An improved model of motion-related signal changes in fMRI.

ABSTRACT: Head motion is a significant source of noise in the estimation of functional connectivity from resting-state functional MRI (rs-fMRI). Current strategies to reduce this noise include image realignment, censoring time points corrupted by motion, and including motion realignment parameters and their derivatives as additional nuisance regressors in the general linear model. However, this nuisance regression approach assumes that the motion-induced signal changes are linearly related to the estimated realignment parameters, which is not always the case. In this study we develop an improved model of motion-related signal changes, where nuisance regressors are formed by first rotating and translating a single brain volume according to the estimated motion, re-registering the data, and then perfo

SUBMITTER: Patriat R 

PROVIDER: S-EPMC5533292 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature