An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2013-01, Vol.64, p.240-256
• Main Authors: Satterthwaite, Theodore D., Elliott, Mark A., Gerraty, Raphael T., Ruparel, Kosha, Loughead, James, Calkins, Monica E., Eickhoff, Simon B., Hakonarson, Hakon, Gur, Ruben C., Gur, Raquel E., Wolf, Daniel H.
• Format: Article
• Language: English
• Subjects: Adolescence; Adolescent; Age; Algorithms; Biological and medical sciences; Brain - physiology; Brain research; Child; Connectivity; Connectome; Connectome - methods; Data Interpretation, Statistical; Development; Female; fMRI; Fundamental and applied biological sciences. Psychology; Head Movements - physiology; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Methods; Motion; Network; Regression Analysis; Reproducibility of Results; Rest - physiology; Resting-state; Scanners; Sensitivity and Specificity; Studies; Vertebrates: nervous system and sense organs; Young Adult
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2012.08.052

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. ► We describe spatial, temporal, and spectral features of rsfc-MRI motion artifact. ► We show how these artifact features impact preprocessing choices. ► We systematically evaluate different confound regression and filtering techniques. ► Our optimized preprocessing approach minimizes rsfc-MRI motion artifact.