GenBrain: A Generative Foundation Model of Multimodal Brain Imaging

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Abstract

Neuroimaging faces a reproducibility crisis, where studies on small, heterogeneous datasets produce unreliable brain-wide associations and AI models that fail to generalize. To address this, we introduce GenBrain, a generative foundation model pretrained on approximately 1.2 million 3D scans from over 44,000 individuals across 34 imaging modalities to learn a population prior of brain structure and function. Crucially, GenBrain enables rapid, data-efficient adaptation, allowing any targeted study to generate biologically valid synthetic cohorts, conditioned on demographics, disease status, or other modalities, to augment statistical power and enhance generalizability. We demonstrate GenBrains transformative utility across 81 independent datasets spanning diverse populations, protocols, and clinical conditions. For image-level tasks, it achieves state-of-the-art performance in image enhancement and cross-modality synthesis while preserving subject-specific neurobiology. In population neuroscience, synthetic cohorts from GenBrain stabilize effect-size estimates and significantly improve the reproducibility of brain-wide association studies. For clinical AI, disease-specific fine-tuning of GenBrain substantially boosts the cross-site generalizability of prediction models. Finally, we prove its direct translational value when adapted to unseen modality and scarce clinical stroke data. GenBrain significantly improves predictions of acute stroke severity and chronic aphasia, demonstrating actionable utility under extreme data scarcity. By empowering small-scale studies with large-scale population priors, GenBrain provides a unified framework for more reproducible and clinically generalizable neuroimaging analysis.

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