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Abstract #2690

Spectrally Segmented Regression of Physiological Noise and Motion in High-Bandwidth Resting-State fMRI

Khaled Talaat1, Bruno Sa de La Rocque Guimaraes1, and Stefan Posse2,3
1Nuclear Engineering, U New Mexico, Albuquerque, NM, United States, 2Neurology, U New Mexico, Albuquerque, NM, United States, 3Physics and Astronomy, U New Mexico, Albuquerque, NM, United States

It has been demonstrated in prior works that whole-band linear nuisance regression can result in the introduction of artifactual connectivity in the high frequency regime in resting-state fMRI. In the present work, an alternative approach is proposed to whole-band linear nuisance regression relying on spectral and temporal segmentation of the motion parameters and the physiological noise signals. The new approach is shown to not only avoid the injection of artifactual connectivity, but it also substantially improves the removal of physiological noise and motion effects throughout the whole frequency spectrum when uncertainties are present in the regression vectors.

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