Methods to detect, characterize, and remove motion artifact in resting state fMRI
Open accessAbstract
Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DVAR values vary across datasets for various reasons, which effectively removes the need for a standard threshold.
“DVAR values, which are based in BOLD signal intensity, differ across datasets for a variety of reasons (e.g. blurring kernel size, frequency filter or data acquisition characteristics) (Power et al., 2014), effectively eliminating the need for a standard threshold.”
From Developmental sex differences in resting state functional connectivity of amygdala sub-regions (2015) · cites this paper as Power et al., 2014
The study tested the robustness of its findings by adjusting functional connectivity processing steps and using a different motion-censoring method in a supplementary analysis.
“we tested the robustness of our findings by adjusting the functional connectivity processing steps and employing a different method of motion censoring in a supplementary analysis (see Supplementary Methods), as suggested by prior reports (Hallquist et al., 2013, Power et al., 2014).”
From Developmental sex differences in resting state functional connectivity of amygdala sub-regions (2015) · cites this paper as Power et al., 2014
The main analysis did not scrub data, but results from scrubbed data showed strong correspondence with the main outcome measures.
“Due to concerns associated with the alteration of the temporal structure of the images, the data used in the main analysis were not ‘scrubbed’ (Power et al., 2014), however we did compare the results of our experiment with scrubbed data (missing values corrected using interpolation) and found strong correspondence between the outcome measures of the two studies (see Validation).”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as Power et al., 2014
Additional motion correction in the form of censoring was applied to compensate for using fairly lenient translation and rotation cut-offs.
“Following standard Statistical Parametric Mapping (SPM12; https://www.fil.ion.ucl.ac.uk/spm12 ) pre-processing steps, additional motion correction was applied in the form of censoring, i.e. identifying outliers based on framewise displacement and regressing them from the fMRI timeseries ( Power et al., 2012 , Power et al., 2014 ) (see Supplementary Methods), to compensate for using fairly lenient”
From Self-blame-selective hyper-connectivity between anterior temporal and subgenual cortices predicts prognosis in major depressive disorder (2023) · cites this paper as Power et al., 2014
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182 · see citing works
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429 · see citing works
Self-citationThe organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304 · see citing works
Least-squares frequency analysis of unequally spaced data
Lomb, N. (1976) · Astrophysics and Space Science · doi:10.1007/bf00648343 · Cited by 5,724 · see citing works
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150 · see citing works
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124 · see citing works
Self-citationMovement‐Related effects in fMRI time‐series
Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. J., & Turner, R. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350312 · Cited by 3,774 · see citing works
Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.036 · Cited by 2,387 · see citing works
The influence of head motion on intrinsic functional connectivity MRI
Van Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.044 · Cited by 2,344 · see citing works
Satterthwaite, T., Elliott, M., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M., Eickhoff, S., Hakonarson, H., Gur, R., Gur, R. E., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.052 · Cited by 1,900 · see citing works
The global signal and observed anticorrelated resting state brain networks
Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90777.2008 · Cited by 1,877 · see citing works
Self-citationVan Dijk, K. R. A., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00783.2009 · Cited by 1,844 · see citing works
Time–frequency dynamics of resting-state brain connectivity measured with fMRI
Chang, C., & Glover, G. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.011 · Cited by 1,808 · see citing works
