Neurogenesis Spring 2021

Neurogenesis

Volume 8 Issue 1

| Spring 2021

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The Journal of Undergraduate Neuroscience

Photo By: K.H. Fung, Science Photo Library

Featured Articles

Efficacy of Caffeine Consumption on Visual Attention and Memory Neuroimaging in Personal Injury Court Cases

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The Undergraduate Journal of Neuroscience

Volume 8 Issue 1 Spring 2021

Copyright © 2021

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Editorial Board Editors-In-Chief Riya Dange Class of 2019 riya.dange@duke.edu

Chris Lai Class of 2019 christopher.lai97@duke.edu

Publishing Editors Shayal Vashisth Class of 2019 shayal.vashisth@duke.edu William Chen Class of 2019 william.j.chen@duke.edu Nidhila Masha Class of 2019 nidhila.masha@duke.edu Preston Bowman Class of 2023 preston.bowman@duke.edu Pierce Hollier Class of 2022 pierce.hollier@duke.edu

Managing Editors Kanav Chhabra Class of 2019 kanav.chhabra@duke.edu Kyle Berlind Class of 2019 kyle.berlind@duke.edu Karan Desai Class of 2022 karan.desai@duke.edu

Design Team Esther Liu Class of 2019 esther.liu@duke.edu Miguel Gonzalez Class of 2020 miguel.gonzalez@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.

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The Undergraduate Journal of Neuroscience

Letter from the Editors In studying neuroscience, we are progressing the work of the ancients.

The earliest recorded reference to the human brain appears in the Edwin Smith Surgical Papyrus, authored by an ancient Egyptian battlefield surgeon in the 17th century B.C. Since then, humanity’s quest to understand how the brain and nervous system work has extended over several millennia. From ancient Greek philosophers, who designated the brain as the “seat” of the mind, to Santiago Ramón y Cajal, who gave us the first images of neurons, we have sought that understanding relentlessly – through a variety of means.

We have come a long way in neuroscience. The anatomy of the brain has been well-determined, and the functionality of various brain regions becomes clearer every day. Through the scientific method and innovative technological developments, we continue to push the boundaries of our own knowledge, piecing together a more complete understanding of why and how the nervous system works. Take, for instance, neurogenesis: the process by which new neurons are spawned. Only thirty years ago, we had no idea that neurogenesis continued to occur in adult brains, and that discovery revolutionized our conceptualization of life, death, neural function and dysfunction. It is no accident that our journal is named for this very process.

This edition of Neurogenesis highlights the work of undergraduates at Duke, our home university, and at other institutions around the world. The authors featured in this issue discuss and utilize cutting-edge techniques, such as functional neuroimaging, transcranial magnetic stimulation, and enzyme-linked immunosorbent assay. They cover a wide variety of fields within neuroscience, from neuropharmacology to neurodegenerative diseases to the intersection of neuroimaging and law.

Our two featured articles embody the ways neuroscience intersects with other arenas of study. Xuanyu Zhou and Yiran Xu's piece explores the efficacy of caffeine consumption in enhancing students’ visual attention and short-term memory capabilities. They conduct human subject trials to examine whether and how acute caffeine intake may improve educational outcomes for students. Meanwhile, Sophia Li and Caleb Rummel’s article illustrates how neuroscientific tools can be applied to the legal field. Specifically, they explain how neuroimaging techniques show promise in standardizing measures of pain for use in chronic pain and personal injury lawsuits.

Neurogenesis serves as a platform for undergraduates at Duke and other institutions to showcase their work in neuroscience to a global audience. Submissions and publications come from all over the United States and the world, and this participation drives the continued success of the journal. To that end, we would like to thank the students from Duke University, University of Notre Dame, University of Toronto, and DePaul University for their submissions and publications. Although we attend different universities, we all share the same passion for neuroscience and the goal of advancing humanity’s collective understanding.

Sincerely, Riya Dange & Chris Lai Editors-in-Chief

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The Undergraduate Journal of Neuroscience

Neurogenesis

TABLE OF CONTENTS ARTICLES 8 Efficacy of Acute Caffeine Consumption on Visual Attention and Short-Term Memory in Chinese High School Students Xuanyu Zhou and Yiran Xu

15 Relating PerspectiveTaking to Objective Distancing at Behavioral and Neural Levels Angeli Sharma

REVIEWS 22 The Role of Neuroimaging in Personal Injury Court Cases Sophia Li and Caleb Rummel

28 Alzheimer's Disease: A Review of Current and Novel Therapies Nathan Luzum and Ian Levitan

33 Repetitive Transcranial Magnetic Stimulation (rTMS) as a Novel Therapeutic Intervention for Obsessive Compulsive Disorder Nicolette Stogios

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Neurogenesis

ARTICLE

Efficacy of Acute Caffeine Consumption on Visual Attention and Short-Term Memory in Chinese High School Students Xuanyu Zhou1 and Yiran Xu1 Duke University, Durham, North Carolina 27708 Correspondence should be addressed to xuanyu.zhou@duke.edu 1

Accepted for Publication: October 21, 2018

The study aimed to evaluate the efficacy of acute caffeine consumption on visual attention and short-term memory in Chinese high school students. A randomized, double-blind, controlled trial was conducted, during which 44 healthy high school students (20 males and 24 females), 16-18 years of age, were randomly assigned to the intervention group (24 cases) and the control group (20 cases).

Participants in the intervention group received 2g of caffeinated coffee (containing 60mg of caffeine) while the control group received 2g of decaffeinated coffee. Visual attention, short-term memory, blood pressure and heart rates were measured at baseline and again at 45 min post-coffee consumption. There was a significant decrease in visual attention scores in both the caffeinated coffee group and the decaffeinated coffee group following intervention. Visual attention scores of the caffeinated coffee group decreased more than the decaffeinated coffee group (-16.33 ± 10.31 vs -10.35 ± 12.42, p<0.05). The short-term memory scores increased in both groups after intervention, while the change was not significantly different between the two groups (7.04 ± 6.49 vs 6.95 ± 7.33, p>0.05). There were no statistically significant differences regarding changes in blood pressure and heart rates, before and after intervention or between the two groups. Acute caffeine consumption was associated with improved visual attention performance in adolescent populations. The relationship between acute caffeine consumption and short-term memory performance is still worthy of further research.

INTRODUCTION

Caffeine (1, 3, 7-trimethylxanthine) is the most commonly consumed psychoactive substance throughout the world, with coffee and tea as the two most prominent sources (Heckman et. al, 2010). The history of caffeine dates back to 2737 BC, when Chinese legend describes that the tea discovered

by Emperor Shen Nung could produce “vigor of body, contentment of mind, and determination of purpose” (Arab et. al, 2008). Caffeine is a widely used psychoactive stimulant, as it easily crosses the blood–brain barrier and acts primarily by blocking adenosine A1 and A2A receptors, leading to centrally stimulating effects (Fredholm et. al, 2016). Acute caffeine consumption can also activate the sym8 | Issue 1 | Volume 8 | Spring 2021

pathetic nervous system (Papakonstantinou et. al, 2016) and increase galvanic skin response (Lyvers et. al, 2013). These stimulant properties may explain the beneficial effects of acute caffeine intake on attention, memory and cardiovascular functions.

A substantial body of evidence demonstrated that caffeine consumption can improve attention (Einother et. al, 2013). Andrew P. Smith et. al conducted a trial in an adult population and found that caffeine intake was linked to faster reaction time, fewer long responses, greater detection of targets in the cognitive vigilance task, and faster encoding of new information (Smith et. al, 2013). A double-blind, placebo-controlled study in young adults concluded that acute habitual doses of caffeine can improve simThe Undergraduate Journal of Neuroscience

Zhou & Xu | ARTICLE

Characteristic

Age (year, x±s)

Height (cm, x±s) Weight (kg, x±s) Male (case, %)

Never Drink (case, %)

Caffeine Group (24 cases)

Decaffeinate Group (20 cases)

P-value

62.08±9.16

63.80±12.85

.62

17.04±.55

171.42±6.96 14, 58.0% 8, 33.3%

Drink Occasionally (case, 9, 37.5% %) Drink a Lot (case, %)

7, 29.2%

Table 1: Comparision of Baseline Data Between the Two Groups

ple and sustained attention and executive updating (Lanini et. al, 2016). Studies also considered caffeine as a potential therapeutic approach in treatment of attention-deficit hyperactivity disorder (ADHD) (Ioannidis et. al, 2014). A growing number of animal experiments have shown that caffeine can improve memory performance and prevent memory impairment (Espinosa et. al, 2013; Han et. al, 2013; Nazario et. al, 2015). However, the beneficial effects of caffeine on human memory performance are still unclear (Warburton et. al, 2001).

In China, more and more teenagers drink caffeinated products as relief from the intense stress of studying. However, few studies have investigated the potential effects of acute caffeine consumption on teenagers’ cognitive abilities, specifically attention and memory. This study investigates the relationship between acute caffeine consumption and visual attention, short-term memory performance and cardiovascular function in Chinese high school students.

The study protocol was approved by the ethics committee, and the high school affiliated with Renmin University of China. All participants provided written informed consent prior to participation.

The trial was conducted and reported according to the principles contained in the CONSORT statement 2010 (Shulz et. al, 2010).

16.30±.47

171.90±6.98 10, 50.0% 2, 10.0%

17, 85.0% 1, 5.0%

.001 .82 .58

.006

mal vision, and sleep 6 to 8 hours a night during weekdays without any sleeping problems. Each participant’s general information, including name, sex, age, height and weight was recorded. Their coffee-drinking habits were classified into three categories: never drink (no history of drinking coffee), drink occasionally (drink coffee at least once a week, but not every day) and drink a lot (drink coffee frequently or daily).

Study Design and Treatment This study was a randomized, double-blind, controlled trial. Participants were randomly assigned to two groups: caffeinated coffee group (intervention group) and decaffeinated coffee group (control group). In this trial, randomization with allocation concealment by opaque sequentially numbered sealed envelopes was conducted. We generated the random allocation sequence, enrolled participants and assigned them to intervention groups. The Figure 1: Visual Attention Test Questionnaire

MATERIALS AND METHODS

Setting and Participants This study was conducted in a high school affiliated with Renmin University of China. Participants were recruited from grades 10 and 11. To be eligible, participants had to be healthy high school students, nonsmokers with normal or corrected-to-norhttp://www.neurogenesis-journal.com

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ARTICLE | Efficacy of Acute Caffeine Consumption on Visual Attention and Short-Term Memory in Chinese High School Students

researchers and participants were blinded to the types of coffee given to the control and intervention group. As there was no previous study upon which to base a sample size calculation, this study was initiated as an exploratory trial, not intended to be definitive but rather to provide the basis for sample size calculation for any future trials. Participants in the caffeinated coffee group received 2g of caffeinated coffee (Dormans®, containing 60mg caffeine) dissolved in 200ml hot water. Participants in the decaffeinated coffee group received 2g of decaffeinated coffee (Kenco), also dissolved in 200 ml hot water.

Outcomes and Measurement of Visual Attention A Schulte table from a previous study was modified in this trial to assess visual attention (Schulte et. al, 2001). During the experimental sessions, participants were seated in a bright classroom and faced a piece of paper on a desk. A black ball pen, a timer, and a quiz paper were placed on each desk. Natural numbers 00 to 99 (00, 01, 02, 03, 04, 05..., 99) were randomly presented in a 10*10 square frame.

Participants were asked to find any 15 continuous natural numbers in the square frame as quickly as possible. The timer recorded the time in which participants completed the task. The visual attention scores, presented in seconds, was a measurement of the time participants took to complete the test, and thus a shorter time indicates better visual attention performance (Figure 1).

Short-Term Memory The short-term memory test contains both numerical and linguistic terms to mitigate bias caused by students’ preference and ability in memorizing a specific type of term. The linguistic terms test conFigure 2: Short-Term Memory Test Questionnaire

Figure 3: CONSORT 2010: Diagram for participant flow through the trial

tains 20 terms (two terms with one character each and 18 terms with two characters each). The participants were asked to memorize as many as possible in 30 seconds, then turn over the paper and write down what they memorized. Each correct term was worth one mark, and the sum of the marks was recorded as T1. The numerical terms test contains a number sequence with 30 numbers set randomly (from 0 to 9, with no consecutive repeats). The participants were asked to memorize as many as possible in 40 seconds, in the same order as the numbers were given then turn over the paper to write down what they memorized. N is the nth correct term the participant successfully memorized in the sequence and T2 is the score of the numbers memorizing portion. The total scores were calculated as T1+T2. The total score is a measure of the number of terms the participants memorized correctly, and thus higher score indicates better short-term memory performance (Figure 2).

