This study investigates resting-state signal fluctuations at high-frequencies (>0.3Hz) using a novel regression method for high-speed fMRI data. Respiration and cardiac related signal changes and motion parameters were regressed using a spectral and temporal segmentation approach. This novel approach was shown to substantially remove physiological noise and motion effects. It reduces artificial high-frequency correlations compared with a recently developed sliding window regression approach. High frequency connectivity maps showed comparable localization to low frequency connectivity maps.
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