Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology

Abstract. Developing a better understanding of how life stress leads to the onset of psychiatric disorders, such as major depression has the potential to transform our ability to prevent and treat these disorders. Unfortunately, the capacity to capture the effects of stress accurately and in real-time has been limited because the assessment of psychiatric phenotypes has traditionally relied on the long-term recall and self-report of symptoms by affected individuals. Mobile electronic technology holds great promise in overcoming these limitations by capturing continuous, real-time, passive measures likely to be related to the progression from stress to . Unfortunately, the results of mobile electronic technology studies conducted, to date, have been limited, in large part because it is difficult to anticipate the onset of stress and depression. To advance, there is a critical need to understand the temporal relationship between stress and depression with real-time, objective measures. Our lab has demonstrated that we can prospectively predict the onset of stress and depression in a large group of individuals utilizing the medical internship paradigm. Medical internship, the first year of professional medical training, is characterized by long work hours, emotionally difficult situations and inconsistent and insufficient sleep. Internship is a rare situation; we can accurately predict that a cohort will experience the onset of a major, uniform stressor and a dramatic increase in depressive symptoms1, 2. This model allows for the same individuals to be followed, first under normal conditions and then under the conditions of high stress. Over the past 10 years, we have enrolled over 13,000 interns from 55 U.S. institutions and are enrolling 3,000-3,500 new interns each year. We have found that rates of depression increase dramatically, from 4% prior to internship to 26% during internship year. Here, we propose to utilize existing and developing technology (see support letter from Dr. Insel, Google/Verily) to examine physiology, behavior, environment, spatial, and temporal dimensions in real-time, and establish how, together, they contribute to the relationship between stress and onset of depression. Specifically we aim to: 1) identify mobile electronic technology factors that change with stress, and 2) develop a weighted mobile electronic technology factor that predicts depressive mood. We believe that completion of these aims will have profound impact in multiple domains: 1) most directly, detecting signature predictors of depression risk will inform micro-randomized intervention trials so that individuals at risk are identified early and provided the right treatment at the right time 2) better temporal and dynamic understanding of how risk factors (such as sleep perturbations) lead to depression, guiding development of new and more effective interventions 3) finally, identifying mobile signatures of depression risk will provide richer and more effective phenotypes that will facilitate genetic and biomarker studies of depression and stress. Principal Investigator: Co-Principal Investigator: Srijan Sen, M.D., Ph.D. Margit Burmeister, Ph.D. Associate Professor of Psychiatry, Medical School Research Professor, MBNI Research Associate Professor, Molecular and Behavioral Associate Chair and Professor, Department of Neuroscience Institute (MBNI) Computational Medicine & Associate Chair for Research/Research Faculty Devel Professor, Department of Psychiatry Email: [email protected]; Tel.: (734) 615-8666 Professor, Department of Human Genetics Website: http://www.srijan-sen-lab.com/ (U-M) Team Members: Susan Murphy, Ph.D. ISR & Medical School, Department of Psychiatry Lawrence C. An, M.D CHCR Rex Timbs, B.S.C.S CHCR Marcio Mourao, Ph.D. CSCAR John Brussolo, Ph.D MSIS Amy Cochran, Ph.D. LSA, Department of Mathematics Daniel Forger, Ph.D. LSA, Department of Mathematics Elena Frank, M.A., Ph.D Medical School, MBNI Zhuo (Joan) Zhao, M.S. Medical School, MBNI Industry Partner(s): Thomas Insel, M.D Verily Life Sciences a division of Google

Technical Description

Problem. According to the World Health Organization, major depression is the second leading cause of disability, affecting more than 350 million people worldwide3. Unfortunately, the burden of depression has been growing over the past decades, with a concomitant increase of death due to suicide while death for other health factors decreased4. The field has struggled to develop new, effective treatments and prevention strategies for the disorder. One important and robust finding in depression research has been that life stress is the single most important trigger for the development of depressive episodes5,6. Approximately 80% of depressive episodes are preceded by a major stressor7, 8, 9. Understanding the mechanisms through which life stress leads to depression has the potential to predict when an episode is imminent, informing preventative and treatment strategies. Unfortunately, the capacity to capture the effects of stress accurately and in real-time has been limited because the assessment of psychiatric phenotypes has traditionally relied on long-term recall and self-report of symptoms by affected individuals. Once depressed, individuals develop negative cognitive biases. In fact, individuals often recall fixed periods of time (like childhood) as more stressful when they are in a depressive episode compared to when they are in a healthy period10. There is a critical need to understand the temporal relationship between stress and depression with real-time, objective measures to intervene effectively in real- time. Mobile electronic technology holds great promise in overcoming these limitations by capturing continuous, real-time, passive measures likely to be related to depression11. Previous studies suggest that insufficient and poor quality sleep, a sedentary lifestyle, social isolation, and decreased cognitive function are associated with mood changes12. Because it is almost impossible to prospectively predict the onset of stress and depression in a large group of subjects, mobile electronic technology studies conducted, to date, have been small and largely focused on patients already experiencing psychiatric symptoms13,14,15.

