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ABSTRACT:

Simultaneous advances in neurotechnology and data science have created new opportunities to measure, modulate and understand the neural activity underlying complex psychiatric disorders such as treatment resistant depression (TRD). Despite promising explorations of deep brain stimulation (DBS) as a therapy for TRD, one of the barriers to scaling this treatment is the lack of objective information supporting clinical decision making during recovery. I will describe our recent efforts collecting and analyzing longitudinal intracranial electrophysiology data from TRD patients undergoing subcallosal cingulate cortex (SCC) DBS, as well as our development of novel explainable AI techniques that use nonlinear dimensionality reduction to identify brain biomarkers of disease recovery. Finally, I will show how this data-driven biomarker can potentially be used in clinical decision support through patient case studies, as well as shed light on the microcircuit structures and complex behaviors that underly TRD pathology and recovery.

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