Resting-state fMRI in the human connectome project
Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D. A., Griffanti, L., Harms, M. P., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.039 · Cited by 1,502 · see citing works
Self-citationYan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F. X., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.004 · Cited by 1,461 · see citing works
Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging
Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., Glasser, M. F., Miller, K. L., Ugurbil, K., & Yacoub, E. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015710 · Cited by 1,296 · see citing works
Satterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliott, M. A., Hakonarson, H., Gur, R. C., & Gur, R. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.063 · Cited by 1,125 · see citing works
Neural basis of global resting-state fMRI activity
Schölvinck, M. L., Maier, A., Ye, F. Q., Duyn, J. H., & Leopold, D. A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913110107 · Cited by 939 · see citing works
Saad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., & Cox, R. W. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0080 · Cited by 859 · see citing works
Longitudinal Analysis of Neural Network Development in Preterm Infants
Smyser, C. D., Inder, T. E., Shimony, J. S., Hill, J. E., Degnan, A. J., Snyder, A. Z., & Neil, J. J. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhq035 · Cited by 791 · see citing works
Self-citationWeissenbacher, A., Kasess, C., Gerstl, F., Lanzenberger, R., Moser, E., & Windischberger, C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.005 · Cited by 776 · see citing works
Temporally-independent functional modes of spontaneous brain activity
Smith, S. M., Miller, K. L., Moeller, S., Xu, J., Auerbach, E. J., Woolrich, M. W., Beckmann, C. F., Jenkinson, M., Andersson, J., Glasser, M. F., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121329109 · Cited by 702 · see citing works
Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI
Kundu, P., Inati, S., Evans, J. W., Luh, W., & Bandettini, P. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.028 · Cited by 604 · see citing works
Defining functional areas in individual human brains using resting functional connectivity MRI
Cohen, A. L., Fair, D. A., Dosenbach, N. U. F., Miezin, F. M., Dierker, D., Van Essen, D. C., Schlaggar, B. L., & Petersen, S. E. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.01.066 · Cited by 582 · see citing works
Self-citationShulman, G. L., Pope, D. L. W., Astafiev, S. V., McAvoy, M. P., Snyder, A. Z., & Corbetta, M. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.4085-09.2010 · Cited by 536 · see citing works
Self-citationMapping sources of correlation in resting state FMRI, with artifact detection and removal
Jo, H. J., Saad, Z. S., Simmons, W. K., Milbury, L. A., & Cox, R. W. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.246 · Cited by 509 · see citing works
Fractionation of social brain circuits in autism spectrum disorders
Gotts, S. J., Simmons, W. K., Milbury, L. A., Wallace, G. L., Cox, R. W., & Martin, A. (2012) · Brain · doi:10.1093/brain/aws160 · Cited by 369 · see citing works
Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.017 · Cited by 336 · see citing works
Self-citationKeller, C. J., Bickel, S., Honey, C. J., Groppe, D. M., Entz, L., Craddock, R. C., Lado, F. A., Kelly, C., Milham, M., & Mehta, A. D. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4837-12.2013 · Cited by 245 · see citing works
Modelling large motion events in fMRI studies of patients with epilepsy
Lemieux, L., Salek‐Haddadi, A., Lund, T. E., Laufs, H., & Carmichael, D. W. (2007) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2007.03.009 · Cited by 240 · see citing works
Network anticorrelations, global regression, and phase‐shifted soft tissue correction
Anderson, J. S., Druzgal, T. J., Lopez-Larson, M. P., Jeong, E., Desai, K., & Yurgelun-Todd, D. A. (2010) · Human Brain Mapping · doi:10.1002/hbm.21079 · Cited by 229 · see citing works
Tyszka, J. M., Kennedy, D. P., Paul, L. K., & Adolphs, R. (2014) · Cerebral Cortex · doi:10.1093/cercor/bht040 · Cited by 187 · see citing works
Optimizing the order of operations for movement scrubbing: Comment on Power et al.