Solution. Medical internship has long been considered the most stressful year during the career of a physician, with long work hours, little sleep and life and death decisions to make. Unlike most stressors, internship is a rare situation where we can accurately predict that a cohort will experience the onset of a major, uniform stressor and a dramatic increase in depressive symptoms16, 17. This model allows for the same individuals to be followed, first under normal conditions and then under the conditions of high stress. Our laboratory has also shown feasibility that mobile monitoring allows prospective, real-time monitoring of continuous, real-time, passive measures from this large National group of medical interns to effectively predict short-term risk for depressive episodes (R01 MH101459, K23 MH095109). Here, we propose to examine physiology, psychosocial behavior, environment, spatial and temporal dimensions in real-time, and establish how, together, they contribute to the relationship between stress and onset of depression. Specifically we aim to: 1) identify mobile electronic technology factors that change with stress, and 2) develop a weighted mobile electronic technology factor that predicts depressive mood.

Approach. Summary statement of work, justification of team, and expected impact is outlined below. I. Objectives and Expected Outcomes Our primary objective in this application is to identify weighted sensor stream signatures that predict near-term risk of a major depressive episode. Specifically, we will collect daily mood ratings along with a rich set of passive and active data streams including sleep duration and quality, physical activity, geolocation, social interactions and cognitive function. We will then employ state-of-the-art analytical methodologies to develop prediction models that will be tested in subsequent cohorts of training physicians and general population samples. There are also a series of important secondary objectives for this study. First, beyond identifying unbiased integrated predictors of near-term depressive episodes, we will also better understand the temporal relationship between specific factors and depression. For instance, the study should better elucidate the timing and nature of sleep changes that precede depression and may suggest potential interventions (see preliminary data below). Finally, mobile interventions hold promise in improving access to effective mental health care but we know little about when these tools will be most effective. Our team proposes to successfully accomplish: Aim #1: Identify mobile electronic technology factors that that change with stress and Aim #2: Identify a weighted mobile electronic technology factor signature that predict depressive mood. At the completion of this project, we expect to have identified mobile signatures that change with stress and prospectively predict changing levels of depressive symptoms. Further, we will establish a nimble platform well positioned to rapidly assess new mobile electronic intervention technologies as they emerge. II. Nature of the Data – The Internship Model To date, we have enrolled over 13,000 interns from 55 U.S. institutions and are enrolling 3,000-3,500 new interns each year. We have found that rates of depression increase dramatically, from 4% prior to internship to 26% during internship year. Several key features of the internship study make it ideal to assess mobile electronic technology tools in mental health: 1) a prospective design that allows the collection of data before and after the onset of stress and depression; 2) likelihood of high compliance in a medically and technologically literate sample; and 3) a large sample with existing rich longitudinal data available to be integrated with mobile data (self-reported mood, anxiety and suicidality data, genomic data, epigenomic-wide methylation data, telomere length data, hair cortisol data and vascular function data). We propose to assess multiple classes of mobile data: 1) ecological momentary assessment of mood; 2) geospatial assessment of location diversity; 3) sleep; 4) physical activity; and 5) speech and text analysis of emotional valence. III. Preliminary Data, Experimental Design and Analytical Methods 1.Preliminary Data We have collected pilot mobile health measures on two small cohorts. Fifty (50) subjects from the 2015-6 cohort were invited to take part in a mobile health pilot and 38 chose to take part. Physical activity and sleep were assessed through Fitbit Trackers and subjects were texted daily to assess their daily mood. In this pilot, 92% of subjects provided sleep, activity, and mood data on at least 80% of days. Data has shown an expected strong association between sleep and mood, but found that sleep predicted mood the following day substantially more strongly than mood predicted subsequent sleep (Sleep Mood b=0.12; p<0.001; Mood Sleep b=0.05; p=0.04 – Fig. 1). We have also found that on a given night, the farther an individual’s sleep midpoint is from their pre-internship baseline midpoint the lower their mood (p<0.001). For the 2016-17 cohort, we are seeking to identify a mechanism to expand our sample size and of potential mobile variables assessed. We worked with a team in the U- M MSIS to develop an iPhone App using the ResearchKit framework. The pilot version of the “Intern Study” app allows us to administer surveys and collect daily mood ratings. Figure 1. Daily Mood and Sleep Recent evidence suggests the pattern of locations individuals Throughout Internship visit changes before a change in mood and, specifically, that individuals visit fewer novel locations immediately before a depressive episode18. Thus, in our pilot app, we are also assessing geolocation data on all places that subjects visit. To date 885 incoming interns have been invited to join the study using the “Intern Study” App and 593 (64%) have agreed to participate. Over the baseline pre-internship phase of the study, 80% of subjects have provided location and mood data on at least 70% of days in this pilot study. These pilots demonstrate: 1) that this target population of training physicians is engaged and willing to provide mobile data; 2) that our team can develop mobile assessment tools to gather active and passive data on a large scale; and 3) that the initial data are promising for the detection of signal streams that predict future mood. If this current proposal is funded, we will work with the MSIS team that developed our skeleton “Intern Study” to build on the established foundation to develop an app to assess the broader set of signal streams described here in a larger sample of subjects. 2. Work Plan 2a. Recruit two cohorts for a total of 6,500 subjects. The Intern Health Study is a prospective longitudinal that began enrolling U.S. subjects in 2007, with an expanding annual cohort enrolling each year. To date, ten (10) cohorts and over 13,000 interns (60% participation rate) from 55 U.S. institutions have taken part in the study, with the current 2016-17 cohort including 3,331 interns

