Flow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics

Conditional flow matching produces realistic out-of-distribution samples across modalities from images to proteins, yet the conditioning signals that enable extrapolation remain poorly understood for biological time series. While considerable work has focused on areas of biology such as proteins and molecules, generative models of neural time series have been largely restricted to categorical conditioning, which precludes compositional and zero-shot generalization. In this work, we propose a per-timestep conditioned diffusion transformer for generating realistic fMRI brain dynamics during unseen cognitive tasks, by injecting both compositional language and optional spatial priors in-context. Such zero-shot generation would support in-silico task design and counterfactual evaluation of novel cognitive experiments before costly scanner acquisition. Leveraging this model, we evaluate across hundreds of held-out task conditions and characterize predictive performance in relation to the training manifold. From language alone, the model recovers region-specific recruitment across tasks and held-out spatial activation patterns. Spatial priors, when available, complement the text pathway by anchoring generation in regions of task space where language alone degrades, while retaining the compositional structure needed for counterfactual task specification. To our knowledge this is the first generative model of whole-cortex fMRI dynamics for unseen cognitive tasks, enabling counterfactual neuroscience and data-driven experimental design.