 9 PM, slept, and have MST performances measured at 9 AM. Wake participants learn MST at 9 AM, stay awake, and have MST performances measured at 9 PM. The MST involves participants using their left hand, which is individually associated with four keys, to repeat a five digit sequence as it appears on a screen. The motor control task (MCT) has the same setup; however, instead of a sequence, the goal is to have a paced (3.3 finger taps/second) 1-2-3-4 series. Each session (MST or MCT) consists of twelve 30-second trials where the member’s speed and accuracy are measured. The training session results indicate that all individuals show MST learning (MCT did not stimulate as high a learning demand.), but only the “sleep” group shows improvement in performance. This confirms with previous findings that claim sleep improves motor task performance (Walker et al.. 2005) and is seen in the fcMRI study later.

For the fcMRI study, participants are separated into two groups. Each group undergoes two scan sessions: A and B. Session A involves cycles of performing the MST and having a rest with fcMRI scans before and after the task. Immediately following the MST, participants complete a questionnaire asking what is on the participant’s mind during the session.

Then 24 hours subsequently, participants complete another MST session to check performance. (The questionnaire results show that conscious thought are not involved with increased connectivity because the performance improvements did not correlate with the amount of time spent thinking about the actual task.) In contrast, Session B involves cycles of performing the MCT and having a rest with fcMRI scans before and after the task. The study alternates the sessions for each group to see if order affects connectivity and performance: one group 16 | Issue 1 | Volume 5 | Fall 2015

undergoes session A then B, while the other group undergoes session B then A. The order of session A and B do not significantly affect the next-day MST improvement.

The study finds that increased resting state connectivity in the bilateral motor cortex (as measured by the fcMRI during the post-task rest) is seen only in individuals whose performance improves the following day. There, however, is no increased connectivity before, during, or after training; the increased connectivity is seen only following the entire training session.

Unfortunately, there are some concerns involving the study: inconsistent sleep and limited brain monitoring. Since sleep is unmonitored, the amount of time each participant sleep is inconsistent, causing potential unknown factors to affect the results.

Additionally, the analyzed brain region was confined to the motor network because of a limited number of participants, which prevented the study from demonstrating any connectivity involved beyond the motor network. This experiment, however, provides evidence that post-task resting state connectivity following learning correlates with the improved performance of motor tasks seen after sleep. Nevertheless, one should note that regardless of the connectivity sleep is still required for improved motor task performance. These results still support Quintilian’s observation from the 1st century.

This correlational study which involves a behavioral approach is the first study of its kind that shows this correlation between increased resting state connectivity and improved task performance.

The results suggests that the connectivity observed after learning may prepare the “motor memories for subsequent consolidation during sleep,” (Gregory et al., 2014) further decoding the memory consolidation mechanism that many neuroscientists have tackled. The study presents a mechanism through post-learning resting state connectivity for the events that lead up to memory consolidation as seen during sleep. This builds strongly upon previous studies that have established pathways and mechanisms for memory consolidation, such as Herz, Eliassen, Beland, & Souza (2004) which demonstrate a mechanism for memory consolidation using emotion and odor.

Gregory et al. (2014) also presents a topic for further memory and connectivity research: The regions of the motor network showing this correlation might present evidence for the function of The Undergraduate Journal of Neuroscience

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post-training rest’s connectivity. Overall, Gregory et al. (2014) creates a new basis from which memory mechanisms can be discovered and provides a new approach for memory consolidation. The study, however, raises a question: What are the practical implications of this research?

Although there are no major known ongoing studies involving resting state connectivity, the implications of increased connectivity correlating to post-sleep motor task performance improvements can be applied to medical treatments. One application involves the treatment of stroke, which affects an individual’s motor performance. In a recent experiment, Sen et al. (2015) shows that acute ischemic stroke patients have improved connectivity after undergoing a medical procedure that breaks down blood clots. The research “suggests that the improvement of resting state network means improved efficiency of brain activity indicated by functional outcome” (Sen et al., 2015). The study hopes that this increased connectivity can act as a potential predictive biomarker for similar procedures. The study, however, noted that his was a pilot experiment and a larger study would need to be conducted to confirm the results.

Another recent paper tackles the improvement of rehabilitation treatments for stroke victims and those with motor task impairments. Gudberg & Johansen (2015) suggest that sleep should be a required component of a treatment plan for those who have suffered from motor task degrading accidents, such as strokes and injuries affecting motor skills.

The paper states that “increasingly incorporating sleep as an integral part of clinical assessments and training paradigms will undoubtedly have important implications for rehabilitation outcomes” (Gudberg & Johansen, 2015).

Gregory et al. (2014) demonstrates that the field of resting state connectivity and sleep are crucial to our understanding of learning. The results of this research contribute a foundation from which the research community can further define the role of resting state connectivity in the memory consolidation mechanism. Additionally, the usefulness of resting state connectivity and sleep is seen by the medical advances already achieved. Although our understanding of sleep and its complete role in the mechanism for memory is not complete, we are on the correct path to its full comprehension.

Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., ... & Meyerand, M. E. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. American Journal of Neuroradiology, 21(9), 1636-1644.

Gregory, M. D., Agam, Y., Selvadurai, C., Nagy, A., Vangel, M., Tucker, M., … & Manoach, D. S. (2014) Resting-state connectivity immediately following learning correlates with subsequent sleep-dependent enhancement of motor task performance. NeuroImage, 102, 666-673.

Gudberg, C., & Johansen-Berg, H. (2015). Sleep and motor learning: implications for physical rehabilitation after stroke. Frontiers in Neurology, 6, 241.

Herz, R. S., Eliassen, J., Beland, S., & Souza, T. (2004). Neuroimaging evidence for the emotional potency of odor-evoked memory. Neuropsychologia, 42(3), 371-378.

Ma, L., Narayana, S., Robin, D. A., Fox, P. T., & Xiong, J. (2011). Changes occur in resting state network of motor system during 4weeks of motor skill learning. Neuroimage, 58(1), 226-233.

Sampley, J., & Lampe, P. (Eds.). (2010). Paul and Rhetoric (p. 196). New York: Bloomsbury T & T Clark.

Sánchez-Andrade, G., James, B. M., & Kendrick, K. M. (2005). Neural encoding of olfactory recognition memory. The Journal of reproduction

and development, 51(5), 547-558.

Sen, S., Fridriksson, J., Hanayik, T., Rorden, C., Hubbard, I., Burdine, J., ... & Fridriksson, J. (2015). Abstract W P29: Tissue Plasminogen Activator Mediated Reperfusion, and Subsequent Improvement in Resting State Connectivity Correlates with Functional Outcome in Acute Ischemic Stroke Patients. Stroke, 46(Suppl 1), AWP29-AWP29.

Smith, D. G., Standing, L., & De Man, A. (1992). Verbal memory elicited by ambient odor. Perceptual and Motor Skills, 74(2), 339-343.

Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., ... & Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences, 106(31), 13040-13045.

Stickgold, R. & Walker, M. P. (2007). Sleep-dependent memory consolidation and reconsolidation. Sleep medicine, 8(4), 331-343.

Tambini, A., Ketz N., & Davachi, L. (2010). Enhanced brain correlations during rest are related to memory for recent experiences. Neuron, 65.2, 280-290.

Walker, M. P., Stickgold, R., Alsop, D., Gaab, N., & Schlaug, G. (2005).

Sleep-dependent motor memory plasticity in the human brain. Neuroscience, 133(4), 911-917.

REFERENCES

http://www.neurogenesisjournal.org

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Bilingualism and executive control function Rachel Gallegos1 1 Duke University, Durham, North Carolina 27708 Correspondence should be addressed to Rachel Gallegos (rachel.gallegos@duke.edu)

Research suggests that bilingualism is associated with a more efficient use of neural resources, especially in executive control regions, which may shed light on the benefits of bilingualism. Despite current debate on the benefit of bilingualism, bilingualism undoubtedly alters the brain structure, with more change occurring with more fluency and for those who learned at a younger age. Bilinguals, unlike monolinguals, may not even need executive control function for phonological competition, and they may be more effective at reducing activity in task-irrelevant regions. Using neural resources more effectively during competition may be due to the constant need for bilinguals to choose between two languages. Future research of the superior frontal gyrus (SFG), an area responsible for executive control working memory, could be an important region in determining whether areas responsible for executive control affect bilingual ability to manage non-phonological competition.

There is growing evidence to support bilingualism as a positive agent in neural functioning and plasticity. However, there is debate regarding the benefit of bilingualism, as it is often tied to social disadvantages and lexical deficiencies. Nonetheless, there is compelling research indicating that bilingualism leads to increased effectiveness of executive control function and possibly working memory, likely due to the necessity of working memory in the process of learning language and the constant demand of bilinguals to choose between two languages (Carlson & Meltzoff, 2008; Bialystok, 2009).

This is a compelling issue as executive function is crucial to what truly defines us as human beings as well as our ability to plan ahead and participate in higher level functioning (Watson & Breedlove, 2012). This paper seeks to analyze how bilingualism affects the ability to manage competition, both phonological and non-phonological, in regions of the brain that are associated with executive control and working memory to better understand how bilingualism may affect our higher level functioning capabilities through neural organization and functioning, which may confirm the benefits of bilingualism in education and aging.

There is debate regarding the benefit of bilin18 | Issue 1 | Volume 5 | Fall 2015

gualism as bilingualism has been correlated with less academic achievement. However, this may be attributed to the fact that many bilinguals today come from socially and economically disadvantaged backgrounds, and academic success of a bilingual individual may be more dependent on social factors than on the bilingualism itself. In a 2012 study by Han, mixed race bilingual children who started with lower math scores in kindergarten than white English-monolingual peers were able to minimize the difference in math scores by 5th grade. Still, non-English-dominant bilinguals (along with non-English monolinguals) had significantly lower test scores in math and reading in 5th grade than their white English monolinguals peers, supporting the claim that social factors greatly influence academic achievement (Han, 2012). Nonetheless, bilinguals have been shown to perform equally as well as monolingual peers on vocabulary assessments, although it may vary by age group and amount of exposure to each language (Smithson et al., 2014).

Despite all debate, bilingualism has been shown to alter the structure of the brain, increasing the grey matter in the left inferior parietal cortex, with more change for those who are more fluent or who learned at a younger age (Mechelli et al., 2004).

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This demonstrates that although all the cognitive repercussions of bilingualism are not known, bilingualism does have a profound effect on the brain.

It has been shown that bilinguals activate the dorsal anterior cingulate cortex (ACC), an executive control region specifically tied to resolving nonverbal cognitive conflict, more effectively than monolinguals. In fact, bilinguals activated the region to a lesser degree while outperforming monolinguals in tests of cognitive conflict (Abutalebi et al., 2011).

This more effective use of the ACC could be due to the constant conflict management of languages for bilinguals. This is evidenced by the fact that semantically equivalent words in different languages activate the same regions in bilinguals (Correia et al., 2014).

Similarly, lifetime bilingualism is correlated with more efficient use of neural resources, as well as better cognitive control functionalities in aging (Gold et al., 2013). In aging, where neural capabilities may decrease in efficiency, the effects of bilingualism might be most evident. Older adults use more brain activation outside of the expected task-related network as a way to compensate for the decrease in neural efficiency. However, it was found that older adult bilinguals switched between perceptual tasks significantly faster than older adult monolinguals while still requiring less brain activation. This shows much larger implications for the potential advantage of using neural resources more effectively, and demonstrates promise for greater understanding of neural organization and plasticity. It implicates that language learning affects all aspects of a person’s mind, and there are other domains that could have similar effects.

