FM-fMRI applies event-conditioned flow matching to synthesize task-based fMRI ROI time series from rsfMRI, showing better spectral, connectivity, and distributional match than diffusion, GAN, and VAE baselines while improving downstream autism classification on augmented data.
Proceedings of the National Academy of Sciences107(49), 21223–21228 (2010)
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BrainSimSiam applies positive-only Siamese self-supervised learning to fMRI data to produce representations that generalize across downstream tasks and outperform supervised baselines.
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FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis
FM-fMRI applies event-conditioned flow matching to synthesize task-based fMRI ROI time series from rsfMRI, showing better spectral, connectivity, and distributional match than diffusion, GAN, and VAE baselines while improving downstream autism classification on augmented data.
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Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
BrainSimSiam applies positive-only Siamese self-supervised learning to fMRI data to produce representations that generalize across downstream tasks and outperform supervised baselines.