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The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?
Open accessAbstract
Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.
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“Some physiological noises, such as cardiac and respiratory rates, can alias into the low-frequency domain of functional MRI signals (Strother, 2006;Murphy et al., 2009).”
From Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis (2012) · cites this paper as Murphy et al., 2009
In the citing paper
“The interpretation of negative correlations is especially controversial because they only become strongly evident after regressing out the global signal; therefore some argue that negative connections in functional data may be purely a statistical artifact (Murphy et al., 2009).”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Murphy et al., 2009
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“Some experts have suggested that these techniques might introduce spurious negative correlations upon regressing out whole brain signals (Murphy et al, 2009;Van Dijk et al, 2009).”
From Understanding the Effects of Stimulant Medications on Cognition in Individuals with Attention-Deficit Hyperactivity Disorder: A Decade of Progress (2011) · cites this paper as Murphy et al, 2009
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“Though depression commonly co-occurs with PTSD in veteran populations (up to 80% by some estimations70)”
From Altered resting-state amygdala functional connectivity in men with posttraumatic stress disorder (2012) · cites this paper as [70]
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A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Co-Planar Stereotaxic Atlas of the Human Brain
Talairach, J., & Tournoux, P. (1988) · Medical Entomology and Zoology · Cited by 11,798
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
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
Co-planar stereotaxic atlas of the human brain
Laitinen, L. (1989) · Clinical Neurology and Neurosurgery · doi:10.1016/0303-8467(89)90128-5 · Cited by 7,565
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Research works printed but not linked: 2
- Fox, M.D., Zhang, D., Snyder, A.Z., Raichle, M.E., 2008. Global signal regression and anticorrelations in resting state fMRI data. Proc. HBM, 575 W-AM.
- Weissenbacher, A., Lanzenberger, R., Moser, E., Windischberger, C., 2008. Increasing specificity of resting-state fMRI-data using multiple regression analysis. Proc. HBM, TH-AM.
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Eckstein, M., Markett, S. A., Kendrick, K. M., Ditzen, B., Liu, F., Hurlemann, R., & Becker, B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.078 · Cited by 70
Sormaz, M., Jefferies, E., Bernhardt, B. C., Karapanagiotidis, T., Mollo, G., Bernasconi, N., Bernasconi, A., Hartley, T., & Smallwood, J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.071 · Cited by 70
Talukdar, T., Nikolaidis, A., Zwilling, C. E., Paul, E. J., Hillman, C. H., Cohen, N. J., Kramer, A. F., & Barbey, A. K. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx232 · Cited by 74
Topological data analysis of task-based fMRI data from experiments on schizophrenia
Stolz, B. J., Emerson, T., Nahkuri, S., Porter, M. A., & Harrington, H. A. (2021) · Journal of Physics Complexity · doi:10.1088/2632-072x/abb4c6 · Cited by 70
Nilsonne, G., Tamm, S., Schwarz, J., Almeida, R., Fischer, H., Kecklund, G., Lekander, M., Fransson, P., & Åkerstedt, T. (2017) · Scientific Reports · doi:10.1038/s41598-017-09744-7 · Cited by 68
Vigilance Effects in Resting-State fMRI
Liu, T. T., & Falahpour, M. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00321 · Cited by 69
Rytty, R., Nikkinen, J., Paavola, L. E., Elseoud, A. A., Moilanen, V., Visuri, A., Tervonen, O. A., Renton, A. E., Traynor, B. J., Kiviniemi, V. J., et al. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00461 · Cited by 74
Spontaneous functional network dynamics and associated structural substrates in the human brain
Liao, X., Yuan, L., Zhao, T., Dai, Z., Shu, N., Xia, M., Yang, Y., Evans, A. C., & Yong He, A. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00478 · Cited by 68
Xu, J., Potenza, M. N., Calhoun, V. D., Zhang, R., Yip, S. W., Wall, J. T., Pearlson, G. D., Worhunsky, P. D., Garrison, K. A., & Moran, J. M. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.08.035 · Cited by 68
Broadwater, M., Lee, S., Yu, Y., Hongtu Zhu, B., Crews, F. T., Robinson, D. L., & Shih, Y. I. (2017) · Addiction Biology · doi:10.1111/adb.12530 · Cited by 74
Identification of Common Thalamocortical Dysconnectivity in Four Major Psychiatric Disorders
Tu, P., Bai, Y. M., Li, C., Chen, M., Lin, W.-C., Chang, W.-C., & Su, T. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby166 · Cited by 71
Takeuchi, H., Taki, Y., Nouchi, R., Sekiguchi, A., Hashizume, H., Sassa, Y., Kotozaki, Y., Miyauchi, C. M., Yokoyama, R., Iizuka, K., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.058 · Cited by 71
