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 counterfactual estimations between pathological and healthy neural states within these personalized models, we predicted clinical responses (TI: AUPR=0.853; DBS: AUPR=0.915), substantially outperforming baselines. External and prospective validations (n=14, n=11) highlight the feasibility of clinical translation. Moreover, our framework provides state-dependent regional patterns linked to response, offering hypothesis-generating mechanistic insights.

翻译：帕金森病（PD）影响全球超过一千万人口。尽管时间干涉（TI）和脑深部刺激（DBS）是有前景的疗法，但个体间差异限制了经验性治疗选择，增加了不可忽视的手术风险和成本。以往的研究要么依赖于有限的、不足以表征个体差异的统计生物标志物，要么采用易出现过拟合和缺乏可解释性的AI驱动方法。我们通过一种预训练-微调框架弥合了这一差距，该框架可直接从静息态fMRI预测疗效。关键之处在于，一个在大规模集体数据集（2707名受试者，5621次扫描）上预训练的生成式虚拟大脑基础模型，通过捕捉普适性疾病模式，并在接受TI（n=51）或DBS（n=55）治疗的PD队列上进行微调，得以生成与经验功能连接（r=0.935）高度保真的个体化虚拟大脑。通过在这些个性化模型中构建病理与健康神经状态之间的反事实估计，我们成功预测了临床反应（TI：AUPR=0.853；DBS：AUPR=0.915），显著优于基线方法。外部验证和前瞻性验证（n=14，n=11）凸显了临床转化的可行性。此外，我们的框架提供了与治疗反应相关的状态依赖性脑区模式，为产生机制性假说提供了洞见。