Neurogenesis Fall 2016

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Fall 2016 | Volume 6 | Issue 1 | 1

2 | Issue 1 | Volume 6 | Fall 2016

The Undergraduate Journal of Neuroscience

Volume 6 Issue 1 Fall 2016

Copyright © 2016

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Editorial Board Editors-In-Chief

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

Megbana Vagwala Class of 2018 meghana.vagwala@duke.edu

Design Team

Tannya Cai Class of 2016 tannya.cai@duke.edu

Gehua Tong Class of 2018

Publishing Editors

Riya Dange Class of 2019 riya.dange@duke.edu

Shaq Junaid Class of 2017 shaqif.junaid@duke.edu Tina Zhao Class of 2018 zirun.zhao@duke.edu

Shobana Subramanian Class of 2017 shobana.subramanian@duke. edu Kirsten Bonawitz Class of 2017 kirsten.bonawitz@duke.edu

Managing Editors

Jackson Xu Class of 2018 shengnan.xu@duke.edu Connor Hile Class of 2018 connor.hile@duke.edu Sarah Hakani Class of 2017 sarah.hakani@duke.edu 4 | Issue 1 | Volume 6 | Fall 2016

gehua.tong@duke.edu

Esther Liu Class of 2019 esther.liu@duke.edu Michelle Dalson Class of 2018 michelle.dalson@duke.edu Shivee Gilja Class of 2017 shivee.gilja@duke.edu

Faculty Advisor Leonard White, Ph.D. Duke University School of Medicine Director of Education Duke Institute for Brain Sciences len.white@duke.edu

*We would like to thank the John Spencer Bassett Memorial Fund for their generous support of this publication.

The Undergraduate Journal of Neuroscience

Letter from the Editors The human mind is truly extraordinary, having more than 70,000 thoughts a day. It is capable of innovation, brilliance, creativity, and most astonishingly, an awareness of itself. In fact, it is the only organ capable of asking questions about itself, and this is what Dr. Jeff MacInnes, a postdoctoral associate at the Center for Cognitive Neuroscience at Duke University, explores in his portrait series titled, Brain Boxes. Our cover features nine such boxes—beginning with a depiction of the brain in its own environment, the subsequent pages of this journal explores the brain’s interactions within, and beyond, its own box.

Neuroscience is an inherently interdisciplinary subject as the brain is involved in nearly everything we do. The list of neuroscience subfields is far reaching and continues to grow, including topics like neuropharmacology, neuroeconomics, neuroimmunology, neurotheology, neuroinformatics, and neuroethics. The interdisciplinary nature of the field is highlighted in this issue of Neurogenesis—you will find topics ranging from the biochemical and developmental mechanism underlying adult heroin abuse and addiction, to the neuroscience of adolescent impulsivity and its legal implications, and even to the relation between visual-motor abilities and high performance in sports.

Furthermore, we would like to bring your attention to our two featured articles. Have you ever wondered how memories that you made long ago in elementary and middle school are currently affecting your ability to regulate your emotions? Julia Kozolwski’s featured review on the relationship between long-term memory and emotion regulation helps to elucidate the answer to that question. In Stellina Lee’s featured article on the visual primacy of food, you will learn about the neurological mechanism underlying the Internet ‘food porn’ sensation.

As you flip through the journal, you will notice work from several different universities. One of the main goals of Neurogenesis is to provide a platform for all undergraduate neuroscience researchers to share their findings with the rest of the world. This is why we are excited to share with you writing from students at universities across the country, including from Duke, Emory, and New York University (NYU). It is truly an honor to collaborate with student researchers from these different research institutions in exploring the commonality of our goal: a greater understanding of the brain.

Finally, we would like to thank Jake Stauch for his time in discussing his work with Neurospire and NeuroPlus. As a young entrepreneur who attended Duke, Stauch has made leaps in merging neuroscience, business and marketing. Game-changers like Stauch highlight the interdisciplinary nature of the field, that work in neuroscience and the study of the command center of our lives is as much applicable to medicine and academia, as to a wide range of, at times unexpected, fields. We sincerely hope that the pages before you inspire your love of unravelling the mysteries of the brain, just as we have been inspired by the leaders in neuroscience today.

Sincerely, Katrina Vokt & Tannya Cai Editors-in-Chief

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The Undergraduate Journal of Neuroscience

Neurogenesis

TABLE OF CONTENTS ARTICLES ARTICLES 8

Visual-motor Abilities as Indicators of On-Field Game Performance Kelly Vittetoe

REVIEWS 14

Seeing before Eating: TheVisual Primacy of Food Yun-Hsuan Lee

18

The Relationship between Long-Term Memory and Emotion Regulation Julia Kozlowski

24

From Herb to Heroin : How Chronic Adolescent THC Exposure May Facilitate Heroin Abuse and Addiction In Adulthood Mark Robles-Long

OPINION OPINION 31

The Neuroscience of Adolescent Impulsivity and its Legal Implications Julie Uchitel

INTERVIEW INTERVIEW 36

Meet Former Duke Student and Neuromarketing Expert Jake Stauch Connor Hile

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Neurogenesis

ARTICLE

Visual-motor Abilities as Indicators of On-Field Game Performance Kelly Vittetoe1 Duke University, Durham, NC 27708 Correspondence should be addressed to kelly.vittetoe@duke.edu 1

Visual-motor skills, a specific type of sensorimotor abilities, are important for athletic performance.

This study investigates visual-motor abilities as they relate to sport performance in two cohorts: collegiate baseball players and professional basketball, baseball, football, and hockey players. Secondary data analysis was performed on nine sensorimotor tasks constituting the Nike Sensory Station assessment battery in conjunction with on-field game statistics and elite athletic status. Correlation matrices and regression models showed no significant association between visual-motor abilities and game performance in the collegiate baseball players studied. However, using binary logistic regressions for the professional athlete cohort, the Near-Far Quickness and Go/No-Go tasks were both found to be significantly predictive of an athlete’s status as elite or sub-elite. These results suggest that elite athletes are distinguished from their sub-elite counterparts in part by visual-motor abilities, a conclusion that can be used to both support and refine sports vision training programs.

INTRODUCTION

Sensorimotor abilities, specifically visual-motor skills, are crucial in deciphering environmental cues, anticipating events, and initiating appropriate motor responses to interact effectively with one’s surroundings. Such abilities are of particular importance in situations in which efficient and precise response to one’s environment can have a substantial impact on outcomes, for example: high stakes game time decisions. A meta-analysis conducted by Mann and colleagues investigated this decision-making ability in athletes and concluded that experts are more accurate in decision-making relative to non-expert athletes, which was attributed to experts’ ability to extract more task-relevant information in each visual fixation (Mann et al, 2007). While additional studies have also shown that visual-motor skills are heightened in more advanced athletes (Starkes & Ericsson, 2003; Williams, Davids, & Williams, 1999), there is currently little quantitative evidence that connects enhanced visual-motor skills with better on-field game performance. If such a relationship exists, it could be 8 | Issue 1 | Volume 6 | Fall 2016

utilized to not only predict athletic performance, but also to improve performance through training focused on visual-motor skills.

Quantifiable metrics for dynamic vision and visual-motor control were measured in the current study using the Nike Sensory Station, a computerized assessment tool intended for measuring and improving sports-relevant sensorimotor skills. Previous research with this device has demonstrated that the nine sensorimotor tasks offered on the Station provide reliable measures that can be used to examine sensorimotor abilities relative to performance in practical tasks (Erickson, Citek, & Cove, 2011).

The current experiment investigates two cohorts of athletes: Division I collegiate baseball players and high-performing professional basketball, baseball, football, and hockey players. This investigation intends to determine the scope to which visual-motor skills predict on-field performance in these athlete populations. Previous research using various sports vision training (SVT) methods such as Dynavision, Tachistoscope, Brock String, Eyeport, Rotary, The Undergraduate Journal of Neuroscience

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Strobe Glasses, and Saccades with Division I baseball players at the University of Cincinnati showed improvements in batting averages and slugging percentages following six weeks of SVT prior to the start of the season (Clark et al., 2012). Similarly, the current study will focus in part on the performance of Division I collegiate baseball players, but rather than using SVT to improve specific measures of game performance, this study will analyze Sensory Station performance in conjunction with measures of athletic performance and achievement to test for the existence of significant relationships between visual-motor skills and athletic performance. If a predictive relationship between quantifiable visual-motor abilities and on-field game performance exists, it could have a substantial impact on sports vision training and revitalize fundamental visual-motor training as it relates to sport performance.

METHODS

Participants In the first analysis, Sensory Station data as well as on-field game statistics were collected for 51 male varsity baseball players from three Division I universities: Duke University, the University of North Carolina at Chapel Hill, and the University of Texas. Following selection for a minimum number of 60 at-bats in the season prior to being tested on the sensory station, the participant pool became 24 batters between the ages of 18 and 24 (mean = 20.96; SD = 1.94).

In the second analysis, 104 professional basketball, football, baseball, and hockey players tested at the Michael Johnson Performance Center were classified as elite or sub-elite in accordance with their league affiliation. Athletes playing in the most competitive league for their sport (NBA, NFL, NHL, MLB) were classified as elite, while athletes in other professional leagues were classified as sub-elite.

Within the sample, 28 athletes were classified as elite and 76 were classified as sub-elite.

ed on a staircase system in which subsequent stimulus difficulty increased following a correct response and decreased following an incorrect response. The task was considered complete when participants scored two correct and two incorrect responses for two contiguous difficulty levels.

The Visual Clarity task measures both monocular and binocular visual acuity for fine details at a distance using a black landolt ring – an incomplete ring with a small gap oriented in one of the four cardinal directions. Participants were asked to swipe on the iPod in the direction that corresponded to the orientation of the gap in the ring.

The Contrast Sensitivity task measures the minimum resolvable difference in contrast at a distance.

Participants were presented with four black rings on a light gray background and asked to indicate which ring contained a pattern of dark gray concentric circles by swiping on the iPod in the direction corresponding to the patterned circle.

The Depth Perception task measures how quickly and accurately participants are able to detect differences in depth at a distance using liquid crystal glasses (NVIDIA 3D Vision, Santa Clara, CA), facing right, left, and forward. Similar to the contrast sensitivity task, four black rings were presented, and participants were asked to swipe in the direction of the ring that appeared to have depth as seen through the glasses.

The Near-Far Quickness task measures the number of near and far targets that can be correctly reported in 30 seconds. Participants aligned the top of the iPod with the bottom edge of the large monitor, then swiped in the direction of the gap in

Figure 1: A) Nike SPARQ Sensory Station Kiosk. B) Illustrations of the 9 sensorimotor tasks in the battery. # indicates that sensitivity thresholds were measured using a staircase procedure. Reproduced with permission from author.

Sensorimotor Assessments The Nike Sensory Station consists of a battery of nine computerized visual-motor tasks, each designed to evaluate a specific facet of a participant’s visual-motor ability. A frontal view of the station as well as illustrations for each task can be found in Figure 1. The first five tasks were completed using a handheld Apple iPod Touch, standing 4.9 m from the station. The Visual Clarity, Contrast Sensitivity, Depth Perception, and Target Capture tasks operathttp://www.neurogenesis-journal.com

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ARTICLE | Visual Motor Abilities as Indicators of Game Performance

Figure 2: Histograms for the nine Sensory Station tasks, shown with means, standard deviation, and N-values for each task.

the landolt ring that appeared on either the iPod or larger monitor screen. Participants were instructed to respond as quickly as possible, and the ring only moved from one screen to another following a correct response, so participants continued to answer until they answered correctly.

The Target Capture task measures the speed at which participants can shift attention and recognize peripheral targets. A small black landolt ring was briefly presented in one of the four corners of the monitor, and participants were asked to swipe on the iPod in the direction corresponding to the gap in the ring.

The Perception Span task measures the capacity of spatial working memory. As participants stood at arm’s length from the monitor, a grid of empty black circles was presented, and in each trial specific circles were briefly filled with green dots that disappeared after several seconds. Participants were then asked to recreate the pattern by touching the circles that had previously contained the green dots.

The Eye-Hand Coordination task measures the 10 | Issue 1 | Volume 6 | Fall 2016

speed at which participants can make visually guided hand responses to rapidly changing targets. A grid of 48 evenly spaced black rings was presented on the screen, and when a green dot appeared in a ring, participants touched the dot as quickly as possible. The dot then relocated to another ring for a total succession of 96 dots.

The Go/No-Go task measures the ability to execute and inhibit visually guided hand responses in the presence of “go” and “no-go” stimuli. Similar to the previous task, a grid of 48 rings was presented; however, in this task the dots could appear either green or red. Participants tried to touch the green dots as quickly as possible while avoiding red dots.

Each dot was presented for only 500 ms before disappearing.

The Response Time task measures how quickly participants react and respond to a simple visual stimulus. Two larger rings were shown on each side of the large monitor, and participants began with their dominant hand in the “starting” ring on the side of the screen corresponding with their dominant hand, while their body was oriented in front of The Undergraduate Journal of Neuroscience

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the “landing” ring on the opposite side of the screen.

When the landing ring turned green, participants moved their hand from the starting ring to the landing ring as quickly and accurately as possible for a total of seven separate trials, which were averaged to obtain the Response Time score.