In a 2014 study, Marian et al. showed that during phonological competition monolinguals and bilinguals recruit different brain regions with varying magnitudes, specifically areas associated with executive control and the primary visual cortex. In this study, participants were characterized as either monolingual, only speaking English, or as bilingual, speaking both Spanish and English, based upon answers to Language Experience and Proficiency Questionnaire (LEAP-Q), an in-depth questionnaire developed to quantify bilingualism based on self-reported and behavioral measures (Marian, Blumenfeld, & Kayshanskaya, 2007). Language proficiency was assessed and ensured. The participants were tested on accuracy, response time, and the BOLD response shown by an fMRI during the phonological competition. http://www.neurogenesisjournal.org

The phonological competition consisted of the participants hearing words aurally, while in an fMRI machine, and indicating through a button box which of four black-and-white photos shown above them the word referred to. The participants were tested over 60 trials total, with 20 competitor trials, 20 unrelated trials, and 20 additional filler trials. In the competitor trials, a competitor item was shown whose name overlapped phonologically with the name of the target item. In unrelated trials, the competitor item was replaced by an item with a name not overlapping with the target name. To ensure that each item picture corresponded with the expected name, the participants were asked to name each picture after testing was complete. If the participant called the item by a different name, that trial was not used.

It was found that accuracy and response time did not differ between monolinguals and bilinguals.

However, measuring response time by a button box may not have been sensitive enough to detect a difference, as the response relied on motor control. A better option to measure response time in this situation, where the difference may be very minuscule but present, could be eye-tracking. Nevertheless, response time was slower during competitor trials for both monolinguals and bilinguals, indicating that they both experienced competition. Both groups showed the same level of accuracy indicating that monolinguals and bilinguals did not differ in their abilities to manage phonological competition.

Functional neuroimaging showed that during competitor trials, monolinguals, compared to bilinguals, had much greater activation in the left superior frontal gyrus, left interior frontal gyrus, left middle frontal gyrus, and the anterior cingulate as well as all regions associated with executive control including the primary visual cortex. This indicates that bilinguals did not use as many neural resources for tasks of phonological competition as monolinguals. Although the abilities to manage competition of each group did not differ, this experiment demonstrates that bilinguals may use neural resources more effectively. In addition, bilinguals may not need to use higher level processing to manage competition, which would come from these executive control regions, rather, managing competition may be more automatic for them. Likewise, the monolinguals’ use of the primary visual cortex is likely indicative of less automatic processing, as they recruit and depend on more neural regions.

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trials within groups showed that monolinguals activated more neural resources in competitor trials than in unrelated trials, whereas bilinguals activated fewer neural resources. Specifically, bilinguals activated the bilateral parahippocampal gyrus and cerebellum less on competitor trials. This shows that when competition is present, bilinguals may be more effective in reducing activation in task-irrelevant regions.

In order to determine whether these results would hold true for general executive control tasks, unspecific to language, monolingual and bilingual performance on the Simon task were compared.

The Simon task is a non-linguistic executive control task where the participant hears the words “right” or “left” in one of their ears and then presses a rightor left-handed key in response, with the correct key following what word was heard regardless of the ear that heard it (Simon & Rudell, 1967). This illustrates a type of executive function in which the participant must manage competing cues. It was found that while monolinguals and bilinguals did not differ in their abilities to perform the tasks, individuals’ differences in response time between competitor and non-competitor trials in the phonological testing were correlated to their response time in the Simon task. This substantiates that individuals who experienced less interference in linguistic competition also experienced less interference in non-linguistic competition. It also further proves that competition in linguistic and non-linguistic tasks may be managed by the same domain-general mechanisms, indicating that bilinguals may use less neural resources than monolinguals for other non-linguistic tasks that involve executive control.

In order to determine whether areas responsible for executive control affect bilingual ability to manage non-phonological competition, this paper proposes an experiment that takes particular interest in the left superior frontal gyrus (SFG), an area important for executive control working memory that was not activated by bilinguals in the Marian et. al study during phonological competition. The experiment seeks to determine how well bilinguals with a lesion in the left SFG can perform a task of competition specific to spatial working memory as compared to healthy bilinguals, healthy monolinguals, and monolinguals with a left SFG lesion.

Furthermore, in a 2006 study by du Boisgueheneuc et al. that used patients who underwent a left side frontal lobe excision for low-grade gliomas, the SFG has been shown to be crucial for execu20 | Issue 1 | Volume 5 | Fall 2015

tive control working memory. Patients with a SFG lesion were much less successful when required to use spatial working memory, as opposed to face or letter memory. This is consistent with findings that the much of the dorsolateral prefrontal cortex manages both executive function and working memory (Watson & Breedlove, 2012).

In this experiment, each of the four different groups (the healthy individuals and those with lesions, and bilinguals and monolinguals) will be shown a computer screen that is split into a 6x6 array of neutral colored squares. In non-competition trials, different squares will light up in red in a particular order that the participant will be asked to remember and repeat as soon as the light ceases.

In competition trials, participants will still be asked to follow the red squares, and repeat that pattern, but various other squares will light up as well. The participants will get a total of 30 competition trials and 30 non-competition trials in each session, for a total of 5 sessions over a month period. And as the trials progress, the sequence of lit up squares will increase in length, making the task increasing difficult. Success will be measured by accuracy and speed.

As the left SFG was not activated in linguistic competition in the study by Marian et al. (2014), it is hypothesized that the bilinguals do not depend on this brain region for other non-linguistic competition tasks. However, as this region was activated by monolinguals in linguistic competition, and it is important for spatial working memory, it is hypothesized that this region is necessary for monolinguals’ success in a test of spatial working memory that involves competition. Therefore, bilinguals with a lesion in the left SFG will not significantly differ from either the healthy bilinguals or monolinguals in either task, while the monolinguals with a lesion in the left SFG will be significantly less successful in these tasks.

If this hypothesis proves true, this study will demonstrate that certain areas (specifically the left SFG) associated with executive control in monolinguals are not necessary for bilinguals to successfully manage competition or specific domain controls.

This would open up the question as to what these specific regions are used for in the bilingual brain and how that affects the “effectiveness” of this brain plasticity. This would shed light on brain plasticity with regards to how language affects brain organization and use, and how bilingualism positively or negatively affects a person’s mind. Determining the The Undergraduate Journal of Neuroscience

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benefits of bilingualism may change the implications of bilingualism in education systems and settings. Furthermore, it may support the claims that brain plasticity in executive control regions due to bilingualism may benefit cognitive control in aging, showing that all levels of language learning may be advantageous.

REFERENCES

Abutalebi, J., Rosa, P., Green, D., Hernandez, M., Scifo, P., Keim, R., Cappa, S., et al. (2011). Bilingualism tunes the anterior cingulate cortex for conflict monitoring. Cerebral Cortex, 22(9). 2076-2086.

Bialystok, E. (2009). Bilingualism: The good, the bad, and the indifferent.

Bilingualism: Language and Cognition, 12(1), 3-11.

Boisgueheneuc, F.d., Levy, R., Volle, E., Seassau, M., Duffau, H., Kinkingnehun, S., Samson, Y., et al. (2006). Functions of the left superior frontal gyrus in humans: a lesion study. Brain, 129, 3315-3328.

Carlson, S., & Meltzoff, N. (2008). Bilingual experience and executive functioning in bilingual children. Developmental Science, 11(2), 282298.

Correia, J., Formisano, E., Valente, G., Hausfeld, L., Jansma, B., Bonte, M. (2014). Brain-based translation: fMRI decoding of spoken words in bilinguals reveals language-independent semantic representations in anterior temporal lobe. The Journal of Neuroscience 34(1), 332338.

Gold, B., Kim, C., Johnson, N., Keyscio, R., Smith, C. (2013) Lifelong Bilingualism Maintains Neural Efficiency for Cognitive Control in Aging.

The Journal of Neuroscience 33(2), 387-396.

Han, Wen-Jui. (2012). Bilingualism and Academic Achievement. Child Development, 83(1), 300-321.

Marian, V., Blumenfeld, H.K., & Kaushanskaya, M. (2007). The language experience and proficiency questionnaire (LEAP-Q): Assessing language profiles in bilinguals and multilinguals. Journal of Speech, Language, and Hearing Research, 50(4), 940.

Marian, V., Chabal, S., Bortolotti, J., Bradley, K., Hernandez, A. (2014). Differential recruitment of executive control regions during phonological competition in monolinguals and bilinguals. Brain & Language, 139, 108-117.

Mechelli, A., Crinion, J., Noppeney, U., O’Doherty, J., Ashburner, J., Frackowiak, R., Price, J. (2004). Structural plasticity in the bilingual brain.

Nature, 431, 757.

Simon, J.R., & Rudell, A.P. (1967). Auditory S-R compatibility: The effect of an irrelevant cue no information processing. Journal of Applied Psychology, 51(3), 300-304.

Smithson, L., Paradis, J., & Nicoladis, E. (2014). Bilingualism and receptive vocabulary achievement: Could sociocultural context make a difference? Bilingualism: Language and Cognition 17(4), 810-821.

Watson, N., & Breedlove, S. (2012). Attention and consciousness. In N. Watson & S. Breedlove, The mind’s machine: foundations of brain and behavior (419-421). Sunderland, MA: Sinauer Associates, Inc.

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Socioeconomic status and addiction: A review of patterns of addiction across the socioeconomic spectrum Leah Hershberger1 1 Duke University, Durham, North Carolina 27708 Correspondence should be addressed to Leah Hershberger (lhershy@att.net)

Drug addiction is a complex phenomenon affected by social, environmental, and biological factors, including socioeconomic status of the addict. This review synthesizes research on the relationship between socioeconomic status and addiction to specific drugs of abuse. Trends indicate that some addictive substances are associated with low socioeconomic status (nicotine) while others are correlated with high socioeconomic status (alcohol). Understanding the relationship between socioeconomic status and drugs of abuse has vital implications for developing successful prevention programs that target individuals during periods of high vulnerability to drug addiction.

Keywords: addiction, drug abuse, socioeconomic status

INTRODUCTION

Substance abuse and drug dependence are serious and often crippling disorders that have dramatic long-term consequences for users, their families, and communities worldwide (National Institute on Drug Abuse, 1999). Although they have been studied extensively, drug abuse and dependence manifest differently in every individual because they are triggered by complex interactions between genetic and environmental factors. One such factor, socioeconomic status (SES), is a demographic feature whose powerful contributions to drug addiction have yet to be convincingly quantified. This review synthesizes research on the relationship between different drugs of abuse and socioeconomic correlates to better conceptualize the etiology of drug addiction. Drug addiction (or dependence) is defined here as a chronic disorder characterized by compulsive drug seeking behavior and use, despite direct negative consequences (NIDA, 2015). SES encompasses measures of childhood and current family income, occupational class, parental educational attainment, personal educational attainment, and 22 | Issue 1 | Volume 5 | Fall 2015

cultural measures of social status.

Although no sweeping trends or conclusive patterns have emerged, reasonable inferences can be made about the relationships between SES and alcohol and nicotine use around the world, but conclusions about other drugs prove much more difficult to draw. By highlighting and attempting to synthesize relevant addiction research – despite a degree of disagreement between studies – this review aims to bring to light salient correlations between drug addicts and their socioeconomic status and guide efforts of other researchers to fill gaps in knowledge.

DRUG NON-SPECIFIC FINDINGS

Most research on SES and addiction examines this interaction in one addictive substance, but there is a body of research that has analyzed SES and addiction more broadly, albeit with some inconsistent findings. This could be a result of a number of confounding influences: different demographics of users of a particular drug, the lack of a standardized measurement for SES, quality and quantity of expoThe Undergraduate Journal of Neuroscience

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sure to different drugs based on SES, etc. Nevertheless, most studies identify low SES, quantified using a variety of measures, as predictive of substance use.

A longitudinal study by Fothergill and Ensminger (2006) found a direct effect of low childhood SES – that is, SES of the subjects’ parents during childhood – and a reduced risk of substance abuse for females but not for males. Across both genders, low childhood SES was correlated with lower educational attainment, which had an indirect effect of increased risk of substance abuse. This suggests that different indirect and direct demographic factors during childhood influence a person’s risk for substance abuse later in life, sometimes in opposing directions (Fothergill and Ensminger, 2006).