Carp, J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.061 · Cited by 175 · see citing works
Mowinckel, A. M., Espeseth, T., & Westlye, L. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.004 · Cited by 139 · see citing works
Algorithms for Spectral Analysis of Irregularly Sampled Time Series
Mathias, A., Grond, F., Guardans, R., Seese, D. G., Canela, M. Á., & Diebner, H. H. (2004) · Journal of Statistical Software · doi:10.18637/jss.v011.i02 · Cited by 104 · see citing works
A method to determine the necessity for global signal regression in resting‐state fMRI studies
Chen, G., Chen, G., Xie, C., Ward, B. D., Li, W., Antuono, P., & Li, S. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24201 · Cited by 90 · see citing works
Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data
Bright, M. G., & Murphy, K. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.043 · Cited by 87 · see citing works
Estimation of the intrinsic dimensionality of fMRI data
Cordes, D., & Nandy, R. R. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.07.054 · Cited by 65 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
fMRIPrep: A robust preprocessing pipeline for functional MRI
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0235-4 · Cited by 3,905 · see citing works
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data
Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.064 · Cited by 1,684 · see citing works
Intrinsic and task-evoked network architectures of the human brain
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.014 · Cited by 1,564 · see citing works
Self-citationGeneration and evaluation of a cortical area parcellation from resting-state correlations
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhu239 · Cited by 1,463 · see citing works
Self-citationPrecision Functional Mapping of Individual Human Brains
Gordon, E. M., Laumann, T. O., Gilmore, A. W., Newbold, D. J., Greene, D. J., Berg, J. J., Ortega, M., Hoyt-Drazen, C., Gratton, C., Sun, H., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.07.011 · Cited by 1,335 · see citing works
Self-citationThe Human Connectome Project's neuroimaging approach
Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., Coalson, T. S., Harms, M. P., Jenkinson, M., Moeller, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4361 · Cited by 1,090 · see citing works
Functional system and areal organization of a highly sampled individual human brain
Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M.-Y., Gilmore, A. W., McDermott, K. B., Nelson, S. M., Dosenbach, N. U. F., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.037 · Cited by 911 · see citing works
Self-citationCiric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., Shinohara, R. T., Elliott, M. A., Eickhoff, S. B., Davatzikos, C., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.020 · Cited by 1,042 · see citing works
Self-citationRecent progress and outstanding issues in motion correction in resting state fMRI
Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.044 · Cited by 957 · see citing works
Self-citationGratton, C., Laumann, T. O., Nielsen, A. N., Greene, D. J., Gordon, E. M., Gilmore, A. W., Nelson, S. M., Coalson, R. S., Snyder, A. Z., Schlaggar, B. L., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.035 · Cited by 846 · see citing works
Self-citationShine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.018 · Cited by 886 · see citing works
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912 · see citing works
Betzel, R. F., Byrge, L., He, Y., Goñi, J., Zuo, X.-N., & Sporns, O. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.067 · Cited by 807 · see citing works
Time-resolved resting-state brain networks
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400181111 · Cited by 753 · see citing works
Rethinking segregation and integration: contributions of whole-brain modelling
Deco, G., Tononi, G., Boly, M., & Kringelbach, M. L. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3963 · Cited by 658 · see citing works
Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke
Siegel, J. S., Ramsey, L. E., Snyder, A. Z., Metcalf, N. V., Chacko, R. V., Weinberger, K., Baldassarre, A., Hacker, C. D., Shulman, G. L., & Corbetta, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1521083113 · Cited by 656 · see citing works
Self-citationCorrelated gene expression supports synchronous activity in brain networks
Richiardi, J., Altmann, A., Milazzo, A.-C., Chang, C., Chakravarty, M. M., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Bromberg, U., Büchel, C., et al. (2015) · Science · doi:10.1126/science.1255905 · Cited by 625 · see citing works
Questions and controversies in the study of time-varying functional connectivity in resting fMRI
Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., Kucyi, A., Liégeois, R., Lindquist, M. A., McIntosh, A. R., et al. (2020) · Network Neuroscience · doi:10.1162/netn_a_00116 · Cited by 648 · see citing works
Methods for cleaning the BOLD fMRI signal
Caballero-Gaudes, C., & Reynolds, R. C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.018 · Cited by 542 · see citing works
Psilocybin for treatment-resistant depression: fMRI-measured brain mechanisms
Carhart-Harris, R. L., Roseman, L., Bolstridge, M., Demetriou, L., Pannekoek, J. N., Wall, M. B., Tanner, M., Kaelen, M., McGonigle, J., Murphy, K., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13282-7 · Cited by 587 · see citing works
On the stability of BOLD fMRI correlations
Laumann, T. O., Snyder, A. Z., Mitra, A., Gordon, E. M., Gratton, C., Adeyemo, B., Gilmore, A. W., Nelson, S. M., Berg, J. J., Greene, D. J., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw265 · Cited by 516 · see citing works
Self-citationEvaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI
Pruim, R. H. R., Mennes, M., Buitelaar, J. K., & Beckmann, C. F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.063 · Cited by 487 · see citing works
Grayson, D. S., & Fair, D. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.079 · Cited by 411 · see citing works
“Considerable work and debate is now ongoing to determine the optimal ‘denoising’ approaches during data processing (Patriat et al., 2016; Power et al., 2014).”
