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Cingulate dynamics track depression recovery with deep brain stimulation.
- Source :
-
Nature [Nature] 2023 Oct; Vol. 622 (7981), pp. 130-138. Date of Electronic Publication: 2023 Sep 20. - Publication Year :
- 2023
-
Abstract
- Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD) <superscript>1</superscript> . However, achieving stable recovery is unpredictable <superscript>2</superscript> , typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting <superscript>3</superscript> . We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.<br /> (© 2023. The Author(s).)
- Subjects :
- Humans
Artificial Intelligence
Biomarkers
Electrophysiology
Treatment Outcome
Local Field Potential Measurement
White Matter
Limbic Lobe physiology
Limbic Lobe physiopathology
Facial Expression
Deep Brain Stimulation methods
Depression physiopathology
Depression therapy
Depressive Disorder, Major physiopathology
Depressive Disorder, Major therapy
Subjects
Details
- Language :
- English
- ISSN :
- 1476-4687
- Volume :
- 622
- Issue :
- 7981
- Database :
- MEDLINE
- Journal :
- Nature
- Publication Type :
- Academic Journal
- Accession number :
- 37730990
- Full Text :
- https://doi.org/10.1038/s41586-023-06541-3