However, the data from in this study has limited generalizability because of a non-random sample population.

From a neurobiological perspective, traits such as impulsivity, risk-taking, and novelty-seeking that are mediated by a person’s genetics also play a role in susceptibility to drug addiction. Especially relevant to addictive behaviors is a phenomenon called delay discounting, discussed at length in a neuroeconomics model of addiction proposed by Bickel et al. (2007): a person who is more vulnerable to impulses or addictive behavior will exhibit a decreased ability to delay rewards in favor of instant gratification. Experimentally, subjects may be asked several reward-delayed gratification questions. Answers are then plotted to form a curve that illustrates subjects’ impulsivity. Steeper curves indicate higher degrees of impulsivity, a characteristic of the limbic system controlled by the amygdala. When the limbic system overrides the executive system, more delay discounting occurs, and it is theorized that in addicts, prefrontal cortex projections are chemically altered by drug use, causing them to be overwhelmed by the impulsive system (Bickel et al., 2007). Sweitzer et al. (2013) found that individuals that were raised in families with lower SES during their childhood, measured by low parental education and occupational status, and who possessed a particular genotype exhibited a distinctly steeper delay discounting curve, indicative of greater susceptibility to addiction later in life. This is an archetypal example of the interplay between environment and genetics, and possibly points to one genetic culprit in the search for biological bases to addiction across substances. This finding has particularly strong implications for identifying at-risk http://www.neurogenesisjournal.org

individuals based on a combination of their genotypes and parental SES. Previous fMRI studies have shown that the impulsive and executive systems activate differently in response to temporal discounting tasks, and Bickel et al. (2007) suggest that treatment for addiction may involve both increasing the activity of the executive region and decreasing the activity of the impulsive region for maximum efficacy.

The last relevant finding in this category corroborates the evidence above, finding associations between low SES and substance use disorders (Wahler and Otis, 2014). However, higher educational level was predictive of substance use in this study’s follow up, and female gender predictive of illicit substance abstinence (Wahler and Otis, 2014). Although this parallels the protective power of female gender cited above, comparisons between this and other studies are limited by the absence of a division by age, as this was not a longitudinal study. Low SES has so far been most consistently associated with substance abuse, and high SES implicated only under specific conditions. The following breakdown of SES correlates by substance compiles evidence that forms a more complete picture of environmental influences on drug addiction.

Alcohol Stratification of alcohol use and abuse by SES exhibits the clearest and most convincing pattern, one that holds true across cultures. A nearly unambiguous positive correlation between SES and alcohol consumption has been documented in Mexican youth, Brazilian youth, Canadian adolescents, American adolescents, and American adults (Caballero et al., 1999, Pratta et al., 2007, Lemstra et al., 2008, Harrell et al., 2013, Humensky, 2010).

Rates of binge drinking and other problematic alcohol-related behavior have been reported in adults with higher parental income and education (Humensky, 2010), a finding supported by the absence of a correlation between alcohol use and social childhood disadvantage (Daniel et al., 2009).

Alcohol consumption as a social habit could factor in this trend, especially because of the prevalence of alcohol on college campuses (Widome et al., 2013) and the role of educational attainment in a calculation of higher SES. Furthermore, Harrell et al.

(2013) found high parental SES to be predictive of college alcohol problems, but student-reported SES was not associated with alcohol problems. Considering that the age of onset of alcohol consumption Fall 2015 | Volume 5 | Issue 1 | 23

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was an important factor in these calculations, there appears to be a critical period of exposure to alcohol that at least partially mediates the relationship between alcohol problems and earlier age of first drink among young adults (Harrell et al., 2013). Harrell et al. (2013) extrapolate that perhaps alcohol use as a coping mechanism in higher SES individuals is responsible for this.

The relationship between SES and alcohol has also been investigated internationally with similar results. Rates of alcohol consumption and risk of hazardous or harmful drinking habits in Mexican youth and college students in the city of Guadalajara have been reported to be associated with individuals of medium and higher socioeconomic strata, especially when monthly family income is used in the calculation of stratum (Caballero et al., 1999).

In Brazil, SES (calculated using occupation and enrollment in secondary school) and self-report data on alcohol use associated higher alcohol use and higher SES of the youth (Pratta et al., 2007).

Not all literature has reported a positive correlation. A study examining alcohol abuse in Canadian adolescents found that alcohol risk behavior was higher for low SES young teens than high SES young teens. Because this study identified risk behavior and not actual alcohol use, this finding did not necessarily predict of alcohol abuse later in life (Lemstra et al., 2008). However, another study of the same population by the same authors discovered that one of the most important risk indicators for Canadian youth is residence in a low-income neighborhood (Lemstra et al., 2009). Despite this ambiguity, the majority of the evidence indicates a positive correlation between high SES and high alcohol consumption.

Nicotine Nicotine use deserves special mention as an addictive substance that doubles as a visible and distinctly social phenomenon. Although social pressure and changing views toward smoking have decreased nicotine dependence in recent decades (Daponte-Codina et al., 2009), smoking as a social habit and as a coping mechanism are still prevalent forms of nicotine use today. Here, indicators of lower SES generally correlate with higher rates of smoking and nicotine addiction.

Smoking and nicotine dependence have been associated with a number of indicators of low SES, including lower educational attainment (Widome et al., 2013), residence in a high-unemployment area 24 | Issue 1 | Volume 5 | Fall 2015

(Daponte-Codina et al., 2009), and lower educational levels in Chinese tobacco farmers (Cai et al., 2012). This evidence was obtained from Chinese, Spanish, and American communities, demonstrating that smoking as a social habit spans cultures.

Not only do Hiscock et al. (2012) acknowledge that disadvantaged groups are disproportionately prone to smoking, they additionally report that quit attempts are less likely to be successful among this demographic. Along with a negative correlation between smoking and SES, a 10-year longitudinal study beginning in adolescence found that prevalence of smoking was highest among those with no secondary education (Widome et al., 2013).

Hard Drugs (Cocaine, Methamphetamine, and Heroin) Many assume that higher SES is associated with addiction to more expensive drugs of abuse. Unfortunately, correlation between SES and hard drugs has remained largely unexplored, and research tends to focus on hard drug use as opposed to abuse. This is problematic precisely because higher SES individuals may be more likely to engage in hard drug-experimentation than lower SES individuals because of access and financial opportunity. There is some evidence that demand for drugs is price-sensitive and thus predicts that illicit substance use will increase with increasing income (Humensky, 2010), but not enough to be conclusive.

In a cocaine study, Humensky (2010) found higher parental income to be associated with cocaine use early in adulthood, but no statistically significant results for other hard drugs like crystal methamphetamine. Alternatively, Palamar and Ompad (2014) found that higher parental education reduced odds for cocaine use, while personal income of more that $50 a week from a job increased odds and income of more than $50 a week from sources other than a job more than doubled odds. This suggests that parental and personal income oppositely impact likelihood of cocaine use and could therefore use further exploration, particularly because these two definitions of income manifest at different periods in a person’s life.

Perhaps the most promising research relevant to SES and hard drug use comes from a study conducted by Williams and Latkin (2007), who found that neighborhood poverty levels were significantly associated with current cocaine and heroin use in an African American sample population. Two main caveats place limitations on the generalizabilThe Undergraduate Journal of Neuroscience

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ity of these findings: substance use (not substance abuse) was the focus of this study, and the subject sample was not representative of drug abusing/ using populations. However, their results demonstrate a clear, if tentative, relationship between low SES and heroin and cocaine use.

Research on the effects of environment on hard drug addiction in rodent models is an important complement to often-limited research in humans.

Puhl et al. (2012) found that rats housed in enriched conditions, e.g. with other social contacts and novel stimuli, exhibited significantly reduced cocaine-seeking behavior and cocaine self-administration when compared with rats housed in nonenriched conditions. If the enriched condition stimuli can be considered analogous to the enhanced social and educational environments of higher SES children and adults, these results would suggest a link between low SES and higher susceptibility to drug addiction. Furthermore, early-life enrichment and stressful early-life rearing conditions in rats have been shown to decrease and increase addiction and addictive behaviors respectively (Solinas et at., 2009, Palm et al., 2013). Solinas et al. (2009) reported that environmental enrichment early in life reduced the behavioral, neurochemical, and even the molecular effects of cocaine in rats. Taken together, these findings suggest a negative correlation between environmental enrichment and susceptibility to drug addiction.

IMPLICATIONS FOR TREATMENT AND PREVENTION

Patterns in SES and substance use/addiction have critical implications for treatment and prevention policy, especially in light of the immense economic and social costs of drug addiction. A study conducted by Saloner et al. (2013) found that addiction treatment patients of low SES were less likely to complete treatment than their high SES counterparts, and cited greater unemployment and housing difficulties as potential socioeconomic barriers to addiction counseling and treatment completion, directly connecting SES and negative outcomes for addicts. Furthermore, disparities in access to and quality of substance abuse treatment disproportionately affected low-income minorities, attributable to differences in treatment options in low and in high SES areas (Saloner et al., 2013).

Importantly, low SES individuals are more than six times as likely to receive substance abuse treatment than high SES individuals, especially when edhttp://www.neurogenesisjournal.org

ucational attainment was a significant contributor in determination of SES (Le Cook & Alegria, 2011).

Disadvantaged communities tend to have fewer preventive measures but greater exposure to governmental or correctional substance abuse treatment services, and this serves as a potential explanation for the considerable divide in treatment receipt (Le Cook & Alegria, 2011).

Psychological factors also appear to play a role in addiction treatment efficacy. The co-morbidity of addiction and psychological disorders has been well established (Wittchen & Reed, 1996), but individuals of low SES appear to be hindered by other less obvious social psychological forces, including weaker social support for quitting, lower motivation to quit, and reduced self-efficacy due to disadvantaged life circumstances (Hiscock et al., 2012).

Understanding gaps in equity to treatment access is a valuable tool for improving recovery success rates and minimizing discriminatory treatment options. Research has also shown that critical periods of vulnerability to different types of addiction also affect individuals of different SES. Recognizing when disparities in substance use behaviors first emerge and later escalate to addiction can better inform interventions by targeting them to age- and circumstance-specific warning signs and can help tailor prevention policies to different vulnerable age groups (Widome et al., 2013). Low childhood SES, for instance, appears to have different effects on vulnerability to drug addiction than does current low SES (Daniel et al., 2009).

LIMITATIONS OF THIS REVIEW

Socioeconomic status can be defined and measured on a variety of objective and subjective scales, and SES is likely not defined or measured comparably across different countries and cultures. This review could not control for these variables. Self-selection for participation in research studies also likely skewed data, especially in communities in which social stigmas associated with substance use would disincentivize potential participants. Finally, findings from both cross-sectional and longitudinal studies were considered, despite the fact that cross-sectional studies have weaker causal predictive abilities because they cannot examine the effects of childhood SES on susceptibility to drug addiction in adulthood.

FURTHER RESEARCH

Many gaps in the scientific understanding of the Fall 2015 | Volume 5 | Issue 1 | 25

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nature and conditions of the relationship between SES and addiction persist. Further investigation of SES-addiction connections is both a necessary and cost-effective undertaking because of the economic burden addictions place on countries, considering the costs of prosecution, incarceration, probation, etc. of criminal drug offenders (Witkiewitz et al., 2014). This review identifies three promising avenues for future research that would deepen scientific understanding of socioeconomic correlates of addiction. The first and most glaring gap in knowledge concerns longitudinal data. Although there have been a number of such longitudinal studies investigating the impact of childhood or adolescent SES on adult drug use for certain substances, few (if any) have procured thorough analyses of the sort for psychostimulants and opiates, although rodent model studies have been more successful. Furthermore, studies have yet to explain the mechanisms through which poverty affects drug abuse and addiction. Hypotheses have been presented for more expensive drugs abuse (e.g. more wealth equals more opportunity) but they have neither been tested nor extended to other addictive substances like alcohol and nicotine. Possible mechanisms of interest might include parental income, parental educational attainment, and occupational prestige.