(http://www.internhealthstudy.org). The Intern Healthy Study has produced important insights into the genes and other biomarkers in depression, the nature and trajectory of depressive symptoms under stress and the factors associated with depression among training physicians. Findings have been published in JAMA, JAMA Psychiatry(2), JAMA Internal Medicine(2), Biological Psychiatry and Academic Medicine(2), garnering an average of over 100 citations, and studies with media coverage in TIME, the New York Times, the New Yorker, Fox News and many others. 2b. Administer surveys and collect active and passive mobile data before internship and through internship. All subjects will download the “Intern Study” app (Fig. 2) and complete the consent and baseline survey through the app. As discussed above in preliminary data, our pilot app assesses survey data, daily mood and geolocation data. Our team will develop the next generation app to 1) assess sleep, activity Figure 2. Intern Study App and physiologic data (both from phones and wearable devices 2) assess voice changes. In addition to demographic and baseline psychological information (Neuroticism using the NEO-FFI and Early Family Environment using the Risk Family ), the baseline survey will assess anxiety symptoms using the Generalized Anxiety Disorder-7 (GAD-7), suicidal symptoms using the Positive and Negative Suicidality Inventory (PANSI) and depressive symptoms using the Patient

30.00% Health Questionnaire (PHQ-9) (61-64). Through the course of internship, we will assess subjects at 3-month

25.00% intervals using a shorter (5-7 minute) web based questionnaire designed to assess 1) current depressive

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5.00% at baseline and months 3, 6, 9 and 12, we will provide participants with general information about depression

0.00% and a list of resources for mental health counseling and Before Internship 3 Months 6 Months 9 Months 12 Months Intern Assessment Time treatment. Our current “Intern Study” app already collects geolocation data and solicits daily mood data Figure 3. Depression Rates Across Internship (Fig. 3). We will work with the U-M MSIS team to expand the functionality of the app by pulling existing technologies to assess sleep, activity, and speech patterns. Further, we anticipate that our partners at Google/Verily will finalize their prototype wearable device for depression in 2017 (See support letter from Dr. Insel). We will utilize this wearable to integrate novel additional passive data into the app. 2c. Build probability models with embedded latent, or unobserved, constructs from the observational data collected with mobile health technology. Often promised as a panacea, mobile health collects real-time data on numerous aspects of patient health. While the amount of data can be a benefit when predicting outcomes, such as done in machine learning, it can also pose a challenge when the goal is to inform/elucidate. The latter is indeed our goal: to inform care-givers on when, how, and for who to intervene; and to inform researchers on how to connect observations to underlying processes and enrich sample populations for testing genetic and physiological differences. Latent provides an approach, wherein a large set of observations are explained as a function of a fewer set of latent variables and a random term; e.g., test scores explained by a latent "intelligence" variable and a random term. There are certain benefits to such an approach. First, probability models can capture the inherent volatility of depressive symptoms and the subjective nature of measuring depressive symptoms. Second, latent constructs (e.g. mood) are already essential in psychiatry for aggregating otherwise complicated set of observations. Probability models with latent constructs simply extends this work by formally defining how to aggregate observations, and mobile health, along with methods such as Monte Carlo Markov Chain,

