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Decoding Depression Severity From Intracranial Neural Activity.
- Source :
-
Biological psychiatry [Biol Psychiatry] 2023 Sep 15; Vol. 94 (6), pp. 445-453. Date of Electronic Publication: 2023 Feb 02. - Publication Year :
- 2023
-
Abstract
- 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.<br />Methods: We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 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.<br />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 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression.<br />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.<br /> (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1873-2402
- Volume :
- 94
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- Biological psychiatry
- Publication Type :
- Academic Journal
- Accession number :
- 36736418
- Full Text :
- https://doi.org/10.1016/j.biopsych.2023.01.020