Game Statistics and Data Analysis On-field game statistics were acquired through collaboration with the athletic departments at Duke University, the University of North Carolina, and the University of Texas. Statistics were obtained for both pitchers and hitters and consisted of the following measures: at bats, batting average, hits, singles, doubles, triples, runs, homeruns, RBI, total bases, slugging percentage, walks, GDP, on base percentage, stolen bases, and stolen base attempts.

RESULTS

Analysis 1: Collegiate Baseball Players The data for the 24 baseball hitters who met the minimum at-bat requirement (60 at-bats on the season) were analyzed in conjunction with the data acquired on the Sensory Station. Initial separate analyses of the descriptive statistics and sensorimotor data showed means and distributions similar to those in previous studies using the Sensory Station (Wang et al., 2015). Histograms detailing the distribution, mean, and standard deviation for each of the nine Sensory Station tasks can be found in Figure 2.

The primary objective of this analysis was to assess for relationships that might exist between visual-motor abilities tested on the Sensory Station and on-field game performance measured by game statistics. To start, correlation matrices were created between the nine sensorimotor tasks and onfield game statistics. Results for these matrices for batting average and total bases are included in Table 1. As seen in the table, no individual correlation reached significance when held up to correction for multiple comparisons with Bonferroni adjusted alpha (α/9 = .0055). Further analyses included separate regression models in which batting average and total bases were taken as the dependent variables. With the nine sensory station tasks acting as predictors, neither the model with batting average as the dependent variable [F(10,20)=.282, p=.971, R=.469], nor the model with total bases as the dependent variable [F(10,20)=1.40, p=.302, R=.764] yielded a significant result in ANOVA. While the R value for the regression model with total bases as http://www.neurogenesis-journal.com

Table 1: Correlations between the nine Sensory Station tasks and Batting Average, Total Bases.

Task

Statistical Results

Batting Total Average Bases

Visual Clarity

Pearson Correlation Sig. (2-tailed) N

-0.023 0.917 23

0.262 0.28 23

Depth Pearson Perception Correlation Sig. (2-tailed) N

0.031 0.891 22

0.223 0.319 22

Contrast Sensitivity

Pearson Correlation Sig. (2-tailed) N

Near-Far Quickness

Pearson Correlation Sig. (2-tailed) N

-0.162 0.448 24

-0.235 0.268 24

Perception Pearson Span Correlation Sig. (2-tailed) N

0.01 0.964 24

0.042 0.846 24

Go-No-Go

0.213 0.317 24

Target Capture

Pearson Correlation Sig. (2-tailed) N

Eye-Hand Coordination

Pearson Correlation Sig. (2-tailed) N

Response Time

Pearson Correlation Sig. (2-tailed) N

Pearson Correlation Sig. (2-tailed) N

0.078 0.716 24

0.123 0.567 24

0.014 0.949 24

0.019 0.931 24

0.23 0.28 24

0.461* 0.023 24

-0.189 0.375 24

0.154 0.473 24

-0.157 0.463 24

the dependent variable was high (R=.764), neither p-value was significant and therefore no relationship could be concluded. In a principal component analysis across the Sensory Station measures, 70% of the total variance could be explained by four components. A principal component analysis of game statistics revealed three components that accounted for 81.8% of the variance. A correlation matrix between the three REGR factors for game statistics and four REGR factors for Sensory Station measures yielded no significant results using Bonferroni adjusted alpha (α/12 = .0042).

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ARTICLE | Visual Motor Abilities as Indicators of Game Performance

Analysis 2: Elite Vs. Sub-Elite Professional Athletes Binary logistic regression models were created for the professional athletes tested at the Michael Johnson Performance Center, using classification as elite or sub-elite as the categorical dependent variable.

The first model included performance on all nine Sensory Station tasks as independent variables. The second model included performance on all nine tasks as well as age and height for each athlete.

Model 1: x2 = 18.363; df = 9; p = .031*; Nagelkerke R2 = .235 Model 2: x2 = 21.407; df = 11; p = .029*; Nagelkerke R2 = .270

While including age and height in the model increases the R2 of the model, neither metric is an individually significant predictor of elite athletic status. Wald test values and p-values for each of the nine Sensory Station tasks as predictors of elite status are included in Table 2. Broken down by task, these results indicate that the Near-Far Quickness and Go/No-Go tasks are both significantly predictive of an athlete’s status as elite or sub-elite.

DISCUSSION

This study sought to investigate the relationship between visual-motor performance and athletic achievement through two modes of analysis. The first examined performance on sensorimotor tasks in conjunction with on-field game statistics for collegiate baseball players. The second considered status as an elite athlete and how performance on sensorimotor measures might be indicative of elite status. No significant relationships were found between visual-motor performance and game statistics for the cohorts of baseball players included in this study. This could be due in part to the relatively small, homogenous sample sizes studied, as well as a need for more comprehensive game statistics such as player efficiency ratings, which were unavailable for the current investigation. However, there do exist significant predictive relationships between visual-motor abilities and elite athletic status, which suggests that such abilities are predictive of athletic success, at least at the professional level. Specifically, better performance on Near-Far Quickness and Go/No-Go tasks are predictive of elite professional status. The Near-Far Quickness task tests an athlete’s ability to shift his or her locus of visual focus quickly, while the Go/No-Go task requires an athlete to make a decision to initiate or desist movement in 12 | Issue 1 | Volume 6 | Fall 2016

Table 2: Wald test values and significance for each Sensory Station task as a predictor of elite athletic status.

Sensory Station Task

Wald

Significance

Visual Clarity

0.029

0.864

Near-Far Quickness

5.028

0.025*

Contrast Sensitivity Depth Perception Target Capture

Perception Span Eye-Hand Coordination Go/No-Go

Response Time

2.178 2.388 1.308 2.615 2.717 4.164 0.872

0.140 0.122 0.253 0.106 0.099

0.041* 0.350

response to a visual cue. While this investigation did not specifically examine the neural underpinnings of such abilities, previous studies can offer insight into possible neurological differences between elite and sub-elite athletes. The neural reorganizations coincident with athletic expertise reflect optimization of neurocognitive resources to best manage the computational load required to perform at the highest level (Debarnot, 2014). A study of golfers found that the disparity in performance between elite and novice golfers lies at the level of the functional organization of neural networks during motor planning (Milton, 2007). Specifically, increases in speed and accuracy in motor task performance are associated with changes in the primary motor cortex that resemble changes seen in the primary visual cortex during perceptual learning. Additionally, elite athletes are thought to have more highly trained decision circuits, allowing them to make quicker and more accurate choices, a skill imperative for performing well on the Go/No-Go task (Yarrow, 2009). Future studies should investigate interactions between game performance, elite status, and sensorimotor abilities to further delineate the visual and motor abilities that underlie high achievement in sports, as well as the neural mechanisms that facilitate such performance and achievement.

The Undergraduate Journal of Neuroscience

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REFERENCES

Abernethy, B., & Wood, J. M. (2001). Do generalized visual training programmes for sport really work? An experimental investigation. J Sports Sci, 19(3), 203-222.

Clark, J. F., Ellis, J. K., Bench, J., Khoury, J., & Graman, P. (2012). High-Performance Vision Training Improves Batting Statistics for University of Cincinnati Baseball Players. PLoS ONE, 7(1).

Davids, K., Savelsbergh, G., Bennett, S., & Van der Kamp, J. (2002). Interceptive actions in sport: Information and movement. London: Routledge.

Debarnot, U., Sperduti, M., Di Rienzo, F., & Guillot, A. (2014). Experts bodies, experts minds: How physical and mental training shape the brain.

Frontiers in Human Neuroscience, 8, 280.

Erickson GB, Citek K, Cove M, et al. Reliability of a computer- based system for measuring visual performance skills. Optometry.

2011;82:528-542.

Laby, D. M. (2014). Sports Vision. Focal Points, XXXII(8).

Mann, D., Williams, A. M., Ward, P., & Janelle, C. M. (2007). Perceptual-Cognitive Expertise in Sport. Journal of Sport and Excercise Physiology, 29, 457-478.

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Milton, J., Solodkin, A., Hlustik, P., and Small, S. L. (2007). The mind of expert motor performance is cool and focused. Neuroimage 35, 804– 813.

Poltavski D, Biberdorf D. The role of visual perception measures used in sports vision programmes in predicting actual game performance in Division I collegiate hockey players. Journal of Sports Sciences.

2015;33:597-608.

Starkes, J. L., & Ericsson, K. A. (2003). Expert performance in sports: Advances in research on sport expertise. Champaign, IL: Human Kinetics.

Wang, L., Krasich, K., Bel-Bahar, T., Hughes, L., Mitroff, S. R., & Appelbaum, L. G. (2015). Mapping the structure of perceptual and visual–motor abilities in healthy young adults. Acta Psychologica, 157, 74-84.

Williams, A. M., Davids, K., & Williams, J. G. (1999). Visual perception and action in sport. London: E & FN Spon.

Yarrow, K., Brown, P., & Krakauer, J. W. (2009). Inside the brain of an elite athlete: the neural processes that support high achievement in sports. Nat Rev Neurosci, 10(8), 585-596.

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Neurogenesis

REVIEW

Seeing before Eating: TheVisual Primacy of Food Yun-Hsuan Lee1 1 Emory University, Atlanta, GA 30322 Correspondence should be addressed to Yun-Hsuan Lee (yun-hsuan.lee@emory.edu)

From predation to high-class food tasting, the visual aspect of food experience has partaken a major role throughout evolution in primates and especially in humans. This paper investigates the visual primacy of food in relating food evaluation to actual food consumption. Specifically, this paper argues that the visual primacy of food perception influences brain and behavior, which mediates the following food consumption experience. Food perception is a multisensory experience, yet humans demonstrate visual primacy toward food. The effect of visual primacy controls behavior physiologically via visual attention and the reward circuitry. As well, visual presentation of food elicits sensory simulation of food which influences our feeling of satiety. As “food art” and “food porn” gradually emerges in our current culture, our exposure to images of desirable food unveils future direction in investigating visual primacy of food. This paper thus could provide comprehensive insight into various fields including health, business, and the overall well-being of society.

Food has assumed an integral role throughout evolution with its ability to provide necessary nutrients and energy for survival. The ability to detect food in predation is rudimentary for a species in terms of reproductive success and evolutionary fitness. In humans, while the food experience integrates multisensory information such as vision, taste, smell, and flavor, visual perception primes and could alter food perception of other modalities. The phrase commonly used by master chefs, “We eat first with our eyes”, denotes the evolution of food from a biological demand to an aesthetic experience (Delwiche, 2012).

The visual perception of food elicits neural activities and subsequent cognitive responses. The mechanism in which visual primacy of food affects brain and behavior involves differential physiological responses. Specifically, the visual appeal of food influences visual attention and the reward circuitry physiologically during food evaluation, which in turn mediates actual food consumption and cycles of hunger (Spence, 2015). Furthermore, sensory simulation of food underlies the mechanism of satiation, where visual perception necessitates imagined consumption of food (Larson, 2014). This paper thus considers the visual primacy of food in its 14 | Issue 1 | Volume 6 | Fall 2016

effects on psychological, physiological, and behavioral responses, aiming to provide comprehensive insight into marketing, health, and research fields.

In doing so, this paper posits that the visual primacy of food perception influences brain and behavior during food evaluation which mediates actual food consumption. The visual primacy of food denotes vision precedence before other modalities in food perception, as well as the weighted importance of vision through its ability to bias perceptions of other modalities. This paper focuses on three approaches-psychological, physiological, and behavioral- toward the visual primacy of food experience: 1. The visual processing of food precedes other modalities and exerts a dominating effect on the overall food perception psychologically.

2. Increased neural activity is correlated with visual-appealing food stimuli in controlling behavior physiologically via visual attention and reward circuitry.

3. Visual presentation of food leads to sensory simulated satiation, which in turn modulates food consumption behavior.

The Undergraduate Journal of Neuroscience

Lee| REVIEW

VISUAL PRIMACY OF FOOD AND ITS PSYCHOLOGICAL EFFECTS

As an evolutionary adaptation, primates exhibit visual primacy when detecting food sources. Humans especially utilize their increased visual acuity in food foraging, where vision predominates other sensory modalities in food perception (Bompas et al., 2013). Color, being a crucial aspect of visual perception, has long been identified as an intrinsic sensory cue dominating perception. (Spence, 2015.) Thus, this paper will be focusing on color as a parameter in the psychological approach towards the visual primacy of food.

Morrot, Brochet, & Dubourdieu (2001) conducted a wine tasting experiment and analyzed the psychophysical data through flavor ratings to investigate the effect of coloring on odor perception. 54 wine experts were recruited and asked to describe a white wine colored with anthocyanins, an odorless, red food coloring. The results showed that participants used description typically used for red wine such as plum, strawberry, and currant.

When asked to describe the same white wine that was uncolored, those wine experts used yellow and clear descriptions such as lemon, pineapple, and hay. This finding suggests that the coloring of wine creates a perceptual illusion of food perception. The effect of coloring in biasing food perception extends to other food and beverages, such as table jellies, sherbet, and noncarbonated, fruit-flavored beverages. (Delwiche, 2004). Subjects show higher error rates in identifying miscolored food items than appropriately colored food items, suggesting the precedence and dominance of color in food identification psychologically.

