DPlocate
GPS analysis pipeline
Blog – Grid 2
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 […]
Rosie 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 […]
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, […]
Mobile 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 […]
Linking 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 […]