Blood Pressure and Heart Rates The differences between the baseline and post-intervention arterial blood pressure (BP) and heart rates (HR) were observed to evaluate the shortterm effect of caffeine on cardiovascular function.

BP and HR were measured using a well-validated electronic sphygmomanometer (HEM-8102A) both before and 45 minutes after coffee consumption.

Additionally, two measurements were taken at 1-2 min intervals, and the average was recorded as the BP value. BP was presented in mmHg and HR was presented in number of beats per minute (bpm).

Procedures The participants were asked to abstain from caf-

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Figure 4: Comparisons of Visual Attention Scores and Short-Term Memory Scores in Two Groups Before and After Intervention (Score, x±s)

Figure 5: Comparisons of BP in Two Groups Before and After Intervention (mmHg, x±s)

feine-containing beverages such as coffee, tea and soft drinks for at least 48 hours prior to the trial. All the tests took place in the morning at a consistent time. At the beginning of the trial, the general information, BP and HR were measured and collected. Then, visual attention and memory tests were conducted. Participants were instructed to complete the questionnaire papers to the best of

their abilities, and were not permitted to talk or disturb any other participant in the laboratory. Then, the participants received 2g of decaffeinated coffee or caffeinated coffee from the same brand dissolved in 200ml hot water. Approximately 45 minutes after the participants consumed coffee, when they reached peak plasma caffeine levels (Lorist et. al, 1996), BP and HR were measured again. Meanwhile, another similar visual attention and shortterm memory tests, using questionnaire papers with different but similarly structured questions, were conducted again.

Statistical Analysis Continuous variables were presented as mean values ± standard deviation (SD) and categorical variables were presented as absolute numbers and percentages. The independent-samples t-test was used to test differences between the two groups and the paired-samples t-test was used to test differences before and after treatment if the variables were normally distributed; otherwise the Mann-Whitney U test was used. The categorical variables were analyzed by χ2 test. All P-values were 2-sided and P-values<0.05 was regarded as statistically significant difference. All the analyses were performed by SPSS17.0 software.

RESULTS

Participant Flow and Baseline Data 44 healthy high school students, 20 males and 24 females, between 16 to 18 years of age, with different habits of drinking coffee (8 drink a lot, 26 drink occasionally, and 10 never drink any) were enrolled and randomly assigned (24 to the caffeinated coffee group and 20 to the decaffeinated coffee group).

Participant flow through the trial is shown in Fig. 3. There were no significant differences in gender,

Table 2: Comparisons of Visual Attention Scores and Short-Term Memory Scores in Two Groups Before and After Intervention (Score, x±s)

Characteristic

Age (year, x±s)

Height (cm, x±s) Weight (kg, x±s) Male (case, %)

Never Drink (case, %)

Caffeine Group (24 cases)

Decaffeinate Group (20 cases)

P-value

62.08±9.16

63.80±12.85

.62

17.04±.55

171.42±6.96 14, 58.0% 8, 33.3%

Drink Occasionally (case, 9, 37.5% %) http://www.neurogenesis-journal.com

16.30±.47

171.90±6.98 10, 50.0% 2, 10.0%

17, 85.0%

.001 .82 .58

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and Fig. 5. The results of HR are shown in table 4 and Fig. 6.

DISCUSSION Figure 6: Comparisons of HR in Two Groups Before and After Intervention (bpm, x±s)

height or weight between the two groups. There were statistical differences in age (17.04 ± 0.55 vs 16.30 ± 0.47, P<0.01), but this difference had little clinical significance. The coffee-drinking habits also showed statistical differences (P<0.01). The baseline data of the participants is shown in table 1.

Visual Attention Scores and Short-Term Memory Scores At baseline, there was no significant difference in either visual attention scores or short-term memory scores between the two groups. After intervention, there was a significant decrease in visual attention scores in both caffeinated coffee group and decaffeinated coffee group. Visual attention scores of the caffeinated coffee group decreased more than those of the decaffeinated coffee group (-16.33 ± 10.31 vs -10.35 ± 12.42, p<0.05). The short-term memory scores increased in both groups after intervention.

However, the difference was not statistically significant between the two groups (7.04 ± 6.49 vs 6.95 ± 7.33, p>0.05). The results are shown in Table 2 and Fig. 4.

Blood Pressure and Heart Rates At baseline, there were no significant differences in either systolic BP (SBP) or diastolic BP (DBP) level between the two groups. After intervention, the changes in SBP, DBP and HR were not statistically significant. The results of BP are shown in table 3

Summary of Main Findings This study shows that acute caffeine consumption is associated with teenagers’ visual attention performance, as caffeinated coffee does not improve an individuals’ visual attention compared to decaffeinated coffee. However, acute caffeine consumption was not significantly associated with short-term memory performance or cardiovascular function in this trial.

Interpretations This trial suggested that caffeine may not be a preferable visual attention enhancer for high school students, providing insights into the impact of caffeine consumption on juvenile drinkers. A previous systematic review concluded caffeine can improve performance on both simple and complex attention tasks in healthy adult volunteers (Einother et. al, 2013). One trial showed that in habitual consumers, high doses of caffeine can produce beneficial changes in visual attention and that caffeine can interact with adenosine and dopamine in brain regions mediating visual attention (Brunye et. al, 2010B). A high dose of caffeine can also improve the visual attention in non-habitual caffeine consumers (Ferre, 2008). Because adenosine imposes the pre- and postsynaptic brakes on dopaminergic neurotransmission by acting on different adenosine receptor heteromers localized in different elements of the stratal spine module, releasing the brakes may be a critical mechanism of the psychostimulant effects of caffeine (Caballero et. al, 2011). Another experiment also hypothesized that caffeine might be useful in management of attention deficit during

Table 3: Comparisons of BP in Two Groups Before and After Intervention (mmHg, x±s)

Outcome

Systolic BP Diastolic BP

Group

Before Intervention After Intervention

Difference

Decaffeinate (20 cases)

113.35±11.31

-0.95±11.23

Caffeine (24 cases) Caffeine (24 cases) Decaffeinate (20 cases)

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114.38±1301

113.42±14.19

69.42±10.62

71.54±10.23

66.20±9.36

112.40±17.02 72.95±19.55

-0.96±7.57 2.13±7.80

6.75±18.44

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Outcome

Heart Rate

Group

Before Intervention After Intervention

Difference

Decaffeinate (20 cases)

75.80±13.52

2.45±8.91

Caffeine (24 cases)

83.17±10.27

Table 4: Comparisons of HR in Two Groups Before and After Intervention (bpm, x±s)

the prepubertal period of attention deficit hyperactivity disorder (ADHD) as caffeine can improve the attention deficits present in 6-hydroxydopamine (6-OHDA) lesioned rats (Heilbronner et. al, 2015).

In the present study, acute caffeine consumption was founded to have no effect on cardiovascular functions in adolescents, as the BP and HR showed no significant change in the caffeinated group. A meta-analysis of randomized controlled trials showed that chronic intake of caffeine increased BP, though the effect was small (Bitar et. al, 2015). Chronic caffeine intake can also increase the HR and acute caffeine intake can increase HR in mice (Bauza et. al, 2015; Waring et. al, 2003). However, there is still no agreement on the actual effect of acute caffeine consumption on peripheral BP and HR. Previous trials found that acute caffeine intake significantly increased central BP but had no effect on peripheral BP in healthy adults, and the effects of caffeine on BP may be significantly underestimated by measurement of BP at the brachial artery [Karatzis et. al, 2005; Kenemans et. al, 1998). In this trial, only measuring BP at the brachial artery may also underestimate the actual effects of caffeine on BP and a more in-depth study should be conducted to offer a thorough conclusion of this issue.

Strengths and Limitations In accordance with former studies (Koppelstaetter et. al, 2008; Smillie et. al, 2010), this trial found no significant effect of caffeine on short-term memory behavioral performance. However, other studies have reported that caffeine can enhance working memory [Herz et. al, 1999; Noordzij et. al, 2005).

These discrepancies might be caused by differences in participants’ characteristics, differences in the testing methods, or varying dosage of caffeine intake. Moreover, the insignificant effect may result from low statistical power due to the small sample size of participants.

While most previous studies were conducted in adult populations, this trial enrolled 16-to-18 yearhttp://www.neurogenesis-journal.com

80.71±10.96 78.25±11.27

-2.46±9.73

old high school students. Moreover, this trial adopted a relatively traditional method—testing on paper—to assess the effect of caffeine on attention and memory. In past studies, electronic screens (Brunye et. al, 2010B), multi-sync monitors and electronic portable devices (Lanini et. al, 2016) were often used as measurement tools.

However, for high school students in China, quizzes and examinations on paper are a relatively more common form, so testing their cognitive abilities on paper in this trial may be a more appropriate method. There were, however, some limitations that should be carefully considered. Firstly, due to time and distance restrictions, this trial only enrolled forty-four participants from one high school. Therefore, it is possible that people from other areas, racial backgrounds or different age groups might have varying baseline cognitive abilities. Additionally, the sample size was not large enough to draw the most unbiased conclusion. Secondly, we failed to find a satisfactory placebo. We used caffeinated and decaffeinated coffee from the same brand, but these two drinks may differ in other unknown ways.

Thirdly, though we randomised the participants, age and habits at baseline still showed statistical differences, as the sample size was too small to truly randomise. Nevertheless, further subgroup analyses did not show the impact of drinking habits on visual attention and short-term memory results in the trial. Lastly, despite controlling the amount of caffeine consumption, we did not control the daily dietary routine, which may also influence the effects.

CONCLUSION AND FURTHER IMPLICATIONS

In summary, there was a relationship between acute caffeine consumption and teenagers’ visual attention performance. The present trial provides evidence suggesting that caffeine consumption can improve visual attention in adolescent populations.

The relationship between acute caffeine consumption and short-term memory performance and carSpring 2021 | Volume 8 | Issue 1 | 13

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diovascular function in adolescents is still worthy of further research. Well-designed future studies with larger sample sizes are necessary to further evaluate the effects of caffeine on teenagers’ cognitive ability and experimental studies should be conducted to explore the mechanisms underlying the effects.

BP and HR were measured using a well-validated electronic sphygmomanometer (HEM-8102A) both before and 45 minutes after coffee consumption.

Additionally, two measurements were taken at 1-2 min intervals, and the average was recorded as the BP value. BP was presented in mmHg and HR was presented in number of beats per minute (bpm).

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Lanini, J., Galduroz, J.C., & Pompeia, S. (2016). Acute personalized habitual caffeine doses improve attention and have selective effects when considering the fractionation of executive functions. Human Psychopharmacology, 31(1), 29-43.

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Crippa, J.A., Bonan, C.D., & da Silva, R.S. (2015). Caffeine protects against memory loss induced by high and non-anxiolytic dose of cannabidiol in adult zebrafish (Danio rerio). Pharmacology Biochemistry and Behavior, 135, 210-216.

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Heilbronner, U., Hinrichs, H., Heinze, H.J., & Zaehle, T. (2015). Caffeine differentially alters cortical hemodynamic activity during working memory: A near infrared spectroscopy study. BMC Research Notes, 8, 520.

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Noordzij, M., Uiterwaal, C.S., Arends, L.R., Kok, F.J., Grobbee, D.E., & Geleijnse, J.M. (2005). Blood pressure response to chronic intake of coffee and caffeine: A meta-analysis of randomized controlled trials. Journal of Hypertension, 23(5), 921-928.

Bitar, A., Mastouri, R., & Kreutz, R.P. (2015). Caffeine consumption and heart rate and blood pressure response to regadenoson. PLOS One, 10(6), 1-9.

Bauza, G., & Remick, D. (2015). Caffeine improves heart rate without improving sepsis survival. Shock, 44(2), 143-148.

Waring, W.S., Goudsmit, J., Marwick, J., Webb, D.J., & Maxwell, S.R. (2003).

Acute caffeine intake influences central more than peripheral blood pressure in young adults. American Journal of Hypertension, 16(11 Pt 1), 919-924.

Karatzis, E., Papaioannou, T.G., Aznaouridis, K., Karatzi, K., Stamatelopoulos, K., Zampelas, A., Papamichael, C., Lekakis, J., & Mavrikakis, M. (2005). Acute effects of caffeine on blood pressure and wave reflections in healthy subjects: should we consider monitoring central blood pressure? International Journal of Cardiology, 98(3), 425-430.

Kenemans, J.L., & Verbaten, M.N. (1998). Caffeine and visuo-spatial attention. Psychopharmacology (Berl), 135(4), 353-360.

