title: An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.
abstract: Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.
authors.affiliation: Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA. sattertt@upenn.edu
authors.forename: Theodore D
authors.initials: TD
authors.lastname: Satterthwaite
authors.forename: Mark A
authors.initials: MA
authors.lastname: Elliott
authors.forename: Raphael T
authors.initials: RT
authors.lastname: Gerraty
authors.forename: Kosha
authors.initials: K
authors.lastname: Ruparel
authors.forename: James
authors.initials: J
authors.lastname: Loughead
authors.forename: Monica E
authors.initials: ME
authors.lastname: Calkins
authors.forename: Simon B
authors.initials: SB
authors.lastname: Eickhoff
authors.forename: Hakon
authors.initials: H
authors.lastname: Hakonarson
authors.forename: Ruben C
authors.initials: RC
authors.lastname: Gur
authors.forename: Raquel E
authors.initials: RE
authors.lastname: Gur
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Wolf
mesh_terms: D000293:Adolescent; D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D002648:Child; D063132:Connectome / Q000379:methods*; D003627:Data Interpretation, Statistical; D005260:Female; D019416:Head Movements / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D012044:Regression Analysis; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology; D012680:Sensitivity and Specificity; D055815:Young Adult