ME 250 Seminar Series: Dr. Christopher Rozell

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. In particular, a major complicating factor is that we aim to control a complex dynamical system state related to the disorder but the system states are not well characterized and we only routinely access subjective readouts of that state. 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 AI techniques to identify electrophysiology biomarkers of disease recovery state. 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. Finally, I will describe briefly our current efforts in multimodal naturalistic behavioral paradigms that aim to better understand the disfunction of brain-body interactions in motivated behavior deficits commonly seen in psychiatric disorders.