The Undergraduate Journal of Neuroscience

Neurogenesis

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Relating PerspectiveTaking to Objective Distancing at Behavioral and Neural Levels Angeli Sharma1 Duke University, Durham, North Carolina 27708 Correspondence should be addressed to angeli.sharma@duke.edu 1

Accepted for Publication: October 21, 2018

This study relates perspective taking to objective distancing using both behavioral and BOLD fMRI data. Participants completed questionnaires including the Interpersonal Reactivity Index (IRI) and its subscore which measures perspective taking in everyday life, trained on objective distancing, and practiced with the task. They then viewed aversive images that elicited an emotional response. Participants were asked to either let the response develop naturally or told to regulate it by taking on the perspective of a neutral, objective observer. Self-report rating screens were used to calculate the efficacy of the objective distancing technique in relation to the natural response scores. For the behavioral data, efficacy was correlated with the perspective taking scores in the IRI. For the fMRI data, brain activation caused by the objective distancing was again correlated to the trait scores. The results yielded no significant correlation between perspective taking scores in either the behavioral (trait scores) or the neural data (fMRI data). The imaging analysis did produce a trend showing a negative correlation between the left Temporo-Parietal Junction and the Interpersonal Reactivity Index suggesting that the neurons in this area show less brain activation because of higher efficiency.

INTRODUCTION

Emotional regulation, the ability to use different mental processes, like imagination or distraction, to influence emotions, is a field that has been studied from a variety of different lenses including social psychology and cognitive neuroscience (Ochsner & Gross, 2008). The field of social psychology focuses on how feelings and behaviors are affected by social interactions. The field of cognitive neuroscience focuses on the neural structures behind mental processes. However, in order to better understand how social processes and emotions interact in the brain, we must draw from both fields. Such an interdisciplinary approach would lead to a greater understanding of the intimate relationship between behavior and neural activity. Cognitive restructuring is a type of emotional regulation that requires people to identify negative thoughts and modify them (Boyes, 2013). There are several points during the thought process at which this therapy can be implemented, as shown in Figure 1 below (Ochsner, Silhttp://www.neurogenesis-journal.com

vers & Buhle, 2012).

Through emotional regulation, people can regulate the degree to which stimuli affect them. This is known as antecedent-focused emotional regulation.

Emotional regulation can be broken down into two broad categories within antecedent focused emotion regulation. The first focuses on changing the stimulus and the second—which is the category of emotion regulation this paper attends to—focuses on changing the interpretation or the perception of the stimulus (Berkman & Lieberman, 2009). Re-appraisal is one way to change the perception of a stimulus, as shown in Figure 1. In this technique, the stimulus itself remains unchanged, however the person uses a different thought process to re-imagine the situation. For example, if a man goes to a job interview and feels like he did poorly, he may believe that he will fail to get the job. This opinion may cause him to believe that his next job interview will also go poorly and will cause him to proactively attach negative emotions to future job interviews.

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Figure 1: Emotional Response Regulation

Using cognitive re-appraisal, he can change his thought process to believe that although his first interview did not go well, his performance on it will not have any effect on his performance on subsequent interviews.

Another way to focus on this regulation is through cognitive distancing which is when you detach yourself from the situation to be less affected by the stimulus. Various cognitive distancing techniques can help vary the emotional responses attached to stimuli. Some distancing techniques include: spatial (Trope & Liberman, 2010), temporal (Trope & Liberman, 2010), and objective (Sokul et. all, 2010). Spatial distancing refers to the ability to regulate emotions through manipulating the mental representations of physical distance between oneself and objects or between oneself and events.

Temporal distancing refers to the ability to regulate emotions due to perceiving events as being far in the past or far in the future. Finally, objective distancing refers to the ability to regulate emotions by making one’s perspective of a stimulus more neutral.

In addition to the various cognitive distancing techniques that have been developed for emotional regulation, technological innovations have provided better tools to study the neural activity associated with these behaviors. The use of functional MRIs has greatly impacted the ability of neuroscience research to measure brain activation (Ochsner, Bunge, Gross & Gabrieli, 2002). This technology is founded on the theory that increased neural activation causes increased blood supply in the activated areas of the brain. Therefore, blood oxygen-level dependent (BOLD) signals can be used as a proxy measure for neural activation.

This development in technology has enabled researchers to better understand how different brain areas relate to various cognitive processes. Previous studies have shown an association between 16 | Issue 1 | Volume 8 | Spring 2021

the Temporo-Parietal Junction (TPJ) and ability to understand others’ perspectives, also known as the “theory of mind” (Saxe & Kanwisher, 2003). The TPJ receives information from the thalamus, the limbic system, as well as the somatosensory, auditory, and visual sensory systems. It has also been linked to empathy (Engen & Singer, 2013).

All in all, the interdisciplinary nature of emotional regulation, improvements in technology, new research showing the relation to the TPJ region of the brain lead to interesting questions. It allows the potential to predict the likelihood for a particular emotional regulation technique to be more effective or successful by using a psychometric tool as a predictive measure. This also opens the possibility of brain activation to be predicted or correlated by psychometric measures. Another possibility is that greater propensity of using a particular technique in everyday life increase or decrease brain activation when it is employed. These are some of the questions that this research focuses on answering.

By focusing on the objective distancing technique in particular, the research hopes to learn more about the questions raised.

The overall purpose of this research is to examine the relationship between propensity for perspective taking (tendency to see the psychological perspective of others) and the emotion regulation technique of objective distancing at behavioral and neural levels. The hypothesis is that greater use of perspective taking will correlate with a better ability to be able to use the objective distancing technique by being able to put himself or herself in the psychological position of an objective observer of the situation. Using fMRI data, this study will also examine if the temporo-parietal junction is the brain area that is most activated during objective distancing technique, and if the area relates to trait perspective taking.

MATERIALS AND METHODS

Figure 2: Natural View Task and Self-Report Image Rating Screen for View

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Participants For this study, we recruited 34 participants (16 F, 18 M), ranging from ages 18-39. The mean age was 24.35 years with a standard deviation of 3.61 years.

The participants were recruited through the DukeUNC Brain Imaging and Analysis Center (BIAC) subject pool, which consisted of individuals affiliated with Duke as well as those from Durham, Orange, and Wake Counties. All participants had been approved to be involved in fMRI studies. Participants with a previous history of neurological illnesses, current psychiatric disorders or psychoactive medication usage, left-handedness, and/or pregnancy were excluded from this study. Participants received no direct benefits from participating in the study, however it was explained to them that this study could be useful in the future for those suffering from emotional regulation disorders. The participants were compensated at $20 per hour for every 5 presses of the active lever. FR5 testing was followed by one FR1 session (ChAT-ChR2 n=3). In order to test for flexibility of learning, 5 FR1 sessions of a contingency switch condition followed, during which the right lever became active and the left lever inactive (ChAT-ChR2 n=2).

Psychometric Instruments Various psychometric tests have been developed to measure the ability of individuals to minimize emotional responses and perspective-taking. One such tool is the Interpersonal Reactivity Index (IRI) which was developed by Dr. Mark Davis in 1980 (Davis, 1980). In this questionnaire, empathy is measured using four subscales: fantasy taking, perspective taking, as well as two subscales which measure reactions to negative experiences. The perspective taking subscale measures the participant’s tendency to see the psychological perspective of others in their life (Davis, 1980). This study used participants’ scores on the perspective taking subscale to measure their level of everyday perspective taking.

Figure 3: Objective Distancing Task and Rating, Success and Effort Screens

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This study also used the Beck Depression Inventory (BDI) which measures symptoms related to depression (Beck, Ward, Mendelson, Mock & Erbaugh, 1961), as an exclusionary tool to ensure the eligibility of study participants.

Procedure This study spanned two days. The first day takes 1 hour and the second day takes 2 hours. On the first day of the study, participants filled out an informed consent form about their participation in both the behavioral and fMRI portion of the study. This was be followed by a personal history form to determine if the participants were fit to be part of the study. They then took the Beck Depression Inventory (BDI) which helped exclude participants with current psychiatric disorders, such as suicide or depression. If their total score was over 20 or the answer to question 9 (regarding suicidal ideation) was rated at 2 or 3, the participant were excluded and the protocol for recommending them to mental health professionals for further full evaluation was followed. If the participant was considered a good fit for the study, the psychometric tool of the Interpersonal Reactivity Index (IRI) was administered with participants and the score for the perspective taking subscale, which measures the participant’s tendency to see the psychological perspective of others in their life, was obtained.

Emotion Regulation Task After the completion of the psychometric questionnaires, participants underwent emotional regulation training by practicing the computer based task. The task will use negative pictures from the International Affective Picture System (IAPS) to elicit emotional responses (Lang, Bradley & Cuthbert, 2008 ). Subjects were instructed as follows: “In the task you will be doing, you will be viewing images on a screen and answering questions about your emotional responses to the images. Before each image, you will see a cue indicating whether you should passively view the image and allow any emotional response to occur naturally or whether you should implement one of three techniques to decrease a potential emotional response. First, you will receive instruction and practice in implementing each technique and answering the questions you will see during the task.” The success of the distancing technique was be measured by comparing the participant’s self-report image rating responses of the distancing trials to the trials where the parSpring 2021 | Volume 8 | Issue 1 | 17

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ticipant responded naturally to the pictures.

For trials prompting participants to passively view images,, the cue word “View” appeared on the screen for 2 seconds followed by an image which will remain on screen for 8 seconds. It will be followed by an image rating screen which will prompt participants to identify the degree of emotional response that they felt the image elicit. A scale of 1 through 7 will be used, where 1 will be very negative, 4 is neutral and 7 is very positive. The technique and screen is illustrated in Figure 2 below.

After practicing “View” three times successfully (meaning that they verbally confirm that they were able to implement the technique), the participants were taught the objective distancing emotional regulation technique, which manipulated the mental representations of the image with regards to the participants’ perspective. For objective distancing, subjects were instructed, “While viewing the image, imagine that you are observing the content depicted in the image from the perspective of an objective observer.” Following the cue word, image presentation, and rating screen, the participant were asked to verbalize their thought process. After verbalizing their thought process they moved on to a success screen which determined if the participant attempted to use the correct technique and was attentive to the cue word. Both measures were self-reported. The effort screen collected data regarding the difficulty of implementing the technique. Figure 4 illustrates how the objective distancing technique will look and presents the three different screens presented after the objective distancing technique.

Two other regulation techniques are taught to the participants in this manner too for the task, but the data and analysis for those techniques will not be presented in this paper.

After the objective distancing technique was taught and successfully completed three times, the participant explained the differences between the “View” versus the “Objective” technique to the researcher.

Once the researcher was confident that the participant is clear on the differences between the two, they proceeded to the mixed practice, which includes all the regulation techniques and the natural view which mimics the real task. Successful completion of the mixed task made a participant eligible for Day 2 of the study which was completed about a week later.

MRI Data Acquisition For the fMRI, the 3 Tesla General Electric MR 750 18 | Issue 1 | Volume 8 | Spring 2021

system with 50-mT/m gradients and an eight-channel head coil for parallel imaging (General Electric, Waukesha, WI, USA) was used. Anatomical images were acquired using a 3D fast SPGR BRAVO pulse sequence: repetition time (TR) = 7.58 ms; echo time (TE) = 2.936 ms; image matrix = 2562; α = 12°; voxel size = 1 × 1 × 1 mm; 206 contiguous slices (Kragel & LaBar, 2015). Functional images for this study was acquired using a spiral-in pulse sequence (TR = 2000 ms; TE = 27 ms; image matrix = 64 x 128; α = 60°; voxel size = 4.0 x 4.0 x 3.8 mm; 34 contiguous slices). In each run, four images were excluded to ensure the magnet field was at a steady state. fMRI Task Day 2 of the study focuses on the neural hypothesis set forth by the research of whether brain activation in the TPJ would correspond with the valence scores. On this day, brain imaging data from BOLD fMRI will be collected while participants complete the mixed task. First, the participant will practice the task using a training refresher. They will be asked to verbalize their understanding of the objective regulation technique and the natural view technique and successfully complete another mixed practice. Prior to the data collection, female participants will be asked to take a pregnancy test to ensure their eligibility for the study. For their safety, all participants will go through a metal screening prior to entering the fMRI machine.

After completing the safety precautions, the participants will enter the fMRI machine and an anatomical scan of their brain will be taken. Then they will be asked to complete the mixed task, as described in Day 1, in the fMRI machine while the following psychophysiological measures-- ECG, GSR, and RSP data—are collected. Each participant will complete the task 7 times. Each run of the task will require them to view and respond to as many images as they are able to get through in 11 minutes; each run of the task is thus expected to take about 11 minutes.