Finally, the most accurate picture of SES-addiction relationships would necessarily include analyses of the middle stratum of SES. Although trends are likely clearest in a comparison of the high and low SES extremes, individuals in the middle stratum cannot be neglected. Such an analysis could examine whether these individuals are protected as middle REFERENCES

Bickel, W. K., et al. “Behavioral and Neuroeconomics of Drug Addiction: Competing Neural Systems and Temporal Discounting Processes.”

Drug and Alcohol Dependence 90 (2007): S85-91. Web. 5 Apr. 2013.

Caballero, R., de Leon, E. M., San Martin, A. H., & Villasenor, A. (1999).

Tobacco, alcohol, and illicit drug intake in adolescents of different socioeconomical strata in Guadalajara. Salud Mental, 22(4), 1-8.

Cai, L., Wu, X. A., Goyal, A., Han, Y. T., Cui, W. L., Xiao, X., . . . Jiao, F. (2012).

Patterns and socioeconomic influences of tobacco exposure in tobacco cultivating rural areas of Yunnan Province, China. Bmc Public Health, 12, 8. doi: 10.1186/1471-2458-12-842 Daniel, J. Z., Hickman, M., Macleod, J., Wiles, N., Lingford-Hughes, A., Farrell, M., . . .Lewis, G. (2009). Is socioeconomic status in early life associated with drug use? A systematic review of the evidence. Drug and Alcohol Review, 28(2), 142-153. doi: 10.1111/j.14653362.2008.00042.x Daponte-Codina, A., Bolivar-Munoz, J., Ocana-Riola, R., Toro-Cardenas, S., & Mayoral Cortes, J. (2009). Patterns of smoking according to individual social position, and to socioeconomic environment in municipal areas, Spain 1987-2001. Health & Place, 15(3), 709 716. doi: 10.1016/j.healthplace.2008.11.002

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SES or exhibit similar susceptibilities that vary by substance or by age.

CONCLUSIONS

Current research suggests a relationship between socioeconomic status and addiction, but more interestingly that this relationship varies among illicit substances. Alcohol alone has been shown to correlate convincingly with higher SES and nicotine with lower SES. There is some evidence from studies on general addiction and abuse that there is an association with low SES, but this is not conclusive.

Genetics have also been broadly implicated, with genotype and delay discounting as possible neurological predictors of addiction. Finally, the evidence for a relationship between SES and drugs like methamphetamine, cocaine, and heroin is incomplete, although rodent model studies have implicated environmental enrichment as a protective force.

This review has been an important synthesis of current evidence for connections between SES and addiction because of its potential to inform intervention methods and a greater understanding of the manifestations of SES in addictive behaviors. By recognizing trends, researchers can 1) more easily recognize at-risk populations, 2) identify critical periods at which vulnerability is highest, and 3) develop more effective prevention programs and policies. Much remains to be explored, but by parsing out the details of the relationship between addiction and socioeconomic status, policymakers, addiction counselors, and addicts themselves can take strides toward alleviating the burden of addiction by identifying and mitigating vulnerabilities.

Fothergill, K. E., & Ensminger, M. E. (2006). Childhood and adolescent antecedents of drug and alcohol problems: A longitudinal study.

Drug and Alcohol Dependence, 82(1), 61-76. doi:10.1016/j.drugalcdep.2005.08.009 Harrell, Z. A., et al. “Brief Report: Affluence and College Alcohol Problems: The Relevance of Parent- and Child-reported Indicators of Socioeconomic Status.” ClinicalKey. Elsevier, INC, 2013. Web. 23 Mar. 2015.

Hiscock, R., Bauld, L., Amos, A., Fidler, J. A., & Munafo, M. (2012). Socioeconomic status and smoking: a review. Addiction Reviews, 1248, 107-123. doi: 10.1111/j.17496632.2011.06202.x Humensky, J. L. (2010). Are adolescents with high socioeconomic status more likely to engage in alcohol and illicit drug use in early adulthood? Substance Abuse Treatment Prevention and Policy, 5, 10. doi: 10.1186/1747-597x-5-19 Le Cook, B., & Alegria, M. (2011). Racial-Ethnic Disparities in Substance Abuse Treatment: The Role of Criminal History and Socioeconomic Status.Psychiatric Services, 62(11), 1273 1281.

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Hershberger | REVIEW Lemstra, M., Bennett, N. R., Neudorf, C., Kunst, A., Nannapaneni, U., Warren, L. M., . . .Scott, C. R. (2008). A meta-analysis of marijuana and alcohol use by socio-economic status in adolescents aged 10-15 years.

Canadian Journal of Public Health-Revue Canadienne De Sante Publique, 99(3), 172-177.

Lemstra, M., Neudorf, C., Nannapaneni, U., Bennett, N., Scott, C., & Kershaw, T. (2009). The role of economic and cultural status as risk indicators for alcohol and marijuana use among adolescents. Paediatrics & Child Health, 14(4), 225-230.

National Institute on Drug Abuse. National Household Survey on Drug Abuse: Main Findings. DHHS Publication No. ADM 90-1682 (1999).

Palamar, J. J., & Ompad, D. C. (2014). Demographic and socioeconomic correlates of powder cocaine and crack use among high school seniors in the United States. American Journal of Drug and Alcohol Abuse, 40(1), 37-43. doi: 10.3109/00952990.2013.838961 Palm, S., Daoura, L., Roman, E. (2013). Effects of rearing conditions on behavior and endogenous opioid in rats with alcohol access during adolescence. PloS one. 8(10), e76591.

Pratta, E. M. M., & dos Santos, M. A. (2007). Adolescence and the consumption of psychoactive substances: the impact of the socioeconomic status. Revista Latino-Americana De Enfermagem, 15, 806811. doi: 10.1590/s0104-11692007000700015 Puhl, M. D., Blum, J. S., Acosta-Torres, S. (2012). Environmental enrichment protects against the acquisition of cocaine self-administration in adult male rats, but does not eliminate avoidance of a drug-associated saccharin cue. Behavioural Pharmacology. 23(1), 43-53.

Saloner, B., & Le Cook, B. (2013). Blacks And Hispanics Are Less Likely Than Whites To Complete Addiction Treatment, Largely Due To Socioeconomic Factors. Health Affairs, 32(1), 135-145. doi: 10.1377/ hlthaff.2011.0983

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Solinas, M,. Thiriet, N., El Rawas, R. (2009). Environmental enrichment during early stages of life reduces the behavioral, neurochemical, and molecular effects of cocaine. Neuropsychopharmacology. 34(5), 1102-1111.

Sweitzer, M. M., Halder, I., Flory, J. D., Craig, A. E., Gianaros, P. J., Ferrell, R. E., & Manuck, S. B. (2013). Polymorphic variation in the dopamine D4 receptor predicts delay discounting as a function of childhood socioeconomic status: evidence for differential susceptibility. Social Cognitive and Affective Neuroscience, 8(5), 499-508. doi: 10.1093/scan/nss020 Wahler, E. A., & Otis, M. D. (2014). Social Stress, Economic Hardship, and Psychological Distress as Predictors of Sustained Abstinence from Substance Use After Treatment. Substance Use & Misuse, 49(13), 1820-1832. doi:10.3109/10826084.2014.935789 Widome, R., Wall, M. M., Laska, M. N., Eisenberg, M. E., & Neumark-Sztainer, D. (2013). Adolescence to Young Adulthood: When Socioeconomic Disparities in Substance Use Emerge. Substance Use & Misuse, 48(14), 1522-1529. doi: 10.3109/10826084.2013.800885 Williams, C. T., & Latkin, C. A. (2007). Neighborhood socioeconomic status, personal network attributes, and use of heroin and cocaine.

American Journal of Preventive Medicine, 32(6), S203-S210. doi: 10.1016/j.amepre.2007.02.006 Witkiewitz, K., Warner, K., Sully, B., Barricks, A., Stauffer, C., Thompson, B. L., & Luoma, J. B. (2014). Randomized Trial Comparing Mindfulness-Based Relapse Prevention with Relapse Prevention for Women Offenders at a Residential Addiction Treatment Center. Substance Use & Misuse, 49(5), 536-546. doi: 10.3109/10826084.2013.856922 Wittchen, H., Perkonigg, A., & Reed, V. (1996). Comorbidity of Mental Disorders and Substance Use Disorders. European Addiction Research, 36-47.

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Essential elements of drug treatment programs in the California Prison System Ella Moberg1 Duke University, Durham, NC 27708 Correspondence should be addressed to Ella Moberg (ella.moberg@duke.edu) 1

The California Prison System has one of the highest needs for drug treatment programs, yet fails to provide inmates with adequate resources and has one of the least comprehensive treatment approaches. Research has shown that there are many dimensions of a successful treatment approach that result in long-term behavioral change and a low recidivism rate. Inside the prison, therapeutic living communities must be implemented to foster relationship building, responsibility, and positive social connections. Psychological consequences of incarceration such as PTSD and antisocial personality traits are also vital aspects that must be addressed, as these play a large role in the revolving door prison cycle. Positive behavioral reinforcement has been proven to foster long-term behavior change, and thus the current methods of inconsistent punishment must be abolished. Likewise, transitional support to assist individuals who often have maladaptive behaviors is key in preventing relapse when returning to an environment filled with salient drug cues. The California Prison System fails to implement proven treatment methods and programs, and consequently, the recidivism rate remains high. In order to eliminate this prison cycle, the California Prison System must develop a comprehensive treatment approach that treats the individual both in mind and body, through therapeutic living communities, mental health treatment, and transitional care. “Addiction is like being on fire and running into the ocean and drowning.�- Karl Menninger The California Department of Corrections and Rehabilitation has tried to develop a system to treat inmates with addiction and mental illness. Though programs and treatment methods have been instituted, there still remains an insufficient availability of treatment programs, large holes in the existing programs, and inadequate funding. Historically, California has had one of the highest prison populations in the United States, with capacity only decreasing in recent years. The few current addiction treatment programs in the California Prison System are ineffective and largely unsuccessful in treating a majority of substance users. Fewer California Prison System inmates participate in treatment and rehabilitation programs than inmates of other state prison systems. In addition, California Prison 28 | Issue 1 | Volume 5 | Fall 2015

System inmates have some of the highest needs for drug and alcohol treatment programs. Over 42 percent of inmates need alcohol treatment and over 56 percent need drug treatment, while only 7.5 percent and nine percent of inmates who are in high need of treatment receive any treatment, respectively. Only 2.5 percent of the 56 percent of inmates needing treatment participate in professionally supervised and run treatment programs, and most often these programs fail to provide a multifaceted approach that is conducive to long term behavioral change.

Overall, California is failing to provide adequate access to treatment to those who are in the most need of treatment and rehabilitation in comparison to other states and to the national average (Petersilia, 2006). Little is being done, however, to modify the few current drug treatment programs. In California, there are no programs specifically designed for general population inmates with addictions, though The Undergraduate Journal of Neuroscience

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there are some groups such as Alcoholics Anonymous that they may participate in. Only those with mental illness are brought into treatment programs, and even so, those inmates in treatment groups are often dispersed in housing throughout the prison.

Finally, the California Prison System lacks a sufficient reward system to encourage participation in treatment programs and fails to deal with the psychological consequences of incarceration, leading to maladapted inmates who are reintegrated into society with little support. All of these failures by the California Department of Corrections and Rehabilitation have led to a revolving door reincarceration cycle, as research has shown that inmates with substance use disorders have some of the highest recidivism rates (Harer, 1994).

