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Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
Open accessAbstract
Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.
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“Importantly, no group differences were observed in frame-wise displacement, a metric of relative head displacement between volumes (Power et al. 2012), suggesting the groups were well-matched for head motion (Table 2).”
From A novel resting-state functional magnetic resonance imaging signature of resilience to recurrent depression (2017) · cites this paper as Power et al. 2012
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In the citing paper
“To avoid spurious correlation due to motion55,56, we choose a priori a more conservative criterion by excluding all participants with more than 1 mm FD.”
From The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse (2020) · cites this paper as [55]
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In the citing paper
“Head motion during fMRI acquisitions was evaluated using the framewise displacement, which was calculated as the L1-norm of the realignment-derived parameters after converting angles to linear displacements (Power et al., 2012).”
From Perception is associated with the brain’s metabolic response to sensory stimulation (2022) · cites this paper as Power et al., 2012
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“Finally there were no significant effects of percent frames scrubbed using a threshold of 0.5 mm (Power et al. 2012) on group, condition, or interactions of group and condition (group:”
From Brain Activity and Functional Connectivity Associated with Hypnosis (2016) · cites this paper as Power et al. 2012
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In the citing paper
“Additionally we examined mean framewise displacement (Power et al. 2012) and found no main effect of group (F(1,40) = 0.01, P = 0.92), condition (F(3,40) = 1.01,P = 0.39), or group by condition interaction (F(3,40) = 1.49,P = 0.22).”
From Brain Activity and Functional Connectivity Associated with Hypnosis (2016) · cites this paper as Power et al. 2012
In the citing paper
“When processing techniques intended to remove motion artifact are applied, developmental effects of this type – both in terms of overall local versus distributed coupling and in terms of changing modular assignments – are substantially attenuated, though still present to a limited extent (Fair et al., 2012b; Power et al., 2012; Satterthwaite et al., 2012).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Power et al., 2012
In the citing paper
“Indeed, there are now at least 3 reports that suggest the network structure in children as defined by community detection (see Figure 3) are largely similar to the adult (Fair et al., 2012b; Marek et al., 2015; Power et al., 2012).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Power et al., 2012
In the citing paper
“it has been revealed that motion increases non-specific local coupling and decreases long-range coupling in ways that are similar to differences found between children and adults (Fair et al., 2012b; Power et al., 2012; Satterthwaite et al., 2012; Yan et al., 2013).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Power et al., 2012
In the citing paper
“head motion has become a substantial concern since 2012 when a trio of papers pointed to systematic motion-related artifacts in rs-fcMRI (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Power et al., 2012
In the citing paper
“head motion increases functional correlations between spatially proximal nodes and decreases correlations between distant nodes (Power et al., 2012).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Power et al., 2012
In the citing paper
“researchers recently discovered that some functional connectivity analyses interpreted as demonstrating developmental increases in long-range connections during rest (i.e., independent from any task) were spurious effects of greater motion in younger participants [62,63], which were eliminated with appropriate controls for motion-induced artifacts.”
From Arrested development? Reconsidering dual-systems models of brain function in adolescence and disorders (2012) · cites this paper as [62]
In the citing paper
“Recent work has suggested that small movements may have a greater impact on resting-state analyses than previously considered (Power et al, 2012).”
From Resting-state cortico-thalamic-striatal connectivity predicts response to dorsomedial prefrontal rTMS in major depressive disorder (2014) · cites this paper as Power et al, 2012
In the citing paper
“Strict motion correction procedures were applied to resting state functional maps, due to the sensitivity of RSFC data to head motion (Power et al., 2012, Satterthwaite et al., 2012, Van Dijk et al., 2012).”
From Developmental sex differences in resting state functional connectivity of amygdala sub-regions (2015) · cites this paper as Power et al., 2012
In the citing paper
“Head motion, even of submillimeter magnitude, has been identified as a nonphysiological source of spurious variance in resting-state fMRI data (Power et al. 2012; Van Dijk et al. 2012; Satterthwaite et al. 2013).”
From Joint attention and brain functional connectivity in infants and toddlers (2017) · cites this paper as Power et al. 2012
In the citing paper
“Diagnostic groups did not differ in age, sex, handedness, FSIQ, or head motion (mean framewise displacement [FD (Power et al., 2012)], translational and rotational motion, Table 2).”
From Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders (2019) · cites this paper as Power et al., 2012
In the citing paper
“Given the known problems of motion for connectivity analyses (90), we used ArtRepair software to correct for excessive movement (91).”
From Adolescent impatience decreases with increased frontostriatal connectivity (2015) · cites this paper as [90]
In the citing paper
“we elected to exclude participants where for either visit, the participant had one or more of the following: movement in any dimension ≥4 mm, and/or ≥25% of volumes identified as outliers according to the DVARS metric72 such that only participants who displayed acceptable amounts of motion under both the OT and PLC visits were included in analysis.”
From Intranasal oxytocin enhances connectivity in the neural circuitry supporting social motivation and social perception in children with autism (2016) · cites this paper as [72]
In the citing paper
“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., 2012
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Cites
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Self-citationNewman, M. (2010) · doi:10.1093/acprof:oso/9780199206650.001.0001 · Cited by 6,595
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
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
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
Esmark, K., Orning, H. J., & Hermanson, L. (2011) · Choice Reviews Online · doi:10.5860/choice.48-3328 · Cited by 2,950
Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging
Beckmann, C. F., & Smith, S. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.822821 · Cited by 2,707
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
Prediction of individual brain maturity using fMRI
Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., et al. (2010) · Science · doi:10.1126/science.1194144 · Cited by 2,183
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Items 101–200 of 372
Adolescent impatience decreases with increased frontostriatal connectivity
van den Bos, W., Rodriguez, C. A., Schweitzer, J. B., & McClure, S. M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1423095112 · Cited by 253
Qin, S., Young, C. B., Duan, X., Chen, T., Supekar, K., & Menon, V. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.10.006 · Cited by 249
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
Development of Thalamocortical Connectivity during Infancy and Its Cognitive Correlations
Alcauter, S., Lin, W., Smith, J. K., Short, S. J., Goldman, B. D., Reznick, J. S., Gilmore, J. H., & Gao, W. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0796-14.2014 · Cited by 228
One-year test-retest reliability of intrinsic connectivity network fMRI in older adults
Guo, C. C., Kurth, F., Zhou, J., Mayer, E. A., Eickhoff, S. B., Kramer, J. H., & Seeley, W. W. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.027 · Cited by 252
The development of hub architecture in the human functional brain network
Hwang, K., Hallquist, M. N., & Luna, B. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs227 · Cited by 226
Brain Activity and Functional Connectivity Associated with Hypnosis
Jiang, H., White, M. P., Greicius, M. D., Waelde, L. C., & Spiegel, D. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw220 · Cited by 246
Temporal circuit of macroscale dynamic brain activity supports human consciousness
Huang, Z., Zhang, J., Wu, J., Mashour, G. A., & Hudetz, A. G. (2020) · Science Advances · doi:10.1126/sciadv.aaz0087 · Cited by 217
Functional connectivity of hippocampal networks in temporal lobe epilepsy
Haneef, Z., Lenartowicz, A., Yeh, H. J., Levin, H. S., Engel, J., & Stern, J. M. (2014) · Epilepsia · doi:10.1111/epi.12476 · Cited by 235
Learning and comparing functional connectomes across subjects
Varoquaux, G., & Craddock, R. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.007 · Cited by 233
King, A. P., Block, S. R., Sripada, R. K., Rauch, S., Giardino, N., Favorite, T., Angstadt, M., Kessler, D., Welsh, R., & Liberzon, I. (2016) · Depression and Anxiety · doi:10.1002/da.22481 · Cited by 225
Roalf, D. R., Quarmley, M., Elliott, M. A., Satterthwaite, T. D., Vandekar, S. N., Ruparel, K., Gennatas, E. D., Calkins, M. E., Moore, T. M., Hopson, R., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.068 · Cited by 199
