Language
English
Publication Date
9-15-2023
Journal
Biological Psychiatry
DOI
10.1016/j.biopsych.2023.01.020
PMID
36736418
PMCID
PMC10394110
PubMedCentral® Posted Date
9-15-2024
PubMedCentral® Full Text Version
Author MSS
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.
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.
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.
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.
Keywords
Humans, Depression, Brain, Prefrontal Cortex, Brain Mapping, Gyrus Cinguli, depression, decoding, intracranial recording, biomarker, anterior cingulate cortex, spatio-spectral features
Published Open-Access
yes
Recommended Citation
Xiao, Jiayang; Provenza, Nicole R; Asfouri, Joseph; et al., "Decoding Depression Severity From Intracranial Neural Activity" (2023). Faculty and Staff Publications. 2505.
https://digitalcommons.library.tmc.edu/baylor_docs/2505