Pangborn and Berg (1963) related the ability to associate color intensity and flavor perception to a learned ability which differentiated experts and novices. The study invited 2 groups of participants to a wine-tasting panel: naïve wine-tasters and wine-tasting experts. Food colorings were added to dry, white table wine to simulate 5 different wine types: rosé, sauterne, sherry, burgundy, and claret. Participants were asked to judge the wine and rate their sweetness. The experienced wine tasters showed a difference in sweetness rating in response to wine with different colors, whereas novices did not exhibit differences in perceived taste. In addition, experienced wine tasters demonstrated a positive correlation of color intensity and perceived flavor intensity. Parr et al (2003) supported the color dominance effect amongst professional wine http://www.neurogenesis-journal.com

tasters and wine makers. Wine experts and novices were asked to discriminate aged white wine and red wine through smell. Mis-colored wine was presented in opaque (black) and clear glasses, respectively, and participants were instructed to sniff the wine.

The opaque glass was employed so participants would not be able to see the color of the wine. The results showed that wine experts manifested worst aroma judgments when the mis-colored wine was presented in the clear glass, supporting the effect of visual primacy on flavor perception.

Novices, on the other hand, demonstrated indiscriminate behavior for miscolored wine and reported difficulty in distinguishing the wine when based solely on the aroma. The intensified color-flavor association in experts underscored the visual primacy of food perception. Since experiences denote the experts’ expectations of other cross-modal perception of the wine, experts exhibit visual primacy when perceiving food as a multisensory experience.

Provided that visual presentation of food accounts for much of food perception in expertise, the visual primacy of food perception might be reinforced through developed expertise to expect the overall food perception. Thus, the visual primacy not only precedes processing of other sensory modalities but is also capable of biasing food perception cognitively in influencing behavior.

PHYSIOLOGICAL RESPONSES ELICITED BY APPEALING FOOD VISUAL STIMULI

As the visual primacy of food elicits differential neural and cognitive activities, this paper is interested in relating food perception to actual consumption through changes in physiological states. In particular, the visual primacy of food perception controls behavior via its effect on attention and the reward circuitry. Wang et al (2004) conducted a representative PET (Positron emission tomography) study and found that the presentation of appealing food stimuli to food-deprived participants led to a 24% increase in whole brain metabolism. The marked metabolism increase in the brain with appealing food visual stimuli is evidence of the human brain’s sensitivity to food stimuli. Laan et al (2011.) further investigated the visual presentation of food vs nonfood images, in which participants exhibited neural activation in the bilateral posterior fusiform gyrus, the left lateral orbitofrontal cortex (OFC), and the left middle insula. The increased neural correlates require active recognition of food as a visual stimulus, inferring implicit knowledge as a prerequisite Fall 2016 | Volume 6 | Issue 1 | 15

REVIEW | The Visual Primacy of Food

in modulating visual sensitivity. The degree of hunger also affected brain activity, where response to food images were modulated in the right amygdala and left lateral OFC. Additionally, the energy content of the food mediated neural activities in the hypothalamus and ventral striatum, suggesting that human’s visual sensitivity to different energy content in food is modulated by physiological state in addition to implicit knowledge.

The visual representation of food not only affects visual attention, but also influences behavior via the reward circuitry in the limbic system, which instructs behavior and serves as an important determinant for motivation (Gazzaniga, 1988).

Specifically, visual presentation of food increases appetitive reward, in which reward anticipation occurs during food evaluation, as compared to consummatory food reward (Stice et al, 2009). Harrar et al (2011) presented different visual stimuli, each with different item presented on the same white plate: high fat, low fat, or non-food images. The high fat images included items such as pizza and fried chicken, the low fat images included tomatoes and cucumbers, and the non-food images included forks and knives. The images were used as cues to direct participants’ attention to the left or the right, black circles that serve as visual targets are then presented either above or below the cues and participants were to made elevation discrimination responses.

Harrar reported that participants’ response was significantly faster when high fat food images were shown versus low fat or non-food images. Similar results were seen for visual cues of high-carbohydrate food items compared to low carbohydrate visual stimuli. These findings support the rapid processing of high energy value, or in a more general sense, the visual appeal of food.

Ohla et al. (2012) conducted an electrical neuroimaging study where high- and low- calorie food visual cues were used to investigate the neural processes and taste perception. A small current was applied to the tongue to present a neutral electric taste. Cognitively, participants reported a more pleasant taste when viewing high-calorie food images compared to low-calorie images with the same neutral taste. With regards to brain activity, increased taste- evoked neural activity in the bilateral insula and adjacent frontal operculum (FOP) was found in the high-calorie visual cues compared to the low-calorie images. Additionally, taste pleasantness is correlated with increased anterior cingulate (ACC) and OFC activity, suggesting visually appeal16 | Issue 1 | Volume 6 | Fall 2016

ing food stimuli’s effect on neural activities through the reward circuitry.

As visually appealing food stimuli preferentially direct visual attention and increases motivation through the reward circuitry, the effect of visual primacy of food during food evaluation may be responsible for subsequent eating behaviors. Dimitropoulos et al (2012) proposed that during fasting, differential neural activation in response to food vs. non-food images was shown in obese individuals, in which responses towards high- calorie food stimuli were especially prominent. Obese individuals demonstrated increased neural responses in the insula and OFC, which is associated with reward processing and anticipation. Conversely, healthy participants showed increased activation in brain regions associated with cognitive control such as the prefrontal cortex (PFC), and decision making such as the OFC, PFC, and thalamus. Petit (2014) extended this finding in directly associating the visual stimulation of food with reward circuitry and satisfaction. In his experiment, participants viewed images of healthy food. When viewing the images, one group was asked to anticipate the pleasure of eating what was in the image and another group was instructed to associate the potential health benefits with eating it. In the group that was asked to anticipate pleasure, there was greater activation in brain areas associated with cognitive control (inferior frontal gyrus) and the reward expectation (insula, OFC) in participants with higher BMI than lean individuals. Visual appealing food stimuli, therefore, can induce changes in neural activities during food evaluation that mediates subsequent consumption behaviors. The physiological mechanism by which the visual primacy of food acts on brain and behavior is mediated through visual attention and reward circuitry.

VISUAL PRESENTATION ALONE MODULATES FOOD CONSUMPTION BEHAVIOR

While the visual primacy of food influences brain and behavior in coupling food evaluation and its subsequent food consumption, visual presentation alone is able to modulate physiological responses.

Rolls et. al. (1981) introduced the concept of sensory-specific satiety, that repeated presentation of a given food leads to satiation. In the experiment, participants were divided into 2 groups and asked to rate the food according to how appetizing it was.

One group was presented with pictures of sweet food such as chocolates, candies, and truffles, while The Undergraduate Journal of Neuroscience

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another group was presented with pictures of salty food such as French fries, chips, and pretzels, but no nuts. After rating the food, participants were given 3 peanuts and asked whether they enjoyed eating the peanuts. Results show that participants presented with the salty food images reported decrease enjoyment when eating the peanuts. On the other hand, there was no change in enjoyment rating for participants that were shown the sweet food images.

The researchers proposed that sensory simulation of salty food decreased pleasure and produced sensory-specific satiation to similar effect as actual consumption of food. This finding supported that visual presentation alone is capable of mediating physiological satiation, therefore affecting the consequent food consumption behavior.

The effect of visual perception related to consumption in mediating sensory simulated satiety not only pertains to the food itself but also the presentation of food. Kim and Chang (2015) designed an experiment relating visual perception of consumption norms to satiety by using modified soup bowls with elevated bottoms. Healthy subjects were recruited and divided in two groups: half of the participants drank soup from the original, unchanged soup bowls (250g), while the other half drank the same soup in the modified bowls (180g). The two subjects reported similar levels of satiation despite the fact that one group drank significantly less soup and had a lower energy intake. This suggests that the visual perception of amounts of food can influence satiation.

REFERENCES

Acree, T.E., American Chemical Society (ACS). (2013, April 11).

‘Seeing’ the flavor of foods before tasting them. ScienceDaily.

Retrieved April 22, 2016 from www.sciencedaily.com/releases/2013/04/130411194017.htm Bompas, A., Kendall, G., Sumner, P. (2013). Spotting fruit versus picking fruit as the selective advantage of human colour vision. i-Perception, 4 (2013), pp. 84–94 Coary, S., & Poor, M. (2016). How consumer-generated images shape important consumption outcomes in the food domain. Journal of Consumer Marketing, 33(1), 1-8. doi:10.1108/jcm-02-2015-1337 Delwiche, J. (2004). The impact of perceptual interactions on perceived flavor. Food Quality and Preference, 15(2), 137-146.

Delwiche, J. F. (2012). You eat with your eyes first. Physiology & Behavior, 107(4), 502-504.

Dimitropoulos, A., Tkach, J., Ho, A., Kennedy, J.(2012) Greater corticolimbic activation to high-calorie food cues after eating in obese vs. normal-weight adults. Appetite, 58(1):303–12. doi:10.1016/j.appet.2011.10.014 Gazzaniga, M. S., Ivry, R. B., & Mangun, G. R. (1998). Cognitive neuroscience: The biology of the mind. New York: W.W. Norton.

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Scheibehenne et al. (2010) also supported the importance of visual perception of food in mediating satiation. Participants were served a two-course meal in a “dark” restaurant. Half of the participants received a larger portion of food without being told.

Directly after the main course, participants were directed to a self-served dessert presented in the light. Participants then filled out a questionnaire to report their satiation level. Results showed that there is no significant difference amongst the experimental versus the control group regarding either their appetite for dessert or their subjective satiation. This finding suggests the visual perception of food as a main mechanism in mediating satiation.

Therefore, the visual primacy of food not only influences brain and behavior in relating food evaluation to consumption, but can mediate satiation and regulate eating behavior by itself.

CONCLUSION

The visual primacy of food precedes and dominates other modalities in food perception psychologically. In addition to altering cognition, visual primacy of food is also correlated with increased neural activity via visual attention and reward circuitry. The effect of visual presentation alone is able to mediate physiological satiation proposes insight into health and marketing fields. The implication of research of this area thus has various potential in the current society which focuses on physical and mental well-being.

Harrar,V., Toepel, U., Murray, M., Spence, C. (2011). Food’s visually-perceived fat content affects discrimination speed in an orthogonal spatial task. Experimental Brain Research, 214, pp. 351–356 Johnson, J.L., Dzendolet, E., Clydesdale, F.M. (1983) Psychophysical relationships between sweetness and redness in strawberry-drinks, Journal of Food Protection, 46 (1), pp. 21–25 Kim, Y., & Chang, U. (2015). Effects of Food Consumption Monitoring Using Modified Rice Bowls on Food Intake, Satiety Rate, and Eating Rate. Journal of the Korean Dietetic Association, 21(3), 194-202.

Larson, J., Redden, J., & Elder, R. (2014). Satiation from Sensory Simulation: Evaluating Foods Decreases Enjoyment of Similar Foods. PsycEXTRA Dataset.

Morrot, G., Brochet. F., Dubourdieu., (2001). The color of odors. Brain Lang, 79, pp. 309–320 Michel, C., Velasco, C., Gatti, E., & Spence, C. (2014). A taste of Kandinsky: Assessing the influence of the artistic visual presentation of food on the dining experience. Flavour, 3(1), 7. doi:10.1186/2044-7248-3-7

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The Relationship between Long-Term Memory and Emotion Regulation Julia Kozlowski1 1 Duke University, Durham, NC 27708 Correspondence should be addressed to julia.kozlowski@duke.edu

Recent research has demonstrated the powerful influence emotion regulation can have on the neural circuitry involved in the encoding and retrieval of long-term declarative memory. This review evaluates studies that investigate the relationships between emotion and memory, emotion regulation and memory, and what goes wrong with emotion regulation and memory in different psychological disorders. The current findings demonstrate a complex relationship between emotion regulation and declarative memory, in which different strategies of emotion regulation can be either beneficial or detrimental to memory performance. Emotion regulation strategies that enhance the neural pathways involved in encoding, such as reappraisal, can improve memory, while strategies such as suppression inhibit these pathways and impair memory. The impact of emotion regulation on memory may explain the memory deficits seen in psychiatric disorders that involve impaired ability to regulate mood.

EMOTION AND MEMORY

While it is clear that emotion and memory influence each other, the exact nuances of the relationship have yet to be fully understood. For centuries, scientists have tried to tease out how our emotional experiences impact the encoding and recall of memories. One hypothesis regarding the interactions between emotions and memory is the modulation hypothesis, which states that emotion improves long-term memory due to amygdala activation in response to emotional stimuli. This activation influences the encoding and consolidation processes of the hippocampal formation (Dolcos, LaBar, and Cabeza, 2004). In 2004, Dolcos, LaBar, and Cabeza looked for evidence of the modulation hypothesis by investigating brain activation in response to the encoding and recall of either emotional or neutral stimuli. They discovered that emotionally arousing pictures were associated with significantly better memory performance as well as medial temporal lobe activation during encoding. Overall, the findings of this study were consistent with the hypothesis that the amygdala interacts with the 18 | Issue 1 | Volume 6 | Fall 2016

hippocampal formation to improve the processing and encoding of emotionally arousing stimuli. In support of this, a study by Erk, von Kalckreuth, and Walter (2010) found that participants were better able to accurately recall negative pictures than neutral pictures and that this enhanced recall ability is correlated with greater activation of the amygdala.