Behavioral Statistical Analysis The objective distancing score was calculated by taking the difference between the average image ratings while participants used the technique and while participants used the natural view. This score was calculated across the 7 runs per participant.

Then a pearson correlation was run between the objective distancing scores and the perspective takThe Undergraduate Journal of Neuroscience

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ing subscores in the IRI for the study.

Imaging Statistical Analysis To analyze the image data, FSL 5.0.9 was used. The images were preprocessed by extracting the brain, correcting for motion using MCFLIRT, high pass filtering to remove noise, and smoothing at 8 mm full- width half maximum (FWHM). The functional scans were co-registered with the anatomical brain extraction and then to the MNI template.

This research determined areas with brain activation during the objective distancing technique and the natural view condition across participants. The brain activation related to just the view condition was subtracted from the brain activation related to the objective distancing to ensure that general brain activation was removed from the analysis.

This enabled the study to determine what the peak voxels relating to objective distancing in each TPJ were. The research used a region of interest (ROI) analysis to average brain activation for each participant due to objective distancing in a 5mm spherical ROI around those voxels.. This was related to the perspective taking sub-score on IRI using a Pearson correlation.

RESULTS

The hypothesis of this study was that greater use of perspective taking in daily life will correlate with an improved performance of the objective distancing technique. To study this the perspective taking score on the IRI was used to determine “perspective taking in daily life”. “An improved performance of the objective distancing technique” was determined by measuring the difference in image ratings of the natural view compared to the objective distancing technique. An improvement was when the image ratings moved towards a more positive (higher) number after the objective distancing technique.

By using the analysis and statistics above, we determined that the hypothesis was not supported. The objective distancing technique, on average, lead to a 0.76 increase in image rating compared to the natural view image ratings. This means that on average participants who used the objective distancing technique rated the images as 1 point more positive. This would be the equivalent of rating something slightly negative with the natural view and rating it neutral with the objective distancing technique. However, there was no significance between the perspective taking score on the IRI of participants compared to this change in image http://www.neurogenesis-journal.com

ratings due to the objective distancing technique.

Using the Pearson Correlation, the line of best fit yielded a slope of -.075231 which was not significantly different from zero . Graph A is included below of the correlation.

Some interesting things to note in this finding is that most of the participants had perspective taking scores that were in the top half of all possible scores. The scores can range from 0 -28 and all scores except one, which was 12, were in the top half of possible scores. The mean PT subscore was 19.21 with a standard deviation of 1.

This was not true of all subscores in the IRI. The fantasy taking subscore in the IRI yielded a wider range with scores as low as 5 and as high as 28.

This lack of equal distribution of perspective taking scores in the sample could have lead to a lack of correlation in the data.

The neural hypothesis this study proposed was that trait scores of perspective taking will correspond to the brain activation related to the objective distancing technique in the temporo-parietal junction (TPJ).

When the Pearson Correlation Analysis was completed, there was no significant brain activation in the TPJ compared to the IRI perspective taking score. However, the left TPJ was almost significant with an R-value of -.293 and a P-value of .093. Since the R value was a negative number, it seems to point to an idea similar to the neural efficiency hypothesis (Neubauer &Fink, 2009). This hypothesis says more intelligent people have lower brain activation when performing cognitive tasks.

DISCUSSION Behavioral

Graph A: Correlation of Objective Distancing vs. IRI Perspective Taking Subscore

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The behavioral results indicate that there is no correlation between the IRI perspective taking score and the objective distancing technique scores.

As mentioned in the results section, this could be because the sample scored in the top half of the perspective taking score. If the sample had a more diversified score range then the IRI score might have correlated with the data as originally was hypothesized. Another interpretation of our results could be that the objective distancing technique is very easy to learn. It is possible that the training that was provided to participants regarding the technique was enough to equalize the performance across the scores. Therefore, even those that scored slightly lower originally on the IRI for how they used perspective taking everyday were still able to use the technique effectively during the task.

Neural The neural results indicate that there was no significant correlation between the brain activation and the IRI perspective taking scores. However, there was a trend observed between the Left TPJ region and the IRI perspective taking score that was not statistically significant. Having increased brain activation within the Left TPJ supports previous research about emotional regulation. The negative correlation however points to a phenomenon similar to that of the neural efficiency theory. It may indicate that those who often use perspective taking in their everyday life need less brain activation in those areas to engage the same pathways because those pathways are used frequently.

Limitations There are a few limitations of this study. The study recruited only 34 participants. Having more subjects could help with representing the general population more accurately and to mitigate the problem of the IRI perspective taking scores being skewed to be higher range of the scale. It also could have helped with finding more statistically significant correlations by finding a greater variation and stronger correlations between IRI and trait scores.

Additionally, the participants had a mean age of about 24, which is on the younger side of the age range we had included for eligibility (18-39). Having subjects who were a little younger may affect the neural component of this study because it is commonly believed that brains are still developing in the teenage years (Dahl, 2004).

Another limitation of the study is that subjects were 20 | Issue 1 | Volume 8 | Spring 2021

taught three different distancing techniques during their training. Although this study only focused on the effects of one of those techniques, it is possible that the subjects confused the different techniques and were not accurately completing the task. By either only teaching the objective technique or teaching the techniques with more time in-between the trainings, participants will be less likely to mix techniques. Another possible limitation is that the IRI perspective taking subscore is not the most effective tool to measure objective distancing. The IRI perspective taking subscore measures the ability of a participant to take the psychological perspective of another, meanwhile the objective distancing technique emphasizes the neutral, detached aspect of the perspective. Therefore, the IRI subscore may not be able to fully predict or model the response from the objective distancing technique.

Future Research The idea that emotional regulation is correlated to some extend to the left TPJ is supported by this research study. In the future, this study could see how the different subscores of the IRI correlated to the objective distancing scores and how they correlated to the brain activation in the Left TPJ. Another interesting aspect to incorporate into this research would be to add more questionnaires that focus on measuring empathy lie the Empathy Quotient (Lawrence et al., 2004) and the Toronto Empathy Questionnaire (Spreng et al., 2009) and see if they have a stronger correlation with the objective distancing technique.

Acknowledgments The study would not have been possible without the help of John Powers and Dr. Kevin LaBar. Thank you to both of them and the LaBar Laboratory at Duke University, Center for Cognitive Neuroscience.

CONCLUSION

Results indicate that greater use of perspective taking in daily life does not correlate with an improved performance on the objective distancing technique.

However, the correlation observed between activation in the left TPJ region and the IRI perspective taking score supports previous research about emotional regulation. The fact that this correlation was an inverse correlation points to a phenomenon similar to that of the neural efficiency theory. It may indicate that those who often use perspective takThe Undergraduate Journal of Neuroscience

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ing in their everyday life need to activate fewer TPJ neurons in order to engage certain pathways because those pathways are used frequently.

REFERENCES

Beck, A.T., Ward, C. H., Mendelson, M., Mock, J., & Erbaugh, J. (1961) An inventory for measuring depression. Archives of General Psychiatry, 4, 561-571.

Berkman, E. T., & Lieberman, M. D. (2009). Using Neuroscience to Broaden Emotion Regulation: Theoretical and Methodological Considerations. Social and Personality Psychology Compass,3(4), 475-493. doi:10.1111/j.1751-9004.2009.00186.x Boyes, A. (2013, January 21). Cognitive Restructuring. Retrieved from https://www.psychologytoday.com/us/blog/in-practice/201301/ cognitive-restructuring Dahl, R. E. (2004). Adolescent Brain Development: A Period of Vulnerabilities and Opportunities. Keynote Address. Annals of the New York Academy of Sciences,1021(1), 1-22. doi:10.1196/annals.1308.001 Davis, M. (1980). Interpersonal Reactivity Index. Retrieved from https:// www.eckerd.edu/psychology/iri/ Engen, H. G., & Singer, T. (2013). Empathy circuits. Current Opinion in Neurobiology, 23(2), 275-282. doi:10.1016/j.conb.2012.11.003 Kragel, P. A., & Labar, K. S. (2015). Multivariate neural biomarkers of emotional states are categorically distinct. Social Cognitive and Affective Neuroscience,10(11), 1437-1448. doi:10.1093/scan/nsv032 Lang, P.J., Bradley, M.M., & Cuthbert, B.N. (2008). International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Technical Report A-8. University of Florida, Gainesville, FL. Lawrence, E. J., Shaw, P., Baker, D., Baron-Cohen, S., & David, A. S. (2004). Measuring empathy: Reliability and validity of the Empathy Quotient. Psychological Medicine,34(5), 911-919. doi:10.1017/ s0033291703001624 Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience & Biobehavioral Reviews,33(7), 1004-1023. doi:10.1016/j. neubiorev.2009.04.001 Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. (2002). Rethinking Feelings: An fMRI Study of the Cognitive Regulation of Emotion. Journal of Cognitive Neuroscience,14(8), 1215-1229. doi:10.1162/089892902760807212 Ochsner, K. N., & Gross, J. J. (2008). Cognitive Emotion Regulation. Current Directions in Psychological Science,17(2), 153-158. doi:10.1111/ j.1467-8721.2008.00566.x Ochsner, K. N., Silvers, J. A., & Buhle, J. T. (2012). Functional imaging studies of emotion regulation: A synthetic review and evolving model of the cognitive control of emotion. Annals of the New York Academy of Sciences,1251(1). doi:10.1111/j.1749-6632.2012.06751.x Saxe, R., & Kanwisher, N. (2003). People thinking about thinking peopleThe role of the temporo-parietal junction in “theory of mind”. NeuroImage,19(4), 1835-1842. doi:10.1016/s1053-8119(03)00230-1 Sokol, B. W., Muller, U., Miller, M., & Giesbrecht, G. (2010). Self and social regulation social interaction and the development of social understanding and executive functions. New York: Oxford University Press.

Spreng, R. N., Mckinnon, M. C., Mar, R. A., & Levine, B. (2009). The Toronto Empathy Questionnaire: Scale Development and Initial Validation of a Factor-Analytic Solution to Multiple Empathy Measures. Journal of Personality Assessment,91(1), 62-71. doi:10.1080/00223890802484381 Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review,117(2), 440-463. doi:10.1037/ a0018963

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The Role of Neuroimaging in Personal Injury Court Cases Sophia Li1 & Caleb Rummel1 Duke University, Durham, NC 27708 Correspondence should be addressed to sophia.li1@duke.edu 1

Accepted for Publication: October 21, 2018

Chronic pain results in enormous health, productivity, and monetary costs, leading to countless legal disputes over compensation for damages in personal injury cases. However, due to the largely invisible nature of chronic pain, assessing chronic pain is inherently subjective and unreliable. In an attempt to ameliorate these issues of subjectivity, many have proposed that courts use neuroimaging evidence to evaluate chronic pain claims in personal injury cases.

Although the greater objectivity of neuroscientific research at measuring chronic pain compared to current scales shows great potential, the field of pain neuroimaging is not yet understood well enough to be used effectively in judicial proceedings. For neuroscientific evidence to be admissible in personal injury trials, there first needs to be a consistent procedure for determining causality between brain activity and chronic pain at the individual level and a standardized protocol for interpreting neuroimaging data. This could be accomplished through further technological advancement, large-scale data acquisition, and formulation of strict codes for introducing neuroscientific evidence in court. Until these objectives are achieved, though, the use of neuroscientific evidence should be limited to educating the court about the general neurobiological mechanisms underlying chronic pain so that jurors can make better informed judgments in pertinent cases.

In a just and equitable society, civil laws exist to provide reimbursement to individuals who suffer injuries due to the wrongful acts of others. Unfortunately, though, these injuries are not always readily visible; in personal injury cases involving chronic pain, juries frequently struggle to determine whether a claimant is truly in pain or merely faking.

Attempting to address these challenges, neuroscientific research has led to the advent of a variety of neuroimaging approaches for measuring the chronic pain of individuals based on brain activity, which litigants increasingly strive to use in personal injury court cases to improve the validity of trial decisions. However, although these methods are more objective than the current scales used for evaluating pain in court, given the considerable limitations and uncertainty surrounding neuroscientific evidence and its effective use in the legal system, neuroimaging of pain is not yet developed enough to be admissible in personal injury court trials. In order to transform the potential judicial applications of 22 | Issue 1 | Volume 8 | Spring 2021

pain neuroimaging into a reality, we argue that scientists must first establish a consistent procedure for determining causality between brain activity and chronic pain at the individual level and standardize the interpretation of neuroimaging data in courtrooms.