ensures there are enough observations to customize the models at the patient-level with precision. Third, latent constructs (either directly modeled or indirectly via instrumental variables) enhance observational studies by evaluating possible intervention strategies. They help distinguish between a precursor to depression, e.g., poor sleep, from a cause of depression. Fourth, well-designed latent constructs are meaningful to the user, which in itself may have a therapeutic effect. In detail, observations collected at baseline and during internship stress will be compressed into important features of depression identified in the literature (demography, sleep and circadian rhythms, physical/sedentary behavior, and mobile phone interactions). These features will then be used to build two types of dynamic models, each of which model mood as a discrete-time continuous-state stochastic process. The first model will consist of a Bayesian nonparametric hierarchical model that includes latent subtypes of individuals, observations at baseline, and patient-specific dynamics of self-reported mood. The idea is to identify individuals at risk for depression based not only their observations at baseline (e.g. gender, sleep phenotype), but also on their dynamics of mood. The revealed subtypes could then be used to divide patients into groups for testing genetic differences. The second model will consist of a probability model with an embedded latent process of "depression", where observations are assumed to relate to each other over time and to the latent process. By including a latent process, we may determine how the current latent state explains current observations and how current behavior might change the current latent state. The result will be a better understanding of the dynamics of mood, what factors to monitor for depression, and what factors can be actively changed to improve depression. IV. Resources  Center for Health Communications Research (CHCR): Lawrence C. An, M.D. and Rex Timbs, B.S.C.S. will work on the Intern Study iOS/Android app development.  CSCAR: Marcio Mourao, Ph.D. will provide statistical analysis.  MSIS: John Brussolo, Ph.D., will provide expertise in data virtualization and app development. V. Team The team will be led by Dr. Sen (PI) and Dr. Burmeister (Co-PI). Members of the team include:  Institute of Social Research (ISR): Susan Murphy, Ph.D. will provide expertise in micro-randomized mobile health treatment trials.  LSA, Department of Mathematics and Computational Medicine & Bioinformatics: Amy Cochran, Ph.D. and Daniel Forger, Ph.D. will lead the efforts in data modeling.  Molecular and Behavioral Neuroscience Institute (MBNI): Elena Frank, M.A., Ph.D., will serve as the Coordinator and Zhuo (Joan) Zhao, M.S. will manage the data as the Research Computer Specialist.  Verily Life Sciences a division of Google, Thomas Insel, M.D., Director of Clinical Neuroscience and former director of the National Institute of Mental Health will share expertise on the monitoring of depression, wearable technology, and high-throughput data analysis (see Letter of Support). VI. Impact The focus of this project – to identify mobile signatures that prospectively predict changing levels of depressive symptoms – is anticipated to directly impact two major directions: 1) precision medicine and 2) access to mental healthcare. First, we will begin to elucidate causal mechanisms linking stress and depression. By identifying high risk time periods, we provide the framework to provide true precise personalized interventions for depression, allowing individuals to receive the right treatment at the right time. If employed in a personal and timely manner, mobile health interventions hold the promise in improving access to effective mental health care. Findings will further inform the development and implementation of effective mobile interventions for those identified at risk for mental illness in real time. We anticipate that the work completed will also set the stage for exciting and impactful future directions. The mobile signals of depression identified here will be used to inform the timing and intervention types for micro-randomized mobile health treatment trials (with Susan Murphy). In addition, in lieu of the low-quality subjective, cross-sectional phenotypes currently in use, the rich mobile signatures of depression identified here will be used as phenotypic targets for genomic and biomarker investigations (led by PI Srijan Sen and Co-PI Margit Burmeister).

References

1 Guille C, Clark S, Amstadter AB, Sen S. Trajectories of depressive symptoms in response to prolonged stress in medical interns. Acta Psychiatr Scand. 2014 Feb;129(2):109-15. 2 Sen S, Kranzler HR, Krystal JH, Speller H, Chan G, Gelernter J, Guille C. A investigating factors associated with depression during medical internship. Arch Gen Psychiatry. 2010 Jun;67(6):557-65. 3 http://www.who.int/mediacentre/factsheets/fs369/en/ 4 Curtin SC, Warner M, Hedegaard H. Increase in Suicide in the United States, 1999-2014. NCHS Data Brief. 2016 Apr;(241):1-8. 5 Dohrenwend BP. Sociocultural and social-psychological factors in the genesis of mental disorders. J Health Soc Behav. 1975 Dec;16(4):365-2. 6 Brown J. Danger: stress at work. Elder Care. 1994 May-Jun;6(3):15-6. 7 Mazure CM, Bruce ML, Maciejewski PK, Jacobs SC. Adverse life events and cognitive-personality characteristics in the prediction of major depression and antidepressant response. Am J Psychiatry. 2000 Jun;157(6):896-903. 8 Mazure CM, Maciejewski PK, Jacobs SC, Bruce ML. Stressful life events interacting with cognitive/personality styles to predict late-onset major depression. Am J Geriatr Psychiatry. 2002 May- Jun;10(3):297-304. 9 Maciejewski PK, Prigerson HG, Mazure CM. Self-efficacy as a mediator between stressful life events and depressive symptoms. Differences based on history of prior depression.Br J Psychiatry.2000 Apr;176:373-8. 10 Joormann J, Waugh CE, Gotlib IH. Cognitive Bias Modification for Interpretation in Major Depression: Effects on Memory and Stress Reactivity. Clinical psychological science : a journal of the Association for Psychological Science. 2015;3(1):126-139. 11 Torous J, Staples P, Onnela JP. Realizing the potential of mobile mental health: new methods for new data in psychiatry. Curr Psychiatry Rep. 2015 Aug;17(8):602. 12 Pemberton R, Fuller Tyszkiewicz MD. Factors contributing to depressive mood states in everyday life: A . J Affect Disord. 2016 Aug;200:103-10. 13 Aschbrenner KA, Naslund JA, Shevenell M, Kinney E, Bartels SJ. A Pilot Study of a Peer-Group Lifestyle Intervention Enhanced With mHealth Technology and Social Media for Adults With Serious Mental Illness. J Nerv Ment Dis. 2016 Jun;204(6):483-6. 14 Tomita A, Kandolo KM, Susser E, Burns JK. Use of short messaging services to assess depressive symptoms among refugees in South Africa: Implications for social services providing mental health care in resource- poor settings. J Telemed Telecare. 2015 Sep 24. 15 Naslund JA, Aschbrenner KA, Barre LK, Bartels SJ. Feasibility of popular m-health technologies for activity tracking among individuals with serious mental illness. Telemed J E Health. 2015 Mar;21(3):213-6. 16 Guille C, Clark S, Amstadter AB, Sen S. Trajectories of depressive symptoms in response to prolonged stress in medical interns. Acta Psychiatr Scand. 2014 Feb;129(2):109-15. 17 Sen S, Kranzler HR, Krystal JH, Speller H, Chan G, Gelernter J, Guille C. A prospective cohort study investigating factors associated with depression during medical internship. Arch Gen Psychiatry. 2010 Jun;67(6):557-65. 18 Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, Mohr DC. Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study. J Med Internet Res. 2015 Jul 15;17(7):e175.