Increased amygdala responses to emotional faces after psilocybin for treatment-resistant depression
Roseman, L., Demetriou, L., Wall, M. B., Nutt, D. J., & Carhart-Harris, R. L. (2018) · Neuropharmacology · doi:10.1016/j.neuropharm.2017.12.041 · Cited by 230
Striatum-Based Circuitry of Adolescent Depression and Anhedonia
Gabbay, V., Ely, B. A., Li, Q., Bangaru, S. D., Panzer, A. M., Alonso, C. M., Castellanos, F. X., & Milham, M. P. (2013) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2013.04.003 · Cited by 222
Hemington, K. S., Wu, Q., Kucyi, A., Inman, R. D., & Davis, K. D. (2016) · Brain Structure and Function · doi:10.1007/s00429-015-1161-1 · Cited by 220
Cross-hemispheric functional connectivity in the human fetal brain
Thomason, M. E., Dassanayake, M. T., Shen, S., Katkuri, Y., Alexis, M., Anderson, A. L., Yeo, L., Mody, S., Hernandez-Andrade, E., Hassan, S. S., et al. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3004978 · Cited by 225
EEG hyper-connectivity in high-risk infants is associated with later autism
The BASIS Team, Orekhova, E. V., Elsabbagh, M., Jones, E. J., Dawson, G., Charman, T., & Johnson, M. H. (2014) · Journal of Neurodevelopmental Disorders · doi:10.1186/1866-1955-6-40 · Cited by 210
Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans
Rebollo, I., Devauchelle, A.-D., Béranger, B., & Tallon-Baudry, C. (2018) · eLife · doi:10.7554/elife.33321 · Cited by 194
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
Self-citationAlexander-Bloch, A., Clasen, L., Stockman, M., Ronan, L., Lalonde, F., Giedd, J., & Raznahan, A. (2016) · Human Brain Mapping · doi:10.1002/hbm.23180 · Cited by 178
Li, J., Duan, X., Cui, Q., Chen, H., & Liao, W. (2019) · Psychological Medicine · doi:10.1017/s0033291718001502 · Cited by 208
Vértes, P. E., & Bullmore, E. T. (2015) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12365 · Cited by 197
Alaerts, K., Swinnen, S. P., & Wenderoth, N. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw027 · Cited by 193
Dimsdale-Zucker, H. R., Ritchey, M., Ekstrom, A. D., Yonelinas, A. P., & Ranganath, C. (2018) · Nature Communications · doi:10.1038/s41467-017-02752-1 · Cited by 205
Graham, A. M., Rasmussen, J. M., Entringer, S., Ben Ward, E., Rudolph, M. D., Gilmore, J. H., Styner, M., Wadhwa, P. D., Fair, D. A., & Buss, C. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.06.023 · Cited by 203
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
Tyszka, J. M., Kennedy, D. P., Paul, L. K., & Adolphs, R. (2014) · Cerebral Cortex · doi:10.1093/cercor/bht040 · Cited by 187
Connectotyping: Model Based Fingerprinting of the Functional Connectome
Miranda-Dominguez, O., Mills, B. D., Carpenter, S. D., Grant, K. A., Kroenke, C. D., Nigg, J. T., & Fair, D. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111048 · Cited by 197
The brain's default network: origins and implications for the study of psychosis
Buckner, R. L. (2013) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2013.15.3/rbuckner · Cited by 196
Estimation of dynamic functional connectivity using multiplication of temporal derivatives
Shine, J. M., Koyejo, O., Bell, P. T., Gorgolewski, K. J., Gilat, M., & Poldrack, R. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.064 · Cited by 166
van den Bos, W., Rodriguez, C. A., Schweitzer, J. B., & McClure, S. M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4105-13.2014 · Cited by 178
Beevers, C. G., Clasen, P. C., Enock, P. M., & Schnyer, D. M. (2015) · Journal of Abnormal Psychology · doi:10.1037/abn0000049 · Cited by 192
Structural and functional rich club organization of the brain in children and adults
Grayson, D. S., Ray, S., Carpenter, S., Iyer, S., Dias, T. G. C., Stevens, C., Nigg, J. T., & Fair, D. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088297 · Cited by 187
Costa Dias, T. G., Wilson, V. B., Bathula, D. R., Iyer, S. P., Mills, K. L., Thurlow, B. L., Stevens, C. A., Musser, E. D., Carpenter, S. D., Grayson, D. S., et al. (2013) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2012.10.015 · Cited by 180
Structural and functional correlates of smartphone addiction