To address these issues, researchers have investigated the most effective treatment methods from studies and meta-analysis of prisons across the nation. In order for the California Prison System to effectively treat and combat addiction, major changes need to be implemented. First and foremost, therapeutic living communities within the prison system need to be created. Additionally, there needs to be treatment for inmates once they are released so that they do not return to a drug cue-filled environment that leads them to relapse. Moreover, there needs to be treatment of PTSD and maladaptive behaviors, and a structure where mental health staff, and not the prison staff, deals with infractions within the community. Finally, much of the focus is on immediate compliance regarding prison and living community policies and regulations, yet psychologists have found that praise and behavioral reinforcement are successful in producing behavioral change. Research that has shown that “rewarding positive behavior is more effective in producing long-term positive change than punishing negative behavior. Indeed, punishment alone is an ineffective public health and safety intervention for offenders whose crime is directly related to drug use” (“Principles of Drug”, 2014). Much of the focus in the California prison system is on disciplining those who commit violations, yet there is little reinforcement for positive behavior. Many of the current reward systems are intermittent and depend on the specific prison staff. Because these positive behavioral reinforcements do not align with established procedure for positive behavioral reinforcement techniques, there is little effect within the prison system (Burdon, De Lore, & Prendergast, 2011).

The California Prison System’s failure to adopt poshttp://www.neurogenesisjournal.org

itive reinforcement programs and reliance solely upon discipline for instituting behavioral change fails to help ensure long-term success. Research has shown that the failure to address these maladaptive consequences of incarceration such as dependence on institutional structure and contingencies, hyper-vigilance and distrust, emotional alienation, social withdrawal, and exploitive norms, diminished sense of self worth, and post-traumatic stress can cause many inmates to fail to adequately survive and adapt to living in society, especially those without support systems (Haney, 2002). Eventually, individuals with these maladaptive behaviors and the psychological consequences of incarceration become overwhelmed and are unable to cope with living in society and return to their previous destructive criminal behavior. Over 60 percent of inmates have been in prison before, and research has shown that post-incarceration syndrome, which is “symptoms, such as PTSD and antisocial personality traits, caused by being subjected to prolonged incarceration in environments of punishment with few opportunities for education, job training, or rehabilitation,” plays a large role in inmates’ reincarceration. Both changes in treatment programs inside the California Prison System and in the community need to be made, such as shifting the focus of the prison system from incarceration to rehabilitation (Gorski, 2001).

In order to effectively address the treatment needs of the inmates of the California Prison System, the number of available treatment programs, especially therapeutic communities, needs to increase dramatically. Where therapeutic communities are available, there are often strict requirements for admission, such as diagnosed mental illness, preventing a large number of inmates from receiving treatment. Therapeutic communities focus on substance abuse as a disorder involving all aspects of the individual. In addition, participants learn to develop positive social identities and bring about change through interpersonal relationships. Behavioral alteration is encouraged through personal ownership of actions and choices and how those actions may affect others. Therapeutic communities provide a space where individuals can receive feedback from other members about their behavior (Campling, 2001). Research on the success of therapeutic communities has been well-documented.

These communities seek to facilitate social learning through interaction, a sense of responsibility, internalization of a positive value system, and the transi Fall 2015 | Volume 5 | Issue 1 | 29

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tion of negative life attitudes into positive attitudes. The theory behind the community approach is that positive social relationships and identities enable learning of the “behaviors, values, and attitudes of healthy living” (De Leon, 2000).

Studies on therapeutic communities such as the pilot therapeutic community program that was adopted at the R.J. Donovan Correctional Facility in San Diego, CA and research from the program further illustrated the benefit of therapeutic communities for the treatment of addiction in the California Prison System. Inmates in the program were housed together and treatment was conducted in close proximity to the housing unit to foster a secluded sense of community. Inmates were also assigned jobs within the prison as well as the therapeutic community. The treatment program was divided into three phases. The first phase was the intake phase, the second phase allowed inmates to gain increased responsibility through continued participation in group therapy, individual therapy, and respect for authority, and the third phase was designed to help inmates transition back into society. Of the inmates who completed the program, only 25 percent were incarcerated within three years, compared to 75 percent of those who did not participate in the program (Wexler et al., 1999). The California Prison System should adopt this program in all prison therapeutic communities designed for addicts, as research has shown that the therapeutic communities fosters a sense of togetherness, promotes responsibility, leadership, and lead to better treatment outcomes and lower recidivism rates.

The research data from a variety of sources illustrates that an abrupt change from a prison community back into society without any transitional measures is associated with a high re-incarceration rate. Upon release from prison, individuals released into society without a treatment program will experience a multitude of stressors, cues, and triggers that will increase their risk of relapse. If there is no treatment program available, many individuals will return to their previous places of residences, in which they are usually surrounded by criminal activity and drugs; this places a vulnerable individual in an environment filled with salient drug cues. These cues have been conditioned to activate the dopamine reward circuitry in the brain, which can lead cravings and eventually a relapse. This is especially relevant to the prison population, as almost all inmates have gone through withdrawal in prison; and when they are released, the drug cues 30 | Issue 1 | Volume 5 | Fall 2015

they are exposed to lead to the activation of an automatic schema and increased desire and drug seeking (Chandler, Fletcher, & Volkow, 2009). Without treatment post-release, individuals with substance use disorders are much more likely to relapse and be reincarcerated. Relapse prevention involves controlling the addict’s environment and teaching him or her to disconnect environmental cues and drug behaviors. Placing inmates back into environments that are filled with salient drug cues and without any treatment or support exponentially increases their chance of relapse, as these cues activate brain regions and automatic schema that lead to drug craving and drug seeking behavior (Travis, 2000).

Researchers from the Vera Institute of Justice gathered data from 49 individuals who had been released from prisons in the New York metro area, looking at the individuals post-release. The first month post-release was identified to be the most crucial; arrest rates were highest the first weeks after release. Many of the individuals were released late at night or early in the morning and often ended up alone in an environment with criminal activity and illicit drugs. Those who did have family support fared better than those without a support system.

Support networks provide a sense of belongingness and inclusion, enhance a sense of meaning and purpose, as well as decrease feelings of loneliness and isolation (Nelson, Deess, & Allen, 2011). Post-release treatment programs are vital to the success of inmates suffering from substance use disorder.

They provide an environment with fewer drug cues that could trigger a craving and eventual relapse, provide a support network to create a sense of belonging and meaning, and work to assist individuals with obtaining employment and other services. The first month is crucial in determining the success of an inmate after release, and not having a post-release treatment program greatly increases the chance of relapse and reincarceration.

Furthermore, research has found that there are serious mental health consequences due to incarceration. Incarceration causes chronic and consistent patterns of maladaptive behaviors, and the effects of incarceration remain as these individuals are reintegrated into society, which can lead to re-incarceration and relapse (Toch, Adams, & Grant, 1989).

Many inmates develop these maladaptive behaviors in response to prison conditions. For example, gang violence can cause prisoners to develop maladaptive behaviors in order to protect themselves, and “there is an awful lot of potential rage coming out of The Undergraduate Journal of Neuroscience

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prison to haunt our future” (Petersilia, 2000). Research has well documented the “condition of institutionalization,” in which long continuous periods of incarceration, monotony, a lack of autonomy, and a loss of contact with the outside world can cause depression and maladaptive behaviors. In addition, vindictive staff and the unnecessary use of power by those in positions of authority can lead to the development of distress syndrome (Walker, 1983). As the prison environment can be extremely dangerous, prisoners adapt quickly to become hyper-vigilant and often use aggressive avoidance strategies.

Because prisoners are regularly exploited by other inmates as well as staff members, many inmates develop extreme distrust. Social distancing and isolation are other defense mechanisms that many inmates quickly learn to employ in order to protect themselves from exploitation. Furthermore, the experiences that many inmates have while incarcerated can trigger post-traumatic stress episodes. Many inmates have previously experienced traumatic events and abuse, and incidents inside the prison can bring about “re-traumatization”. The unwanted violence, the abuse of authority and absence of respect, as well as frequent exploitation is often remarkably similar to previous traumatic experiences (Haney, 2002).

Not only are the distressing incidents in prison associated with PTSD, they are associated with a multitude of other disorders. Many prisoners develop antisocial personality traits; these patterns of behaviors are developed as coping and as defense mechanisms. The harsh penal system promotes antisocial behaviors of passive aggression with authority figures and confrontational aggression with other inmates. Inmates often view the prison system as vindictive and seek to challenge the authority of those in power as well as to victimize others to feel powerful in a system that seeks to strip one of power and control. In addition to antisocial behavior patterns, inmates frequently develop social-sensory deprivation syndrome. This syndrome is associated with solitary confinement and isolation. Patterns of behavior include impaired impulse and desire control, suppressed rage, concentration problems, and the incapacity to foresee potential consequences of actions. Along with identifying potential disorders resulting from incarceration, researchers have identified six post-incarceration phases due to the maladaptive behaviors developed in prison. These phases are marked by feelings of despair and depression, and anxiety and PTSD. The http://www.neurogenesisjournal.org

maladaptive behaviors of avoidance and isolation culminate with violence and aggression. The survival skills of avoidance, distrust, isolation, aggression, and exploitation are not patterns of behavior that are successful in the outside world (Gorski, 2001).

These patterns reinforced in prison cause inmates to become almost unable to reintegrate into society. For many prisoners, these behaviors have been reinforced over many years, even decades, and reinforced with the strongest of reinforcers: survival. Prisoners develop automatized schema for responding in particular ways and inmates have been conditioned to this behavior. The effects of incarceration are clearly demonstrated by research findings, and the environments inside prisons lead to prisoners with antisocial personalities, PTSD, sensory deprivation, and repressed aggression.

The mental health effects of incarceration and the development of post incarceration syndrome (PICS) are directly correlated with higher relapse rates and recidivism rates. The main factors that are associated with post incarceration syndrome are: a state of helplessness in the face of prison authorities, trauma experienced inside the prison, antisocial defenses developed to cope with abuse, sensory deprivation caused by solitary confinement, and limited social contact, as well as substance use disorders caused by untreated alcohol and drug use.

Inmates with PICS have a much higher risk of relapse, violence, criminal behavior, and chronic unemployment and homelessness. All of these factors contribute to increased rates of recidivism (Gorski, 2001). The return to a rehabilitation and treatment-focused public service is vital in eliminating a prison environment that leaves inmates with maladaptive behaviors and untreated substance abuse.

The adoption of positive behavioral reinforcement within the California Prison System is vital if long-term behavior change is desired. These positive reinforcement strategies must follow proven, effective reinforcement methods. In addition to tangible rewards, social rewards can be just as effective. In the Stay’n Out program in New York, inmates were rewarded for positive participation in the treatment program with social acknowledgement as well as monetary rewards. These rewards were successful in increasing program attendance and participation (“Principles of Drug”, 2014). One model that may serve as an example for the California Prison System is the BRITE Model. The BRITE Model was a four-year program that examined positive behavioral reinforcement in both a medium se Fall 2015 | Volume 5 | Issue 1 | 31

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curity male prison and a minimum security female prison. Positive reinforcement was provided in systemic and structured method and in a timely manner. Inmates were rewarded for prosocial conduct and behavior that encouraged recovery. Motivational incentive points were given for attending meetings, job assignments, being infraction free, etc.

Inmates could then redeem their points for tangible rewards or privileges, or donate them to a charity.

Project BRITE increased participation in treatment programs within the prison system, as well as post release in the reduction of drug use, participation in post-release treatment programs, and decreased return to custody (Burdon, De Lore, & Prendergast, 2011). Positive behavioral reinforcement has been shown to have positive effects on treatment program participation and treatment program retention. The California Prison System should adopt a positive behavioral reinforcement model and reward system promoting self-efficacy, which would have long- term benefits, instead of instituting a discipline and punishment focused system that is not shown to promote long-term behavior change.

