NeurIPS 2026Diffusion modelsOralSouthern University of Science a Shenzhen southern university of science a

BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics

BrainWorld uses anatomical MRI to condition whole-brain 4D fMRI generation, producing stable long-horizon trajectories and improving downstream performance via augmentation and transferable representations.

Junfeng Xia, Wenhao Ye, Junxiang Zhang, Xuanye Pan, Mo WANG, Quanying Liu

Published 2026Sydney Poster Session 1 · Tue, Dec 8, 10:00 AM–1:00 PM local time · Hall 1-4arXiv ↗OpenReview ↗

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Abstract

Whole-brain 4D fMRI generation is valuable for modeling functional brain dynamics, yet existing fMRI foundation models mainly target representation learning and downstream prediction rather than conditional predictive generation. We introduce BrainWorld, a structural-prior-conditioned generative model for whole-brain 4D fMRI dynamics. BrainWorld uses sMRI as subject-level anatomical context to guide future fMRI generation, integrating structural information into the denoising process rather than treating it as a parallel modality. Evaluated on 22 datasets spanning diverse cohorts and brain states, BrainWorld generates stable 4D fMRI trajectories up to 400 frames, improves downstream performance through generated-example augmentation, and learns transferable multimodal representations that outperform baselines. Together, these results establish BrainWorld as a condition-aware generative framework for long-horizon brain dynamics modeling and multimodal representation learning.