THE SIGNIFICANCE OF CHRONIC PAIN IN NEUROLAW

The subject of innumerable legal disputes, chronic pain is associated with enormous health, productivity, and monetary costs. Unlike acute pain, which is sudden and sharp, chronic pain persists for an extended period of time, typically greater than three months (Seminowicz, 2015). This leads to longterm complications with ability to work, medical expenses, and quality of life. In the United States alone, the annual financial cost due to chronic pain is approximately $150 billion (Tracey and Bushnell, 2009). Affecting up to 35% of the population (Davis et al., 2017), chronic pain is a national public The Undergraduate Journal of Neuroscience

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health issue that deserves serious attention. In fact, chronic pain is the center of about half of all personal injury cases, many of which involve lawsuits for work-related injuries where workers feel personally harmed by their employers (Miller, 2009).

Considering the huge amounts of money at stake in such cases, the ability to accurately judge resultant harms has significant implications for the livelihood of the individual and the financial stability of the company in question. These concerns have put chronic pain and the need for a means to objectively measure it at the forefront of both neuroscience and the law.

CURRENT SCALES FOR MEASURING PAIN ARE TOO SUBJECTIVE

Current methods for assessing chronic pain are crude and subjective, frequently relying on subjects’ self-reports. Despite the major advances in health and medicine in the last century, society has yet to establish a consistent, objective method for measuring chronic pain. As of today, the primary mode for assessing pain is to ask subjects to rate their pain on a scale from one to ten or to choose from a row of cartoon faces whose expressions range from varying degrees of happy to anguished (Reardon, 2015). While these scales can be useful for tracking changes in the pain levels of a single subject over time, such as in the case of a patient recovering from a surgical procedure, they do not provide an objective means for measuring pain across multiple individuals at a given moment. This is due to the fact that people have different pain tolerances and therefore rate their experiences of pain differently.

For example, what one person rates as a four might be an eight for someone else (Davis, 2016). These inconsistencies make such scales unsuitable for use in personal injury court cases, as they depend solely on the testimony of the injured party, who usually has monetary incentive to exaggerate his pain. Because the central debate in personal injury cases revolves around the level of pain an individual suffers as a result of his or her injuries, there is an urgent need for an objective method of measuring pain.

NEUROIMAGING AS A POTENTIAL “PAIN-O-METER”

As a consequence, increasing attention has been turned toward the potential use of brain imaging techniques as a “pain-o-meter” in courts. The inadequacy of verbal-visual scales has driven litigants to try to introduce neuroimaging evidence to suphttp://www.neurogenesis-journal.com

port their chronic pain claims in court, according to the recent rise of private companies offering brain scanning services for lawyers and their clients (Davis, Racine, & Collett, 2012). Because many people exaggerate or sometimes even fake their pain, defense lawyers are constantly suspicious of claims for damages. On the other hand, plaintiffs with actual chronic pain often have difficulty expressing the quality of their pain, unable to prove existence of their suffering. Given the invisible nature of chronic pain, establishing an objective scale to measure chronic pain is crucial for distinguishing between litigants’ legitimate and fake claims of chronic pain so that deserving individuals receive fair compensation for their losses.

VISUALIZING CHRONIC PAIN IN THE BRAIN

THROUGH NEUROIMAGING

Currently, neuroscientific research provides evidence of functional, anatomical, and neurochemical distinctions between the brains of chronic-pain patients and healthy individuals (Camporesi & Bottalico, 2011). It is important to note that there is no single unified neural area devoted to the processing of chronic pain; rather, chronic pain activates a wide network of brain regions known as the “pain matrix” (Salmanowitz, 2015). Scientists believe that neuroimaging techniques will enhance their understanding of this multifaceted “matrix.”

Using neuroimaging techniques for investigating anatomical differences in the brain such as magnetic resonance imaging (MRI) and voxel-based morphometry (VBM), studies have shown that patients suffering from chronic pain exhibit decreased grey matter in the thalamus and lateral prefrontal cortex, regions involved in pain modulation, compared to age-matched control subjects (Apkarian et al., 2004). Furthermore, data obtained through another structural neuroimaging method, diffusion tensor imaging (DTI), have indicated that chronic pain patients exhibit impaired white matter tract connectivity, another structure involved in regulating pain levels (Lutz et al., 2008). However, while these observed relationships between certain brain features and the presence of chronic pain are promising, they are not conclusive and are correlations rather than causations.

In addition to altered morphology, chronic pain-afflicted individuals’ brains display several functional changes (Camporesi & Bottalico, 2011); by acquiring fMRI data in the absence of any overt stimulus or task, scientists have revealed that “restSpring 2021 | Volume 8 | Issue 1 | 23

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ing-state brain activity,” or brain activity relating to physiological maintenance processes, differs between people with and without chronic pain.

However, a distinct pattern in these differences has not been determined, as observed changes in resting-state brain activity vary in a range of clinical conditions (Davis et al., 2017). It is unclear whether any particular pattern is related to pain itself, spontaneous thought, or other related processes, raising the pivotal question of causation. Studying differences between brain responses to evoked stimuli also provides scientists clues for understanding pain intensity, especially in conditions in which chronic pain is accompanied by changes in the central nervous system that increase sensitivity to pain and ordinary touch (Davis et al., 2017).

These changes in pain sensitization are evidenced in functional molecular imaging studies known as positron emission tomography (PET), which have indicated a decrease in opioid receptors, proteins that work to block pain signals in the brain, in patients with central neuropathic pain and rheumatoid arthritis (Jones et al., 2004). Because it is possible for an evoked response to be completely disconnected from the timing and duration of the applied stimulus (Davis et al., 2017), establishing direct association between activity and the experience of chronic pain remains a major challenge.

Experimentation is undoubtedly still at a nascent stage, but the aggregate results of aforementioned studies clearly demonstrate that the biological mechanisms of chronic pain are rooted in identifiable structural and functional changes within the brain. Therefore, an objective answer to measuring chronic pain should theoretically lie in the brain, where the experience of pain is ultimately constructed. By analyzing these common, established characteristics of chronic pain in the brain, scientists have developed several neuroimaging methods that many hope to be able to apply in court to create a more objective scale for chronic pain in personal injury cases.

BENEFITS OF APPLYING NEUROIMAGING IN PERSONAL INJURY COURT CASES: PREDICTIVE MODELING

There is much anticipation surrounding the possibility that introducing neuroimaging evidence in court cases involving personal injury could improve the consistency and reliability of trial outcomes by providing a more objective, scientific model that decodes chronic pain from brain activity. A trend 24 | Issue 1 | Volume 8 | Spring 2021

that has emerged recently known as predictive modeling promises a potential solution. Through a pattern-recognition technique known as “machine learning,” researchers can use computer algorithms created with preliminary sets of pain neuroimaging data to construct integrated models of activity across multiple brain regions to predict brain activation patterns in future datasets (Salmanowitz, 2015; Woo, Chang, Lindquist, & Wager, 2017).

The basis of this approach lies in the identification of “neurological signatures of pain,” (Wager et al., 2013), which manifest themselves in the form of functional patterns of pain related activity, functional connectivity patterns at rest, or anatomical patterns (Seminowicz et al., 2015). For example, activity patterns in the medial prefrontal cortex and right insula correlate strongly with pain intensity and duration of chronic pain, respectively, in people with chronic back pain (Miller, 2009), making them strong pain signatures for chronic back pain.

By comparing and incorporating all available brain data on such pain signatures into a single “best guess” (Woo et al., 2017), these predictive models aim to detect the presence of chronic pain and distinguish them from other unrelated sensations.

Though not quite foolproof yet, using machine learning in conjunction with pain neuroimaging has already yielded impressive results. In an experiment in which painful electrical stimulations were administered to the lower back of both patients with chronic back pain and healthy controls, machine learning algorithms correctly differentiated between pain perceptions in the two subject groups with 92.3% accuracy (Callan et al., 2014). However, a similar study involving patients with chronic pelvic pain reported a much lower accuracy rate of 73% (Bagarinao, et al., 2014), indicating that there is much still room for improvement. While neuroimaging-based predictive modeling definitely provides a means for measuring pain that is far more objective than current methods of self-reporting, as it is founded on tangible measurements rather than individuals’ imprecise statements about their perceived feelings, future research is needed to increase accuracy rates and to collect data on a greater range of pain locations. Nonetheless, the relatively high success rates that have been produced so far reflect the strong statistical power that these methods hold, which litigants could leverage in court to validate their chronic pain claims in personal injury cases. Thus, allowing such technology to be used in court as evidence to prove one’s chronic pain would The Undergraduate Journal of Neuroscience

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enable courts to justify their decisions with concrete data and potentially increase the legitimacy of trial decisions.

TECHNICAL LIMITATIONS OF CHRONIC PAIN NEUROIMAGING

Despite the potential for neuroimaging to decrease the subjectivity of measuring chronic pain, the method clearly still faces many challenges. The most notable is the lack of brain region specificity and pain experience variability, which prevent its use as an absolute objective measurement of pain. As mentioned earlier, no one brain region has been exclusively linked to chronic pain, making it extremely difficult to identify biological neuromarkers that are specific to pain. Many abnormal brain processes observed in chronic pain conditions are also implicated in physical and emotional pain (Davis, 2016), as well as in mental disorders such as depression and anxiety (Davis et al., 2017). Additionally, neural networks which serve to maintain homeostatic equilibrium among attentional, cognitive, emotional, and sensory functions overlap extensively with neural areas typically included in the “pain matrix” (Davis et al., 2017). These confounding variables lead to an issue known as the “reverse inference” problem: while neuroscientific research shows that people who suffer from chronic pain consistently show activity in certain brain regions, it is logically erroneous to assume the converse—that any particular pattern of brain activity necessarily indicates the presence of chronic pain—is also true (Camporesi & Bottalico, 2011). In other words, the existence of activity in the “pain matrix” could very well be due entirely to a myriad of brain processes completely unrelated to chronic pain. These technical challenges prevent pain neuroimaging from being wholly objective.

Variability of chronic pain experience in the brain is another large obstacle that limits the objectiveness of pain neuroimaging evidence. Consistency across hundreds of brain imaging studies has shown correlation between activity in a core set of brain regions and chronic pain at a group level, but extrapolating these results to the individual level ignores between-subject variation that creates subjectivity (Davis et al., 2017). For example, demographic factors such as age, gender, and ethnicity; personality traits; past experiences of pain; and perceived gain or loss from the injury all have the capability to shape an individual’s experience of pain (Camporesi & Bottalico, 2011). Moreover, http://www.neurogenesis-journal.com

the capacity for connections between brain cells to change during regulation of pain differs depending on the person, leading to different degrees of pain processing (Davis et al., 2017). Even within individuals, there exists great variability in the experience of pain. Chronic pain can vary slowly during the course of an individual’s brain imaging session, as each moment in time is marked by a unique combination of sensory, cognitive, emotional, and motivational processes that contribute to the perception of pain. Therefore, attentional focus and an expectation of pain or pain relief can also influence the experience of chronic pain in the brain (Davis et al., 2017), variables that current predictive models for pain fail to capture. Due to this between and within individual variability, scientists have been unable to establish a baseline to which the chronic pain experience can be measured against—a key component necessary for the creation of an objective scale.

ADMISSIBILITY OF CHRONIC PAIN NEUROIMAGING EVIDENCE IN COURT: LEGAL CONSIDERATIONS In addition to scientific barriers, many legal barriers to the effective use of pain neuroimaging in personal injury court cases exist. To this day, there is a lack of consensus over what qualifies neuroscientific evidence as “good enough” to be admissible in court. Although neuroimaging techniques are still demonstrably subjective, proponents for its admissibility in court (Miller, 2009) assert that it is nevertheless an improvement over the verbal and visual scales currently being implemented. Such opinions have become a major point of contention between the scientific and legal communities; in general, scientists tend to be relatively more concerned with technicalities and proving causation, while lawyers must be more pragmatic in how they approach evidence due to the nature of their jobs, capitalizing on the opportunity to use neuroscientific data as tool in trials as long as correlation is high enough (Miller, 2009).

However, while it is true that neuroimaging as a “pain-o-meter” may be better than current alternatives, we argue that this alone is not enough to justify its admission in personal injury trials at this stage. In fact, promoting the use of chronic pain neuroimaging on the basis of such reasoning is not only unwarranted, but it is also unwise; permitting the application of chronic pain neuroimaging as evidence in court attributes to the technology a high degree of objectivity that in reality does not yet exist, creating the impression that chronic pain neuSpring 2021 | Volume 8 | Issue 1 | 25

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roimaging is much more reliable than it actually is.