The memory-enhancing effect of emotion has also been demonstrated within modalities other than vision, in one case by pairing picture slides with narrated stories (Frank and Tomaz, 2000).

Two subject groups looked at the same picture slides but listened to different narrated stories that corresponded with the images on the slide. One group heard a story that was emotionally arousing while the other group heard one that was neutral.

The subjects who listened to the emotional story had significantly better recall for details of the story than the neutral group. Altogether, this study reveals that emotionally arousing content in narrated stories can improve declarative memory (Frank and Tomaz, 2000).

Despite all of the evidence that demonstrates the The Undergraduate Journal of Neuroscience

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beneficial effect of emotion on declarative memory, there is also research that suggests that emotional arousal can sometimes impair memory. A study

by Porter et al. (2010) investigated the effects of emotional valence and post-event misinformation on the accuracy of declarative memory over time.

Subjects who were shown negative images were more likely to incorporate major misleading details than subjects who were shown positive images.

These results suggest that the emotional valence of an experience could make an episodic memory more susceptible to distortion. However, Porter et al. (2010) did not compare the emotional images to a neutral control for the non-misled condition.

Therefore, it may be that emotional memories are recalled in more detail, and those details are more susceptible to distortion.

HOW DOES EMOTION REGULATION IMPACT MEMORY? Once it was well established that emotions could affect memory, researchers began to investigate whether modulating one’s emotional responses has an impact on memory. A study by Erk, von Kalckreuth, and Walter (2010) investigated how emotion regulation impacts long-term memory. The fMRI data revealed significantly reduced amygdala activity during emotion regulation. It also showed significantly stronger activation of a prefrontal-parietal network during regulation that was positively correlated with the amount of amygdala downregulation. Despite these changes in brain activity, memory performance was unaffected by regulation; however they also found that amygdala activation was greater for successful memory, suggesting that stronger emotional activation is associated with stronger memory circuits and better retrieval.

In contrast, another study found that the use of emotion regulation could indeed impact memory (Richards and Gross, 2006). In this study, participants viewed an unpleasant film clip in a room with other participants and subsequently watched a videotape of a couple fighting. Subjects were either told to keep their facial expression constant (use expressive suppression) or to try to control their cognitive response (use emotional reappraisal). Both spontaneous and manipulated efforts to regulate emotional responses were associated with poorer memory performance. In fact, the more effort put into suppression or reappraisal, the worse the memory of the film.

A study by Hayes et al. (2010) investigated the

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Key Structures Functions Hippocampal Formation

Essential Structure in the medial temporal lobe for the encoding and subsequent retrieval of long-term declarative memories (Hariri, 2015)

Amygdala

Subcortical Structure in the medial temporal lobes; integrates sensory and regulatory inputs fron the thalamus and prefrontal cortex respectively; provides emotional context of memories (Dere, Pause, and Pietrowsky, 2010) Prefrontal Cor- Controls action mechanisms tex (PFC) (Mecklinger, 2010) Dorsolateral Provides top-down regulation Prefrontal Cor- of many bottom-up drives tex (dlPFC) (Mansouri, Tanaka, and Buckley, 2009); focuses hippocampal encoding on important information (Simon and Spiers, 2003); essential for working memory (Mottagphy et al., 2002) Ventrolateral Helps with mental rehearsal of Prefrontal Cor- information; involved in worktex (vlPFC) ing memory of faces (Mottaphy et al., 2002) Dorsomedial Essential for spatial working Prefrontal Cor- memory (Mottaphy et al., 2002) tex (dmPFC) Plays a role in executive control Dorsal Anterior Cingulate (Mansouri, Tanaka, & Buckley, Cortex (dACC) 2009)

Table 1: List of relevant key structures invovled in memory and emotion regulation and their respective functions.

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Box 1: Key Terms and Definitions Declarative Memory

Semantic Memory

Episodic Memory

Executive Control

Attention

Working Memory

Memories that are Response Selection consciously created and recalled (Dickerson and Eichenbaum, 2009) Declarative memExpressive ory involving facts Suppression (Simons and Spiers, 2003)

Ability to direct behavioral responses (Hariri, 2015)

Regulation of facial expression to hide visible reaction to negative stimuli (Richards and Gross, 2006) Regulation of cogDeclarative memory Emotional Reappraisal nitive processing to involving personal experiences (Simons reduce emotional and Spiers, 2003) valence of negative stimuli (Dillon et al., 2007; Richards and Gross, 2006) Conscious formation Conrticolimbic Circuit Centered around the amygdala; responsiand execution of ble for recognition goal-directed plans of and reaction to and behaviors (Manthreatening stimuli souri, Tanaka, and (Hariri, 2015) Buckley, 2009) The selective conCorticostriatal Circuit Centered around the centration on certain ventral striatum; aspects of the enviresponsible for motironment vating and generating goal-oriented behavior (Hariri, 2015) Ability to maintain Corticohippocampal Centered around the and manipulate inCircuit dlPFC; responsible for formation necessary memory and execufor a task without tive control storing/encoding it into long-term memory; only remembered for the brief period of time that one is actively engaged with the information

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mechanisms and neural pathways through which emotion regulation affects memory formation and produced findings that contradicted the notion that all forms of emotion regulation decrease memory.

In this experiment, subjects were told to passively view photos, utilize cognitive reappraisal, or suppress their emotional expression. The use of either of the two emotion regulation strategies significantly reduced amygdala and insular activity in response to the pictures, but reappraisal was found to be more successful in reducing emotional responses than expressive suppression. Interestingly, expressive suppression reduced the functional connectivity between the amygdala and hippocampus, while functional connectivity between these structures was maintained during reappraisal. They also found, surprisingly, that reappraisal led to better memory performance, which could be related to the maintained functional connectivity between the amygdala and hippocampus. This can altogether be taken to mean that engaging in emotional reappraisal is advantageous to both emotional responsiveness and declarative memory (Hayes et al., 2010).

HOW DO DIFFERENT PSYCHOLOGICAL DISORDERS IMPACT EMOTION REGULATION AND MEMORY? Many psychological disorders that stem from dysfunctional regulation of emotion are also associated with deficits in declarative memory. One study explored the efficiency of two emotion regulation strategies, the recollection of positive memories and distraction, in improving the mood of patients currently diagnosed with major depressive disorder (MDD), formerly depressed patients, and healthy controls (Joormann, Sierner, and Gotlib, 2007). The distraction task was an effective strategy for both depressed and non-depressed subjects.

However, the recollection strategy was only effective for non-depressed subjects and actually increased the level of sadness in depressed subjects.

This suggests that MDD disturbs the normal patterns of amygdala activation and creates deficits in the ability of MDD patients to use autobiographical recall as an emotion regulation strategy (Joormann, Sierner, and Gotlib, 2007). In addition, another study used an autobiographical memory test (AMT) and memory narratives to evaluate the specificity of autobiographical memory in relation to depressive symptoms (Sumner, Mineka, and McAdams, 2013). Patients with increased levels of depressive http://www.neurogenesis-journal.com

symptoms had decreased autobiographical memory specificity, which suggests that the emotion dysregulation that occurs in depression may impact the ability of patients to accurately recall autobiographical memories (Sumner, Mineka, and McAdams, 2013).

Deficits in memory have also been observed in patients with Bipolar Disorder (BD). Malhi et al.

(2007) used neuropsychological testing to investigate the different neural deficits and behavioral impairments in BD across all three states: depression, euthymia, and hypomania. Interestingly, while other cognitive impairments were seen only during depressive or manic states, reduced verbal memory and attention were seen across all three states (Malhi et al., 2007). This suggests that the neural dysfunction that leads to unregulated emotions in bipolar disorder also has a lasting negative impact on declarative memory, even when mood is stabilized.

The impact of dysfunctional emotion regulation is seen across many neuropsychiatric disorders.

For instance, both patients with Post-Traumatic Stress Disorder (PTSD) and MDD show reduced hippocampal volume. This reduced hippocampal volume could reflect how prolonged stress and resulting elevated cortisol levels can lead to hippocampal atrophy and thus impaired memory (Smith, 2005). Individuals with anxiety disorders often show enhanced memory for threat-related material, but show deficits in the encoding and retrieval of neutral episodic memories and information (Dere, Pause, and Pietrowsky, 2010). According to these findings that memory and emotion play critical roles across a broad range of neuropsychiatric disorders, it is apparent that emotion and emotion regulation strategies are extremely important to the understanding of human mental health.

DISCUSSION

By surveying the vast amounts of research done on the subject, it becomes clear that there are many complex interactions between memory, emotion, and emotion regulation. Taken together, the literature suggests that emotional activation of the amygdala can interact with the hippocampus as it encodes episodic memories. This interaction between the amygdala and hippocampus strengthens the encoded circuits for these memories and improves recall. This is important because it reveals that episodic memories can be shaped by the emotional responses to the events themselves. Howev Fall 2016 | Volume 6 | Issue 1 | 21

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er, not all research claims that emotional arousal is beneficial for memory. In fact, there is evidence that memories associated with negative emotions and more likely to be distorted than positive memories (Porter et al., 2010). This could mean that the encoding and recall of negative episodic memories require different neural mechanisms than those of positive memories. Given these findings, it seems that emotional memories are recalled with more detail than neutral memories, but the emotional valence of a memory may make it more susceptible to distortion. This demonstrates the intricacies of the relationship between emotional arousal and the encoding and retrieval of declarative memories.

Moreover, harnessing this interaction between emotion and memory, emotion regulation has been found to modulate the neural processes responsible for memory. Different types of emotion regulation strategies have different effects and different magnitudes of impact on long-term memory. For example, Hayes et al. (2010) found that expressive suppression and reappraisal were not equivalent: reappraisal was more effective at reducing amygdala activity and actually had a positive impact on recall. Overall, current literature provides convincing evidence for interactions between emotion regulation and memory that shape the neural circuits responsible for declarative memory encoding and retrieval.

The importance of the interactions between emotion regulation and memory becomes most apparent in neuropsychiatric disorders. Patients with major depressive disorder, bipolar disorder, anxiety disorders, and Post-Traumatic Stress Disorder all have dysfunctional regulation of emotions as a major symptom; however, these disorders also all have more subtle, but still significant cognitive deficits, including deficits in memory. For example, magnetic resonance imaging studies have shown decreased hippocampal volume in patients with

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MDD (Bremner, 2000; Frodl et al, 2002), and there is evidence that MDD impairs the ability of patients to use autobiographical recall of happy memories as an effective method for regulating emotion (Joormann, Sierner, and Gotlib, 2007). These deficits in encoding and retrieval of declarative memories also extend to patients with anxiety disorders. Despite the fact that patients with anxiety disorders (AD) often show enhanced threat-related memory (potentially due to the large magnitude of amygdala activation that occurs in AD in response to threatening stimuli), patients with AD show problems with encoding and recall of neutral episodic memories (Dere, Pause, and Pietrowsky, 2010). It follows that there must be a deficit in the pathway for either encoding or recall that is weakened when emotions cannot be regulated properly and used in a way that can improve memory.

In conclusion, emotion and emotion regulation influence the neural processes involved in the encoding and recall of declarative memory. Often, emotional arousal during encoding of a memory can improve episodic memory recall; however, the relationship between emotion and memory is elaborate, and negative memories can be more susceptible to distortion. Emotion regulation can enhance declarative memory when strategies such as reappraisal, which increase the neural activity associated with an event, are employed. Other strategies that inhibit the neural processes engaged during encoding, such as suppression, can actually impair declarative memory. The multifaceted relationship between emotion regulation and memory manifests itself as deficits in memory in psychiatric disorders with impaired emotion regulation, such as MDD, BD, and PTSD. More research must be done to understand the exact biological basis of how emotion regulation affects memory, which could improve our ability to diagnose and treat psychological disorders.

