Spectrally and temporally segmented regression of nuisance signals in high-speed resting-state fMRI

PurposePrior work has shown that whole-band linear regression of nuisance signals can introduce artifactual connectivity in high-frequency resting-state fMRI. Errors of motion regressors and non-stationarity of nuisance signals exacerbate artifacts. Here, we introduce spectral-temporal segmentation of regression vectors to decouple regression in different frequency bands to reduce motion artifacts. MethodsAn alternative approach to whole-band linear nuisance regression is introduced in the present work relying on spectral segmentation of the motion parameters into k-bands using non-causal or FIR filters, with whole-band regression of the filtering residual, and temporal segmentation of regression vectors. The methodology was tested in computer simulations and in-vivo data. Resting-state networks in five healthy controls and two brain tumor patients using high-speed fMRI (TR >= 205 ms) were mapped using the present approach combined with spectrally constrained regression of physiological noise and the results were compared to the conventional whole band regression approach. ResultsComputer simulations showed high tolerance to frequency dependent errors in regression vectors. Motion and physiological noise artifacts in-vivo were substantially reduced without introducing artifactual connectivity. Artifactual connectivity decreased asymptotically with increasing number of frequency bands without decreasing connectivity in major resting-state networks. Connectivity above 0.3 Hz in-vivo was consistent with that in traditional low-frequency networks. ConclusionsSpectral-temporal segmentation of regression vectors is a powerful approach to reduce artifacts from non-stationary high-bandwidth nuisance signals.