Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.29176 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908924124332032 |
|---|---|
| author | Du, Siyuan Li, Siyi Bai, Shuwei Li, Ang Li, Haolin Xiao, Mingqing Pan, Yang Li, Dongsheng Xie, Weidi Wang, Yanfeng Zhang, Ya Zhang, Chencheng Yao, Jiangchao |
| author_facet | Du, Siyuan Li, Siyi Bai, Shuwei Li, Ang Li, Haolin Xiao, Mingqing Pan, Yang Li, Dongsheng Xie, Weidi Wang, Yanfeng Zhang, Ya Zhang, Chencheng Yao, Jiangchao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29176 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model Du, Siyuan Li, Siyi Bai, Shuwei Li, Ang Li, Haolin Xiao, Mingqing Pan, Yang Li, Dongsheng Xie, Weidi Wang, Yanfeng Zhang, Ya Zhang, Chencheng Yao, Jiangchao Neurons and Cognition Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition 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. |
| title | Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model |
| topic | Neurons and Cognition Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.29176 |