[2603.29176] Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model

Quantitative Biology > Neurons and Cognition arXiv:2603.29176 (q-bio) [Submitted on 31 Mar 2026] Title:Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model Authors:Siyuan Du, Siyi Li, Shuwei Bai, Ang Li, Haolin Li, Mingqing Xiao, Yang Pan, Dongsheng Li, Weidi Xie, Yanfeng Wang, Ya Zhang, Chencheng Zhang, Jiangchao Yao View a PDF of the paper titled Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model, by Siyuan Du and 12 other authors View PDF HTML (experimental) Abstract:Parkinson's disease (PD) affects over ten million people worldwide. Although temporal interference (TI) and deep brain stimulation (DBS) are promising therapies, inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost. Previous explorations either resort to limited statistical biomarkers that are insufficient to characterize variability, or employ AI-driven methods which is prone to overfitting and opacity. We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI. Critically, a generative virtual brain foundation model, pretrained on a collective dataset (2707 subjects, 5621 sessions) to capture universal disorder patterns, was finetuned on PD cohorts receiving TI (n=51) or DBS (n=55) to yield individualized virtual brains with high fidelity to empirical functional connectivity (r=0.935). By constructing count...