This would exacerbate the problem of juror bias—a concern that is especially salient in court cases involving neuroscientific evidence because jurors frequently overstate its significance. Several studies suggest that the general public is more likely to believe poor arguments if they are accompanied by neuroscientific data (Weisberg, 2008) or even just irrelevant brain images (Miller, 2009). Moreover, according to Rule 403 of the Federal Rules of Evidence, judges may exclude relevant evidence if they deem it likely to prejudice the jury (Staff, 2011). Evidently, there is a substantial risk that introducing chronic pain neuroimaging evidence in personal injury trials may mislead or confuse the jury, resulting in a partial rather than objective ruling.

CRITERIA FOR EFFECTIVE USE OF PAIN NEUROIMAGING EVIDENCE IN PERSONAL INJURY COURT CASES

Although neuroscience in its present state may not be ready to be admissible in personal injury trials to objectively prove that an individual has chronic pain, rapid progress in the field and technology may eventually make it possible to do so in the not-sodistant future. Before chronic pain neuroimaging evidence can be effectively used in court though, several criteria must first be met to address issues of causality, variability of pain experience, and disparate interpretations of neuroscientific data due to jury biases.

First, scientists must identify salient biological neuromarkers for different forms and components of chronic pain. In order to serve as a pain neuromarker, any given brain measure must be precisely defined, specifying precise volumetric units of interest within involved brain regions and the expected magnitude of activity across these units (Davis et al., 2017). This will require additional research about how neurological pain signatures vary depending on the type of chronic pain condition. To create a protocol for evaluating the degree to which such neuromarkers indicate causality, scientists must also determine measurements relevant to their accuracy, sensitivity, and specificity for detecting chronic pain. As Davis notes, accurate assessment of whether a reverse inference is true requires not only assessment of how often a pattern of brain activity occurs when chronic pain is experienced, but also of how often the pattern is present in the absence of such pain (Davis et al., 2017).

Furthermore, researchers need to experiment more with the inclusion and prioritization of different neuromarkers in machine learning algorithms to determine which predictive models work best.

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With regards to the lack of a general baseline pain measurement due to variability of pain experience within an individual, positive controls (patterns of brain activity independent of pain that must be present) and negative controls (patterns that must be absent) should be established for each individual tested to demonstrate validation of chronic pain within that given individual (Davis et al., 2017).

This would in effect create a baseline standard for each individual to which subsequent neuroimaging scans could be compared and analyzed in conjunction with machine learning predictions, reducing the impact of confounding variables for trials within a single individual. On the other hand, to address the existence of between-individuals variability, more studies investigating the link between different manifestations of chronic pain in the brain and various categorical populations should be conducted to determine how different facets of people’s lives and their personality characteristics affect the nature of their pain experiences. One way to achieve this would be through compiling a database of how neural pain responses differ across age, sex, ethnicity, and other relevant factors. Identifying these relationships would enable scientists to employ a predictive model tailored to a specific profile that matches closest with the individual in question, adding a new layer of objectivity.

While there is no way to completely eliminate or control people’s inherent biases toward neuroscientific evidence, as is true for any type of evidence being introduced in court, measures can be taken to mitigate the potential adverse effects of such biases on court decisions. Currently, under both the Daubert standard for admissibility of scientific evidence (Daubert v. Merrell Dow Pharmaceuticals, 1993) and Federal Rule of Evidence 702 (Staff, 2011), it is the duty of the judge to act as a “gatekeeper” to determine whether expert evidence is based on valid science and is sufficiently reliable to be helpful to the jury. To complement these regulations, we propose that courts adopt a policy or standard protocol mandating judges overseeing personal injury trials to issue an advisory or cautionary statement delineating the existing limitations of neuroimaging evidence to the court prior to the introduction of such evidence.

CONCLUSION

The creation of objective measures is crucial to the legal system’s purpose of ascertaining the truth and maintaining fairness in society. With regards to personal injury cases, many have proposed that courts use neuroimaging evidence to ameliorate issues of subjectivity in assessing damages due to chronic pain. Although neuroscience has great potential to The Undergraduate Journal of Neuroscience

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increase the objectivity of chronic pain evaluations, it is imperative to remember that chronic pain neuroimaging is still a developing field. Considering the powerful sway that neuroimaging evidence holds over the juries in spite of its technical shortcomings, we believe that the dangers of using chronic pain neuroimaging evidence in legal proceedings at this time overshadow the benefits. However, in the future, further technological advancement, largescale data acquisition, and formulation of strict codes for introducing neuroscientific evidence may be able to remove many of the obstacles blocking the introduction of chronic pain neuroimaging evidence in court. Until validation and standardization of such methods are achieved, though, we recommend their use be limited to educating the court about the general neurobiological mechanisms underlying chronic pain as a foundation on which to judge the evidence pertaining to a specific case.

REFERENCES

Apkarian, A.V., Sosa, Y., Sonty, S., et al. (2004) Chronic back pain is associated with decreased prefrontal and thalamic gray matter density, Journal of Neuroscience, 24 (46), pp. 10410–10415.

Bagarinao, E., Johnson, K. A., Martucci, K. T., Ichesco, E., Farmer, M. A., Labus, J., … Mackey, S. (2014). Preliminary structural MRI based brain classification of chronic pelvic pain: A MAPP Network Study. Pain, 155(12), 2502–2509. https://doi.org/10.1016/j.pain.2014.09.002 Camporesi, S., & Bottalico. (2011). Can We Finally “See” Pain?: Brain Imaging Techniques and Implications for the Law. Journal of Consciousness Studies, 18(9–10), 257–276.

Staff, L. I. I. (2011, November 30). Rule 702. Testimony by Expert Witnesses. Retrieved November 28, 2017, from https://www.law.cornell.edu/rules/fre/rule_702 Daubert v. Merrell Dow Pharmaceuticals, 509 U.S. 579 (1993).

Davis, K. D., Flor, H., Greely, H. T., Iannetti, G. D., Mackey, S., Ploner, M., …

Wager, T. D. (2017). Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations. Nat Rev Neurol, 13(10), 624–638.

Davis, K. D., Racine, E., & Collett, B. (2012). Neuroethical issues related to the use of brain imaging: Can we and should we use brain imaging as a biomarker to diagnose chronic pain? Pain, 153(8), 1555–1559. https://doi.org/10.1016/j.pain.2012.02.037 Davis, K. Journal, A. B. A. (2016). Personal injury lawyers turn to neuroscience to back claims of chronic pain. Retrieved November 11, 2017, from Dobbs, D. B. (2000). The Law of Torts. St. Paul, MN: West Group.

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Jones, A.K., Cunningham, V.J., Ha-Kawa, S., et al. (1994) Changes in central opioid receptor binding in relation to inflammation and pain in patients with rheumatoid arthritis, British Journal of Rheumatology, 33, pp. 909–916.

Lutz, K., Jäger, D., de Quervain, D., et al. (2008) White and gray matter abnormalities in the brain of patients with fibromyalgia: A diffusion-tensor and volumetric imaging study, Arthritis & Rheumatism, 58 (12), pp. 3960–3969.

Miller, G. (2009). Brain Scans of Pain Raise Questions for the Law. Science, 323(5911), 195–195. https://doi.org/10.1126/science.323.5911.195 Reardon, S. (2015). Neuroscience in court: The painful truth. Nature News, 518(7540), 474. https://doi.org/10.1038/518474a Salmanowitz, N. (2015). The case for pain neuroimaging in the courtroom: lessons from deception detection. Journal of Law and the Biosciences, 2(1), 139–148. https://doi.org/10.1093/jlb/lsv003 Seminowicz, D., Pustilnik, A., Rigg, S., Davis, A., Davis, K., & Greely, H. (2015). Panel 1: Legal and Neuroscientific Perspectives on Chronic Pain. Journal of Health Care Law and Policy, 18(2), 207.

Weisberg, D. S., Keil, F. C., Goodstein, S., Rawson, E. & Gray, J. R. (2008). J. Cogn. Neurosci. 20, 470–477.

Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017). Building better biomarkers: brain models in translational neuroimaging.

Nature Neuroscience, 20(3), nn.4478. https://doi.org/10.1038/ nn.4478.

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Alzheimer’s Disease: A review of current and novel therapies Nathan Luzum1 & Ian Levitan1 Duke University, Durham, NC 27708 Correspondence should be addressed to nathan.luzum@duke.edu 1

Accepted for Publication: October 21, 2018

Alzheimer’s disease (AD) is a neurodegenerative disease classically identified by the presence of senile plaques and neurofibrillary tangles. These two specific cellular pathologies were first described

by Dr. Alois Alzheimer in 1906. Although the exact roles of each species in contributing to the disease state are unknown, the result is dysfunction in neuronal and synaptic activity and subsequent cell death. Areas directly surrounding diseased neurons are also affected and lead to states of severe and persistent neuroinflammation, which exacerbate AD severity. Because the disease is neurodegenerative, treatments for AD do not cure the disease, but rather attempt to ameliorate symptoms. Ultimately, greater understanding of molecular mechanisms of AD is essential to increase the therapeutic potential of AD treatments.

Alzheimer’s disease (AD), as the most common neurodegenerative disease, has been recognized as a pressing area for medical research, given the aging of the world population and lack of available therapies to arrest or slow the disease’s progression (Feng et al., 2010). In 2018, an estimated 5.7 million Americans are living with AD, 96% of whom are older than 65 and 37% of whom are older than 85 (Hebert et al., 2013). This number is expected to more than double by 2050 as the human lifespan increases, eventually giving rise to an estimated AD population of 13.8 million Americans and 114 million people globally (Alzheimer’s Association, 2016). Impacting a broad range of cognitive and behavioral functions, AD is perhaps best known to induce memory loss and a concomitant neuronal loss in the hippocampus and entorhinal cortex, two areas vital for learning and memory (Jahn, 2013).

There are several hypotheses regarding the molecular mechanisms that govern AD. One of these hypotheses asserts that the disease is caused by the brain’s inability to clear aggregated extracellular amyloid-β (Aβ) plaques, leading to impairment in cognitive and behavioral functions (Masters et al., 2015). The Aβ pathway involves amyloid precursor protein (APP) and its cleavage by the enzymes β- and γ-secretase, yielding two major forms of Aβ known as Aβ40 and Aβ42, numbered according to 28 | Issue 1 | Volume 8 | Spring 2021

their amino acid cleavage site (Murphy & Levine, 2010). Aβ40 is the more benign version and makes up around 80-90% of amyloid product, whereas Aβ42 is only 5-10% of amyloid product but more neurotoxic and apt to aggregate into insoluble plaques (Selkoe, 2001). In turn, Aβ can penetrate neuronal membranes to allow the influx of excessive calcium ions and alter membrane conductance (Sepulveda et al., 2010). Research has also suggested that Aβ plaques can indirectly interact with mitochondria to produce free radicals and oxidative stress via disruption of the electron transport chain (Mao & Reddy, 2011).

Late-onset AD—which makes up around 95% of cases—has associated genetic risk factors, including specific alleles for apolipoprotein E (ApoE), but no known cause. On the other hand, familial AD— which accounts for less than 1% of cases—is caused by specific mutations in genes encoding the amyloid precursor protein or proteins associated with Aβ processing (Scheuner et al., 1996; Saunders et al., 1993).

Chief among other AD theories is hyperphosphorylation of tau, the major microtubule-associated protein in mature neurons (Iqbal et al., 2010).

When tau becomes hyperphosphorylated, it assembles into a mixture of paired helical filaments and straight filaments. These pathogenic forms generThe Undergraduate Journal of Neuroscience

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ate neurofibrillary tangles (NFT), impairing the protein’s proper biological function (Martin et al., 2013).

In addition to implicating these molecular markers of AD, researchers have also proposed circuit-level neurotransmitter imbalance in Alzheimer’s. Drawing on acetylcholine’s role in learning and memory, the cholinergic hypothesis for AD posits that dysfunction of acetylcholine-containing neurons is a driving force behind memory loss and a number of other symptoms (Bartus et al., 1982).

The glutamatergic hypothesis complements the cholinergic hypothesis by suggesting that elevated levels of glutamate, the brain’s major excitatory neurotransmitter, may also drive AD pathology.

When high levels of glutamate bind to the N-methyl-D-aspartate (NMDA) receptor, excessive calcium influx can cause neuronal death and impaired synaptic function.

Currently available therapies aim not to target these putative molecular mechanisms driving AD pathogenesis, but rather to treat its symptoms by repairing the imbalance of neurotransmitters and circuit-level aberrances. Despite years of research into the molecular mechanisms behind the disease and potential drug development, no approved therapy currently exists to prevent or slow neurodegeneration in AD (Kumar et al., 2017).

