Abstract
In resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correction pipelines consisting of head motion, independent components analysis, and non-neuronal physiological signal regressors in sequential or concatenated combinations. The pipelines are evaluated on a dataset of cognitively normal individuals with repeat imaging and on datasets of studies of Autism Spectrum Disorder, Major Depressive Disorder, and Parkinson’s Disease. Extensive metrics of motion artifact removal are measured, including resting state network recovery, Quality Control-Functional Connectivity (QC-FC) correlation, distance-dependent artifact, network modularity, and test–retest reliability of multiple rs-fMRI analyses. The results reveal limitations in previously proposed metrics, including the QC-FC correlation and modularity quality, and identify more robust artifact removal metrics. The results also reveal limitations in the concatenated regression approach, which is outperformed by the sequential regression approach in the test–retest reliability metrics. Finally, pipelines are recommended that perform well based on quantitative and qualitative comparisons across multiple datasets and robust metrics. These new insights and recommendations help address the need for effective motion artifact correction to reduce noise and confounds in rs-fMRI.









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Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2010). A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration. NeuroImage, 54, 2033–2044. https://doi.org/10.1016/j.neuroimage.2010.09.025
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical transactions of the Royal Society of London. Series B, Biological Sciences, 360, 1001–1013. https://doi.org/10.1098/rstb.2005.1634
Bianciardi, M., Fukunaga, M., van Gelderen, P., Horovitz, S. G., de Zwart, J. A., Shmueli, K., et al. (2009). Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: A 7 T study. Magnetic Resonance Imaging, 27, 1019–1029. https://doi.org/10.1016/j.mri.2009.02.004
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Bright, M. G., & Murphy, K. (2015). Is fMRI “noise” really noise? Resting state nuisance regressors remove variance with network structure. NeuroImage, 114, 158–169. https://doi.org/10.1016/j.neuroimage.2015.03.070
Burgess, G. C., Kandala, S., Nolan, D., Laumann, T. O., Power, J. D., Adeyemo, B., et al. (2016). Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project. Brain Connectivity, 6, 669–680. https://doi.org/10.1089/brain.2016.0435
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14, 140–151. https://doi.org/10.1002/hbm.1048
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., et al. (2017). Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage, 154, 174–187. https://doi.org/10.1016/j.neuroimage.2017.03.020
Cox, R. W. (1996). AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Computers and Biomedical Research, 29, 162–173. https://doi.org/10.1006/cbmr.1996.0014
Craddock, C., Sikka, S., Cheung, B., Khanuja, R., Ghosh, S. S., Yan, C., et al. (2013). Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC). Frontiers in Neuroinformatics. https://doi.org/10.3389/conf.fninf.2013.09.00042
Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., et al. (2014). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19, 659–667. https://doi.org/10.1038/mp.2013.78
Fornito, A., & Bullmore, E. T. (2010). What can spontaneous fluctuations of the blood oxygenation-level-dependent signal tell us about psychiatric disorders? Current Opinion in Psychiatry, 23, 239–249. https://doi.org/10.1097/YCO.0b013e328337d78d
Franco, A. R., Pritchard, A., Calhoun, V. D., & Mayer, A. R. (2009). Interrater and intermethod reliability of default mode network selection. Human Brain Mapping, 30, 2293–2303. https://doi.org/10.1002/hbm.20668
Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., & Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35, 346–355. https://doi.org/10.1002/mrm.1910350312
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016). Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. Cerebral Cortex, 26, 288–303. https://doi.org/10.1093/cercor/bhu239
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 100, 253–258. https://doi.org/10.1073/pnas.0135058100
Huang, P., Carlin, J. D., Alink, A., Kriegeskorte, N., Henson, R. N., & Correia, M. M. (2018). Prospective motion correction improves the sensitivity of fMRI pattern decoding. Human Brain Mapping, 39, 4018–4031. https://doi.org/10.1002/hbm.24228
Iglesias, J. E., Liu, C.-Y., Thompson, P. M., & Tu, Z. (2011). Robust brain extraction across datasets and comparison with publicly available methods. IEEE Transactions on Medical Imaging, 30, 1617–1634. https://doi.org/10.1109/TMI.2011.2138152
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17, 825–841. https://doi.org/10.1016/S1053-8119(02)91132-8
Jo, H. J., Gotts, S. J., Reynolds, R. C., Bandettini, P. A., Martin, A., Cox, R. W., et al. (2013). Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI. Journal of Applied Mathematics. https://doi.org/10.1155/2013/935154
Lindquist, M. A., Geuter, S., Wager, T. D., & Caffo, B. S. (2019). Modular preprocessing pipelines can reintroduce artifacts into fMRI data. Human Brain Mapping, 40, 2358–2376. https://doi.org/10.1002/hbm.24528
Liu, T. T. (2016). Noise contributions to the fMRI signal: An overview. NeuroImage, 143, 141–151. https://doi.org/10.1016/j.neuroimage.2016.09.008