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REFERENCES

Bremner, J. D., Narayan, M., Anderson, E. R., Staib, L. H., Miller, H. L., & Charney, D. S. (2000). Hippocampal Volume Reduction in Major Depression. American Journal of Psychiatry, 157(1), 115–118. http:// doi.org/10.1176/ajp.157.1.115 Dere, E., Pause, B. M., & Pietrowsky, R. (2010). Emotion and episodic memory in neuropsychiatric disorders. Behavioural Brain Research, 215(2), 162–171. http://doi.org/10.1016/j.bbr.2010.03.017 Dickerson, B., & Eichenbaum, H. (2009). The Episodic Memory System: Neurocircuitry and Disorders. Neuropsychopharmacology, 35(1), 86–104. http://doi.org/10.1038/npp.2009.126 Dillon, D. G., Ritchey, M., Johnson, B. D., & LaBar, K. S. (2007). Dissociable effects of conscious emotion regulation strategies on explicit and implicit memory. Emotion, 7(2), 354–365. http://doi. org/10.1037/1528-3542.7.2.354 Dolcos, F., LaBar, K., & Cabeza, R. (2004). Interaction between the Amygdala and the Medial Temporal Lobe Memory System Predicts Better Memory for Emotional Events. Neuron, 42(5), 855–863. http://doi. org/10.1016/S0896-6273(04)00289-2 Erk, S., von Kalckreuth, A., & Walter, H. (2010). Neural long-term effects of emotion regulation on episodic memory processes. Neuropsychologia, 48(4), 989–996. http://doi.org/10.1016/j.neuropsychologia.2009.11.022 Frank, J. E., & Tomaz, C. (2000). Enhancement of declarative memory associated with emotional content in a Brazilian sample. Brazilian Journal of Medical and Biological Research, 33(12), 1483–1489. http:// doi.org/10.1590/S0100-879X2000001200013 Frodl, T., Meisenzahl, E., Zetzsche, T., Born, C., Groll, C., Jäger, M., Bottlender, R., Hahn, K., Möller, H.-J. (2002). Hippocampal Changes in Patients With a First Episode of Major Depression. American Journal of Psychiatry, 159(7), 1112–1118. http://doi.org/10.1176/appi. ajp.159.7.1112 Hariri, A. (2015). Looking inside the disordered brain: an introduction to the functional neuroanatomy of psychopathology. Sinauer Associates, Incorporated.

Hayes, J., Morey, R., Petty, C., Seth, S., Smoski, M., McCarthy, G., & LaBar, K. (2010). Staying Cool when Things Get Hot: Emotion Regulation Modulates Neural Mechanisms of Memory Encoding. Frontiers in Human Neuroscience, 4. http://doi.org/10.3389/fnhum.2010.00230 Joormann, J., Siemer, M., & Gotlib, I. H. (2007). Mood regulation in depression: Differential effects of distraction and recall of happy memories on sad mood. Journal of Abnormal Psychology, 116(3), 484–490. http://doi.org/10.1037/0021-843X.116.3.484 LaBar, K., & Cabeza, R. (2006). Cognitive neuroscience of emotional memory. Nature Reviews Neuroscience, 7(1), 54–64. http://doi. org/10.1038/nrn1825 Malhi, G., Ivanovski, B., Hadzi-Pavlovic, D., Mitchell, P., Vieta, E., & Sachdev, P. (2007). Neuropsychological deficits and functional impairment in bipolar depression, hypomania and euthymia. Bipolar Disorders, 9(1-2), 114–125.

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Mansouri, F. A., Tanaka, K., & Buckley, M. J. (2009). Conflict-induced behavioural adjustment: a clue to the executive functions of the prefrontal cortex. Nature Reviews Neuroscience, 10(2), 141–152. http://doi. org/10.1038/nrn2538 Mecklinger, A. (2010). The control of long-term memory: Brain systems and cognitive processes. Neuroscience & Biobehavioral Reviews, 34(7), 1055–1065. http://doi.org/10.1016/j.neubiorev.2009.11.020 Mottaghy, F. M., Gangitano, M., Sparing, R., Krause, B. J., & Pascual-Leone, A. (2002). Segregation of Areas Related to Visual Working Memory in the Prefrontal Cortex Revealed by rTMS. Cerebral Cortex, 12(4), 369–375. http://doi.org/10.1093/cercor/12.4.369 Porter, S., McDougall, A., Bellhouse, S., ten Brinke, L., & Wilson, K. (2010).

A Prospective Investigation of the Vulnerability of Memory for Positive and Negative Emotional Scenes to the Misinformation Effect. Canadian Journal of Behavioural Science, 42(1), 55–61. Retrieved from http://search.proquest.com/docview/220494932/abstract/35B56FAD32AC469APQ/1 Richards, J. M., & Gross, J. J. (2006). Personality and emotional memory: How regulating emotion impairs memory for emotional events.

Journal of Research in Personality, 40(5), 631–651. http://doi. org/10.1016/j.jrp.2005.07.002 Simons, J. S., & Spiers, H. J. (2003). Prefrontal and medial temporal lobe interactions in long-term memory. Nature Reviews Neuroscience, 4(8), 637–648. http://doi.org/10.1038/nrn1178 Smith, M. E. (2005). Bilateral hippocampal volume reduction in adults with post-traumatic stress disorder: A meta-analysis of structural MRI studies. Hippocampus, 15(6), 798–807. http://doi.org/10.1002/ hipo.20102 Sumner, J. A., Mineka, S., & McAdams, D. P. (2013). Specificity in autobiographical memory narratives correlates with performance on the Autobiographical Memory Test and prospectively predicts depressive symptoms. Memory, 21(6), 646–656. http://doi.org/10.1080/0 9658211.2012.746372 Harrar,V., Toepel, U., Murray, M., Spence, C. (2011). Food’s visually-perceived fat content affects discrimination speed in an orthogonal spatial task. Experimental Brain Research, 214, pp. 351–356 Johnson, J.L., Dzendolet, E., Clydesdale, F.M. (1983) Psychophysical relationships between sweetness and redness in strawberry-drinks, Journal of Food Protection, 46 (1), pp. 21–25 Kim, Y., & Chang, U. (2015). Effects of Food Consumption Monitoring Using Modified Rice Bowls on Food Intake, Satiety Rate, and Eating Rate. Journal of the Korean Dietetic Association, 21(3), 194-202.

Larson, J., Redden, J., & Elder, R. (2014). Satiation from Sensory Simulation: Evaluating Foods Decreases Enjoyment of Similar Foods. PsycEXTRA Dataset.

Morrot, G., Brochet. F., Dubourdieu., (2001). The color of odors. Brain Lang, 79, pp. 309–320 Michel, C., Velasco, C., Gatti, E., & Spence, C. (2014). A taste of Kandinsky: Assessing the influence of the artistic visual presentation of food on the dining experience. Flavour, 3(1), 7. doi:10.1186/2044-7248-3-7

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From Herb to Heroin : How Chronic Adolescent THC Exposure May Facilitate Heroin Abuse and Addiction In Adulthood Mark Robles-Long1 1 New York University, New York, NY 10012 Correspondence should be addressed to Mark Robles-Long (mrl427@nyu.edu)

As the heroin epidemic in the US continues to develop, an objective evaluation of potential causes is crucial. Among the many biopsychosocial analyses that seek to explain what promotes drug abuse in adulthood, the gateway hypothesis remains one of the most prevalent and contested. It posits marijuana use, typically in adolescence, as the first step toward becoming a user of “harder” drugs by fostering the user’s curiosity. Through a comprehensive literary review, I assess the validity of the hypothesis through neurobiological examination of a mouse model, providing relevant and ethical understanding of the degree to which adolescent THC exposure affects the tendency toward using drugs such as heroin. Coupling the fundamental and consequential changes in gene expression that occur in response to adolescent THC exposure with the innately hyper-reactive adolescent stress response, it may be the case that early use of cannabis, high in THC content, exacerbates the vulnerability of adolescents to the initiation and persistence of heroin-seeking behavior, as well as the propensity to becoming addicted.

INTRODUCTION: HEROIN, ADOLESCENCE, AND THE GATEWAY HYPOTHESIS Heroin abuse is at an all-time high in the US. Opioids, including prescription pain relievers as well as heroin, comprised 61% of all drug overdose deaths in 2014. This trend has manifested itself most notably in the mid-west and the northeast where epidemics were declared in at least two states: Massachusetts and New Hampshire, the latter of which has had a 76% increase in opioid-deaths–totaling 325 persons–in 2014 (NYT, 2015). It is likely the case that the difficulty in attaining prescription opiates and/ or the reformulation of them to include ingredients that make abuse more difficult could explain this rise in usage (Figure 1).

“Hard” drugs (heroin, methamphetamine, cocaine) historically have not infiltrated homes nor lives as easily as some other drugs. “Soft” drugs, however, permeate the lives of people of all ages and

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Figure 1: Trends in prescription opioid-related deaths and heroinrelated deaths in the United States (US News, 2015)

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some have even become an expected rite of passage during the transition from adolescence into adulthood: alcohol, caffeine, nicotine, and cannabis. The lattermost drug is the centerpiece of the popular “gateway hypothesis”. The adolescent population is of primary interest regarding the “gateway hypothesis” for two reasons: (1) in adolescence, immense physical and neurobiological changes occur rendering these individuals vulnerable to long-term, drug-induced structural alterations and resultant behaviors, and (2) adolescence has been characterized as a time for “experimentation” as one begins to pursue certain, oftentimes hedonistic interests.

NEUROBIOLOGICAL EFFECTS OF ADOLESCENT THC EXPOSURE It has been set forth that chronic adolescent exposure to ∆-9-tetrahydrocannabinol (THC), the primary psychoactive component in cannabis, resulted in a sustained enhancement of striatal preproenkephalin mRNA expression in the nucleus accumbens (NAc) shell, a component governing the reward system in the brain, and potentiation of μ opioid receptor (μOR) GTP-coupling in mesolimbic and nigrostriatal brainstem regions (Ellgren et al., 2006). Collectively, these results support the “gateway hypothesis” but do not have enough evidence to verify it. This review seeks to fill the gaps between adolescent exposure to THC and vulnerability to heroin addiction by focusing on THC-induced effects on brain-derived neurotrophic factor (BDNF) and its role in potentiating the acquisition or extinction of a drug memory, as well as effects on the hypothalamic-pituitary-adrenal (HPA) axis and the subsequent involvement of the axis in the stress response. The link between BDNF and HPA reactivity will be discussed as it is crucial in explaining what may initiate drug-seeking behavior after adolescent THC exposure.

THC AND HEDONIC DEFICIT

Modern forms of cannabis are more potent than ever before, containing higher levels of the main psychoactive component, THC, responsible for the “high” experienced by its consumers (PBS, 2014). Work has been conducted to link early-onset cannabis use to the development of neurological disorders later in life, including schizophrenia and anxiety, as well as to the increased risk of initiation of illicit drug use (Lessem et al., 2006). THC binds to and activates CB1, the type-1 cannabinoid receptor which is co-localized with μOR on neurons in the ventral tegmental area http://www.neurogenesis-journal.com

(VTA) and in the striatal output projection neurons of the NAc and dorsal striatum (DS) containing the caudate and putamen (Ellgren et al., 2006). These regions are known to modulate reward, goal-directed behavior, and habit formation relevant to addiction. Ellgren et al. assessed changes in both CB1 and μOR density and functionality (e.g. activation capacity). Figure 2 depicts the number of responses on active and inactive levers that would administer heroin (15μg/kg/ i.v.) to THC-pretreated (1.5mg/ kg i.p. once every three days from P28-49) or naive rats. This data suggests THC-pretreated rats exhibited a hedonic deficit wherein satiety (a leveling off of number of responses) was not met at the same point as is in control, if it was met at all–this is an example of a rapidly-developed tolerance.

Though the data did not exceed 19 sessions, a plateau effect was not inferred from the existing data.

Although no significant change in μOR density was observed, there was enhanced G-protein activation, assayed by administration of DAMGO-stimulated [35S]GTPγS , in the substantia nigra and the VTA which project to the striatum and NAc, respectively.

It must be noted that heroin intake behavior and CB1 binding in the substantia nigra are correlated.

PENK EXPRESSION

Their finding that proenkephalin (PENK) mRNA expression increased in the NAc shell indicated a substantial and lasting change at the transcriptional level. Proenkephalin is a precursor to enkephalin peptides, endogenous opioid hormones which bind to μOR and delta opioid receptors (δOR). PENK mRNA is greater in the THC-pretreated rats after heroin self-administration compared to the naive rats, attributable to adolescent exposure and not solely heroin consumption.

While repeated opiate exposure typically decreases PENK mRNA expression, facilitating tolerance and desensitization, there is a persistent up-regulation of PENK transcription in THC-exposed rats. This increase in enkephalin concentration, particularly in the NAc shell and VTA, paired with the substantiated GTP-coupling at the receptor level in μOR’s in substantia nigra and VTA, suggests a THC-dependent change in neural machinery that would facilitate an opiate response. These findings could explain the increased heroin intake and the apparent lack of satiability inferred from Figure 2.

BDNF EXPRESSION

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Figure 2: Heroin acquisition behavior on active and inactive levels following either vehicle or THC pretreatment (Ellgren et al., 2006).

Reproduced with permission from author. .

BDNF is involved in regulating neurogenesis and synaptogenesis, including neuronal differentiation, survival, and repair. Most relevant, BDNF is an integral part of neuroplasticity and adaptive processes underlying learning. There is conflicting evidence regarding the effect of THC on BDNF levels. In one human study conducted by D’Souza et al., a group of light cannabis users was poised against healthy controls, ranging in subject age from 18-55. The results of this study indicated that light users (1) had lower baseline serum BDNF levels relative to control, and (2) were insensitive upon acute THC administration, maintaining a level even lower than baseline. Contrarily, healthy controls exhibited a spike in serum BDNF levels upon acute THC administration. According to this study, chronic THC exposure leads to significantly decreased serum BDNF levels whereas acute THC administration in naive subjects increases serum BNDF levels (D’Souza et al., 2008). This data disputes another study that used mice (12-15 weeks old) and found chronic THC-exposure (7 days) to elicit increased BDNF mRNA and protein predominantly in NAc (medial shell) with smaller increases in the posterior VTA, medial PFC (mPFC), and paraventricular nucleus (PVN) of the hypothalamus presumably through the mechanism proposed in Figure 4; no changes were observed in the anterior VTA, DS, or hippocampus.

