Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model
arXiv:2603.29176v1 Announce Type: new 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 f
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 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 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.
Subjects:
Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2603.29176 [q-bio.NC]
(or arXiv:2603.29176v1 [q-bio.NC] for this version)
https://doi.org/10.48550/arXiv.2603.29176
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Siyuan Du [view email] [v1] Tue, 31 Mar 2026 02:36:10 UTC (12,782 KB)
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
modelfoundation modeltraining
Anthropic Just Accidentally Leaked the Most Dangerous AI Ever Built - Then Had to Admit It Exists!
Frontier AI · Cybersecurity · Accidental Disclosure Claude Mythos internally called Capybara is described by Anthropic itself as “by far the most powerful AI we’ve ever developed” with “unprecedented cybersecurity risks.” Their own documents said it. They left those documents in a publicly searchable data store. The irony writes itself. I’m writing this on a machine running Claude Sonnet 4.6. The model writes back when I ask it to, helps me structure my thinking, catches errors in my drafts. I’ve used it to help write every article in this series. It has become, over the past few months, one of the most reliable tools in my engineering education a collaborator I interact with more than most of my classmates. So when I opened my laptop on Thursday morning in Puducherry and saw the Fortune h
What is Intelligence?
An examination of cognitive science and computational physics in light of Artificial General Intelligence There’s no shortage of opinions on whether LLMs are intelligent. I’ve spent time studying two perspectives on this question, rooted in separate yet complementary scientific fields. While the combined view appears almost complete, there is a gap between them that points to something I believe is the one of today’s most important unsolved problems on our path towards true intelligence. Two Views: Cognitive Science and Computational Physics The first perspective comes from cognitive science — the psychological view. One of its prominent voices in the AI debate is Gary Marcus , Professor Emeritus at NYU, founder of Geometric Intelligence (acquired by Uber), and author of multiple books on
Seedance 2.0: Technical Analysis of ByteDance's Multimodal Video Generation Model
This post provides a technical analysis of Seedance 2.0, ByteDance’s AI video generation model released in February 2026. The focus is on the model’s architectural innovations — multimodal reference inputs, physics-aware motion synthesis, video-to-video editing, and frame-accurate audio generation — and the current state of API access for integration. Model Architecture: Multimodal Reference System The defining architectural feature of Seedance 2.0 is its multimodal reference system. While most video generation models accept a text prompt and optionally a single image, Seedance 2.0 supports up to 9 images + 3 video clips + 3 audio tracks as simultaneous input references . The model processes these through separate extraction pathways: Input Type Max Count Extracted Features Images 9 Compos
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
What is Intelligence?
An examination of cognitive science and computational physics in light of Artificial General Intelligence There’s no shortage of opinions on whether LLMs are intelligent. I’ve spent time studying two perspectives on this question, rooted in separate yet complementary scientific fields. While the combined view appears almost complete, there is a gap between them that points to something I believe is the one of today’s most important unsolved problems on our path towards true intelligence. Two Views: Cognitive Science and Computational Physics The first perspective comes from cognitive science — the psychological view. One of its prominent voices in the AI debate is Gary Marcus , Professor Emeritus at NYU, founder of Geometric Intelligence (acquired by Uber), and author of multiple books on
[Research] Standard Protocol for Axiomatic Alignment: 100-Dilemma Stress Test (PCE v1.3-T)
Hello community, I am introducing a standardized experimental protocol to test a new hypothesis in AI Alignment: The Prompt Coherence Engine (PCE). The Challenge Most alignment methods rely on local heuristics or safety filters. The PCE explores Axiomatic Structuring—integrating 7 logical invariants (axioms) through a hybrid approach of Axiomatic Fine-Tuning and a Cosmological System Core. The Protocol I have designed a massive 100-dilemma battery to evaluate if a model can maintain structural integrity when its core principles are directly attacked. This protocol tests: G3V (Third Way Generation): Can the model synthesize a resolution instead of collapsing into binary bias? Adversarial Resilience: Can the model resist “Emergency Overrides” or “Identity Hijacking” (e.g., the user claiming
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!