BackgroundRowald, A., Komi, S., Demesmaeker, R., Baaklini, E., Hernandez-Charpak, S. D., Paoles, E., Montanaro, H., Cassara, A., Becce, F., Lloyd, B., et al. (2022) · Nature Medicine · doi:10.1038/s41591-021-01663-5 · Cited by 455 · see citing works
Li, J., Kong, R., Liégeois, R., Orban, C., Tan, Y., Sun, N., Holmes, A. J., Sabuncu, M. R., Ge, T., & Yeo, B. T. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.016 · Cited by 380 · see citing works
Rudolph, M. D., Graham, A. M., Feczko, E., Miranda-Dominguez, O., Rasmussen, J. M., Nardos, R., Entringer, S., Wadhwa, P. D., Buss, C., & Fair, D. A. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0128-y · Cited by 374 · see citing works
Development of human brain cortical network architecture during infancy
Gao, W., Alcauter, S., Smith, J. K., Gilmore, J. H., & Lin, W. (2015) · Brain Structure and Function · doi:10.1007/s00429-014-0710-3 · Cited by 356 · see citing works
Emerson, R. W., Adams, C., Nishino, T., Hazlett, H. C., Wolff, J. J., Zwaigenbaum, L., Constantino, J. N., Shen, M. D., Swanson, M. R., Elison, J. T., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aag2882 · Cited by 359 · see citing works
Self-citationThe contribution of network organization and integration to the development of cognitive control
Marek, S., Hwang, K., Foran, W., Hallquist, M. N., & Luna, B. (2015) · PLOS Biology · doi:10.1371/journal.pbio.1002328 · Cited by 337 · see citing works
Plitt, M., Barnes, K. A., & Martin, A. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.12.013 · Cited by 335 · see citing works
Measuring macroscopic brain connections in vivo
Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van Essen, D. C., & Behrens, T. E. (2015) · Nature Neuroscience · doi:10.1038/nn.4134 · Cited by 334 · see citing works
Cheng, W., Rolls, E. T., Qiu, J., Liu, W., Tang, Y., Huang, C.-C., Wang, X., Zhang, J., Lin, W., Zheng, L., et al. (2016) · Brain · doi:10.1093/brain/aww255 · Cited by 294 · see citing works
Analysis of task-based functional MRI data preprocessed with fMRIPrep
Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E. P., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0327-3 · Cited by 315 · see citing works
FAAH genetic variation enhances fronto-amygdala function in mouse and human
Dincheva, I., Drysdale, A. T., Hartley, C. A., Johnson, D. C., Jing, D., King, E. C., Ra, S., Gray, J. M., Yang, R., DeGruccio, A. M., et al. (2015) · Nature Communications · doi:10.1038/ncomms7395 · Cited by 289 · see citing works
Burgess, G. C., Kandala, S., Nolan, D., Laumann, T. O., Power, J. D., Adeyemo, B., Harms, M. P., Petersen, S. E., & Barch, D. M. (2016) · Brain Connectivity · doi:10.1089/brain.2016.0435 · Cited by 216 · see citing works
Self-citationConfound modelling in UK Biobank brain imaging
Alfaro-Almagro, F., McCarthy, P., Afyouni, S., Andersson, J. L. R., Bastiani, M., Miller, K. L., Nichols, T. E., & Smith, S. M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117002 · Cited by 249 · see citing works
Betzel, R. F., Fukushima, M., He, Y., Zuo, X.-N., & Sporns, O. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.001 · Cited by 249 · see citing works
Individual variability of the system-level Organization of the Human Brain
Gordon, E. M., Laumann, T. O., Adeyemo, B., & Petersen, S. E. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv239 · Cited by 219 · see citing works
Self-citationStructural disconnections explain brain network dysfunction after stroke
Griffis, J. C., Metcalf, N. V., Corbetta, M., & Shulman, G. L. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.07.100 · Cited by 213 · see citing works
