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

arXiv:2603.29176v1 Announce Type: cross 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 over