Utilization of machine learning approaches on multimodal and ambulatory data to characterize obsessive-compulsive disorder and predict treatment response
Funded by : SC CTSI (NIH/NCRR/NCATS) through grant KL2TR000131
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The goal of this study is to address the gap in our understanding of the heterogeneity of OCD and how this impacts illness course and treatment outcomes. We take a multimodal and longitudinal approach by collecting behavioral, physiologic, and self-report measures in a naturalistic setting using smartphone and wearable devices.
These data will be coupled with functional neuroimaging and symptom severity scores and analyzed by machine learning approaches to classify patients, uncover longitudinal symptom patterns at the individual level, and estimate response to treatment.
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Perspectives on Wearable and Mobile Technology use in Individuals with OCD: a Qualitative Study
Funded by : Brain & Behavior Research Foundation Young Investigator Award
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Wearable biosensors, smartphone applications (apps), and advanced analytic approaches such as machine learning (ML) and artificial intelligence (AI) are increasingly used in daily life and are also finding applications in healthcare and psychiatry. However, little is known about patient experience with, knowledge of, and perspective on these technologies, particularly with respect to their use in symptom monitoring, diagnosis, and prognosis for mental healthcare.
This study addresses this knowledge gap by utilizing qualitative methods to uncover and map themes regarding use of wearable biosensors, smartphone apps, and AI/ML in psychiatry.
Breaking through OCD Genetics
Funded by : Foundation for OCD Research
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OCD affects approximately 1–2% of the population, with onset in most cases occurring in childhood, adolescence or early adulthood implying significant genetic loading. Evidence from family-based studies supports a genetic contribution to the disorder. This multi-site study will aim to recruit the largest cohort of OCD subjects to identify novel common and rare variants associated with OCD.