Machine Learning to Infer and Control Brain State

Описание: Presented By: David Carlson, PhD

Speaker Biography: David Carlson is an Associate Professor at Duke University in the Departments of Biostatistics & Bioinformatics, Civil & Environmental Engineering, and Computer Science. His research develops machine learning methods for data‐driven science—emphasizing probabilistic and deep learning approaches. He has published on a broad range of algorithmic challenges, including efficient inference, stochastic optimization, and interpretable models. His work applies these techniques to generate testable hypotheses and design confirmatory experiments, having previously uncovered manipulable brain networks linked to depression, social processing, aggression, and anxiety.

Webinar: Machine Learning to Infer and Control Brain State

Webinar Abstract: Advances in neuroscience now make it possible to develop treatments for psychiatric disorders through targeted brain interventions. However, designing effective protocols requires careful consideration of many factors. We propose a method that identifies electrical dynamics across brain regions associated with illness states or behaviors and leverages these patterns to design precise intervention protocols. Specifically, we statistically model the brain’s electrical activity as a superposition of latent electome networks, functional connectomes that collectively define a “brain state” predictive of disease, behavior, or outcomes. These networks are interpretable through their spectral power and the directional relationships between brain regions, which in turn facilitates the design of testable intervention strategies. We then use these techniques with case studies on both social aggression and anxiety. In an animal model of aggression, we identified an electome network linked to aggressive activity across multiple assays and developed a closed-loop intervention protocol that selectively reduces aggression while sparing pro-social behavior—a significant improvement over open-loop stimulation, which suppresses both. We also discuss our ongoing efforts to apply similar approaches to anxiety and conclude with continued methodological developments to better design and track the impact of interventions.

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