Decoding Depression Severity from Intracranial Neural Activity

Background: Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis. Methods: We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in three human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings. Results: Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all three subjects. Relaxing this constraint revealed unique, individual-specific sets of spatio-spectral features predictive of symptom severity, reflecting the heterogeneous nature of depression. Conclusions: The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.

Jiayang Xiao, Nicole R. Provenza, Joseph Asfouri, John Myers, Raissa K. Mathura, Brian Metzger, Joshua A. Adkinson, Anusha B. Allawala, Victoria Pirtle, Denise Oswalt, Ben Shofty, Meghan E. Robinson, Sanjay J. Mathews, Wayne K. Goodman, Nader Pouratian, Paul R. Schrater, Ankit B. Patel, Andreas S. Tolias, Kelly R. Bijanki, Xaq Pitkow, Sameer A. Sheth