Personnel % Year 1 Year 2 Year 3 Fringe Name Position Effort Cummulative Rate Srijan Sen PI 20% $ 38,611 $ 39,769 $ 40,962 $ 119,342 32% Margit Burmeister Co-I 10% $ 16,604 $ 17,102 $ 17,615 $ 51,321 Elena Frank Clinical research 40% $ 19,530 $ 20,116 $ 20,719 $ 60,365 Zhuo (Joan) Zhao Research computer 50% $ 34,446 $ 35,479 $ 36,543 $ 106,468 Amy Cochrane Data modeling 10% $ 7,316 $ 7,535 $ 7,761 $ 22,612 LSA Mathematics Danny Forger Data modeling 5% $ 6,835 $ 7,040 $ 7,251 $ 21,126 LSA Mathematics Marcio Mourao (mdam) Statistical analysis 5% $ 4,151 $ 4,276 $ 4,404 $ 12,831 CSCAR Micro-randomized mobile health treatment Susan Murphy trials 5% $ 16,066 $ 16,548 $ 17,044 $ 49,658 SCR Quant methods Total salaries and wages $ 143,559 $ 147,865 $ 152,299 $ 443,723 Total fringe benefits $ 45,939 $ 47,317 $ 48,736 $ 141,991 Total salaries, wages and fringe benefits $ 189,498 $ 195,182 $ 201,035 $ 585,714

Other Costs/non salary items $ 287,500 $ 248,025 $ 98,566 $ 634,091 data infrastructure, storage, database $ - $ - $ - $ - apple watches/fit bits $ 17,500 $ 18,025 $ 18,566 $ 54,091 license fees for use of statistical packages $ - $ - $ - $ - iOS development…CHCR $ 120,000 $ 80,000 $ 80,000 $ 280,000 239800 Interal Med participant fees $ 150,000 $ 150,000 $ - $ 300,000

Total Direct Costs $ 476,998 $ 443,207 $ 299,601 $ 1,219,805

6/30/2016 3:34 PM BIOGRAPHICAL SKETCH NAME: Sen, Srijan ERA Commons name: SRIJAN POSITION TITLE: Associate Professor of Psychiatry, Medical School; Research Associate Professor, Molecular and Behavioral Neuroscience Institute; Associate Chair for Research & Research Faculty Development EDUCATION/TRAINING: INSTITUTION AND LOCATION DEGREE COMPLETION DATE FIELD OF STUDY CORNELL UNIV BA 01/1997 Biology, Neurobiology UNIV OF MICHIGAN MD 05/2005 Medicine UNIV OF MICHIGAN PHD 05/2005 Neuroscience UNIVERSITY OF OXFORD, Oxford Other training 06/1996

