Given the substantial variations in the shape and timing of the hemodynamic response function (HRF) across the brain, it is critical to develop methods to characterize these variations for proper interpretation of the BOLD fMRI signal. Here, we identified significant differences in spectral properties of resting state fMRI signals between voxels with fast and slow hemodynamics. We found that these spectral properties can be used to classify fast and slow voxels, suggesting that information from the resting state can provide a way to understand and predict the temporal dynamics of the HRF across the brain.
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