Network-based virtual perturbation for modeling DBS-related network effects in obsessive-compulsive and major depressive disorders

Deep brain stimulation (DBS) efficacy for psychiatric disorders remains heterogeneous due to incomplete mechanistic insights and insufficient individualized treatment planning. A biologically grounded computational framework is urgently needed to simulate DBS-related differences for treatment-resistant patients.

Resting-state fMRI data from 11 patients with obsessive-compulsive disorder (OCD) and 11 with major depressive disorder (MDD), scanned in DBS-ON and DBS-OFF states, were used to derive functional connectivity (FC) via network deconvolution. Whole-brain dynamic modeling incorporated stimulation targets, amplitudes, and frequencies to simulate DBS effects in silico.

The model recapitulated syndrome-specific DBS-related network patterns in exploratory analyses. OCD stimulation primarily altered frontal-subcortical connectivity, whereas MDD stimulation predominantly affected default mode network hubs (p < 0.05). Under leave-one-out cross-validation, correlations between predicted and observed clinical outcomes were r = 0.625 in OCD (p = 0.040) and r = 0.659 in MDD (p = 0.028). Applying the same FC signatures to simulated FC yielded r = 0.465 in OCD (p = 0.149) and r = 0.627 in MDD (p = 0.039). In a proof-of-concept application to a separate OCD cohort (N = 92), three individuals showed high model-predicted NAc-response profiles and predicted improvement exceeding 35%.

This hypothesis-generating framework retrospectively models DBS-related network effects using individualized resting-state FC and virtual perturbations. Linking simulated FC states with empirical DBS-ON/OFF patterns may help characterize how baseline functional architecture shapes DBS-related network responses and inform future individualized DBS modeling.