A. Personal Statement My recent work has been built on the Intern Health Study, a prospective of stress during medical internship that I initiated in 2007. Medical internship is a rare situation where we can accurately predict that a cohort will experience the onset of a major, uniform stressor and a dramatic increase in depressive symptoms. This model allows us to follow the same individuals, first under normal conditions and then under the conditions of high stress. To date, we have enrolled over 13,000 interns from 55 institutions around the country, adding between 2,000 subjects to the study each year. The research design overcomes the problems of recall bias and heterogeneous precipitants that have limited traditional studies of depression under stress. For the proposed MIDAS project, I will lead all aspects of the clinical assessments and analyses of the data and work closely with the experts in computational biology to manage and analyze the data gathered from the mobile device. I will be involved with the evaluation of the modeling data and outcomes from the perspectives of both translational research and clinical utility. I am dedicated to the success of this project and view it as a critical in better understanding the onset of depression, a condition that is not truly homogeneous in terms or mechanisms of action, so that individuals at risk are identified early and appropriately treated. 1. Sen S, Kranzler HR, Krystal JH, Speller H, Chan G, Gelernter J, Guille C. A prospective cohort study investigating factors associated with depression during medical internship. Arch Gen Psychiatry. 2010 Jun;67(6):557-65. PubMed PMID: 20368500; PubMed Central PMCID: PMC4036806. 2. Sen S, Duman R, Sanacora G. Serum brain-derived neurotrophic factor, depression, and antidepressant medications: meta-analyses and implications. Biol Psychiatry. 2008 Sep 15;64(6):527- 32. PubMed PMID: 18571629; PubMed Central PMCID: PMC2597158. 3. Fried EI, Nesse RM, Guille C, Sen S. The differential influence of life stress on individual symptoms of depression. Acta Psychiatr Scand. 2015 Feb 4;PubMed PMID: 25650176. 4. D Mata, M Ramos, N Bansal, R Khan, C Guille, E Di Angelantonio and S Sen. 2015. of Depression and Depressive Symptoms among Resident Physicians. JAMA. 314 (22), 2373-2383. 5. C Guille, J Krystal, B Nichols, J Zhao, K Brady and S Sen. 2015. Web-Based Cognitive Behavioral Therapy Intervention for the Prevention of Suicidal Ideation in Medical Interns: A Randomized Controlled Trial. JAMA Psychiatry. 72(12), 1192-1198

B. Positions and Honors 2005 – 2009 Psychiatry Resident and Research Fellow, Yale University 2009 – 2014 Assistant Professor, Department of Psychiatry, University of Michigan (U-M) 2011 – 2015 Research Assistant Professor, Molecular and Behavioral Neuroscience Institute, U-M Associate Professor, Department of Psychiatry, U-M 2015 – Present Associate Chair for Research and Faculty Development, Department of Psychiatry, U-M Research Associate Professor, Molecular and Behavioral Neuroscience Institute, U-M Honors: 2002 Associate Fellowship, Michigan Society of Fellows 2005 Raymond Waggoner Award, University of Michigan 2006 Seymour Lustman Award, Honorable Mention, Yale University 2007 Weinshilboum Prize, Runner-up, Mayo Clinic 2007 Daniel X. and Mary Freedman Fellowship in Academic Psychiatry, Yale University 2008 Seymour Lustman Award, Yale University 2008 Research Colloquium for Junior Investigators, American Psychiatric Association 2008 Outstanding Resident Award, Indo-American Psychiatry Association 2009 Laughlin Fellowship, American College of Psychiatry 2009 Research Resident Award, American Psychiatric Association/Lilly 2012 Oscar Stern Award for Depression Research, University of Michigan Depression Center 2013 BRAINS Award, National Institute of Mental Health 2016 University of Michigan Endowment for Basic Sciences Award

C. Contribution to Science 1) Most clinical research on depression has focused on cross-sectional assessment of depression diagnosis, an approach that can miss important information about the disorder. We have found that: i) under stress, three distinct longitudinal trajectories of depressive symptoms emerge and ii) different depressive symptoms behave differently under stress and are associated with different predictive variables. These findings highlight the utility of longitudinal phenotypes that go beyond DSM diagnoses. a) Fried EI, Nesse RM, Zivin K, Guille C, Sen S. Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors. Psychol Med. 2013 Dec 2;PubMed PMID: 24289852; PubMed Central PMCID: PMC4104249. b) Guille C, Clark S, Amstadter AB, Sen S. Trajectories of depressive symptoms in response to prolonged stress in medical interns. Acta Psychiatr Scand. 2014 Feb;129(2):109-15. PubMed PMID: 23581856; PubMed Central PMCID: PMC4073633. 2) Medical internship is a time of high stress, which can affect both the interns and the patients that they treat. We have identified psychological, demographic and work condition variables that are associated with depression during internship stress. Further, we documented the low-level of treatment received by depressed interns and identified important barriers to treatment. We evaluated the effect of these changes on the mental health and performance of interns. a) Guille C, Speller H, Laff R, Epperson CN, Sen S. Utilization and barriers to mental health services among depressed medical interns: a prospective multisite study. J Grad Med Educ. 2010 Jun;2(2):210- 4. PubMed PMID: 21975622; PubMed Central PMCID: PMC2941380. b) Guille C, Sen S. Prescription drug use and self-prescription among training physicians. Arch Intern Med. 2012 Feb 27;172(4):371-2. PubMed PMID: 22371930; PubMed Central PMCID: PMC3654840. c) Grant F, Guille C, Sen S. Well-being and the risk of depression under stress. PLoS One. 2013;8(7):e67395. PubMed PMID: 23840872; PubMed Central PMCID: PMC3698120. d) Sen S, Kranzler HR, Didwania AK, Schwartz AC, Amarnath S, Kolars JC, Dalack GW, Nichols B, Guille C. Effects of the 2011 duty hour reforms on interns and their patients: a prospective longitudinal cohort study. JAMA Intern Med. 2013 Apr 22;173(8):657-62; discussion 663. PubMed PMID: 23529201; PubMed Central PMCID: PMC4016974. All Citations: http://www.ncbi.nlm.nih.gov/sites/myncbi/srijan.sen.1/bibliograpahy/41447881/public/?sort=date&direction=ascending