Horvath, J., Mundinger, C., Schmitgen, M. M., Wolf, N. D., Sambataro, F., Hirjak, D., Kubera, K. M., Koenig, J., & Christian Wolf, R. (2020) · Addictive Behaviors · doi:10.1016/j.addbeh.2020.106334 · Cited by 190
Alterations in amygdala–prefrontal circuits in infants exposed to prenatal maternal depression
Posner, J., Cha, J., Roy, A. K., Peterson, B. S., Bansal, R., Gustafsson, H. C., Raffanello, E., Gingrich, J., & Monk, C. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.146 · Cited by 184
Intrinsic organization of the anesthetized brain
Liang, Z., King, J., & Zhang, N. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.1020-12.2012 · Cited by 168
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
Blank, I., Kanwisher, N., & Fedorenko, E. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00884.2013 · Cited by 181
Brain–phenotype models fail for individuals who defy sample stereotypes
Greene, A. S., Shen, X., Noble, S., Horien, C., Hahn, C. A., Arora, J., Tokoglu, F., Spann, M. N., Carrión, C. I., Barron, D. S., et al. (2022) · Nature · doi:10.1038/s41586-022-05118-w · Cited by 167
Finding the Elusive Psychiatric “Lesion” With 21st-Century Neuroanatomy: A Note of Caution
Weinberger, D. R., & Radulescu, E. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.15060753 · Cited by 173
Exploring early human brain development with structural and physiological neuroimaging
Vasung, L., Abaci Turk, E., Ferradal, S. L., Sutin, J., Stout, J. N., Ahtam, B., Lin, P.-Y., & Grant, P. E. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.041 · Cited by 177
Anticevic, A., Hu, X., Xiao, Y., Hu, J., Li, F., Bi, F., Cole, M. W., Savic, A., Yang, G. J., Repovs, G., et al. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2310-14.2015 · Cited by 178
Connolly, C. G., Ho, T. C., Blom, E. H., LeWinn, K. Z., Sacchet, M. D., Tymofiyeva, O., Simmons, A. N., & Yang, T. T. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.09.026 · Cited by 177
Functional Connectome of the Fetal Brain
Turk, E., van den Heuvel, M. I., Benders, M. J., de Heus, R., Franx, A., Manning, J. H., Hect, J. L., Hernandez-Andrade, E., Hassan, S. S., Romero, R., et al. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2891-18.2019 · Cited by 176
Decreased Functional Brain Connectivity in Adolescents with Internet Addiction
Hong, S.-B., Zalesky, A., Cocchi, L., Fornito, A., Choi, E.-J., Kim, H.-H., Suh, J.-E., Kim, C.-D., Kim, J.-W., & Yi, S.-H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057831 · Cited by 175
“emerged as an important confound in functional neuroimaging [38,88].”
BackgroundStructural and Maturational Covariance in Early Childhood Brain Development
Geng, X., Li, G., Lu, Z., Gao, W., Wang, L., Shen, D., Zhu, H., & Gilmore, J. H. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw022 · Cited by 175
Wang, L., Xia, M., Li, K., Zeng, Y., Su, Y., Dai, W., Zhang, Q., Jin, Z., Mitchell, P. B., Yu, X., et al. (2015) · Human Brain Mapping · doi:10.1002/hbm.22663 · Cited by 166
Wei, D., Yang, J., Li, W., Wang, K., Zhang, Q., & Qiu, J. (2014) · Cortex · doi:10.1016/j.cortex.2013.09.004 · Cited by 164
Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder
Korgaonkar, M. S., Goldstein-Piekarski, A. N., Fornito, A., & Williams, L. M. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0574-2 · Cited by 163
Alderson-Day, B., McCarthy-Jones, S., & Fernyhough, C. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.04.016 · Cited by 165
Changing value through cued approach: an automatic mechanism of behavior change
Schonberg, T., Bakkour, A., Hover, A. M., Mumford, J. A., Nagar, L., Perez, J., & Poldrack, R. A. (2014) · Nature Neuroscience · doi:10.1038/nn.3673 · Cited by 161
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
“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
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
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
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
Underconnectivity of the superior temporal sulcus predicts emotion recognition deficits in autism
Alaerts, K., Woolley, D. G., Steyaert, J., Di Martino, A., Swinnen, S. P., & Wenderoth, N. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nst156 · Cited by 149
