ArticleFrontiers in psychiatry2026
Combinatorial effects of multi-site stimulation on depression-related brain regions: clinical data analysis and predictive modeling.
Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Stimulation-evoked orbitofrontal activity as a biomarker for DBS personalization in depression.medRxiv : the preprint server for health sciences · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
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.
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