These increases were chronic exposure-dependent (Butovsky et al., 2005). It is known that acute THC transiently activates ERK in the DS, NAc, and hippocampus whereas chronic THC exposure activates ERK in the prefrontal cortex (PFC) and hippocampus.

Regarding long-term exposure, the role of ERK has been shown to be crucial to developing tolerance to THC (Rubino et al., 2004). While activation in the DS and NAc are critical parts in the reward system, BD26 | Issue 1 | Volume 6 | Fall 2016

Figure 3: PENK mRNA expression levels of adult rats with adolescent exposure to THC or vehicle at start of heroin self-administration (Ellgren et al., 2006). Reproduced with permission from author.

NF-dependent plasticity in the PFC plays a large role in the development of addiction as it is responsible for both memory consolidation and extinction in the dmPFC and vmPFC regions, respectively. In rodents, these regions are referred to as prelimbic (PL) and infralimbic (IL) PFC’s. The two studies’ opposing outcomes most likely resulted from their different methodologies which included types of subjects (humans vs. rats), definition of chronicity (lifetime exposure threshold vs. 7 days), THC dosage and administration method (0.0285 mg/kg/i.v. vs. 1.5 mg/ kg/i.v.) and localization of observed BDNF (serum vs. particular regions). Nevertheless, both conclusions indicate THC affects BDNF levels (Figure 4).

It may be the case that the location of this BDNF flux is responsible for the change in consummatory behavior and apparent vulnerability observed in Ellgren et al.

THE HPA AXIS AND BDNF FLUX

Cannabinoids are potent activators of the HPA axis which mediates the stress response. One hypothalamic part of the HPA axis, PVN, was observed to have enhanced BDNF mRNA upon chronic THC exposure in rats. This same increase was observed after acute or repeated immobilization stress in rats. (Smith and Vale, 2006). Enhanced BDNF in PVN increases the firing rate of PVN neurons (Noble et al., 2011). In adolescence, there is a shift in HPA responsiveness involving the posterior PVN of the thalamus (pPVT), a converging point for sensory and limbic inputs The Undergraduate Journal of Neuroscience

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relevant to emotional control and regulation of the HPA system. This decline in stress reactivity causes adolescents to be more susceptible to abnormal behavioral phenotypes and hormonal hyper-reactivity compared to adults. In response to chronic restraint stress, adolescent rats had significantly increased corticosterone levels, demonstrating anhedonia, increased locomotion, and increased latency to immobility in a forced swim test. This locomotor activity, prompted by corticosterone-dependent secretion of epinephrine and norepinephrine, was considered to be stress-related (Eiland et al., 2011). Chronic THC exposure, when likened to chronic (restraint) stress in terms of its effect on HPA functionality, can potentially lead to increased drug-seeking, addiction, and relapse–states as coping methods when undergoing chronic and/or early-life stress (Sinha, 2009).

Furthermore, with regards to the THC-induced enhancement of the mesolimbic system, it is known that the VTA, NAc, and the mPFC are involved in the HPA response, responsible for regulating distress and related cognition. Their hyper-sensitization due to BDNF flux and the up-regulation in PENK mRNA in NAc shell could give rise to downstream enhancements of the HPA response. Similar to the inverse effect of THC on PENK, up-regulation of BDNF in these three HPA-related areas could oppose an organism’s natural way of habituating itself to a stress response through enhanced activity potential.

IMBALANCE AND RELAPSE

BDNF is a critical player in excitatory synaptic signaling. A delicate balance between excitatory and inhibitory neurotransmission is believed to be necessary for long-term potentiation (LTP) and long-term memory (LTM). Long-term potentiation involving numerous neural adaptations in areas such as VTA and NAc has been found to be integral to the development of addiction (Kauer and Malenka, 2007).

One supposition is that the flux of BDNF itself in either direction could disrupt the excitatory/inhibitory balance. BDNF flux about the HPA axis, in particular the PVN, and subsequent cortisol secretion, would therefore elicit these drug-related behaviors accordingly.

The effect of adolescent THC exposure on reinstatement or “relapse” could reveal information regarding the relationship between THC and cognitive control centers such as the vmPFC and dmPFC as well as the hippocampal and amygdala memory center, all of which are sensitive to cortisol. Partichttp://www.neurogenesis-journal.com

THC —> CB1 —> ERK —> CREB —> BDNF mRNA expression Figure 4: Flow chart of effect of THC on BDNF mRNA expression: THC binds to CB1, activating ERK (extracellular signal-related kinase) which activates CREB (cAMP response element-binding protein), a transcription factor that stimulates BDNF mRNA expression.

ularly, a greater increase in NMDA receptor subunit expression has been observed in response to corticosterone in prepubertal male rodents compared to adult males. This enhanced potential for excitotoxicity may contribute to the stress-induced, long-lasting dendritic atrophy observed in CA3 and volumetric deficits in CA1 and CA3 (Romeo and McEwen, 2006).

These deficiencies were not observed until three weeks after the stress sessions, implying the effects of chronic stress on the hippocampus were delayed.

Hippocampal deficiency has been postulated to reduce the capacity to react to stress appropriately due to reduced memory sources, thus promoting drug-seeking behavior in adulthood.

POTENTIAL ROLE OF IL IN EXTINCTION

Regarding cognitive control, two regions of the mPFC have been implicated in drug-seeking behavior and addiction: vmPFC and dmPFC in humans, IL and PL in rodents. The latter region, believed to be involved in memory consolidation (fear and drug seeking [Peters et al., 2007]), experienced pyramidal reduction in response to chronic restraint stress; however, neuronal enhancement was found to occur upon exposing cues previously associated with heroin self-administration three weeks following extinction (Schmidt et al., 2005). The former region typically facilitates extinction of drug-seeking in rodents treated with cocaine; however, one study has proposed its involvement in facilitating relapse in rodents treated with heroin (Barker et al., 2014). Further investigating the role of IL in extinction memory, work was done evaluating the role of protein kinase M ζ (PKMζ) in both IL as well as the amygdala, where it was hypothesized to maintain drug (morphine) reward and aversion memory in the basolateral amygdala (BLA).

Through selective inhibition, they found PKMζ to be location-specific and responsible for both expression and extinction of conditioned place preference (CPP) and aversion (CPA) memories: inhibition in the intra-BLA prevented expression while inhibition in the IL prevented extinction (He et al., 2011). This evidence substantiates the idea that a region-specific Fall 2016 | Volume 6 | Issue 1 | 27

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change in LTP could affect relapse behavior. Although BDNF increased in the NAc of THC-exposed rats and did not change in the BLA, it has been affirmed that exogenous BDNF may act locally in the NAc or undergo retrograde transport to regions such as the BLA, mPFC, and VTA where it may act there (Schoenbaum et al., 2007). Inferably, increases in BLA BDNF via retroactivity may explain the consolidation of memories seen in CPP and CPA. More work must be done to determine the exact role and change in expression of BDNF in IL as this could explain the inability to extinguish a consolidated memory such as that of a drug.

Regarding stress-induced relapse, adolescent THC-exposure has been shown to increase propensity of reinstatement of heroin use. Administration of a chemical stressor, yohimbine, that elicited noradrenaline release and increased cell firing was shown to induce robust heroin seeking in rats chronically exposed to THC following extinction (Stopponi et al., 2014). Abstinence from heroin is characterized by reduced hippocampal LTP levels, perhaps due to reduced BDNF, while re- exposure but not extinction is characterized by a restoration of this LTP (Barker et al., 2014). This may suggest a change in homeostatic LTP wherein sobriety is now a deviation from baseline rather than baseline itself. This is assuming a level of LTP prior to heroin abuse conferred by adolescent THC-exposure. This data connects the THC-induced effect of BDNF imbalances in both the HPA axis and corticolimbic centers which promote addiction, most notably drug-seeking behavior and relapse.

of neurogenesis, have more BDNF than adults, it may be inferable that THC-exposure at that age would increase it further especially since the downstream effects on behavior inferred from the Butovsky et al. data are observed in adolescents (i.e. HPA hyper-reactivity, enhanced BLA-mediated drug memory consolidation). Replicating the study using adolescent rats would bolster the findings. Regarding the dosage of THC, 1.5 mg/kg was used in both Butovksy et al. as well as Ellgren et al., contrasting the human study which assessed serum BDNF after administration of 0.0286 mg/kg THC. This relatively minuscule dose (50-fold difference) was deemed “socially relevant”, modeling recreational cannabis use, taken to be one standard National Institute of Drug Abuse (NIDA) marijuana cigarette. Social studies must be conducted to accurately model current cannabis usage among adolescents. Pertaining to the molecular work, data was lacking regarding the effect of THC exposure on BDNF levels in IL. This would be paramount to the hypothesis regarding facilitated consolidation via BLA BDNF enhancement at the expense of extinction capacity typically mediated by IL. Additionally, using an inhibitor of PKMζ to assess the level of LTP in certain regions and its role in addictive behaviors could serve as a proxy for BDNF effects or potentiation in general.

CONCLUSION

The literature cited substantiates and extends the hypothesis and inferences made by Ellgren et al. by elucidating the consequences of THC-induced up-regulations of PENK mRNA and potentiation of μOR binding. Furthermore, Butovsky et al. contributed crucial information pertinent to the disruption of LTP balance in various memory, cognitive, and emotional centers, including the HPA axis, that could explain the exacerbated vulnerability of adolescents to the initiation and persistence of heroin-seeking behavior, as well as the enhanced propensity to becoming addicted. Still, there are relevant caveats in the works. Although Butovsky et al. has sound methodology, the data reflects the effect of adult THC-exposure on BDNF levels. However, since It is known that adolescents, due to an inherently elevated level 28 | Issue 1 | Volume 6 | Fall 2016

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REFERENCES

Barker, Jacqueline M., Jane R. Taylor, and L. Judson Chandler. “A Unifying Model of the Role of the Infralimbic Cortex in Extinction and Habits.”

Learning & Memory 21.9 (2014): 441–448. PMC. Web. Brangham, William. "Is Pot Getting More Potent?" PBS. PBS, 2 Apr. 2014.

Web. 18 Nov. 2016.

Butovsky, Elena, Ana Juknat, Igor Goncharov, Judith Elbaz, Raya Eilam, Abraham Zangen, and Zvi Vogel. "In Vivo Up-regulation of Brain-derived Neurotrophic Factor in Specific Brain Areas by Chronic Exposure to Delta9-tetrahydrocannabinol." Journal of Neurochemistry J Neurochem 93.4 (2005): 802-11. Web. D’Souza, Deepak Cyril, Brian Pittman, Edward Perry, and Arthur Simen.

"Preliminary Evidence of Cannabinoid Effects on Brain-derived Neurotrophic Factor (BDNF) Levels in Humans." Psychopharmacology 202.4 (2008): 569-78. Web. Eiland, Lisa et al. “Chronic Juvenile Stress Produces Corticolimbic Dendritic Architectural Remodeling and Modulates Emotional Behavior in Male and Female Rats.” Psychoneuroendocrinology 37.1 (2012): 39–47. PMC. Web. Ellgren, Maria, Sabrina M. Spano, and Yasmin L. Hurd. "Adolescent Cannabis Exposure Alters Opiate Intake and Opioid Limbic Neuronal Populations in Adult Rats." Neuropsychopharmacology 32.3 (2006): 607-15. Web. He, Ying-Ying et al. “PKMζ Maintains Drug Reward and Aversion Memory in the Basolateral Amygdala and Extinction Memory in the Infralimbic Cortex.” Neuropsychopharmacology 36.10 (2011): 1972–1981.

PMC. Web. "The Heroin Epidemic, in 9 Graphs." US News. U.S.News & World Report, 19 Aug. 2015. Web. 17 Kauer, Julie A., and Robert C. Malenka. "Synaptic Plasticity and Addiction." Nature Reviews Neuroscience Nat Rev Neurosci 8.11 (2007): 844-58. Web. Lessem, Jeffrey M., Christian J. Hopfer, Brett C. Haberstick, David Timberlake, Marissa A. Ehringer, Andrew Smolen, and John K. Hewitt.

"Relationship between Adolescent Marijuana Use and Young Adult Illicit Drug Use." Behav Genet Behavior Genetics 36.4 (2006): 498506. Web.

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Noble, Emily E. et al. “The Lighter Side of BDNF.” American Journal of Physiology - Regulatory, Integrative and Comparative Physiology 300.5 (2011): R1053–R1069. PMC. Web. "The Numbers Behind America’s Heroin Epidemic." The New York Times.