Pannekoek, J. N., van der Werff, S. J. A., Meens, P. H. F., van den Bulk, B. G., Jolles, D. D., Veer, I. M., van Lang, N. D. J., Rombouts, S. A. R. B., van der Wee, N. J. A., & Vermeiren, R. R. J. M. (2014) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12266 · Cited by 150
Ray, S., Miller, M., Karalunas, S., Robertson, C., Grayson, D. S., Cary, R. P., Hawkey, E., Painter, J. G., Kriz, D., Fombonne, E., et al. (2014) · Human Brain Mapping · doi:10.1002/hbm.22603 · Cited by 149
Wass, S. V., Forssman, L., & Leppänen, J. (2014) · Infancy · doi:10.1111/infa.12055 · Cited by 146
Intrinsic functional connectivity variance and state‐specific under‐connectivity in autism
Chen, H., Nomi, J. S., Uddin, L. Q., Duan, X., & Chen, H. (2017) · Human Brain Mapping · doi:10.1002/hbm.23764 · Cited by 137
Chronnectomic patterns and neural flexibility underlie executive function
Nomi, J. S., Vij, S. G., Dajani, D. R., Steimke, R., Damaraju, E., Rachakonda, S., Calhoun, V. D., & Uddin, L. Q. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.026 · Cited by 131
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Within-patient correspondence of amyloid-β and intrinsic network connectivity in Alzheimer’s disease
Myers, N., Pasquini, L., Göttler, J., Grimmer, T., Koch, K., Ortner, M., Neitzel, J., Mühlau, M., Förster, S., Kurz, A., et al. (2014) · Brain · doi:10.1093/brain/awu103 · Cited by 132
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Bertero, A., Liska, A., Pagani, M., Parolisi, R., Masferrer, M. E., Gritti, M., Pedrazzoli, M., Galbusera, A., Sarica, A., Cerasa, A., et al. (2018) · Brain · doi:10.1093/brain/awy111 · Cited by 120
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Gao, W., Elton, A., Zhu, H., Alcauter, S., Smith, J. K., Gilmore, J. H., & Lin, W. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.5072-13.2014 · Cited by 131
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Self-citationCottam, W. J., Iwabuchi, S. J., Drabek, M. M., Reckziegel, D., & Auer, D. P. (2018) · Pain · doi:10.1097/j.pain.0000000000001209 · Cited by 110
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Emerson, R. W., Gao, W., & Lin, W. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.3980-15.2016 · Cited by 112
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Baum, G. L., Roalf, D. R., Cook, P. A., Ciric, R., Rosen, A. F. G., Xia, C., Elliott, M. A., Ruparel, K., Verma, R., Tunç, B., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.041 · Cited by 116
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Wang, L., Su, L., Shen, H., & Hu, D. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044530 · Cited by 115
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
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
Gordon, I., Jack, A., Pretzsch, C. M., Vander Wyk, B., Leckman, J. F., Feldman, R., & Pelphrey, K. A. (2016) · Scientific Reports · doi:10.1038/srep35054 · Cited by 114
Metadata
- Authors
- Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Power, Jonathan D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 59
- Issue
- 3
- Pages
- 2142-2154
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3254728
- OpenAlex
- W1990134753
- Cited by
- 7,429
- References
- 42
- Citations by year
- 2026: 404 · 2025: 506 · 2024: 475 · 2023: 525 · 2022: 604 · 2021: 671 · 2020: 696 · 2019: 647 · 2018: 604 · 2017: 587 · 2016: 504 · 2015: 439 · 2014: 357 · 2013: 294 · 2012: 104
- FWCI
- 123.02
- Type
- journal article
- Language
- en
- Update notices
- erratum (2012-11-01)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5p60dk020579-35, 5u54mh091657-05)
- National Institute of Mental Health (P50MH071616, U54-MH091657, P50MH71616)
- National Institute on Deafness and Other Communication Disorders (K23DC006638)
- National Institute of Diabetes and Digestive and Kidney Diseases (P60 DK020579-31)
- National Institute of Neurological Disorders and Stroke (R01NS32979, R01NS032979, R21NS061144)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD057076)
- 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
- subject motion, resting-state functional connectivity, spurious correlations, motion correction, spatial registration, brain development
- MeSH terms
- Algorithms, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Motion, Oxygen, Software, Cohort Studies, Artifacts, Head Movements
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