Bryce Gillis presents poster ‘Leveraging ecological assessments to derive robust predictors of mania, psychosis, and depression’ at the 2023 Technology in Psychiatry Summit (TIPS) Annual Meeting in Tampa, FL. See abstract below:
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Adolescence is characterized by profound change, including increases in negative emotions. Approximately 84% of American adolescents own a smartphone, which can continuously and unobtrusively track variables potentially predictive of heightened negative emotions (e.g. activity levels, location, pattern of phone usage). The extent to which built-in smartphone sensors can reliably predict states of elevated negative affect in adolescents is an open question.
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Locations of people moving about their lives are now commonly tracked through smartphones and wearable devices that access the Global Positioning System (GPS). Immediate measures include the estimated locations that identify visited map points and the travel paths between them. Here we introduce DPLocate, an open-source GPS data analysis pipeline designed to derive measures that abstract away from the original locations (and hence the identity of the individuals) and capture dynamics related to social, vocational, sleep, and clinical behaviors.
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https://www.medrxiv.org/content/10.1101/2022.07.05.22277276v1
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Mobile technology enables unprecedented continuous monitoring of an individual’s behavior, social interactions, symptoms, and other health conditions, presenting an enormous opportunity for therapeutic advancements and scientific discoveries regarding the etiology of psychiatric illness. Continuous collection of mobile data results in the generation of a new type of data: entangled multivariate time series of outcome, exposure, and covariates. Missing data is a pervasive problem in biomedical and social science research, and the Ecological Momentary Assessment (EMA) using mobile devices in psychiatric research is no exception. However, the complex structure of multivariate time series introduces new challenges in handling missing data for proper causal inference. Data imputation is commonly recommended to enhance data utility and estimation efficiency. The majority of available imputation methods are either designed for longitudinal data with limited follow-up times or for stationary time series, which are incompatible with potentially non-stationary time series.
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https://arxiv.org/abs/2206.14343
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Mapping individual differences in behavior is fundamental to personalized neuroscience, but quantifying complex behavior in real world settings remains a challenge. While mobility patterns captured by smartphones have increasingly been linked to a range of psychiatric symptoms, existing research has not specifically examined whether individuals have person-specific mobility patterns. We collected over 3000 days of mobility data from a sample of 41 adolescents and young adults (age 17–30 years, 28 female) with affective instability. We extracted summary mobility metrics from GPS and accelerometer data and used their covariance structures to identify individuals and calculated the individual identification accuracy—i.e., their “footprint distinctiveness”.
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https://www.nature.com/articles/s41386-022-01351-z
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Psychiatry has long needed a better and more scalable way to capture the dynamics of behavior and its disturbances, quantitatively across multiple data channels, at high temporal resolution in real time. By combining 24/7 data—on location, movement, email and text communications, and social media—with brain scans, genetics, genomics, neuropsychological batteries, and clinical interviews, researchers will have an unprecedented amount of objective, individual-level data. Analyzing these data with ever-evolving artificial intelligence could one day include bringing interventions to patients where they are in the real world in a convenient, efficient, effective, and timely way. Yet, the road to this innovative future is fraught with ethical dilemmas as well as ethical, legal, and social implications (ELSI).
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https://www.jmir.org/2022/2/e31146/
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Bryce received his Bachelor’s in psychology from Wesleyan University in 2022 after studying experimental psychopathology using behavioral and electrophysiological methods. Working in two research labs, he helped with data acquisition and analysis for cognitive, judgment and decision-making tasks and auditory oddball tasks using EEG. Now, he is a clinical RA in the Baker Lab working on longitudinal deep phenotyping studies involving functional neuroimaging and clinical interviewing of patients with major depression, bipolar disorder, schizophrenia, obsessive-compulsive disorder, and related mental illnesses. Bryce plans to pursue a PhD in Clinical Psychology following his time at McLean.
In collaboration with Elizabeth Olson, this project aims to identify the contribution of a specific neural circuit to social withdrawal during the transition to adulthood (ages 16-20) in individuals with a history of interpersonal trauma exposure. Active and passive digital phenotyping metrics will be used to assess social withdrawal over the course of a 12-month follow-up. The hypothesis is that baseline functional connectivity within basolateral amygdala-ventromedial prefrontal cortex-nucleus accumbens circuitry will prospectively predict social withdrawal.