D. Research Support 2015/02/28-2018/12/31 Department of Defense, Department of the Navy AKIL, HUDA (PI), Identification of Biomarkers for Stress Vulnerability and Resilience (Role: Co-Investigator)

2013/08/01-2018/06/30 R01 MH101459-02, National Institute of Mental Health (NIMH) SEN, SRIJAN (PI), Broad Scale Genomic Analysis to Find Genes Associated with Depression Under Stress

2014/01/01-2016/12/31 Emerging Scholar Grant, A. Alfred Taubman Medical Institute SEN, SRIJAN (PI), Novel Approaches to Identifying Biological Links Between Stress and Depression

2013/07/01-2016/06/30 Young Investigator Award, Oscar Stern Memorial Award SEN, SRIJAN (PI), Hair Cortisol as a Biomarker for Depression under Stress BIOGRAPHICAL SKETCH NAME: Burmeister, Margit ERA Commons name: Margit POSITION TITLE: Research Professor, Molecular and Behavioral Neuroscience, Vice Chair and Professor of Computational Medicine & Bioinformatics, and Professor of, Human Genetics and of Psychiatry EDUCATION/TRAINING DEGREE Completion FIELD OF STUDY INSTITUTION AND LOCATION (if Date

applicable) MM/YYYY Free University Berlin, Germany Diplom 07/1983 Biochemistry Weizmann Inst. of Science, Israel Dipl.thesis 07/1983 Molecular Biology European Molecular Biology Lab PhD work 05/1987 Molecular Genetics University of Heidelberg, Germany Dr.rer.nat. 11/1987 Biology University of California, San Francisco postdoc 04/1991 Human Genetics

A. Personal Statement My laboratory’s interest has been the influence of genes on both Mendelian neurological disorders and complex brain disorders related to depression and addiction. Supported by NIH and many foundation grants, my lab’s work has led to identification of more than a dozen Mendelian genes as well as downstream analyses. With the new ~omics techniques and NGS sequencing, genetic approaches have changed dramatically. A focus of my current work on Mendelian genes uses a pipeline from recruitment to gene identification with a combination of molecular approaches, exome sequencing, animal models, and machine learning pathways. Depression and addictions are affected by many genes and in with environmental factors in interaction. Gene x gene and gene x environment interactions are understudied topic in the era of large scale meta-analyses in which many studies are pooled, necessitating ignoring complex phenotypes and environments. The MIDAS proposal aims to identify real-time data predictors of depression using mobile technology by investigating environmental components such as stress and sleep on stress, in a paradigm that has the potential to expand to truly large datasets in the future. I am strongly dedicated to the team as I trained and then collaborated with Dr. Sen since 2002, and will contribute my expertise in quantitative endophenotypes, meta-analyses, and data interpretation to understand the complexities of depression. All my publications are in: http://www.ncbi.nlm.nih.gov/myncbi/browse/collection/44967389/

B. Positions and Honors

1987-1991 Postdoctoral fellow, UCSF with David R Cox and Richard M Myers 1991-1997 Asst. Res Scientist, Mental Health Research Institute; Asst. Prof. of Human Genetics in Psychiatry, Asst. Professor of Human Genetics, University of Michigan 1994-pres. Faculty member, Neuroscience Program, University of Michigan 1997-2005 Research Associate Prof., Mental Health Research Institute; Assoc. Prof. (with tenure) of Psychiatry, Assoc. Prof. of Human Genetics; Univ. Michigan 2003-pres. Faculty Member, Bioinformatics Graduate Program 2005-pres. Research Professor, Molecular & Behavioral Neuroscience Institute (formerly Mental Health Res. Inst.), Prof. (with tenure) of Psychiatry, Prof. of Human Genetics 2008-pres. co-Director, Bioinformatics Graduate Program, Univ. of Michigan 2001 Member, NIMH Strategic Planning Workgroup for Mood Disorders 1994-pres Adhoc member of study sections: e.g. NIDCD, NINDS & NIMH Centers & PPs; Genomics, Computational Biology & Technology (GCAT, many times), Genetics, Health & Disease (GHD), Brain Disorders & Clinical Neuroscience (BDCN-A), 2008 NARSAD Distinguished Investigator Award 2009-pres. Board of Directors, International Society for Psychiatric Genetics (ISPG) 2009-pres. Adjunct Prof., U of Michigan–Shanghai Jiao Tong Univ. Joint Inst., Shanghai, China 2010 Michael Weston Visiting Professor, Weizmann Institute of Science, Rehovot, Israel 2014 2 mo Foreign Expert, Bio-X Center, Shanghai Jiao Tong Univ., Shanghai, China 2012-2016 Permanent Member, GCAT study section of NIH 2016 Teaching Award - Endowment in the Basic Sciences – Molecular & Behavioral Neuroscience Institute

