A Habenula Neural Biomarker Simultaneously Tracks Weekly and Daily Symptom Variations during Deep Brain Stimulation Therapy for Depression

Abstract

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. A clinically-viable neural biomarker is desired to classify depression symptom states, track both slow and fast symptom variations during the treatment, and respond to DBS parameter alterations, which is currently lacking. Here, 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. The identified neural biomarker classified high and low depression symptom severity 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. It finally responded to DBS frequency alterations. Our results hold promise to identify clinically-viable neural biomarkers to facilitate future aDBS for treating TRD.