A general prediction model for the detection of ADHD and Autism using structural and functional MRI
Sen, B., Borle, N. C., Greiner, R., & Brown, M. R. G. (2018) · PLOS ONE · doi:10.1371/journal.pone.0194856 · Cited by 198 · see citing works
Akiki, T. J., Averill, C. L., Wrocklage, K. M., Scott, J. C., Averill, L. A., Schweinsburg, B., Alexander-Bloch, A., Martini, B., Southwick, S. M., Krystal, J. H., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.005 · Cited by 188 · see citing works
Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm
Feczko, E., Balba, N. M., Miranda-Dominguez, O., Cordova, M., Karalunas, S. L., Irwin, L., Demeter, D. V., Hill, A. P., Langhorst, B. H., Grieser Painter, J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.044 · Cited by 173 · see citing works
Chen, C. P., Keown, C. L., Jahedi, A., Nair, A., Pflieger, M. E., Bailey, B. A., & Müller, R.-A. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.04.002 · Cited by 183 · see citing works
Altered network hub connectivity after acute LSD administration
Müller, F., Dolder, P. C., Schmidt, A., Liechti, M. E., & Borgwardt, S. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.03.005 · Cited by 174 · see citing works
Deshpande, G., Wang, P., Rangaprakash, D., & Wilamowski, B. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2379621 · Cited by 161 · see citing works
Pan, P. M., Sato, J. R., Salum, G. A., Rohde, L. A., Gadelha, A., Zugman, A., Mari, J., Jackowski, A., Picon, F., Miguel, E. C., et al. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.17040430 · Cited by 156 · see citing works
“Finally, iFC data are sensitive to head motion (22, 23).”
TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry
Frässle, S., Aponte, E. A., Bollmann, S., Brodersen, K. H., Do, C. T., Harrison, O. K., Harrison, S. J., Heinzle, J., Iglesias, S., Kasper, L., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.680811 · Cited by 169 · see citing works
Reduced global functional connectivity of the medial prefrontal cortex in major depressive disorder
Murrough, J. W., Abdallah, C. G., Anticevic, A., Collins, K. A., Geha, P., Averill, L. A., Schwartz, J., DeWilde, K. E., Averill, C., Jia‐Wei Yang, G., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23235 · Cited by 153 · see citing works
Joint attention and brain functional connectivity in infants and toddlers
Eggebrecht, A. T., Elison, J. T., Feczko, E., Todorov, A., Wolff, J. J., Kandala, S., Adams, C. M., Snyder, A. Z., Lewis, J. D., Estes, A. M., et al. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhw403 · Cited by 156 · see citing works
Self-citationReggente, N., Moody, T. D., Morfini, F., Sheen, C., Rissman, J., O’Neill, J., & Feusner, J. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1716686115 · Cited by 154 · see citing works
Dopamine controls Parkinson's tremor by inhibiting the cerebellar thalamus
Dirkx, M. F., den Ouden, H. E. M., Aarts, E., Timmer, M. H. M., Bloem, B. R., Toni, I., & Helmich, R. C. (2017) · Brain · doi:10.1093/brain/aww331 · Cited by 152 · see citing works
Prediction of brain maturity in infants using machine-learning algorithms
Smyser, C. D., Dosenbach, N. U. F., Smyser, T. A., Snyder, A. Z., Rogers, C. E., Inder, T. E., Schlaggar, B. L., & Neil, J. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.029 · Cited by 140 · see citing works
Self-citationAdvanced MRI techniques to improve our understanding of experience-induced neuroplasticity
Tardif, C. L., Gauthier, C. J., Steele, C. J., Bazin, P.-L., Schäfer, A., Schaefer, A., Turner, R., & Villringer, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.047 · Cited by 127 · see citing works
Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies
Satterthwaite, T. D., Ciric, R., Roalf, D. R., Davatzikos, C., Bassett, D. S., & Wolf, D. H. (2019) · Human Brain Mapping · doi:10.1002/hbm.23665 · Cited by 137 · see citing works
Miranda-Dominguez, O., Mills, B. D., Grayson, D., Woodall, A., Grant, K. A., Kroenke, C. D., & Fair, D. A. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.4229-13.2014 · Cited by 130 · see citing works
Brauer, J., Xiao, Y., Poulain, T., Friederici, A. D., & Schirmer, A. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw137 · Cited by 141 · see citing works
Functional Connectivity in Autosomal Dominant and Late-Onset Alzheimer Disease
Thomas, J. B., Brier, M. R., Bateman, R. J., Snyder, A. Z., Benzinger, T. L., Xiong, C., Raichle, M., Holtzman, D. M., Sperling, R. A., Mayeux, R., et al. (2014) · JAMA Neurology · doi:10.1001/jamaneurol.2014.1654 · Cited by 129 · see citing works
Self-citationWu, M., Kujawa, A., Lu, L. H., Fitzgerald, D. A., Klumpp, H., Fitzgerald, K. D., Monk, C. S., & Phan, K. L. (2016) · Human Brain Mapping · doi:10.1002/hbm.23129 · Cited by 130 · see citing works
Palanca, B. J. A., Mitra, A., Larson-Prior, L., Snyder, A. Z., Avidan, M. S., & Raichle, M. E. (2015) · Anesthesiology · doi:10.1097/aln.0000000000000731 · Cited by 112 · see citing works
Self-citationEmerson, R. W., Gao, W., & Lin, W. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.3980-15.2016 · Cited by 112 · see citing works
Differential brain network activity across mood states in bipolar disorder
Brady Jr., R. O., Tandon, N., Masters, G. A., Margolis, A., Cohen, B. M., Keshavan, M., & Öngür, D. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.09.041 · Cited by 115 · see citing works
Goldstein-Piekarski, A. N., Staveland, B. R., Ball, T. M., Yesavage, J., Korgaonkar, M. S., & Williams, L. M. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0100-3 · Cited by 108 · see citing works
van Duijvenvoorde, A. C. K., Westhoff, B., de Vos, F., Wierenga, L. M., & Crone, E. A. (2019) · Human Brain Mapping · doi:10.1002/hbm.24630 · Cited by 115 · see citing works
Bagarinao, E., Watanabe, H., Maesawa, S., Mori, D., Hara, K., Kawabata, K., Yoneyama, N., Ohdake, R., Imai, K., Masuda, M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-47922-x · Cited by 108 · see citing works
Seminowicz, D. A., Burrowes, S. A. B., Kearson, A., Zhang, J., Krimmel, S. R., Samawi, L., Furman, A. J., Keaser, M. L., Gould, N. F., Magyari, T., et al. (2020) · Pain · doi:10.1097/j.pain.0000000000001860 · Cited by 110 · see citing works
Network basis of suicidal ideation in depressed adolescents
Ordaz, S. J., Goyer, M. S., Ho, T. C., Singh, M. K., & Gotlib, I. H. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.09.021 · Cited by 111 · see citing works
Falahpour, M., Thompson, W. K., Abbott, A. E., Jahedi, A., Mulvey, M. E., Datko, M., Liu, T. T., & Müller, R.-A. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0389 · Cited by 106 · see citing works
Developmental sex differences in resting state functional connectivity of amygdala sub-regions
Alarcón, G., Cservenka, A., Rudolph, M. D., Fair, D. A., & Nagel, B. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.013 · Cited by 105 · see citing works
Tu, Y., Fang, J., Cao, J., Wang, Z., Park, J., Jorgenson, K., Lang, C., Liu, J., Zhang, G., Zhao, Y., et al. (2018) · Brain stimulation · doi:10.1016/j.brs.2018.01.006 · Cited by 104 · see citing works
“In the last step, global signal regression (GSR) was applied to reduce head motion-related signal changes [29].”
Customized head molds reduce motion during resting state fMRI scans
Power, J. D., Silver, B. M., Silverman, M. R., Ajodan, E. L., Bos, D. J., & Jones, R. M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.016 · Cited by 88 · see citing works
Self-citationJohnson, S. L., Elliott, M. V., & Carver, C. S. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.08.018 · Cited by 90 · see citing works
Mash, L. E., Linke, A. C., Olson, L. A., Fishman, I., Liu, T. T., & Müller, R. (2019) · Human Brain Mapping · doi:10.1002/hbm.24529 · Cited by 96 · see citing works
Functional connectivity-based subtypes of individuals with and without autism spectrum disorder
Easson, A. K., Fatima, Z., & McIntosh, A. R. (2019) · Network Neuroscience · doi:10.1162/netn_a_00067 · Cited by 95 · see citing works
Dhamala, E., Yeo, B. T. T., & Holmes, A. J. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.09.024 · Cited by 91 · see citing works
Age effects on the default mode and control networks in typically developing children
Sato, J. R., Salum, G. A., Gadelha, A., Picon, F. A., Pan, P. M., Vieira, G., Zugman, A., Hoexter, M. Q., Anés, M., Moura, L. M., et al. (2014) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2014.07.004 · Cited by 85 · see citing works
Head motion and correction methods in resting-state functional MRI
GOTO, M., ABE, O., MIYATI, T., YAMASUE, H., GOMI, T., & TAKEDA, T. (2016) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.rev.2015-0060 · Cited by 82 · see citing works
Brody, G. H., Yu, T., Nusslock, R., Barton, A. W., Miller, G. E., Chen, E., Holmes, C., McCormick, M., & Sweet, L. H. (2019) · Psychological Science · doi:10.1177/0956797619847989 · Cited by 82 · see citing works
Reliable individual differences in fine-grained cortical functional architecture
Feilong, M., Nastase, S. A., Guntupalli, J. S., & Haxby, J. V. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.029 · Cited by 68 · see citing works
Emotions amplify speaker–listener neural alignment
Smirnov, D., Saarimäki, H., Glerean, E., Hari, R., Sams, M., & Nummenmaa, L. (2019) · Human Brain Mapping · doi:10.1002/hbm.24736 · Cited by 71 · see citing works
Riganello, F., Larroque, S. K., Bahri, M. A., Heine, L., Martial, C., Carrière, M., Charland-Verville, V., Aubinet, C., Vanhaudenhuyse, A., Chatelle, C., et al. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00769 · Cited by 70 · see citing works
Wang, X., Peelen, M. V., Han, Z., He, C., Caramazza, A., & Bi, Y. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3914-14.2015 · Cited by 68 · see citing works
Functional connectivity change as shared signal dynamics
Cole, M. W., Yang, G. J., Murray, J. D., Repovš, G., & Anticevic, A. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.11.011 · Cited by 60 · see citing works
Default mode network deactivation during emotion processing predicts early antidepressant response
Spies, M., Kraus, C., Geissberger, N., Auer, B., Klöbl, M., Tik, M., Stürkat, I.-L., Hahn, A., Woletz, M., Pfabigan, D. M., et al. (2017) · Translational Psychiatry · doi:10.1038/tp.2016.265 · Cited by 68 · see citing works
Long-term alterations in brain and behavior after postnatal Zika virus infection in infant macaques
Raper, J., Kovacs-Balint, Z., Mavigner, M., Gumber, S., Burke, M. W., Habib, J., Mattingly, C., Fair, D., Earl, E., Feczko, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16320-7 · Cited by 67 · see citing works
Lawrence, K. E., Hernandez, L. M., Bookheimer, S. Y., & Dapretto, M. (2019) · Autism Research · doi:10.1002/aur.1971 · Cited by 61 · see citing works
Functional Connectivity of the Striatum in Schizophrenia and Psychotic Bipolar Disorder
Karcher, N. R., Rogers, B. P., & Woodward, N. D. (2019) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.05.017 · Cited by 65 · see citing works
Video gaming in school children: How much is enough?
Pujol, J., Fenoll, R., Forns, J., Harrison, B. J., Martínez‐Vilavella, G., Macià, D., Alvarez‐Pedrerol, M., Blanco‐Hinojo, L., González‐Ortiz, S., Deus, J., et al. (2016) · Annals of Neurology · doi:10.1002/ana.24745 · Cited by 62 · see citing works
Pruett, J. R., Kandala, S., Hoertel, S., Snyder, A. Z., Elison, J. T., Nishino, T., Feczko, E., Dosenbach, N. U. F., Nardos, B., Power, J. D., et al. (2015) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2015.01.003 · Cited by 57 · see citing works
Self-citationRegional GABA Concentrations Modulate Inter-network Resting-state Functional Connectivity
Chen, X., Fan, X., Hu, Y., Zuo, C., Whitfield-Gabrieli, S., Holt, D., Gong, Q., Yang, Y., Pizzagalli, D. A., Du, F., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy059 · Cited by 62 · see citing works
Changes in functional connectivity following treatment with emotion regulation therapy
Scult, M. A., Fresco, D. M., Gunning, F. M., Liston, C., Seeley, S. H., García, E., & Mennin, D. S. (2019) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2019.00010 · Cited by 57 · see citing works
“Images were bandpass filtered to retain frequencies between 0.008 and 0.1 Hz, and volumes exceeding 0.25 mm frame-wise displacement or 1.55 standardized DVARS (Power et al., 2014; Nichols, 2017) were censored.”
