Connectomic Determinants of Subcallosal Cingulate Deep Brain Stimulation for Depression

OBJECTIVE: Subcallosal cingulate (SCC) deep brain stimulation (DBS) has shown promise for treatment-resistant depression. This study retrospectively evaluated a large curated SCC-DBS dataset to identify connectomic factors associated with sustained clinical improvement across patients, focusing on the white matter bundles consistently implicated in patient-specific connectome-based targeting. METHODS: A total of 131 patients (mean age=46.67 years, SD=9.79; 60.31% female) with treatment-resistant depression from three independent cohorts received bilateral SCC-DBS using site-specific targeting methods. Clinical improvements were assessed using the 17-item Hamilton Depression Rating Scale (HAM-D). Based on individual DBS settings, volumes of tissue activated were reconstructed, and voxel-wise stimulation mapping and normative fiber-wise analyses were performed to assess contributions of white matter bundles to therapeutic response. RESULTS: Responders (patients with ≥50% reduction in HAM-D score) exhibited a less variable spatial distribution of volumes of tissue activated than nonresponders. Probabilistic stimulation mapping identified a focal cluster associated with clinical improvement. Fiber-wise analyses showed that stimulation of predefined white matter tracts targeted by connectome-based SCC-DBS contributed to clinical efficacy. Notably, failure to activate the left cingulum bundle and medial uncinate fasciculus decreased the therapeutic effects of SCC-DBS. CONCLUSIONS: This study highlights the significance of stimulating critical white matter bundles for effective SCC-DBS. Available evidence supports the idea that precisely targeting the lateralized cingulum bundle and medial uncinate fasciculus is essential for sustained clinical improvement. These findings suggest potential advantages of connectome-based targeting for SCC-DBS over targeting based solely on anatomical landmarks or normative atlases, using a method that is precise, reliable, and scalable for future use in individual patients.