%0 Journal Article %1 satterthwaite2013improved %A Satterthwaite, Theodore D. %A Elliott, Mark A. %A Gerraty, Raphael T. %A Ruparel, Kosha %A Loughead, James %A Calkins, Monica E. %A Eickhoff, Simon B. %A Hakonarson, Hakon %A Gur, Ruben C. %A Gur, Raquel E. %A Wolf, Daniel H. %D 2013 %J NeuroImage %K fmri functional_connectivity motion_artifact %P 240--256 %R 10.1016/j.neuroimage.2012.08.052 %T An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data %U http://dx.doi.org/10.1016/j.neuroimage.2012.08.052 %V 64 %X 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.

@article{satterthwaite2013improved, 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. \^{a}–º We describe spatial, temporal, and spectral features of rsfc-MRI motion artifact. \^{a}–º We show how these artifact features impact preprocessing choices. \^{a}–º We systematically evaluate different confound regression and filtering techniques. \^{a}–º Our optimized preprocessing approach minimizes rsfc-MRI motion artifact.}}, added-at = {2018-12-07T09:10:16.000+0100}, author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.}, biburl = {https://www.bibsonomy.org/bibtex/21c7cdaf9f53c4df2a76c0159a1c957b3/jpvaldes}, citeulike-article-id = {11537927}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neuroimage.2012.08.052}, doi = {10.1016/j.neuroimage.2012.08.052}, interhash = {25e07a3da5b7d649b5f8c2b8ff23d5f2}, intrahash = {1c7cdaf9f53c4df2a76c0159a1c957b3}, issn = {10538119}, journal = {NeuroImage}, keywords = {fmri functional_connectivity motion_artifact}, month = jan, pages = {240--256}, posted-at = {2017-03-16 16:04:14}, priority = {2}, timestamp = {2018-12-07T09:37:57.000+0100}, title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}}, url = {http://dx.doi.org/10.1016/j.neuroimage.2012.08.052}, volume = 64, year = 2013 }