Research has clearly identified the essential elements of addiction treatment that should be instituted in prison systems. Due to the uniqueness of a prison population, specific treatment programs have been shown to be more effective others. Positive reinforcement that follows established psychological principles is crucial. Therapeutic communities are successful because of the community approach that fosters behavioral and attitude change through interpersonal relationships and prosocial identity building. Post-incarceration programs have been shown to be vital as many individuals fail to cope with the environmental drug cues and fall back into the maladaptive behavioral patterns developed in prison. Recognizing the effect that incarceration has upon an individual is crucial for successful reentry back into society. The prison environment promotes maladaptive behavior strategies and can lead to the development of various disorders. Treating these disorders and behavioral patterns is key to the sustained sobriety of an individual post-release. The California prison population has the highest need for treatment programs, and thus the California Prison System requires a comprehensive treatment approach that is reflective of the prison environment and the unique background of the incarcerated population.   32 | Issue 1 | Volume 5 | Fall 2015

REFERENCES

Burdon, W. M., St. De Lore, J., & Prendergast, M. L. (2011). Developing and Implementing a Positive Behavioral Reinforcement Intervention in Prison-Based Drug Treatment: Project BRITE. Journal of Psychoactive Drugs, 43(sup1), 40-50. Retrieved March 21, 2015, from Taylor and Francis.

Campling, P. (2001). Therapeutic communities. Advances in Psychiatric Treatment, 7(5), 365-372. Retrieved March 17, 2015, from BJ Psych Advances.

Chandler, R., Fletcher, B., & Volkow, N. (2009). Treating Drug Abuse And Addiction In The Criminal Justice System: Improving Public Health And Safety. JAMA: The Journal of the American Medical Association, 301(14), 183-190. Retrieved March 19, 2015, from Journal of American Medicine.

De Leon, G. (2000). The therapeutic community: Theory, model, and method. New York City, New York: Springer Publishing Company.

De Leon, G., & Wexler, H. (2009). The Therapeutic Community for Addictions: An Evolving Knowledge Base. Journal of Drug Issues, 39(1), 167-177. Retrieved March 16, 2015, from Sage Journals.

Gorski, T. (2001). Post Incarceration Syndrom and Relapse. Retrieved March 21, 2015, from http://www.tgorski.com/criminal_justice/ cjs_pics_&_relapse.htm Haney, C. (2002). The psychological impact of incarceration: Implications for post-prison adjustment. Prisoners once removed: The impact of incarceration and reentry on children, families, and communities, 33-66.

Harer, M. D. (1994). Recidivism among federal prison releasees in 1987: A preliminary report. Federal Bureau of Prisons. Office of Research and Evaluation. Retrieved March 17, 2015, from National Criminal Justice Reference Service.

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Petersilia, J. (2006). Understanding California Corrections. Retrieved March 14, 2015, from http://static.prisonpolicy.org/scans/carc/understand_ca_corrections.pdf Toch, H., Adams, K., & Grant, J. D. (1989). Coping: Maladaptation in prisons. New Brunswick, NJ: Transaction. Retrieved March 18, 2015, from National Criminal Justice Service Reference.

Travis, J. (2000, May). But They All Come Back: Rethinking Prisoner Reentry. Retrieved March 24, 2015, from https://www.ncjrs.gov/txtfiles1/nij/181413.txt.

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Wexler, H., Melnick, G., Lowe, L., & Peters, J. (1999). Three-Year Reincarceration Outcomes for Amity In-Prison Therapeutic Community and Aftercare in California. The Prison Journal, 79(3), 321-336. Retrieved March 20, 2015, from Sage Journals.

The Undergraduate Journal of Neuroscience

Neurogenesis

REVIEW

The brain on music Gabriela Gomez1 Duke University, Durham, NC 27708 Correspondence should be addressed to Gabriela Gomez (gtgomez11@gmail.com) 1

If you’ve been listening to Adele’s new hit “Hello” on repeat; if your heart has ever stopped in anticipation of the beat drop in Skrillex’ “Scary Monsters and Nice Sprites”; if you’ve ever witnessed music therapy bring life to individuals lost in the fog of dementia, then you have experienced the inimitable and transcendent power of music. Anthropologically, sound patterns and pitches have shaped communication, language, emotion, brain function and development, self-expression, spirituality— ultimately human evolution. The role of music in society continues to change with the evolution of culture, disease, medicine, and scientific inquiry. Today, modern insight into the human brain and the development of innovative techniques for measuring our perception and psychophysiological response to music advances our understanding and appreciation of this powerful phenomenon. Exploring the intricate relationship between music and our brains in the context of normal functioning, music disorder, and music-mediated healing will continue to illuminate the brain on music.

Belief in the power of music is no recent phenomenon. Music is the oldest art form associated with helping the ill (Bunt & Stige, 2014). Evidence from early civilizations—including ancient Greece, India, and China—reveals the use of music in healing and balance (Solanki, Zafar, & Rastogi, 2013; Hanser, 2009). With roots in remote antiquity, Ayurveda’s doctrine of doshas and chakras suggests that human consciousness is modulated and balanced through music (Stamou, 2002; Narayanaswamy, 1981). Well-known philosophers, such as Pythagoras, Aristotle and Plato, provide numerous accounts of the practical and philosophical importance of music in curing mental and physical affliction (Solanki, Zafar, & Rastogi, 2013; Stamou, 2002; Hanser, 2009). Aristotle is believed to have practiced psychocatharsis, a method to cure those suffering from emotional disorder with music listening, which “raised their souls to ecstasy” (Solanki, Zafar, & Rastogi, 2013). Plato posited that the harmonic and mathematical structure of music was an earthly reflection of the vibrations and proportions of the macrocosmos (Solanki, Zafar, & Rastogi, 2013).

Our understanding and perception of music’s http://www.neurogenesisjournal.org

footprint on the human mind has evolved as a reflection of the philosophies and beliefs of the time period. The rise of Western civilization brought about increased focus on scientific inquiry and discrete analysis of the human body. Similarly, Plato’s metaphysical musings on the mind have changed and evolved to make way for our modern understanding of the brain as a network of neurons communicating by chemical and electrical signals. With this in mind, how does the current state of research and medicine impact how we study music therapy?

Over the past thirty years, our understanding of the musical brain has increased significantly. From functional imaging methods, including hemodynamic (PET, fMRI) and electrophysiological (EEG, MEG) techniques, to new constructs for analyzing musical perception, the advancements of modern research have aided scientists in studying the anatomical bases and changes associated with music listening.

Additionally, music-processing studies of the normal brain have been coupled with examinations of the disordered brain to provide richer insight into the neural substrates of music function. Finally, the music therapy phenomenon has sparked renewed Fall 2015 | Volume 5 | Issue 1 | 33

REVIEW | The Brain on Music

exploration into the potential healing qualities of music, reminiscent of Aristotelian thought, yielding even further insight into music and the brain. Analyzing the brain in the context of normal functioning, music disorder, and music-mediated healing will continue to advance our understanding of the brain on music.

MUSIC AND NORMAL FUNCTIONING

Studying the brain on music, however, presents a serious complication: music listening evokes response from multiple brain regions and integrates a complex network of subcortical and interhemispheric pathways. This abstract, multimodal character of music listening has prompted researchers to systematically break down musical functioning into its fundamental components—pitch, timbre, temporal structure, and emotion—in order to better understand how our brains process music (Stewart et al., 2006).

A synthesis of recent literature suggests that even within these fundamental components of music there exists a gradient of simple to complex levels of processing. Regarding pitch, the individual musical percept has been found to implicate auditory cortices and adjacent association areas in the superior-temporal lobes, a network similarly activated by speech. However, when pitch is perceived as a construct for melody, harmony, and chords— more complex musical concepts—modern research suggests a wider distribution of neural activity, incorporating the frontal lobes, responsible for higher-level cognitive function (Penagos et al., 2004; Bendor & Wang, 2005; Stewart et al., 2006).

Another fundamental component of music listening is timbre. Translating to mean “sound-color,” timbre allows us to distinguish musical streams, and may play a role in recognition of voices and other environmental sounds. Superior-temporal lobes and superior-temporal sulcus have been implicated in neural processing of timbre (Stewart et al., 2006).

Temporal structure—an umbrella term encompassing several subfunctions of the time domain of music, such as rhythm and meter—has been less explored than other music components. Implicated brain structures include the cerebellum, basal ganglia, and superior-temporal lobes. As these brain areas are typically associated with motor processing—specifically the cerebellum and basal ganglia—a general clamoring can be heard among researchers about a potential motor theory of rhythm, “whereby our perception of rhythm might 34 | Issue 1 | Volume 5 | Fall 2015

depend on the motor mechanisms required for its production” (Stewart et al., 2006).

Emotion processing is considered to be the fusion of all components of musical listening discussed thus far. A few studies have explored the emotional effects experienced by many people when listening to music. Referred to as a “shiver down the spine”, the physiological response often experienced during a moving piece of music has been shown to activate the same brain regions—ventral striatum, amygdala, and orbitofrontal cortex—associated with the reward¬¬ response. Such studies, which broaden our awareness of the individual brain substrates involved in music listening and the networks they form, further our understanding of the complex neural processes surrounding the brain on music phenomenon (Stewart et al., 2006).

MUSIC AND DISORDER

Disorder has played a significant role in our understanding of the neural substrates of music. In one case study, the “shiver down the spine” phenomenon was no longer experienced by a patient with damage to the amygdala and insula (Griffiths et al., 2004). In other similar studies, patients exhibited appropriate emotional responses despite impaired perceptual processes. These patients were able to perceive music but with impaired emotional processing, or vice versa, suggesting a possible dissociation between the perception of music and one’s emotional response to music. For example, such patients would not be able to perceive or reproduce the pitches in the chorus of Adele’s new hit single, and yet the song might still bring them to tears.

Congenital Amusia The systematization of the developmental disorders of musical listening has only recently emerged in the auditory literature in the past few years. However, as in the case of congenital amusia—a perceptual-emotional processing dissociation—studying musical disorder has already enhanced our understanding of music and the brain. Congenital amusia is characterized by deficits in musical perception, typically identified symptomatically by the inability to sing (Stewart et al., 2006). Most significantly affected in amusic patients is pitch processing. Competing arguments exist across experiments regarding the root of these deficits, but the impairment likely stems from disorder in the most basic levels of pitch processing (Stewart et al., 2006). Amusic patients also show deficits in meter and rhythm The Undergraduate Journal of Neuroscience

Gomez | REVIEW

processing, the higher-order components of temporal structure (Stewart et al., 2006). Exploration of this disorder suggests that the pitch and time domains are circumstantially linked and that perhaps these fundamental components of music are not discretely processed by the brain.

Musical Hallucination While congenital amusia involves deficiencies in musical processing, musical hallucination involves “aberrant” processing (Stewart et al., 2006). Another abnormality in musical perception, musical hallucination involves vivid musical imagery in the absence of a musical stimulus. Halpern & Zatorre (1999) describe mental imagery as “mental acts in which we seem to re-enact the experience of perceiving an object when the object is no longer available.” While musical hallucinations have been reported in association with lesions, epilepsy, and psychosis, most cases often occur in conjunction with acquired deafness. Stewart et al. (2006) point to a general consensus among studies of a possible involvement of the superior temporal lobes in the phenomenon of musical hallucination. We have already seen this brain area involved as a neural correlate with components of musical listening, such as pitch and timbre (Stewart et al. 2006; 2547). As in the case of congenital amusia, research has identified several neural culprits complicit in disordered music processing.

Neuroimaging techniques have also been employed to explore the underlying mechanisms of musical hallucinations among patients suffering from acquired deafness. Griffiths (2000) implemented PET imaging to assess regionalized cerebral blood flow (using the intravenous oxygen-15 water bolus technique) as a measure of localized brain activity. A subject survey followed, outlining the occurrence and severity of any musical hallucinations experienced during the scan. Reports ranged from an orchestral performance of ‘Edelweiss’ to an organ rendition of ‘Auld Lang Syne’ with vocal accompaniment. Scans were then analyzed to explore how regional blood flow—i.e. brain activity—correlated with reported musical symptoms. (Griffiths, 2000).