Liu, T. T., Nalci, A., & Falahpour, M. (2017). The global signal in fMRI: Nuisance or Information? NeuroImage, 150, 213–229. https://doi.org/10.1016/j.neuroimage.2017.02.036
Maknojia, S., Churchill, N. W., Schweizer, T. A., & Graham, S. J. (2019). Resting State fMRI: Going Through the Motions. Frontiers in Neuroscience, 13, 825. https://doi.org/10.3389/fnins.2019.00825
Maziero, D., Rondinoni, C., Marins, T., Stenger, V. A., & Ernst, T. (2020). Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion. NeuroImage, 212, 116594. https://doi.org/10.1016/j.neuroimage.2020.116594
Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009). The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? NeuroImage, 44, 893–905. https://doi.org/10.1016/j.neuroimage.2008.09.036
Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103, 8577–8582. https://doi.org/10.1073/pnas.0601602103
Parkes, L., Fulcher, B., Yücel, M., & Fornito, A. (2018). An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage, 171, 415–436. https://doi.org/10.1016/j.neuroimage.2017.12.073
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59, 2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018
Power, J. D., Plitt, M., Gotts, S. J., Kundu, P., Voon, V., Bandettini, P. A., et al. (2018). Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data. Proceedings of the National Academy of Sciences of the United States of America, 115, E2105–E2114. https://doi.org/10.1073/pnas.1720985115
Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. NeuroImage, 105, 536–551. https://doi.org/10.1016/j.neuroimage.2014.10.044
Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage, 112, 267–277. https://doi.org/10.1016/j.neuroimage.2015.02.064
Saccenti, E., Hendriks, M. H. W. B., & Smilde, A. K. (2020). Corruption of the Pearson correlation coefficient by measurement error and its estimation, bias, and correction under different error models. Scientific Reports, 10, 438. https://doi.org/10.1038/s41598-019-57247-4
Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014). Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. NeuroImage, 90, 449–468. https://doi.org/10.1016/j.neuroimage.2013.11.046
Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., et al. (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage, 64, 240–256. https://doi.org/10.1016/j.neuroimage.2012.08.052
Satterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliott, M. A., Hakonarson, H., et al. (2012). Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. NeuroImage, 60, 623–632. https://doi.org/10.1016/j.neuroimage.2011.12.063
Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17, 143–155. https://doi.org/10.1002/hbm.10062
Smitha, K. A., Akhil Raja, K., Arun, K. M., Rajesh, P. G., Thomas, B., Kapilamoorthy, T. R., et al. (2017). Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. The Neuroradiology Journal, 30, 305–317. https://doi.org/10.1177/1971400917697342
Specht, K. (2019). Current Challenges in Translational and Clinical fMRI and Future Directions. Frontiers in Psychiatry, 10, 924. https://doi.org/10.3389/fpsyt.2019.00924
Thomason, M. E., Dennis, E. L., Joshi, A. A., Joshi, S. H., Dinov, I. D., Chang, C., et al. (2011). Resting-state fMRI can reliably map neural networks in children. NeuroImage, 55, 165–175. https://doi.org/10.1016/j.neuroimage.2010.11.080
Thouless, R. H. (1939). The Effects of Errors of Measurement on Correlation Coefficients. British Journal of Psychology. General Section, 29(4), 383.
Trivedi, M. H., McGrath, P. J., Fava, M., Parsey, R. V., Kurian, B. T., Phillips, M. L., et al. (2016). Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design. Journal of Psychiatric Research, 78, 11–23. https://doi.org/10.1016/j.jpsychires.2016.03.001
van Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. NeuroImage, 59, 431–438. https://doi.org/10.1016/j.neuroimage.2011.07.044
van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., et al. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62, 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018
Zaitsev, M., Akin, B., LeVan, P., & Knowles, B. R. (2017). Prospective motion correction in functional MRI. NeuroImage, 154, 33–42. https://doi.org/10.1016/j.neuroimage.2016.11.014
Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. (2004). Regional homogeneity approach to fMRI data analysis. NeuroImage, 22, 394–400. https://doi.org/10.1016/j.neuroimage.2003.12.030
Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20, 45–57. https://doi.org/10.1109/42.906424
Zou, Q.-H., Zhu, C.-Z., Yang, Y., Zuo, X.-N., Long, X.-Y., Cao, Q.-J., et al. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. Journal of Neuroscience Methods, 172, 137–141. https://doi.org/10.1016/j.jneumeth.2008.04.012
Acknowledgements
We thank Cooper Mellema and Alex Treacher for their feedback during the writing of this manuscript.
Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
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PPMI-–a public–private partnership-–is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbvie, Allergan, Avid Radiopharmaceuticals, Biogen, Biolegend, Bristol-Myers Squibb, Celgene, Denali, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, Verily, and Voyager Therapeutics.
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Raval, V., Nguyen, K.P., Pinho, M. et al. Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI. Neuroinform 20, 879–896 (2022). https://doi.org/10.1007/s12021-022-09565-8
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DOI: https://doi.org/10.1007/s12021-022-09565-8
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