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Measures of Behavior and Life Dynamics from Commonly Available GPS Data (DPLocate): Algorithm Development and Validation
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.
For more information:
https://www.medrxiv.org/content/10.1101/2022.07.05.22277276v1
MoreRosie Sokoll, BA
Rosie graduated with a BA in Psychology, Classics, and Neuroscience from Williams College, where she researched opioid use disorder and evaluated novel pharmacological treatments for post-acute withdrawal syndrome using a rodent model. Rosie joined the Biological Psychiatry Lab in June 2022 and has been working closely with Dr. Brennan on his study investigating individually targeted neuromodulation for OCD. As a shared RA with Dr. Justin Baker’s lab, she is also working on a project tracking neurobiological changes in patients at the McLean Hospital OCDI. Rosie hopes to attend medical school after her time at McLean.
Brien Culhane, BS
Brien received his Bachelor’s of Science with honors in psychology from Davidson College in 2022. At Davidson, Brien studied positive psychology and self-compassion, culminating in an honors thesis on the effects of a 90-minute single session loving-kindness meditation intervention for undergraduate wellbeing. As an RA in the Baker Lab, Brien works on a longitudinal deep phenotyping study performing clinical interviews of patients with major depression, bipolar disorder, schizophrenia, and related mental illnesses. Most recently, he has begun work on a personal research project helping to implement a clinical measurements initiative at McLean and assess the clinical utility of wearable devices for monitoring sleep and activity in psychiatric inpatients. Brien intends to pursue a Ph.D. in Clinical Psychology succeeding his time at McLean.
State space model multiple imputation for missing data in non-stationary multivariate time series with application in digital psychiatry
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.
For more information:
https://arxiv.org/abs/2206.14343
MoreMobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity
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”.
For more information:
https://www.nature.com/articles/s41386-022-01351-z
MoreLinking bipolar disorders to metabolism through machine learning-based behavioral analysis
In collaboration with the Harvard Brain Initiative Bipolar Seed Grant Program, the goals of this project were to use Motion Sequencing, an unsupervised behavioral discovery platform, to characterize the behavior of two mouse models of bipolar disorder in which behavioral deficits are linked to metabolic changes (hyperthyroid and AKAP11 deleted mice), and to characterize the behavior of patients with bipolar disorder in which endocrine and metabolic parameters are measured.
McLean-1000 Project to Characterize Anhedonia Phenotypes and Biomarkers
In collaboration with Kerry Ressler, the goal of this project is to improve our understanding of the neurobiology and treatment of anhedonia.
An Ethics Checklist for Digital Health Research in Psychiatry: Viewpoint
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).
For more information:
https://www.jmir.org/2022/2/e31146/
MoreContext-Adaptive Multimodal Informatics for Psychiatric Discharge Planning
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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