Statistical Improvements in Functional Magnetic Resonance Imaging Analyses Produced by Censoring High-motion Data Points

Overview

Journal Hum Brain Mapp

Publisher Wiley

Specialty Neurology

Date 2013 Jul 18

PMID 23861343

Citations 286

Authors

Joshua S Siegel

Jonathan D Power

Joseph W Dubis

Alecia C Vogel

Jessica A Church

Bradley L Schlaggar

Steven E Petersen

Affiliations

Soon will be listed here.

Abstract

Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement.

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