The New York Times, 29 Oct. 2015. Web. Peters, Jamie, Peter W. Kalivas, and Gregory J. Quirk. “Extinction Circuits for Fear and Addiction Overlap in Prefrontal Cortex.” Learning & Memory 16.5 (2009): 279–288. PMC. Web. Romeo, R. D., and B. S. Mcewen. "Stress and the Adolescent Brain." Annals of the New York Academy of Sciences 1094.1 (2006): 202-14. Web. Rubino, Tiziana, Greta Forlani, Daniela Viganò, Renata Zippel, and Daniela Parolaro. ”Modulation of Extracellular Signal-regulated Kinases Cascade by Chronic Δ9- tetrahydrocannabinol Treatment." Molecular and Cellular Neuroscience 25.3 (2004): 355-62. Web. Schmidt, E. Donné, Pieter Voorn, Rob Binnekade, Anton N. M. Schoffelmeer, and Taco J. De Vries. "Differential Involvement of the Prelimbic Cortex and Striatum in Conditioned Heroin and Sucrose Seeking following Long-term Extinction." European Journal of Neuroscience 22.9 (2005): 2347-356. Web. Schoenbaum, Geoffrey, Thomas A. Stalnaker, and Yavin Shaham. "A Role for BDNF in Cocaine Reward and Relapse." Nature Neuroscience Nat Neurosci 10.8 (2007): 935-36. Web. Sinha, Rajita. “Chronic Stress, Drug Use, and Vulnerability to Addiction.”

Annals of the New York Academy of Sciences 1141 (2008): 105–130.

PMC. Web. Smith, Sean M., and Wylie W. Vale. “The Role of the Hypothalamic-Pituitary-Adrenal Axis in Neuroendocrine Responses to Stress.” Dialogues in Clinical Neuroscience 8.4 (2006): 383–395. Web. Stopponi, Serena, Laura Soverchia, Massimo Ubaldi, Andrea Cippitelli, Giovanni Serpelloni, and Roberto Ciccocioppo. "Chronic THC during Adolescence Increases the Vulnerability to Stress-induced Relapse to Heroin Seeking in Adult Rats." European Neuropsychopharmacology 24.7 (2014): 1037-045. Web.

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The Neuroscience of Adolescent Impulsivity and its Legal Implications Julie Uchitel1 Duke University, Durham, NC 27708 Correspondence should be addressed to Julie Uchitel (julie.uchitel@duke.edu) 1

Should criminal courts treat adolescents differently than adults on the basis of their neurological immaturity? When comparing adolescents and adults, difficulty arises in assigning culpability to defendants of similar criminality yet different stages of development. The growing brain possesses many features that predispose juveniles to impulsive behavior. In light of these considerations, this article will examine the neuroscience of adolescent behavior, the evolutionary basis of impulsivity, and how neuroscience can shape decisions of criminal culpability for adolescents.

THE ADOLESCENT BRAIN Neuroanatomy The brain undergoes a multitude of structural changes during adolescence (Lenroot et al., 2006; Spear, 2000; Casey et al., 2008; Galvan, 2010). Although the brain reaches 90% of its adult size by age six, grey and white matter continue to grow until the mid-twenties (Casey et al., 2008a) Additionally, pruning and myelination in the prefrontal cortex, an important measure of brain maturity, continue from adolescence into adulthood (Lenroot et al., 2006). (See Figure 1.) These developmental changes are critical to maturation, yet this state of flux may manifest as impulsivity and pleasure-seeking behavior (Casey et al., 2008b).

The brain’s executive control functions play a critical role in mediating impulsive behavior. Suppressing inappropriate thoughts in favor of goal-oriented ones is the cornerstone of cognitive maturation, yet adolescent brains are not developed enough to reach this state (Casey et al., 2008b). The frontal cortex, the most underdeveloped region of the adolescent brain, does not reach full maturity until the early twenties, whereas subcortical regions, responsible for emotional processing, reach full maturation much earlier (Figure 2, Figure 3). To explore this relationship, one fMRI study evaluated http://www.neurogenesis-journal.com

children’s, adolescents’, and adults’ feelings of reward when gambling for coins (Galvan et al., 2006).

Adolescents showed similar activity as adults in the nucleus accumbens, an indicator of subcortical maturity, but they also showed the same orbitofrontal cortex activity as children, an indicator of frontal immaturity. This discrepancy predisposes adolescents to make decisions based on their emotions as opposed to reasoning in a bottom-up judgment system. Thus, in emotionally charged situations, an adolescent’s subcortical activity can override its frontal activity, resulting in irrational and emotional behavior (Casey et al., 2008b).

Figure 1: Sequence of grey matter maturation over the cortical surface, reflective of synaptic pruning. The dorsolateral prefrontal cortex is the last area to reach maturation (Lenroot et al., 2006). Reproduced with permission from author.

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Figure 2: Spatial representation of subcortical/limbic development before prefrontal cortex development in an adolescent brain. Green and blue areas indicate greater white matter density and purple indicate lower white matter density (Casey et al., 2008b). Reproduced with permission from author.

Figure 3: Graphical representation of the maturational gap in adolescent brain. The difference in maturation between the limbic regions and the prefrontal regions predispose adolescents to impulsivity (Casey et al., 2008a). Reproduced with permission from author.

Neurochemistry Adolescent impulsivity not only arises from neuroanatomical development, but also from neurochemical changes. The neurotransmitter dopamine governs the development of connections between the amygdala and the prefrontal cortex (Floresco & Tse, 2007). This pathway, necessary for cognitive and emotional processing, does not fully develop until late adolescence when dopamine levels reach their peak (Galvan, 2010; Floresco & Tse, 2007). Dopamine also targets the striatum, a region that stimulates feelings of pleasure. The connection between adolescent pleasure perception and the striatum remains unknown; neuroscientists debate whether the adolescent striatum is hyperresponsive or hyporesponsive to dopamine (Galvan, 2010). A hyperresponsive striatum would magnify pleasure perception in response to dopamine, whereas a hyporesponsive striatum would diminish pleasure perception. Most adolescent reward studies con32 | Issue 1 | Volume 6 | Fall 2016

verge on the former view, yet some neuroimaging studies support the latter (Galvan, 2010). Overall, both views support that adolescents will seek highly pleasurable behaviors, either to reach a pleasure baseline or sustain intensified feelings of pleasure.

Adolescents also differ in their stress response from children and adults, perceiving situations as more stressful (Spear, 2000). They exhibit an increased physiological reactivity to stressors, such as accelerated heart rate and heightened hormonal responses as compared to other age groups when exposed to the same stressor (Spear, 2000; Romeo, 2013). In animal models, this exacerbated stress response correlates with higher drug use: increased stress levels are linked to increased ethanol consumption (Casey et al., 2008a). These findings may explain poor stress management and alcohol abuse in adolescents.

An Evolutionary Advantage Although modern society condemns extreme impulsivity, such behavior may have been evolutionarily adaptive for prehistoric adolescents (Casey et al., 2008b). In order to leave the protection of their families and find mates, adolescents needed to engage in high-risk behavior such as exploring new territories or hunting. This risk-taking coincided with sexual maturation and an increase in sexual hormones, thus driving adolescents to explore and find mates (Casey et al., 2008a; Bogin, 1994).

Additionally, young adults that were emotionally reactive could better protect their territories when faced with competitors or predators (Casey et al., 2008b). Thus, the impulsive survived and reproduced, passing impulsivity down through human evolution.

Recent studies have shown that moderate risk-taking in adolescents is normal (Spear, 2000; Shedler & Block, 1990). Shedler & Block (1990) found that adolescents who had engaged in some drug experimentation became well-adjusted adults (Shedler & Block, 1990). Those who overused drugs became maladjusted, ostracized, overly impulsive, and emotionally distressed adults, while those who entirely avoided drugs became anxious, emotionally constricted, and socially gauche adults. Thus, risk-taking not only serves a purpose in evolutionary history, but also in modern social development.

ADOLESCENT CRIMINAL CULPABILITY Should adult criminal courts try 16-18 year olds?

Opponents evoke psychology and neuroscience, arThe Undergraduate Journal of Neuroscience

Uchitel | OPINION

guing that adolescents should not be compared to more cognitively mature adults, while proponents argue that particularly heinous crimes should be tried more strictly in adult criminal court (Steinberg, 2009; Means et al., 2012). These conflicting viewpoints have directly shaped how adolescents are tried in juvenile and adult criminal court systems.

Protections for Adolescents Established by the Supreme Court Adolescent transfer to adult court first began in the 1970s as a way to account for more serious crimes (Lyons et al., 2012). As transfer rates increased, questions arose as to whether criminal courts could issue adolescents the death sentence or life in prison. In 1988, the Supreme Court held in Thompson v. Oklahoma that the execution of a 15-yearold offender would constitute cruel and unusual punishment, and thus violate the 8th amendment (“Thompson v. Oklahoma,” 1988). Stanford v. Kentucky later overturned this ruling in 1989 after defendant Kevin Stanford raped and shot a female victim, in which the Court sanctioned issuing the death penalty to those who were 16 years or older at the time of the crime. In 2005, this jurisdiction was again revoked in Roper v. Simmons (“Roper v. Simmons,” 2005). At age 17, defendant Christopher Simmons committed capital murder and was issued the death sentence at age 18. In a petition, the defendant argued that the precedent established in Atkins v. Virginia, which prohibited capital punishment for the mentally retarded, should also extend to juveniles (“Roper v. Simmons,” 2005; “Atkins v. Virginia,” 2002; Scott, 2005). In other words, the defendant argued that punishing an adolescent for acting with cognitive restrictions (relative to adults) differed little from charging an individual whose cognitive abilities prevented him from controlling the nature of his actions (“Roper v. Simmons,” 2005). Similarly, Graham vs. Florida established in 2010 that juveniles couldn’t be sentenced to life in prison without parole for non-homicidal crimes (Stevens, 2010). Cumulatively, these cases established protections for juveniles in adult criminal court and acknowledged that adolescents and adults neurologically differ.

Process of Transfer to Adult Criminal Court Throughout the US in 2000, more than 14,500 youths were incarcerated in adult jail on any given day, with 12% of offenders under the age of 15 http://www.neurogenesis-journal.com

Figure 4: Adolescent age of jurisdiction and transfer to adult courts by state and crime (Teigen, 2014). Reproduced with permission from author.

(Lyons et al., 2012). Original juvenile jurisdiction in most states lasts until age 18, but some states set the limit at 15 or 16 (Lyons et al., 2012). (See Figure 4.) Additionally, all states have judicial mechanisms in place by which some offenders below these limits may be transferred to adult criminal court.

Several factors influence the decision to transfer, including the defendant’s age, their potential risk to the public, the nature of the crime, and their maturity, character, and amenability to treatment (Means et al., 2012). This decision carries great weight not only for the current offense but also for all future offenses in some cases: many states possess “once an adult, always an adult” provisions, in which the defendant will be tried as an adult in all future cases (Griffin et al., 2011). This decision falls to the case’s judge, who relies on a forensic evaluator to offer a clinical opinion on the defendant’s emotional maturity, potential for success, and amenability to reform (Lyons et al., 2012). One study reviewed cases of transfer to adult court in 161 cases that occurred before Roper v. Simmons. This study found that judges relied more upon amenability to treatment and public safety risk when making decision to recommend transfer, rather than emotional maturity of the defendant (Means et al., 2012).

Since Roper v. Simmons, courts have become increasingly aware of the adolescents’ neurological immaturity and how this may impact their behavior (Lyons et al., 2012). Beginning in 2012, judges in New York’s Adolescent Diversion Program receive training in areas such as adolescent brain development, trauma, substance abuse, mental health, and co-occurring disorders (Lyons et al., 2012).

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adolescents lack adequate impulse control, may experience transient immaturity associated with the developing brain, and that the state of their brains should be considered when determining culpability (“Resolution Regarding Judicial Training,” 2016).

Altogether, these institutions’ stances recognize the importance of neuroscience in shaping legal policy for adolescents.

Comparison Between Juvenile and Adult Facilities Although transfer to adult criminal court achieves the goal of handling particularly serious and violent offenses more strictly, many studies have shown that transfer does not benefit the adolescent or the public in the long run (Lyons et al., 2012; Taylor, 2015; “Transfer of Youth to Adult Criminal Court,” 2014).

Juvenile facilities greatly differ from adult facilities, aiming to rehabilitate rather than solely punish criminal offenders. Some facilities offer daily classes and structured recreation time in which adolescents report that they still feel capable of achieving goals, learning skills, and improving their social relationships (Lyons et al., 2012). Additionally, these facilities recognize that the developing identity of an adolescent does not predict a depraved adult character and that adolescents are more responsive to rehabilitation than their adult counterparts (“Roper v. Simmons,” 2005; “Resolution Regarding Judicial Training,” 2015). As detailed above, adolescents drastically change neurologically, and thus are likely to outgrow behaviors characteristic of adolescence as their brains develop (Spear, 2000; Casey et al., 2008a; Casey et al., 2008b).

CONCLUSION The developing adolescent brain cannot be treated as an adult brain nor as a child brain. Adolescents are entirely unique: they have adult-like intelligence yet child-like reasoning due to the heterogeneous maturation of the brain. During development, the adolescent brain undergoes many changes that may manifest as impulsive or emotionally reactive behavior. Although this reckless behavior is often destructive, it once served an evolutionary purpose. From these findings, legal policy questions arise as to how youth should be treated in criminal court and how the law could define an age of neurological maturity. Although one must consider the differences in adolescent and adult brain maturity when assigning criminal culpability, a line dividing the neurologically mature from the immature cannot be cleanly drawn, thereby limiting the extent to 34 | Issue 1 | Volume 6 | Fall 2016

which neuroscience can currently influence policy.

Future neuroscience studies on adolescent impulsivity may solve these issues, improving the proceedings of juvenile and adult criminal court.

REFERENCES

Lenroot RK, Giedd JN. Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev. 2006;30(6):718-729. doi:10.1016/j.neubiorev.2006.06.001.

Spear LP. The adolescent brain and age-related behavioral manifestations. Neurosci Biobehav Rev. 2000;24(4):417-463. doi:10.1016/ S0149-7634(00)00014-2.

Casey BJ, Jones RM, Hare TA. The Adolescent Brain. Ann N Y Acad Sci. 2008;1124:111-126. doi:10.1196/annals.1440.010.

Galvan A. Adolescent Development of the Reward System. Front Hum Neurosci. 2010;4. doi:10.3389/neuro.09.006.2010.

Casey BJ, Getz S, Galvan A. The adolescent brain. Dev Rev DR. 2008;28(1):62-77. doi:10.1016/j.dr.2007.08.003.

Galvan A, Hare TA, Parra CE, et al. Earlier Development of the Accumbens Relative to Orbitofrontal Cortex Might Underlie Risk-Taking Behavior in Adolescents. J Neurosci. 2006;26(25):6885-6892. doi:10.1523/ JNEUROSCI.1062-06.2006.

Floresco SB, Tse MT. Dopaminergic Regulation of Inhibitory and Excitatory Transmission in the Basolateral Amygdala–Prefrontal Cortical Pathway. J Neurosci. 2007;27(8):2045-2057. doi:10.1523/JNEUROSCI.5474-06.2007.

Romeo RD. The Teenage Brain: The Stress Response and the Adolescent Brain. Curr Dir Psychol Sci. 2013;22(2):140-145. doi:10.1177/0963721413475445.

Bogin B. Adolescence in evolutionary perspective. Acta Pædiatrica.

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Shedler J, Block J. Adolescent drug use and psychological health. A longitudinal inquiry. Am Psychol. 1990;45(5):612-630.

Steinberg L. Should the science of adolescent brain development inform public policy? Am Psychol. 2009;64(8):739-750. doi:10.1037/0003066X.64.8.739.

Means RF, Heller LD, Janofsky JS. Transferring Juvenile Defendants From Adult to Juvenile Court: How Maryland Forensic Evaluators and Judges Reach Their Decisions. J Am Acad Psychiatry Law Online.

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Anne Teigen, The National Conference of State Legislature. Juvenile Age of Jurisdiction and Transfer to Adult Court Laws.; 2014. http://www. ncsl.org/research/civil-and-criminal-justice/juvenile-age-of-jurisdiction-and-transfer-to-adult-court-laws.aspx.

Lyons CL, Adams AN, Dahan AL. Commentary: Nuances of Reverse-Waiver Evaluations of Adolescents in Adult Criminal Court. J Am Acad Psychiatry Law Online. 2012;40(3):341-347.

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Neurogenesis

INTERVIEW

Meet Former Duke Student and Neuromarketing Expert Jake Stauch An Interview by Connor Hile 36 | Issue 1 | Volume 6 | Fall 2016

The Undergraduate Journal of Neuroscience

Jake Stauch is a former Blue Devil turned entrepreneur who is integrating neuroscience and economics through neuromarketing and EEG analysis. He dropped out of Duke after he experienced an explosion of success through NeuroSpire while working alongside companies such as Sony and Porsche and using brain-imaging analysis to study the brain’s response to marketing strategies. He is now leading his new company, NeuroPlus, by using game-based systems to target children suffering from ADHD to improve their cognitive abilities.

Q: Tell us about yourself.

Q: How did you get interested in neuroscience?

A: My name is Jake and I am the founder and CEO of NeuroPlus. Before founding NeuroPlus, I studied neuroscience at Duke University while also working in a neuroscience lab for approximately three years. I dropped out during my junior year to start my first company called NeuroSpire. At NeuroSpire, we used neuromarketing by testing advertisements and product designs with an EEG on consumers who were part of a focus group. We looked at how they would respond to advertising materials, and we noticed subtle changes in their brain activity which correlated with fluctuations of certain emotional and attentional responses. This company worked with brands such as Wal-Mart, Samsung, Sony, and Porsche, and one day I was approached by an advertising executive who asked if I could build a product to help his son who was struggling with ADHD. I was not entirely sure how to solve this, but I gave it a shot and created a game in which an individual could play and practice improving their attention. This eventually developed into my new company called NeuroPlus, with the goal of helping children who are struggling with attention issues and self-control through brain-controlled videogames.

A: Initially, I was interested in biology. When registering for classes, I could not fit a certain biology course into my schedule, so I decided to register for Neuroscience 101. My TA posted an advertisement for an opening in an ecology lab, and since I needed a job, I applied and accepted the job as a lab assistant. Here, I picked seeds and stems out of soil samples. After taking this neuroscience course, I realized I enjoyed the class and switched from an ecology lab into a neuroscience lab. I initially had no underlying interest in neuroscience. I think I just went down the path, and the further I went the more I became intrigued. The aspect which is extremely interesting to me about neuroscience, is how many fundamental questions remain unanswered relative to other fields of science. We know very little about the subject, and how little we know is exciting.

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Q: Before getting involved with neuromarketing, what were your career plans?

A: I was always interested in starting a business at some point. I think if it did not happen during college then I probably would have tried after graduation. I knew I was interested in entrepreFall 2016 | Volume 6 | Issue 1 | 37

neurship and this happened to collide well with my interest in neuroscience.

Q. Was it a hard decision to drop out of college your junior year and what was your family’s response to your decision?

A. In retrospect it seems like a tough decision, but at the time it was not a difficult decision at all.

I knew what I wanted to do, and it was the most logical decision at the time. At first, it did not start out as entirely dropping out of Duke. Rather, it started out as me not taking any classes for a semester. I had a project I was working on, and I wanted to see where it went. At the end of the semester, I realized we had made good progress, so I decided to take another semester off. I did not suddenly realize I was no longer going to attend school. Rather, it was a slow, gradual process that I took one step at a time. In terms of what my parents thought, I told them I was going to do this a year before I actually did it. It was a slow realization which ended up being helpful in the long run. I did not just suddenly tell my mom and dad that I was dropping out of school the next day, but I instead told them my goals. I told them my plan, and by that time there wasn’t really an argument.

Instead, it was just me deciding to take a semester off to see where things went, and this eventually led to me deciding that it did not make sense to spend a year’s tuition and take a bunch of classes that would not contribute to my career. So, I decided to drop out of Duke, and pursue my already established career at NeuroSpire.

Q: Where do you see your business going in the future and do you have any aspirations to tackle any other disorders?

A: Right now, we want to solve the ADHD crisis that is occurring. In many areas, we are diagnosing 20-30% of children in school districts with ADHD. What I think many people do not realize is these kids are definitively having problems that we need to solve, but whether we need to solve them with the current medicinal approach is a different question. I believe medication can do 38 | Issue 1 | Volume 6 | Fall 2016

wonders for many individuals, but the majority of children could improve their attention and self-control in other ways. Medication is not the only way. We can give these children tools to practice improving their self-control and ability to focus just as we give these children tools to improve their math and English skills. This is where we see NeuroPlus coming in, as this intervention to help these kids with cognitive skills in the same way we intervene to improve academic skills. We want to make this as big as possible and hopefully see these tools in school systems and parents’ homes around the world. There are many conditions where we could implement these kinds of training approaches to other mental health conditions. This is not a one size fits all that will help everyone. Furthermore, I am not someone who is against medicine and the wonders it has done for many individuals suffering from ADHD and other conditions. I believe there are many individuals looking for ways to improve their mental health and supplement their medication through other strategies, and in our case these individuals are guided through gaming. You have seen several companies try to tackle anxiety and depression through social games, and we are interested in that space as well. We are also considering tackling anxiety, depression, dementia, and other kinds of issues to try and improve people’s quality of life through training exercises using software and brain imaging.

Q: Will you be able to tackle other issues dealing with attention, etc. such as autism?

A: It is tough to say as autism is a disorder that is not well understood, and I need to understand this disorder more before making too big of a statement. I believe there are many skill deficits in autistic children which products such as ours could help address around focus and self-control. Practicing these skills will no doubt make an improvement in children lacking these skills. This is really all we are saying. It is not some magical concept where if you practice some sort of skills then you can become better at those skills. In that sense, individuals with autism who have The Undergraduate Journal of Neuroscience

trouble with self-control and paying attention can improve those skills. Now, whether or not this is something that can target some underlying cause of autism is something I do not know, and it would be too bold to say at this point. However, I do believe that we could definitely help improve these kids’ quality of lives.

Q: How do you see your business evolving as brain imaging progresses?

A: A major part of what we do is a concept of neural feedback, which is often misconstrued as there are many people who are doing crazy things in the world of neural feedback. There are both right and wrong ways of doing neural feedback, so we like to think that what we are doing is adhering to the best protocol. We look at brain activity that is well associated with certain types of responses, such as what does attention look like, what does it look like when a child is paying attention, and what do children with attention problems in EEG’s look like. Marginal improvements in brain technology include how comfortable these sensors are and how easy the system is to set up. What we are excited about is what comes after EEG and if there are other technologies that can be shrunken down and used to make the kinds of work we are doing better. There is interesting work being done in areas such as fMRI neural feedback, but this is not feasible for the vast majority of people who we want to target with our products. We are looking to target these finer brain structures and different activity patterns, which are never going to be available with EEG neural feedback, but we hope to someday address these issues in a scalable manner. http://www.neurogenesis-journal.com

Q: What are your thoughts on the Human Connectome Project?

A: It is a controversial idea. We have had these historical moonshot projects starting with the actual moonshot and most recently the Human Genome Project. You have to applaud the ambition of these projects, but projects such as the Human Connectome Project do not have the same end-goal as landing on the moon or mapping the genome. In neuroscience, we find that the more we learn about neuroscience, the more we realize we actually know less than originally thought.

Every finding seems to beg more questions than answers. I think the main answer is how does this end, but several of these neural connections are much more complex than we thought. So, at some point you have to consider if the funding and resources towards mapping out the brain could be diverted towards other major research areas. However, every field is going to argue who deserves the most money and who will most likely find results that will pay off the most. In many ways, though, it is exciting to see all the money and resources go towards the project, so we should be optimistic if the project ends up accomplishing anything major.

Q: What advice would you give to neuroscience students who are not interested in the prehealth field but rather the commercial industry?

A: The most important takeaway is to develop a skill set that is marketable in industry. I think this is where several schools are letting down their neuroscience majors. Neuroscience is awesome and the major provides you with ample information and insight that prepares you well for fields Fall 2016 | Volume 6 | Issue 1 | 39

in neurology and medicine, but it does not necessarily provide you for a job outside of those fields.

This is where I think it ends up creating a big challenge. We get more than a rĂŠsumĂŠ a day from extremely intelligent neuroscience students who cannot do anything for us because we need engineers, marketers, salesmen, customer support, and management. Neuroscience is a good complement to those skills but neuroscience for our application is not a skill in and of itself. In the industry of business, it is more about what skills can you bring to the table other than your neuroscience degree.

If you have lab experience and you go work in a lab, then that can be great, but there are only so many lab positions since there are usually several individuals with neuroscience degrees who are also applying to those same lab positions. If you are just hoping to work for a neuroscience company, and you are not going to be working in a lab or a field tangential to the work you did at your university, then it will be very difficult for you to land a job unless you cultivated a skill set that fits well into the company’s needs. Most companies do not want to invest all of this time to train you when they can find some else in the market who has those same set of skills. Because of this, it is kind of a tough love we have had to give several neuroscience graduates after we ask them what do they have to offer. We ask what can they do, and if they cannot write a computer program, market, or

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have sales experience, we cannot teach the students these skills and thus cannot hire them.

Q: Do you even need a neuroscience degree to work in the neuro-industry?

A: No. The big takeaway is we do not really need people with neuroscience experience for most jobs. However, that is not true for several of the things we do such as technological work with EEGs. However, individuals can oftentimes learn this information over a period of several months if they have the skills around software development, machine learning, and data analytics. If they have those skills coming in, then oftentimes it is an easier transition then someone who knows how an EEG works and who has worked with an EEG in the lab. We prefer people to come in with experience in neuroscience so we are speaking the same language. First and foremost though, we want the individuals to have the skills that we need to run the company, but it is easier to teach a new skill if background knowledge is already there. This is why I keep going back to the importance of learning a skill that you want to implement and let neuroscience enhance that skill. It is important to realize a neuroscience degree is not a skill in and of itself, but only a skill within a few select positions.

The Undergraduate Journal of Neuroscience

Neurogenesis Editors Editors-in-Chief

Katrina Vokt

Tannya Cai

Publishing Editors

Shaq Junaid

Tina Zhao

Shobhana Subramanian

Kirsten Bonawitz

Managing Editors

Jackson Xu

Connor Hile

Sarah Hakani

Ruth Melka

Meghana Vagwala

Michelle Dalson

Shivee Gilja

Design Editors

Gehua Tong

Riya Dange

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Esther Liu

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