IEEE J Biomed Health Inform. 2025 May 1;PP. doi: 10.1109/JBHI.2025.3566057. Online ahead of print.
ABSTRACT
OBJECTIVE: Deep brain stimulation (DBS) targeting the lateral habenula (LHb) is a promising therapy for treatment-resistant depression (TRD) but its clinical effect has been variable, which can be improved by adaptive DBS (aDBS) guided by a neural biomarker of depression symptoms. Existing neural biomarkers, however, cannot simultaneously track slow and fast symptom dynamics, do not sufficiently respond to stimulation parameters, and lack neurobiological interpretability, which hinder their use in developing aDBS.
METHODS: We conducted a study on one TRD patient who achieved remission following a 41-week LHb DBS treatment, during which we assessed slow symptom variations using weekly clinical ratings and fast variations using daily self-reports. We recorded daily LHb local field potentials (LFP) concurrently with the reports during the entire treatment process. We then used machine learning methods to identify a personalized depression neural biomarker from spectral and temporal LFP features.
RESULTS: The neural biomarker was identified from classification of high and low depression symptom states with a cross-validated accuracy of 0.97. It further simultaneously tracked both weekly (slow) and daily (fast) depression symptom variation dynamics, achieving test data explained variance of 0.74 and 0.63 respectively and responded to DBS frequency alterations. Finally, it can be neurobiologically interpreted as indicating LHb excitatory and inhibitory balance changes during DBS treatment.
CONCLUSION: By collecting and analyzing a unique personalized dataset of weekly and daily LFP recordings and symptom evaluations, we identified a high-performance neural biomarker for depression during LHb DBS.
SIGNIFICANCE: Our results hold promise to facilitate future aDBS for treating TRD.
PMID:40310745 | DOI:10.1109/JBHI.2025.3566057
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