High-Frequency Resting-State Connectivity using Spectrally Segmented Regression of Movement, Physiological Noise and Spectral Residuals

Regression of filtering residuals is introduced to spectrally segmented regression of nuisance parameters in high-speed fMRI to enable the application of finite impulse response filters for spectral segmentation of regressors. This extension of our previously introduced method of spectrally and temporally segmented regression improves the removal of noise and mitigates the introduction of artefactual correlations in high frequency resting-state fMRI. Simulations and in-vivo data demonstrate significant advantages of spectrally segmented regression compared to whole-band regression when frequency-dependent errors are present in the regression model. High-frequency resting-state connectivity is detected with high sensitivity during normo-, hypo- and hypercapnic state.