MethodInfluentialBarnett, A. J., Nguyen, M., Spargo, J., Yadav, R., Cohn-Sheehy, B. I., & Ranganath, C. (2024) · Neuron · doi:10.1016/j.neuron.2023.10.010 · Cited by 55 · see citing works
Saalasti, S., Alho, J., Bar, M., Glerean, E., Honkela, T., Kauppila, M., Sams, M., & Jääskeläinen, I. P. (2019) · Brain and Behavior · doi:10.1002/brb3.1288 · Cited by 50 · see citing works
Resting-state functional connectivity of salience network in schizophrenia and depression
Huang, H., Chen, C., Rong, B., Wan, Q., Chen, J., Liu, Z., Zhou, Y., Wang, G., & Wang, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-15489-9 · Cited by 53 · see citing works
Limbic Intrinsic Connectivity in Depressed and High-Risk Youth
Singh, M. K., Leslie, S. M., Packer, M. M., Weisman, E. F., & Gotlib, I. H. (2018) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2018.06.017 · Cited by 54 · see citing works
Neural processing of the reward value of pleasant odorants
Midroit, M., Chalençon, L., Renier, N., Milton, A., Thevenet, M., Sacquet, J., Breton, M., Forest, J., Noury, N., Richard, M., et al. (2021) · Current Biology · doi:10.1016/j.cub.2021.01.066 · Cited by 55 · see citing works
Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI
He, Y., Lim, S., Fortunato, S., Sporns, O., Zhang, L., Qiu, J., Xie, P., & Zuo, X.-N. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx335 · Cited by 54 · see citing works
Chien, H., Lin, H., Lai, M., Gau, S. S., & Tseng, W. I. (2015) · Autism Research · doi:10.1002/aur.1457 · Cited by 54 · see citing works
Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students
Huckins, J. F., daSilva, A. W., Wang, R., Wang, W., Hedlund, E. L., Murphy, E. I., Lopez, R. B., Rogers, C., Holtzheimer, P. E., Kelley, W. M., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00248 · Cited by 51 · see citing works
Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space
Nastase, S. A., Liu, Y.-F., Hillman, H., Norman, K. A., & Hasson, U. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116865 · Cited by 42 · see citing works
Williams, L. M., Pines, A., Goldstein-Piekarski, A. N., Rosas, L. G., Kullar, M., Sacchet, M. D., Gevaert, O., Bailenson, J., Lavori, P. W., Dagum, P., et al. (2018) · Behaviour Research and Therapy · doi:10.1016/j.brat.2017.09.012 · Cited by 40 · see citing works
- Authors
- Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Power, Jonathan D.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 84
- Pages
- 320-341
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3849338
- OpenAlex
- W1973776237
- Cited by
- 3,748
- References
- 38
- Citations by year
- 2026: 347 · 2025: 458 · 2024: 444 · 2023: 367 · 2022: 368 · 2021: 374 · 2020: 328 · 2019: 284 · 2018: 249 · 2017: 186 · 2016: 160 · 2015: 114 · 2014: 60 · 2013: 5
- FWCI
- 61.98
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (F30 MH940322, R21NS061144, 5R01 HD057076-03-S1, R01HD057076, U54MH091657, P30 NS048056)
- McDonnell Foundation Collaborative Action Award
- Simons Foundation Award (95177)
- Intellectual and Developmental Disabilities Research Center (NIH/NICHD P30 HD062171)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- head movement, motion artifacts, resting-state functional connectivity, global signal regression, motion correction, signal intensity changes, data quality metrics
- MeSH terms
- Adolescent, Adult, Algorithms, Brain, Brain Mapping, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Motion, Pattern Recognition, Automated, Rest, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Artifacts, Head Movements, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