While no significant activity was noted in the primary auditory cortices of these deaf participants, the participants did demonstrate hallucinations that correlated with activity in the posterior temporal lobes, basal ganglia, cerebellum, and inferior frontal cortices. The author notes that this activity is typical of normal neural functioning during http://www.neurogenesisjournal.org

patterned-segmented sound—i.e. music. Griffiths also introduces a model of a proposed mechanism for musical hallucinations, involving spontaneous activity associated with the neural network responsible for pattern perception. The absence of sound input due to acquired deafness may cause a positive feedback loop within the now “impoverished” network interaction amongst the substrates responsible for patterned sound recognition and perception (Griffiths, 2000).

MUSIC AND HEALING

Considering the power of music on a more holistic scale, music therapists have taken a different but complementary approach to music research: music as a means of relieving disorder. Modern research has provided evidence that music therapy may provide an inexpensive and noninvasive alternative for relieving symptoms of certain incurable diseases, such as Alzheimer’s type dementia, Parkinson’s Disease and related disorders. Again, this understanding of music and the application of music to cure disorder is nothing new. But what does music therapy look like today?

Guided by the development of behaviorism and psychotherapy, modern music therapy has evolved from a therapeutic accessory to a distinct intervention for disease. Within the clinical practice of music therapy, subtypes of this intervention include active and passive models. Active music therapy engages participants by allowing them either to choose their own music or to participate in music making, often by singing or playing percussion instruments. Conversely, passive or receptive music therapy involves a relaxation-based listening paradigm (Guetin et al., 2009).

Music therapy may take place in groups or in individual sessions with a music therapist, involving generalized music selections or personalized playlists catered to an individual’s interests. A group setting for receiving services is standard in a nursing home or daycare environment as this arrangement is often more practical and cost efficient than one-on-one interventions (Solé, Mercadal-Brotons, Galati, & De Castro, 2014). As most music therapy studies are conducted by nurses or music therapists, the group model is common across the literature (Sherratt, Thornton, & Hatton, 2004; Ueda, Suzukamo, Sato, & Izumi, 2013). However, individually administered non-pharmacological therapies have been reported to produce more positive outcomes among dementia patients than group theraFall 2015 | Volume 5 | Issue 1 | 35

REVIEW | The Brain on Music

pies (Brodaty & Burns, 2012). Additionally, adjusting a music therapy intervention to the individual on an autobiographical and cultural scale has been shown to increase engagement as well as the potential for positive therapeutic results (Gerdner, 2000).

Music is relieving symptoms of disorder in the brain, but how? Music therapy literature emphasizes the potential of intervention methods to alleviate dementia symptoms but fails to methodically address the mechanism of this therapeutic model.

Nevertheless, several hypotheses address the neural mechanisms of music therapy. Fachner, Gold, & Erkkila (2013) implemented a music intervention among depressed individuals also suffering from anxiety and found not only a clinical reduction in depressive and anxiolytic symptoms but a corresponding neuroanatomical effect as well. A change in resting EEG was seen, specifically alpha and theta changes in the fronto-temporal and temporoparietal areas indicative of decreased anxiety and depression. This fronto-temporal region has been implicated in emotion recognition and processing in music (Fachner, Gold, & Erkkila, 2013).

Blood & Zatorre (2001) investigated the emotional response evoked during music listening, incorporating PET imaging to investigate the underlying neural mechanisms of the aforementioned “shiversdown-the-spine”—the intense psychophysiological response one may experience while listening to an evocative piece of music (Blood & Zatorre, 2001).

During exposure to participants’ self-selected music, imaging revealed activity in neural regions associated with reward, emotion, and arousal, including the ventral striatum, midbrain, amygdala, orbitofrontal cortex, and ventral medial prefrontal cortex. As noted in Krout (2007), integral to the music-modulated emotional processing within the limbic system are the amygdala and hippocampus; their respective roles in behavior and memory have implications in the neural mechanisms underly-

ing music therapy. Results from Blood & Zatorre (2001) support the potential of music to improve mental and physical well-being. This lends credence to not only the therapeutic effects of music on mood evidenced by the literature, but also the power of personally significant music to affect neural change.

Increased functional connectivity between key neural networks in response to music may also mediate relief of cognitive, affective and/or behavioral disorder (Wu et al., 2012; Menon & Levitin, 2005).

Menon & Levitin (2005) demonstrated that music modulates functional connectivity within the reward network in the brain. The study implicated reward processing areas—nucleus accumbens and ventral tegmental area—as well as the neural regulators of autonomic and physiological responses to music—hypothalamus and insula—in this functional network. The authors reasoned that the observed interactions between the reward network and frontal regions suggested high correlations between the affective and cognitive subsystems of music listening (Menon & Levitin, 2005). Other studies reported similar correlated activity in limbic and cortical areas, suggesting that the auditory cortex is the hub of an extensive network involved in emotion, attention, reward, and language (Koelsch & Skouras, 2013; Alluri et al., 2012; Levitin & Menon, 2003).

Collectively, this exploration into music and the brain may explain the power of music to affect behavior, mood states, and perhaps even cognition.

But there is still much work to be done. Significant progress is needed to understand the neural mechanisms behind music in the context of the brain, the disordered brain, and relief for the disordered brain. Music is a complex, multimodal phenomenon; it is only natural that music research requires complex, interdisciplinary approaches engaging auditory neuroscientists, clinicians, and music therapists.

Aigen, K. (2013). How are Psycho-Biological Concerns Addressed in Music Therapy? In The Study of Music Therapy: Current Issues and Concepts (pp. 169-214). Routledge.

Alluri, V., Toiviainen, P., Jääskeläinen, I. P., Glerean, E., Sams, M., & Brattico, E. (2012). Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm. Neuroimage, 59(4), 3677-3689 Bendor D, Wang X. The neuronal representation of pitch in primate auditory cortex. Nature. 2005; 436: 1161–5.

Blood, A. J., & Zatorre, R. J. (2001). Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion.Proceedings of the National Academy of Sciences,

98(20), 11818-11823.

Brodaty, H., & Burns, K. (2012). Nonpharmacological management of apathy in dementia: a systematic review. The American Journal of Geriatric Psychiatry, 20(7), 549-564.

Bunt, L., & Stige, B. (2014). Music therapy: An art beyond words. Routledge.

Fachner, J., Gold, C., & Erkkilä, J. (2013). Music therapy modulates fronto-temporal activity in rest-EEG in depressed clients. Brain topography, 26(2), 338-354.

Gerdner, L. A. (2000). Effects of individualized versus classical “relaxation” music on the frequency of agitation in elderly persons with Alzheimer’s disease and related disorders. International Psychogeri

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Gerdner, L. A., & Schoenfelder, D. P. (2010). Evidence-based guideline.

Individualized music for elders with dementia. Journal of gerontological nursing, (36), 7-15.

Griffiths TD. Musical hallucinosis in acquired deafness. Phenomenology and brain substrate. Brain. 2000; 123: 2065–76.

Halpern AR, Zatorre RJ. When that tune runs through your head: a PET investigation of auditory imagery for familiar melodies. Cerebral Cortex, 1999; 9: 697-704.

Hanser, S. B. (2009). From Ancient to Integrative Medicine Models for Music Therapy. Music and Medicine, 1(2), 87-96.

Koelsch, S., Skouras, S., Fritz, T., Herrera, P., Bonhage, C., Küssner, M. B., & Jacobs, A. M. (2013). The roles of superficial amygdala and auditory cortex in music-evoked fear and joy. NeuroImage, 81, 49-60.

Krout, R. E. (2007). Music listening to facilitate relaxation and promote wellness: Integrated aspects of our neurophysiological responses to music. The arts in Psychotherapy, 34(2), 134-141.

Levitin, D. J., & Menon, V. (2003). Musical structure is processed in “language” areas of the brain: a possible role for Brodmann Area 47 in temporal coherence.Neuroimage, 20(4), 2142-2152.

Menon, V., & Levitin, D. J. (2005). The rewards of music listening: response and physiological connectivity of the mesolimbic system.

Neuroimage, 28(1), 175-184.

Narayanaswamy, V. (1981). Origin and development of Ayurveda:(a brief History). Ancient science of life, 1(1), 1.

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Penagos, H., Melcher, J. R., & Oxenham, A. J. (2004). A neural representation of pitch salience in nonprimary human auditory cortex revealed with functional magnetic resonance imaging. The Journal of neuroscience, 24(30), 6810-6815.

Sherratt, K., Thornton, A., & Hatton, C. (2004). Music interventions for people with dementia: a review of the literature. Aging & Mental Health, 8(1), 3-12.

Solanki, M. S., Zafar, M., & Rastogi, R. (2013). Music as a therapy: Role in psychiatry. Asian journal of psychiatry, 6(3), 193-199.

Solé, C., Mercadal-Brotons, M., Galati, A., & De Castro, M. (2014). Effects of Group Music Therapy on Quality of Life, Affect, and Participation in People with Varying Levels of Dementia. Journal of music therapy, 51(1), 103-125.

Stamou, L. (2002). Plato and Aristotle on music and music education: Lessons from ancient Greece. International Journal of Music Education, (1), 3-16.

Stewart, L., von Kriegstein, K., Warren, J. D., & Griffiths, T. D. (2006) Brain, 129(10), 2533-2553.

Ueda, T., Suzukamo, Y., Sato, M., & Izumi, S. I. (2013). Effects of music therapy on behavioral and psychological symptoms of dementia: a systematic review and meta-analysis. Ageing research reviews, 12(2), 628-641.

Wu, J., Zhang, J., Liu, C., Liu, D., Ding, X., & Zhou, C. (2012). Graph theoretical analysis of EEG functional connectivity during music perception.

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The Undergraduate Journal of Neuroscience

Neurogenesis

INTERVIEW

Helping paralyzed patients walk again

An Interview with Miguel Nicolelis by Katrina Vokt With help transcribing by Jennie Xu

http://www.neurogenesisjournal.org

Fall 2015 | Volume 5 | Issue 1 | 39

Duke neuroscientist Miguel Nicolelis is no stranger to thinking outside of the box — he has been making waves in the world of neuroengineering for the past 25 years. Neurogenesis had a chance to pick his brain about the 2014 FIFA World Cup, his innovative research on “Brainets”, and what inspires him as a scientist.

Could you give us some background on the Walk Again Project? How did it begin?

We started this around 2009 when we realized that the basic science work that we have done could be translated into clinical applications, but of course in 2009 we thought that it would take a long time if we didn’t have a very clear goal, an objective.

That’s when I had this idea to create an international consortium that became known as the Walk Again Project to work on this. To bring scientists from all over the world to collaborate so we could have a demonstration during the opening ceremony of the World Cup, more as a gesture, and also as a catalyzer in the field to get people to come together and donate their expertise. We would literally accelerate the development that needed to be done. So that’s how the project was conceived.

It turned out that we had about 166 people from 25 countries collaborating over a pretty crazy 18-month period. When we heard that everything was approved, that was December of 2012. So we had 18 months from that day until the opening ceremony, which of course had to happen at 3:30 in the afternoon on June 12. There was no escaping that deadline, which is kind of unusual for scientists to have a hard deadline. At that point we didn’t have the patients, we didn’t have the exoskeleton, we had just the ideas, the plans, and of course the collaboration of all these people from around the world. So we set up a laboratory in Brazil in São Paulo and one in Natal near the coast of Brazil, which are about 10,000 km apart, and we started working. Basically, during those 18 months we built the exoskeleton, the first prototype, we are already on the third prototype, and this is the first brain-controlled exo, that not only allows the patient to voluntarily control walking, but also to provide tactile feedback, so the feet and from the surface of the feet and the joints, to the skin of the 40 | Issue 1 | Volume 5 | Fall 2015

arms of the patients where they still had sensitivity. This proved to be a very key component.

What was the training process that the patients had to go through to be eligible for the World Cup kick off?