CURRENT THERAPIES

One category of drugs currently available to attenuate AD symptoms is reversible cholinesterase inhibitors (CIs), which arrest the function of acetylcholinesterase. This enzyme is found on postsynaptic membranes and degrades the neurotransmitter acetylcholine (ACh) via hydrolysis at a rate of approximately 25,000 molecules of ACh per second (Taylor & Radic, 1994; Yiannopoulou & Papageorgiou, 2013). In the central nervous system, ACh is found primarily in interneurons but also in neuronal projections from the basal forebrain to the neocortex and limbic system structures, which is one of the pathways degraded by AD (Perry et al., 1999).

Donepezil, rivastigmine, and galantamine are the three CIs currently on the market for AD patients, each of which binds to a different portion of the acetylcholinesterase active site to prevent or slow the rate of ACh breakdown (Colovic et al., 2013).

Trials of these drugs have shown improvements in cognitive markers, activities of daily living, and overall function of patients with more mild forms of the disease (Farlow et al., 2010). These treatments http://www.neurogenesis-journal.com

are not without potential side effects, which typically come in the form of gastrointestinal problems, including vomiting and diarrhea. However, donepezil has a lower likelihood of side effects compared to rivastigmine and galantamine (Alva & Cummings, 2008). Patients on cholinesterase inhibitors experience an increase in cognitive function in the first three months of treatment compared to groups treated with a placebo, and the subsequent decrease in function is less severe in CI-treated patients until one year after first treatment (Birks, 2006; Hansen et al., 2008). Overall, cholinesterase inhibitors are only capable of slightly ameliorating AD symptoms, without slowing the disease progression.

The other option for patients with more severe AD is memantine, a low-affinity uncompetitive antagonist of the N-methyl-D-aspartate (NMDA) glutamate receptor. Excessive activation of these receptors by increased glutamate levels can result in calcium influx and subsequent free radical generation and mitochondrial dysregulation, which can in turn generate reactive oxygen species (Folch et al., 2018). Some preclinical studies have suggested that memantine may also have an effect on the pathology of Alzheimer’s by reducing Aβ levels in the brain, but similar studies arrived at different conclusions, so the possibility of memantine affecting Aβ requires more testing to arrive at a definitive answer (Guerrero-Munoz et al., 2015; Scholtzova et al., 2008). Several studies have also extended these protective effects of memantine to the hyperphosphorylation of tau, which can be caused by the overactivation of cyclin-dependent kinase 5 and glycogen synthase kinase 3β (Guerrero-Munoz et al., 2015). Calcium influx via NMDA receptors can drive the activation of these kinases, so NMDA antagonists including memantine can block excessive activation and suppress this calcium influx (Lee & Tsai, 2003).

Reviews of studies with memantine have concluded that the drug improved cognition, behavior, and activities of daily life in patients (McShane et al., 2006). However, drug treatments need not be limited to one compound—trials have suggested that treating patients with moderate to severe AD with a combination of memantine and donepezil is more effective than memantine alone in improving some symptoms (Howard et al., 2012).

Studies involving a variety of other drugs, including selective serotonin reuptake inhibitors, antipsychotics, and anticonvulsants, have also shown some effectiveness against the multitude of symptoms Spring 2021 | Volume 8 | Issue 1 | 29

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associated with AD (Yiannopoulou & Papageorgiou, 2013).

EXPERIMENTAL THERAPIES

Alzheimer’s pathogenesis does not result solely from neuronal interactions. Microglia and astroglia contain receptors that can respond to misfolded and aggregated proteins. This activation results in the release of inflammatory mediators and a chronic inflammatory state that exacerbates the disease (Heneka et al., 2015). An M1 glial phenotype is characterized by an increased concentration of pro-inflammatory cytokines and a reduced phagocytic capacity, which results from microglia priming. In contrast, M2 represents a virtually complete phenotypic reversal (Orihuela et al., 2016; Tang et al., 2016). As it remains, it is ill-advised to pharmacologically target proinflammatory signaling pathways like the NLRP3 inflammasome due to non-specific actions in the body. However, a once promising alternative remedy to alleviate the severity of AD symptoms pharmacologically targeted heterodimeric type II nuclear receptors or solely the retinoid X receptor (Mandrekar-Colucci et al., 2011).

The effects led to an anti-inflammatory response and increased receptor-induced phagocytosis of Aβ oligomers—essentially, a switch from the M1 to M2 activation state. This would facilitate attenuation of the consequences of persistent neuroinflammation (Colton et al., 2006). Certain pro-inflammatory cytokines, like interleukin-1 beta, modulate APP and contribute to the formation of more Aβ 42 soluble oligomers. Agonists designed to target the glial receptors have difficulty crossing the blood-brain barrier and lead to increased fatty deposits on the liver (Mandrekar-Colucci et al., 2011). However, novel therapeutics may achieve a greater outcome than what currently exists (Kawahara et al., 2014).

Cyclooxygenase-inhibiting nonsteroidal anti-inflammatory drugs are traditionally used to reduce inflammation in a variety of other conditions. Their use in AD is limited due to widespread differences in context-dependent and stage-dependent glial activation throughout the course of the disease (Choi et al., 2013).

The 2018 AD treatment pipeline consists of 112 agents in Phases I-III of clinical trials that target many facets of the disease state (Brookmeyer et al., 2018). Formation of Aβ 42 requires the joint action of beta-secretase (BACE1) and gamma-secretase on APP. Of the 26 agents in Phase III, 65% are disease modifying therapies largely existing as BACE 30 | Issue 1 | Volume 8 | Spring 2021

inhibitors, anti-aggregation agents, or immunotherapies. 31% target neuropsychiatric symptoms such as agitation and sleep disorders, and a small minority are symptomatic cognitive enhancers like acetylcholinesterase inhibitors (Cummings et al., 2018). Novel multi-target-directed ligands containing multiple unique pharmacophores can inhibit Aβ peptide amyloid fibrillization and BACE1 activity while also inhibiting AChE activity (Gazova et al., 2017). Conceptually similar treatments may target any number of irregularities in the disease state, and challenge the “one molecule, one target” paradigm (Knez et al., 2018). Furthermore, the recent discovery of tau-mediated excitotoxicity contributing to the disease state has led to the possibility of interfering with NMDA receptor-coupling to postsynaptic density-95, a protein specialization known to activate excitotoxic signaling cascades (SpiresJones et al., 2014).

CONCLUSION

There are a variety of biological markers associated with AD pathogenesis, but the effectiveness of such treatments relies heavily on the progression of the disease state. Unless scientists develop technology capable of regenerating destroyed neurons, future treatments should instead seek to intervene in the molecular mechanisms underlying AD. The clinical utility of such treatments, however, presumably rests on the ability to target the early disease stage before widespread degeneration occurs. It may, therefore, prove beneficial to develop more advanced screening techniques to identify the early stages of AD in the human brain. As it stands, currently approved medications target circuit-level defects resulting from neurodegeneration, whereas a majority of drugs in clinical trials are designed to disrupt the Aβ pathway. As researchers continue to gain insight into the molecular pathology of AD, the future of AD treatments will likely adapt in turn to target downstream effects of Aβ signaling, which are thought to be more destructive than the presence of the classical cellular pathologies themselves. Overall, relating the various theories of AD by further pinpointing the mechanisms driving the disease remains crucial in the pursuit of creating a more effective treatment for AD.

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Alva, G., & Cummings, J. L. (2008). Relative Tolerability of Alzheimer’s Disease Treatments. Psychiatry (Edgmont), 5(11), 27–36.

Alzheimer’s Association (2016). “2016 Alzheimer’s disease facts and figures,” Alzheimer’s & Dementia, vol. 12, pp. 459–509.

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RepetitiveTranscranial Magnetic Stimulation (rTMS) as a NovelTherapeutic Intervention for Obsessive Compulsive Disorder Nicolette Stogios1 University of Toronto, Toronto, Canada Correspondence should be addressed to Nicolette Stogios (nicolette.stogios@mail.utoronto.ca) Accepted for Publication: October 21, 2018 1

Obsessive Compulsive Disorder (OCD) is a psychiatric condition characterized by clinically significant obsessions and compulsions that interfere greatly with one’s daily functioning. In addition to the standard OCD treatment of cognitive behavioural therapy (CBT) and psychotropic therapy, Jahangard et al. (2016) explored repetitive transcranial magnetic stimulation (rTMS) as a possible treatment method. rTMS is a non-invasive procedure that involves activating or modifying the activity of neurons in the brain through hyperpolarizing or depolarizing them using electromagnetic fields. Numerous studies have indicated growing support for rTMS as an effective treatment for refractory OCD, but the study by Jahangard et al. (2016) was the first to investigate whether rTMS could possibly improve the cognitive performance of those with OCD. The results of their study revealed that the application of high frequency rTMS, particularly over the dorsolateral prefrontal cortex (DLPFC), improved symptom severity, and was also associated with increasing certain aspects of cognitive performance. This review will highlight its major results and conclusions, critically analyze their research methods, as well as provide guidelines for future directions.

INTRODUCTION

Obsessive Compulsive Disorder (OCD) is a psychiatric condition characterized by obsessions (undesired persistent and intrusive thoughts that cause anxiety), and compulsions (repetitive and ritualistic behaviours performed to temporarily relieve the individual of the obsessions) (Jahangard et al., 2016, Berlim, Neufeld, and den Eyned, 2013, RuiMa and Shi, 2014). OCD has a prevalence in the general population of 1-3% (Jahangard et al., 2016, Berlim, Neufeld, and den Eyned, 2013, Rui-Ma and Shi, 2014), and its symptoms cause clinically significant distress and impairment in interpersonal and occupational functioning (Berlim, Neufeld, and den Eyned, 2013, Rui-Ma and Shi, 2014). Although the full extent of OCD’s etiology and pathophysiology is still widely unknown, it is thought that this condition could result from abnormalities in certain neural circuits, such as the dorsolateral pre-frontal http://www.neurogenesis-journal.com

cortex (DLPFC), orbitofrontal cortex (OFC), medial prefrontal cortices, and supplementary motor area (SMA) (Berlim, Neufeld, and den Eyned, 2013). Insufficient inhibition or hyperactivity in these brain regions possibly account for the reduced ability of OCD patients to inhibit intrusive thoughts, impulses, and repetitive motor responses (Mantovani et al., 2014).

Standard treatment for OCD includes psychotropic medications, such as selective serotonin reuptake inhibitors (SSRIs) and clomipramine, and/ or cognitive behavioural therapy (CBT) (Jahangard et al., 2016, Berlim, Neufeld, and den Eyned, 2013). Serotonin-norepinephrine reuptake inhibitors (SNRIs) and atypical antipsychotics are used in treatment-resistant cases (Berlim, Neufeld, and den Eyned, 2013). However, this line of treatment is only successful in 40-70% of patients; therefore, alternative therapeutic approaches are required, Spring 2021 | Volume 8 | Issue 1 | 33

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one such being repetitive transcranial magnetic stimulation (rTMS) (Jahangard et al., 2016, Berlim, Neufeld, and den Eyned, 2013, Rui-Ma and Shi, 2014). rTMS is a neuromodulatory procedure that can either hyperpolarize or depolarize local neurons by sending an electric current through a coil placed on the skull (Jahangard et al., 2016, Berlim, Neufeld, and den Eyned, 2013, Rui-Ma and Shi, 2014, Slotema, 2009). Low frequency rTMS (LF-rTMS; frequencies < 1 Hz) is generally inhibitory, while high frequency rTMS (HF-rTMS; frequencies > 5 Hz) is excitatory(Berlim, Neufeld, and den Eyned, 2013, Rui-Ma and Shi, 2014). Application of rTMS produces a long lasting effect that is presumed to be due to the cortical changes involved in long term potentiation or depression at neuronal synapses(Siebner and Rothwell). These enduring effects are thought to be the reasons why rTMS may be a valuable tool for studying and potentially treating various neuropsychiatric disorders. rTMS as a novel treatment option for treatment-resistant OCD has generated great interest.

In OCD, rTMS targets include the DLPFC and SMA because of their connections to deeper structures involved in OCD. Standard TMS coils have limited penetrability, but these structures are more superficial in nature; therefore, more accessible as a target for treatment (Hedge et al., 2016). Greenberg et al. (1997) was the first to introduce the possibility of using rTMS for OCD therapy (Greenberg et al., 1997). In their study, bilateral application of HFrTMS to the DLPFC for 20 minutes resulted in significantly decreased compulsions and depressive symptoms for several hours after stimulation, but no change in obsessions was found. However, their study included only one session of stimulation, so the results are difficult to interpret (Sachdev, 2007).