C. Contributions to Science (selection due to space restrictions- underlined with Dr. Sen):

The role of the serotonin transporter promoter polymorphism in neuroticism and depression risk My lab has for a long time interacted with Dr. Sen on depression, particularly, on quantitative endophenotypes such as neuroticism, meta-analyses, and gene x environment interactions, with many citations.

a) Sen S, Villafuerte S, Nesse R, Stoltenberg SF, Hopcian J, Gleiberman L, Weder AB and Burmeister M: Serotonin Transporter and GABA(A) α6 Receptor Variants are Associated with Neuroticism. Biol. Psych. 55: 244-249, 2004. b) Sen S, Burmeister M, Ghosh D: Meta-Analysis of the Association Between a Serotonin Transporter Promoter Polymorphism (5-HTTLPR) and Anxiety-Related Personality Traits. Amer. J. Med. Genet. (Neuropsychiatric Genetics): 127B1: 85-89, 2004. 600 citations c) Karg K, Burmeister M, Shedden K, Sen S: The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: Evidence of genetic moderation. Arch Gen Psych. 68(5):444-54, 2011. >900 citations

Identifying gene x environment interactions in GABRA2 polymorphisms’ effect on alcoholism risk In a large family-based longitudinal study of subjects at risk for alcoholism, we have studied how a well-known set of SNPs in GABRA2 affects risk by investigating impulsivity as a mediator, their effect on brain response using imaging, and by studying gene x environment interactions. Our results in >6 publications show that the “risk” alleles for alcoholism are “response” alleles that increase subjects’ response to the environment. a) Villafuerte S, Heitzeg MM, Foley S, Yau WYW, Majczenko K, Zubieta JK, Zucker RA, Burmeister M: Impulsiveness and Insular activation during reward anticipation are associated with genetic variants in GABRA2 in a family sample enriched for alcoholism. Mol Psychiatry, 17(5):511-9, 2012. b) Villafuerte S, Trucco EM, Heitzeg MM, Burmeister M, Zucker RA: Genetic variation in GABRA2 moderates peer influence on externalizing behavior in adolescents. Brain Behav 4(6):833-40, 2014. c) Trucco EM, Villafuerte S, Heitzeg MM, Burmeister M, Zucker RA: Susceptibility effects of GABA receptor subunit alpha-2 (GABRA2) variants and parental monitoring on externalizing behavior trajectories: Risk and protection conveyed by the minor allele. Dev Psychopathol 23:1-12, 2015.

Identification of genes involved in ataxias Identifying genes involved in neurological disorders has been a major emphasis of my career. After identifying the Cayman Ataxia gene with the help of a mouse mutation, we have identified several additional novel ataxia genes, often in families with severe, early onset ataxia and developmental delay and cognitive disabilities. Our findings about ATG5 suggest that autophagy may be important in ataxia even without poly-glutamine- containing misfolded proteins. My newest research includes machine learning network modeling in ataxia. a) Burns R, Majczenko K, Xu JS, Peng W, Yapici Z, Dowling JJ, Li JZ, Burmeister M: Homozygous splice mutation in CWF19L1 in a Turkish family with recessive ataxia syndrome. Neurology 83:2175-82, 2014. b) Kim M, Sandford E, Gatica D, Qiu Y, Zheng Y, Schulman BA, Xu J, Semple I, Ro S-H, Mavioglu RN, Tolun A, Jipa A, Takats S, Karpati M, Li JZ, Yapici Z, Juhasz G, Lee JH, Klionsky DJ, Burmeister M: Mutation in ATG5 Reduces Autophagy and Leads to Ataxia with Developmental Delay, eLife, 2016

D. Current Research Support (completed research support dropped due to 2 pg space restrictions) Title : Ataxia gene identification by integrated genomic analysis NIH - NINDS R01 NS078560 (Burmeister PI/LI) Period: 03/1/12 - 02/29/17, no cost ext until 2/2017 Title: Environmental exposures in early life: Epigenetics and neurodevelopment role: Co-I 1R21 ES025456 (Dolinoy, D, PI) Period: 09/18/14-8/31/15 in no cost extension until 8/2016 Title: The Prechter Neuropsychiatric Disorders Network PI: S. Watson, my role: Co-I Agency: Nancy Pritzker Neuropsychiatric Disorders Research consortium (gift) Period: ongoing since 2004