The patients were training in a six-step procedure.

They progressed from just learning how to use the brain to activity to a noninvasive EEG system to record electrical activity from the surface of the patients’ scalp. They started with that, and then got into a virtual reality stadium where they learned to control an avatar, a soccer player that was an avatar of themselves, and they learned to interpret these tactile signals coming from the feet of the avatar walking past. Finally, they progressed to walking on a standalone robot. When they were really confident about that, they moved to this custom-designed exoskeleton that we built. This way, patients, and seven of them had complete spinal cord lesion multiple years ago. Some of them had 11, 12 years of lesion, as I said, 7 of them were clinically diagnosed with complete lesions where you are completely paralyzed below the knee, and you don’t have tactile sensation below the knee.

And basically we got all these guys to a proficient level where they all could control the exoskeleton mentally, and they all had feeling of what it was to walk with the device. All these patients developed the phantom limb delusion.

Phantom limb is when a person suffers an amputation, about 90% of these patients after the amputation still feel that the limb that was amputated remains there, even though it is not; it has been physically removed. They have the tactile feeling that the arm is still there, or the leg, or in about 80% of these patients, they can feel pain in the part of the body that does not exist anymore. It is well known and well described, but it is still very The Undergraduate Journal of Neuroscience

mysterious how it is generated but even in our patients there was a kind of feeling of the phantom limb delusion when they are walking on the exoskeleton. They report to us that they did not feel like they were inside of a machine, but that they were walking by themselves, that their legs had recovered the ability to walk. And that was created by this tactile feedback that I had described. So we got to the World Cup, and Juliano was in charge of the kick, but all eight patients were on the sideline of the stadium that day, and they all could have done it, it’s just that Juliano was the most skillful and showed reliability -- it was really unbelievable.

I never expected someone to be so proficient.

Have there been any changes in the patients since the World Cup? After the World Cup, we continued to work with patients, and we’re working with them to this day.

But the most amazing part is that months after the World Cup, we did, as we had done every three months, a full neurological examination of all our patients, and to our shock all these patients showed signs of partial neurological recovery below the level of the lesion, which has never been reported in this field. All patients showed that they could feel the body below the level of the lesion. We have filed tactile sensation below the level of the lesion on an average of five segments of the spinal cord, which is big. Also, some of these patients acquired the ability to contract muscles voluntarily below the level of the lesion, and in three patients, we can see now that they are making leg movements on their own. They can move their legs again. Two of these patients, we can suspend them with a device for rehabilitation to get them upright in the air, and you just put them slightly on the surface of the ground—they can step on it. They can generate steps by themselves, which is -- when you look at the movies, I have been showing these movies all over the world and no one can believe it – it’s stunning.

Would you say that this is the result of the patients walking with the exoskeleton or from something else?

http://www.neurogenesisjournal.org

One of the first exoskeleton prototypes, like the one used in the 2014 World Cup

I think what is happening is the whole training paradigm requires them to use their brain activity to control the device to receive feedback of some process of brain plasticity. The functional recovery may have turned on a few fibers of the spinal cord that may have survived the original trauma years ago. It is known that, clinically the patients are totally paralyzed, it is well known that some nerves of the spinal cord may have survived the original trauma. Between 2-20% of the nerves we call white matter may have survived but they turn off, they shut off. But I think that our training paradigm may have turned some of these nerves on again and this is why we are seeing patients able to control their muscles again.

Are the patients continuing to improve, or have they stopped since those first findings?

We noticed this about a month after the World Cup but we have been tracking these patients since then, in fact we just sent a paper with these results. And by the end of last year, this recovery was so obvious, it was clear. And the amazing part, we are in November now, we have done this test 3 more times this year, and one more time in December, and every time we do the test, the patients are continuing to improve. We haven’t plateaued yet in this recovery so we are really intensifying the training with the exo now with the new prototype we just completed because we incorporate all the suggestions made for us and all the improvements yet to be done. And we are going to repeat this test in a couple of weeks because I am Fall 2015 | Volume 5 | Issue 1 | 41

still waiting to see the plateau in recovery. Now we are recruiting 13 more patients because we feel that if this is correct we could actually help a lot of people through this.

That’s like a miracle. That’s fantastic.

Well I have to tell you, I went to medical school about 30 years ago and every professor I had in neurology said that this would be impossible. And now when I saw with my own eyes, well its very shocking to see the evidence because it is counter to everything I have heard from neuroscientists all these years.

You mentioned that some improvements were made to the exoskeleton prototype. Could you explain what modifications were made since we last saw it at the World Cup? These are very technical changes. We got a more robust balance. We had to add more gyroscopes to be more autonomous in terms of balance. We improved the cockpit so the patient has more mobility inside the cockpit so he feels more comfortable and they are not so tight in the exo. We improved the entire system of hydraulic control to make it lighter. We increased the foot area to give more space to make the stepping more comfortable. That reduce a little bit of the speed but the patients don’t care too much about that they really care about feeling completely in balance while they are inside the exo. The patients have now

Dr. Nicolelis is working on a protoype with rats to tackle Parkinson’s disease

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developed the ability to control the individual legs of the exo with each side of their brains so it’s almost like what we do when we walk and its very finer control. We found a new way to control the legs and they have much more control themselves.

And also the tactile feedback we have learned a lot about what patterns they like and what creates a more realistic sense of the ground of the surface that they are stepping on.

Right now our patients can distinguish three types of floors with this feedback. They can distinguish a floor that feels like grass, something that looks like walking on a paved street versus walking on sand, like if they are on the beach running on soft sand.

And that’s amazing because they are only getting 3 micro vibrating elements per forearm, which is not much if you think about it, but that’s enough to make this kind of a texture feeling about the ground. So these are just some of the improvements that we have made in this new version.

The cockpit is where the patient is residing inside the exo. And literally in this new design he is suspended in there, so he doesn’t feel the weight of the exo. He is attached by a harness from his hip level so he is suspended in the cockpit. And the way we redesigned the cockpit now he has no feeling of the weight of the exo right now. We also changed the inclination of the cockpit so he has a total upright posture now which is for them is more comfortable. We also changed a lot of details of the controller system, which is the key component of the exo.

Have these brain machine interfaces been implemented in the medical field as of yet for spinal cord injury patients? Do you anticipate this happening, and if so, how soon?

In parallel to the Walk Again Project in 2009, we developed a new type of therapy for Parkinson’s disease of a stimulator of the spinal cord, which is a very novel approach to Parkinson’s, very different to what is out there. And it turns out that about 50 patients in the world have already tried it successfully. We have seen that it is growing very quickly now. I just got information about 4 patients done in Brazil too at the University of São The Undergraduate Journal of Neuroscience

Visual representation of Nicolelis’ novel “Brainet” research

Paulo, by a group that knows our work very well, and I just looked at the movies and it is incredible.

The fact is that the clinical effect is really very robust, and it is much cheaper and much safer than the techniques that we know so far. I think that the more we learn about how to interface brains to devices, all the applications are going to come very quickly.

What projects are you thinking of tackling in the future and how would brain-machine interfaces be involved?

Well we have many projects running parallel here in the lab. We are working on a prototype with rats now using the same technique we used for Parkinson’s, which is showing a preliminary effect that is very interesting. On a more basic science level, we are exploring what happens when you connect multiple brains to control a device – what I like to call a shared brain machine interface, or a “Brainet”. So it is a network of brains interconnected and cooperating mentally to get a particular behavior done. So we are exploring a variety of domains and using this not only as a potential way to develop new clinical applications but also as a tool to understand how brain circuits operate which is the original motivation that got me into this field anyway.

I recently watched your Ted Talk, and one quote that I was wondering if you could elaborate on http://www.neurogenesisjournal.org

was, “we will always absorb technology but technology will never absorb us”. Do you think it is possible to create true artificial intelligence?

I actually just wrote a little monograph debating exactly this issue. It is about 100 pages long and it is called The Relativistic Brain and why it cannot be simulated by a machine or computer. Basically what I mean in a nutshell is, no, there is no such thing, it will never happen with the human brain because they simply don’t work like human brains.

A human brain has mechanisms and operations that cannot be reproduced by a turing machine.

And in that little booklet I basically describe the mathematical computation of biological arguments against that. What I meant in the quote you just cited is that given everything that we have looked into our brain machine studies, every tool that we use in our daily routine, to increase our enriching world, you know our cars, bikes, silverware, everything that we use. What we have proposed many years ago and now we have a lot of data to support the labs too is that when you become proficient in using these tools, they actually, the reason you are proficient is because they become an extension of our own bodies of the brains. The brain literally dedicates neurons to these tools and these tools become an extension of the body maps that we all have in our brains, as if they were a part of the body schema. This reason you have pianists, violinists, and pilots are extremely proficient, because in reality at that point that device is treated by the brain as the subject’s body. And that theory fits very well with all the results that people have reported in including the results we get from brain machine interfaces.

What motivates you as a scientist?

Curiosity. I like to say that a scientist is someone who is paid to be a kid forever. I think I try to keep that in mind, in perspective, even though these days it’s a pretty harsh and difficult profession, you know because of the contagiousness of the way things are here now in the US for scientists. I tried to keep that in perspective, it is a very big privilege to try and live everyday by curiosity.

So that’s my main motive.

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Nicolelis Lab Research Timeline Monkey uses mind to move two virtual arms

Monkey thought made robot walk in Japan

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1990s

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2009

2008 f

Pioneering studies on brain-machine interfaces first published

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The first potential therapy to target the spinal cord instead of the brain, may offer an effective and less invasive approach for Parkinson’s disease treatment

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4 2013

Prototype neuroprosthetic enables wireless recording of 2,000 neurons across multiple cortical areas

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First brain-to-brain interface allows transmission of tactile and motor information between rats

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2014

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Exoskeleton World Cup Kick Off!

Credit: Laboratory of Dr. Miguel Nicolelis Design: Katrina Vokt, Gehua Tong Fall 2015 | Volume 5 | Issue 1 | 45

REFERENCES 1. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex, Nature Neuroscience, Volume 2, 664 - 670 (1999) John K. Chapin, Karen A. Moxon, Ronald S. Markowitz & Miguel A. L. Nicolelis 2. Controlling Robots with the Mind, Scientific American, Volume 18, 72 - 79 (2008) Miguel A. L. Nicolelis & John K. Chapin

3. Spinal Cord Stimulation Restores Locomotion in Animal Models of Parkinson’s Disease, Science, Volume 23, 1578 – 1582 (2009) Romulo Fuentes, Per Petersson, William B. Siesser, Marc G. Caron, Miguel A. L. Nicolelis

4. A Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys, Science Translation Medicine, Volume 5, 1-13 (2013) Peter J. Ifft, Solaiman Shokur, Zheng Li, Mikhail A. Lebedev, Miguel A. L. Nicolelis

5. Building an organic computing device with multiple interconnected brains, Sci. Rep. 5, 11869; doi: 10.1038/srep11869 (2015).

Miguel Pais-Vieira, Gabriela Chiuffa, Mikhail Lebedev, Amol Yadav & Miguel A. L. Nicolelis

6. Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys, Nature Methods, doi:10.1038/nmeth.2936 (2014) David A Schwarz, Mikhail A Lebedev & Miguel A L Nicolelis

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The Undergraduate Journal of Neuroscience

Neurogenesis Editors Editors-in-Chief

Parth Chodavadia

Lefko Charalambous

Publishing Editors

Syed Adil

Jennie Xu

Sagar Patel

Tannya Cai

Shreya Ahuja

Audra York

Shangnon Fei

Managing Editors

Katrina Vokt

Danielle Scarano

Devon DiPalma

Design Editors

Shivee Gilja http://www.neurogenesisjournal.org

Gehua Tong Fall 2015 | Volume 5 | Issue 1 | 47

Undergraduate Publications Board Duke University

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Neurogenesis is a member of the Duke Undergraduate Publication Board Š Copyright 2015

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