This is a neuroimaging study conducted at McLean Hospital which is focused on studying changes in functional brain network architecture in patients with OCD over the course of intensive, residential treatment.
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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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Determining when a patient is ready for discharge is an open problem, as currently 20% of those hospitalized for psychiatric illness will be hospitalized again within 30 days. To address this issue, we are working to identify behavioral biomarkers for severe mental illness in 400 inpatients hospitalized for psychosis at McLean. The Multisense project is a collaboration with Dr. with LP Morency‘s team at Carnegie Mellon University, where information is extracted from both audio and video of both clinician and patient during a series of semi-structured inpatient interviews. Multimodal analysis techniques are used both to predict clinical scale and discharge-readiness scores, as well as to visualize a summary of each interview and extract relevant interpretable features.
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Deep Phenotyping of Sleep: Open source pipeline to analyze raw actigraphy data and extract sleep parameters