FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis
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arXiv
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| Format: | Preprint |
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2026
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| author | Duan, Peiyu Wang, Jiyao Dvornek, Nicha C. Yang, Junlin Gao, Ziqi Staib, Lawrence H. Duncan, James S. |
| author_facet | Duan, Peiyu Wang, Jiyao Dvornek, Nicha C. Yang, Junlin Gao, Ziqi Staib, Lawrence H. Duncan, James S. |
| contents | Task-based fMRI provides a direct readout of task-evoked neural dynamics, but it is expensive and difficult to acquire at scale, motivating rest-to-task synthesis from widely available resting-state fMRI (rsfMRI). We propose FM-fMRI, an event-conditioned flow-matching model that learns a continuous-time conditional vector field to generate task ROI time series from a subject's rsfMRI and the task event information. The formulation enables fast ODE-based sampling and flexible conditioning over heterogeneous event schedules. Rather than optimizing for pointwise reconstruction, we evaluated generated signals using complementary criteria that probe temporal and spectral structure, subject and group-level connectome consistency, and distributional alignment. On the public Human Connectome Project and internal BioPoint autism cohort, FM-fMRI achieves the strongest spectral and connectivity agreement and improved distribution-level matching over conditional diffusion, generative adversarial networks (GANs), and variational autoencoders (VAEs) baselines. Furthermore, we augment the BioPoint cohort by synthesizing task-fMRI ROI time series with our method, improving downstream autism classification and demonstrating practical utility in data-limited clinical settings. The code will be available on GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26423 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis Duan, Peiyu Wang, Jiyao Dvornek, Nicha C. Yang, Junlin Gao, Ziqi Staib, Lawrence H. Duncan, James S. Machine Learning Image and Video Processing Task-based fMRI provides a direct readout of task-evoked neural dynamics, but it is expensive and difficult to acquire at scale, motivating rest-to-task synthesis from widely available resting-state fMRI (rsfMRI). We propose FM-fMRI, an event-conditioned flow-matching model that learns a continuous-time conditional vector field to generate task ROI time series from a subject's rsfMRI and the task event information. The formulation enables fast ODE-based sampling and flexible conditioning over heterogeneous event schedules. Rather than optimizing for pointwise reconstruction, we evaluated generated signals using complementary criteria that probe temporal and spectral structure, subject and group-level connectome consistency, and distributional alignment. On the public Human Connectome Project and internal BioPoint autism cohort, FM-fMRI achieves the strongest spectral and connectivity agreement and improved distribution-level matching over conditional diffusion, generative adversarial networks (GANs), and variational autoencoders (VAEs) baselines. Furthermore, we augment the BioPoint cohort by synthesizing task-fMRI ROI time series with our method, improving downstream autism classification and demonstrating practical utility in data-limited clinical settings. The code will be available on GitHub. |
| title | FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis |
| topic | Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2605.26423 |