To further the study of rTMS treatment in OCD, Jahangard et al. (2016) performed a randomized sham-controlled trial on 10 patients with refractory OCD. Pharmacological intervention (SSRI or clomipramine) was continued throughout the duration of the study. Patients were randomly assigned to either the rTMS-first-sham-second condition (where they would receive active rTMS 5 sessions per week for 2 weeks and then switch to 2 weeks of sham rTMS 5 times a week), or the sham-first-rTMS-second condition. The sham condition consisted of placing an inactive coil on the skull in a way that produced both sound and somatic sensation (as the active condition would), but with minimal effect on 34 | Issue 1 | Volume 8 | Spring 2021

the brain. Using a sham condition provides the advantage of acting as a control and also being able to explore the prolonged effect of rTMS on the brain and on behaviour (Haghighi, 2015). In addition to exploring the effects of rTMS on symptom severity, they also investigated whether rTMS can influence the cognitive performance of patients with OCD. An earlier study revealed that rTMS has the potential of improving the cognitive performance of individuals with major depressive disorder (Tortella, 2014), while another stated that bilateral HF-rTMS to the DLPFC also had a significant therapeutic effect for cognitive function in Alzheimer’s Disease (Liao, 2015), but the study by Jahangard and colleagues (2016) is the first to explore rTMS on cognitive function in OCD. In line with previous reports, they hypothesized that rTMS would improve both symptom severity and cognitive performance of individuals with refractory OCD. The Jahangard et al. (2016) study is an important contribution to the field as it believed to be the first to investigate the effects of HF-rTMS on both symptom severity and cognitive performance in refractory OCD. Given the significance of this study, the remainder of this review will highlight its major results and conclusions, critically analyze their research methods, as well as provide guidelines for future directions.

MAJOR RESULTS

rTMS Improves Symptoms of OCD Symptom severity of OCD was measured using the 5-point Yale-Brown Obsessive Compulsive Scale (Y-BOCS), with higher sum scores reflecting more severe OCD. The Y-BOCS scale is commonly used in clinical practice because it can define the percentage of symptom response to treatment, which can help determine whether the treatment should be continued (Pallanti, 2014). Measurements were taken at three different time points: baseline, after 2 weeks, and after 4 weeks (Jahangard et al., 2016).

After active HF- rTMS (with a frequency of 20 Hz) was applied bilaterally to the DLPFC, symptoms of OCD improved significantly, or, in other words, Y-BOCS scores decreased significantly (large effect size) over the 4-week protocol. Specifically, Y-BOCS scores decreased during the active-rTMS condition, but not during the sham condition. This was the case for both groups (rTMS first and sham first).

Furthermore, in the rTMS-first group, baseline scores were significantly higher than scores at the second and third time points, but there was no The Undergraduate Journal of Neuroscience

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difference between the second and third time points.

In contrast, in the sham-first group, the third time point scores were significantly lower than the baseline and second time point scores, and the baseline and second time point scores barely differed (Figure 1).

Additionally, the Clinical Global Impression Scale (CGI) was used to assess illness severity and improvement. It consists of one item that asks how mentally ill a patient currently is; with higher scores indicating a greater illness severity. Symptom severity according to this scale decreased significantly, in a similar trend as the Y-BOCS scores. Improvements were only observed after active rTMS, and not after the sham condition. Symptom improvements did not differ between the two groups (i.e., magnitude of overall symptom improvements were the same in both the rTMS-first group and the sham-first group, but just at different times).

Figure 1: Y-BOCS scores decrease significantly after the 4-week intervention. Baseline values are significantly higher than the other two time points in the rTMS-first condition, whereas the third-time point is significantly lower than the baseline and second time point in the sham-first condition (Jahangard et al., 2016).

Comparison to Other Publications These results replicate the findings of the group’s earlier research, which found that active rTMS led http://www.neurogenesis-journal.com

to symptom improvements, but the sham condition did not (Haghighi, 2015). This finding is also supported by a 2013 meta-analysis based on 10 RCTs, which showed that, after a mean of 14 active rTMS sessions, OCD-related anxiety and depressive symptoms were significantly reduced (Berlim, Neufeld, and den Eyned, 2013).

Additionally, because the patients continued taking either SSRIs or clomipramine throughout the study, the results indicate that rTMS augmented pharmacological therapy. This is in accordance with Rui Ma & Shi’s (2014) meta-analysis, which revealed that the addition of active rTMS to ongoing medication treatment produced a statistically significant reduction in Y-BOCS scores, compared to subjects receiving the sham intervention (Rui-Ma and Shi, 2014).

Moreover, supporting and furthering the research of Greenberg et al. (1997), the results showed that rTMS application to the DLPFC leads to improved symptomatology. However, this finding has not been consistent across all reports of the efficacy of rTMS to the DLPFC. For instance, Elbeh et al. (2016) showed that HF-rTMS application to the DLPFC does not improve OCD symptoms, and that LF-rTMS is more effective (Elbeh et al., 2016).

In their study, LF-rTMS of 1 Hz over the DLPFC improved Y-BOCS scores significantly more than sham and 10 Hz-HF-rTMS conditions (Figure 2). This contradicts what Jahangard et al. (2016) found, as they applied 20 Hz HF-rTMS over the DLPFC and found symptom improvement. Elbeh et al. (2016) provided a rationale for their results, stating that because OCD is caused by increased neural activity in prefrontal circuits, inhibitory rTMS protocol over these areas may alleviate OCD-related symptoms (recall LF-rTMS is inhibitory, HF-rTMS is excitatory). Alternatively, LF-rTMS may have normalized DLPFC activity, thereby enhancing the patients’ ability to inhibit obsessive thoughts, impulses and compulsions. The 2013 meta-analysis by Berlim et al. also found that HF-rTMS to the DLPFC was not any more effective than sham-rTMS in terms of improving symptoms.

The present study’s finding also differs from the results of the meta-analysis by Soltema et al.

(2008), which revealed that active-rTMS was not significantly more favourable than the sham condition in treating OCD, as it was for treating depression, auditory verbal hallucinations and negative symptoms of schizophrenia (Slotema, 2009).

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Figure 2: LF-rTMS (1Hz) led to significantly lower Y-BOCS scores than baseline immediately after the session, and 3 months later, in comparison to the sham and HF-rTMS (10 Hz) conditions (Elbeh et al., 2016) .

rTMS Has a Positive Correlation with Improved Cognitive Performance In addition to studying the effects of rTMS on symptoms of OCD, Jahangard et al. (2016) also investigated its effects on the cognitive performance of individuals with OCD. Cognitive performance was assessed through various tests for auditory and visual information processing speed and flexibility, and short term memory. Results showed that all cognitive processes tested improved significantly over the 4-week protocol, with the greatest improvements being observed after active rTMS, and not after the sham condition. This point is illustrated in Figure 3, which displays the results of a digit-symbol substitution test to assess auditory information processing speed. The greater the number of digit spans reported, the greater the cognitive performance.

This finding is very important and relevant to consider because it is the first to indicate that rTMS could be beneficial in terms of improving the cognitive functioning of individuals with refractory OCD. Although no other study has assessed rTMS effects on cognitive function in patients with OCD, the results of this study are in line with the findings that rTMS improves cognitive function in patients with other neuropsychiatric disorders.

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Figure 3: Cognitive performance, assessed through the number of digit spans reported during an auditory information processing task, increased significantly during the rTMS condition, but not during the sham condition (Jahangard et al., 2016).

DISCUSSION

In terms of improving symptom severity, their finding is important because it replicates and endorses the results of the authors’ previous study, as well as other studies, that indicated positive symptom improvement after active-rTMS application (Berlim, Neufeld, and den Eyned, 2013, Rui-Ma and Shi, 2014, Mantovani et al., 2010, Greenberg et al., 1997, Haghighi, 2015, Elbeh et al., 2016). Taking the results of all studies together, rTMS could be considered an efficacious treatment method for refractory OCD. With respect to cognitive performance, the present study is the first to indicate that there is a positive relationship between cognitive performance and rTMS in OCD patients. The novelty of this outcome provides greater insight into the extent of benefits rTMS for OCD therapy provides, adding to the current literature available on this topic.

Based on their results, the authors concluded that rTMS is a safe and efficient treatment of refractory OCD. However, despite the promise in their results, several limitations to the study prevent further interpretation and conclusions from being made. These will be highlighted in the critical analysis section below.

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Critical Analysis and Future Directions There are various strengths of this study that contribute to its significance as a publication. For example, because review of previous literature on rTMS revealed that rTMS is efficacious for cognitive performance in other neuropsychiatric disorders, the authors took the initiative to investigate whether this is also the case for OCD. The results indicated that indeed there is a relationship, and that rTMS application to the DLPFC in refractory OCD patients seemed to improve their cognitive functioning. The weakness in this is that it is simply a correlational finding, and does not imply causation. The authors could not conclude from their study whether cognitive performance improved because of the influence rTMS had on the neural circuits for those cognitive functions, or more so as a side effect of improved symptoms. Along these lines, the study cannot provide any details on how rTMS impacted the neural circuits involved in OCD that then resulted in a reduction of symptom severity. This was one of the shortcomings of their previous publication in 2015, and they failed to address it in this follow up study.

Further investigation of the molecular and neuronal changes resulting from rTMS would provide greater support for the usefulness of rTMS in OCD. As suggested in another study, using neuroimaging and neurophysiologic techniques alongside clinical symptom assessment could help demonstrate the neuronal changes in the brain that are underlying the clinical improvement (Gomes et al, 2012).

Moreover, since rTMS was done in addition to pharmacological treatment, the results could be due to a possible synergistic effect between the two interventions, meaning that improvements of one treatment were enhanced by those of the other.

To address this, and to fully determine the efficacy of rTMS alone in treating OCD, subjects not on any OCD-treating medications should be used.

The authors could also compare HF-rTMS with LF-rTMS over various OCD-related brain regions (not just DLPFC) to determine which form of rTMS is most effective and over which brain regions. Jahangard et al. (2016) only used HF-rTMS in their study, but seeing as OCD is believed to be caused by hyperactivity in certain neural circuits, it makes sense that LF-rTMS could be a useful treatment. It could cause inhibition and normalize the activity in these overactive brain areas. This would be able to either confirm or disprove the findings of Elbeh et al. (2016), which reported that HF-rTMS to the http://www.neurogenesis-journal.com

DLPFC was not any more effective than the sham, and was inferior to LF-rTMS. Different brain regions, in addition to the DLPFC, should be targeted as well, as other brain regions may provide a greater therapeutic effect. For example, Modirrousta et al. (2015) showed that LF-rTMS over the medial pre-frontal cortex resulted in a mean 39% decrease in Y-BOCS score, and this persisted 1 month after the intervention (Modirrousta et al., 2015). So, by targeting both these brain regions in a single study, it would be possible to determine which brain regions provide the most advantageous results.

Lastly, two of the most important improvements that could be made in this study design is increasing the sample size and follow up period time. As reported by Gomes et al. (2012), at least 23 subjects per treatment condition is required for results to reach a power of 85% (alpha = 0.05). The present study is limited in generalizing its results as only a total of 10 subjects were used. Also, the study was only able to provide short term results of the intervention with the last measurement being at the end of the 4-week protocol. A longer follow up period (e.g. 3 months after intervention) would be needed to determine the long-term effects of rTMS treatment.

CONCLUSION

The major findings of this study are as follows: in comparison to a sham condition, bilateral HF-rTMS application to the DLPFC improved symptom severity, as indicated by Y-BOCS and CGI scores, and was also positively correlated with cognitive performance on information processing and short term memory tasks. This is the first study to show that rTMS treatment to the DLPFC can improve cognitive performance in refractory OCD patients.

This review of the Jahangard et al. (2016) study provides a critical analysis of their methods and results, and compares it with previously reported literature to confirm or dispute their findings. The takeaway from this review is meaningful in that it suggests that the finding of improved symptom severity following HF-rTMS has been replicated in numerous trials, and therefore should be considered as a novel and efficacious treatment of refractory OCD. Furthermore, important limitations of the study were highlighted and recommendations were made that could potentially improve their methodology and provide more concrete evidence on the efficacy of this treatment.

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Elbeh, K.A.M, Elserogy, Y.M.B., Khalifa, H.E., Ahmed, M.A., Hafex, M.H., & Khedr, E.M. (2016). Repetitive transcranial magnetic stimulation in the treatment of obsessive-compulsive disorders: double blind randomization clinical trial. Psychiatry Research, 238, 264-269.

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