Network-based virtual perturbation for modeling DBS-related network effects in obsessive-compulsive and major depressive disorders
Jiahui Li, Shiqi Yu, Xiaoyu Chen, Hongyi Li, Ronghui Ju, Shan Cong, Xiaohui Yao, Egill Axfiord Fridgeirsson, Guido van Wingen, Damiaan Denys, Wenting Mu, Zheng WangAbstract
Background
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.
Methods
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.
Results
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%.
Conclusions
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.