Combinatorial effects of multi-site stimulation on depression-related brain regions: clinical data analysis and predictive modeling

Background Despite growing evidence supporting deep brain stimulation (DBS) for treatment- resistant depression (TRD), how stimulation delivered across hemispheres or across multiple targets interact to shape large-scale network activity remains poorly characterized. Objective Using a unique opportunity to simultaneously stimulate the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS) in subjects with TRD while recording neural activity across putative prefrontal networks underlying depression via intracranial electrodes, we investigated whether bilateral or multi-target stimulation has additive, synergistic/super-additive, or antagonistic/sub-additive effects on power modulation across depression-related brain networks. Methods Four DBS leads, and ten stereo-electroencephalography (sEEG) leads were implanted in depression-related prefrontal brain regions in three subjects with TRD. Power modulation in response to unilateral and bilateral stimulation, as well as interaction classes of combinatorial stimulations, were evaluated across various combinations of frequency bands and region of interests (ROI) using marginal predictions from a linear mixed-effects model which were then used as input for machine learning classifiers to predict the additive interaction class of combinatorial stimulations. Results Bilateral and multi-target stimulation produced additive or sub-additive interactions in most cases. A decision tree classifier identified ROI as the most important feature for predicting interaction class, followed by stimulation target and spectral frequency band.