Gozdas, E., Parikh, N. A., Merhar, S. L., Tkach, J. A., He, L., & Holland, S. K. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1707-0 · Cited by 75
Exploring brain functional plasticity in world class gymnasts: a network analysis
Wang, J., Lü, M., Fan, Y., Wen, X., Zhang, R., Wang, B., Ma, Q., Zheng, S. M., Yong He, A., Wang, J., et al. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1116-6 · Cited by 69
Guo, W., Liu, F., Liu, J., Yu, M., Zhang, Z., Liu, G., Xiao, C., & Zhao, J. (2015) · Medicine · doi:10.1097/md.0000000000000560 · Cited by 70
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Laufs, H., Rodionov, R., Thornton, R., Duncan, J. S., Lemieux, L., & Tagliazucchi, E. (2014) · Frontiers in Neurology · doi:10.3389/fneur.2014.00175 · Cited by 68
The impact of image smoothness on intrinsic functional connectivity and head motion confounds
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Venkatesan, U. M., Dennis, N. A., & Hillary, F. G. (2014) · Journal of Neurotrauma · doi:10.1089/neu.2013.3318 · Cited by 72
Khalili‐Mahani, N., Rombouts, S. A. R. B., van Osch, M. J. P., Duff, E. P., Carbonell, F., Nickerson, L. D. H., Becerra, L. R., Dahan, A., Evans, A. C., Soucy, J., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23516 · Cited by 63
The neural network for face recognition: Insights from an fMRI study on developmental prosopagnosia
Zhao, Y., Zhen, Z., Liu, X., Song, Y., & Liu, J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.023 · Cited by 74
Enhanced Network Efficiency of Functional Brain Networks in Primary Insomnia Patients
Ma, X., Jiang, G., Fu, S., Jin, F., Wu, Y., Liu, M., Xu, G., & Wang, T. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00046 · Cited by 70
Collin, G., Seidman, L. J., Keshavan, M. S., Stone, W. S., Qi, Z., Zhang, T., Tang, Y., Li, H., Anteraper, S. A., Niznikiewicz, M. A., et al. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0288-x · Cited by 68
Abnormal Brain Network Connectivity in a Triple-Network Model of Alzheimer’s Disease
Li, C., Initiative, A. A. D. N., Li, Y., Zheng, L., Zhu, X., Shao, B., Fan, G., Liu, T., & Wang, J. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-181097 · Cited by 65
Eickhoff, S. B., & Müller, V. I. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00212-8 · Cited by 80
FMRI minimal preprocessing pipeline
Nieto-Castañón, A. (2020) · Hilbert Press eBooks · doi:10.56441/hilbertpress.2207.6599 · Cited by 73
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Zhu, L., Yang, F., Zou, Q., Wang, J., Gao, J., & Niu, Z. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085880 · Cited by 72
Tang, Y., Wang, L., Cao, F., & Tan, L. (2012) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-11-50 · Cited by 69
Impaired Parahippocampus Connectivity in Mild Cognitive Impairment and Alzheimer’s Disease
Liu, J., Zhang, X., Yu, C., Duan, Y., Zhuo, J., Cui, Y. J., Liu, B., Li, K., Jiang, T., & Liu, Y. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150727 · Cited by 66
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Lu, F.-M., Liu, C.-H., Lu, S.-L., Tang, L.-R., Tie, C.-L., Zhang, J., & Yuan, Z. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00214 · Cited by 67
Kwak, S., Lee, T. Y., Jung, W. H., Hur, J., Bae, D., Hwang, W. J., Cho, K. I. K., Lim, K., Kim, S. Y., Park, H. Y., et al. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00101 · Cited by 75
Rahnev, D., Bahdo, L., de Lange, F. P., & Lau, H. C. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00184.2012 · Cited by 71
Identifying the Functional Brain Network of Motor Reserve in Early Parkinson's Disease
Chung, S. J., Kim, H., Jung, J. H., Lee, P. H., Jeong, Y., & Sohn, Y. H. (2020) · Movement Disorders · doi:10.1002/mds.28012 · Cited by 68
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Zhang, D., Liu, X., Chen, J., Liu, B., & Wang, J. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00006 · Cited by 68
Anticevic, A., Repovš, G., Krystal, J. H., & Barch, D. M. (2012) · Schizophrenia Research · doi:10.1016/j.schres.2012.07.007 · Cited by 69
Clos, M., Diederen, K. M. J., Meijering, A. L., Sommer, I. E., & Eickhoff, S. B. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0519-5 · Cited by 70
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Park, B., Palomares, J. A., Woo, M. A., Kang, D. W., Macey, P. M., Yan‐Go, F. L., Harper, R. M., & Kumar, R. (2016) · Brain and Behavior · doi:10.1002/brb3.441 · Cited by 66
Zhang, J., Yao, Y., Potenza, M. N., Xia, C., Lan, J., Liu, L., Wang, L.-J., Liu, B., Ma, S., & Fang, X. (2016) · Scientific Reports · doi:10.1038/srep28109 · Cited by 66
Modular‐level alterations of structure–function coupling in schizophrenia connectome
Sun, Y., Dai, Z., Li, J., Collinson, S. L., & Sim, K. (2016) · Human Brain Mapping · doi:10.1002/hbm.23501 · Cited by 65
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Zwilling, C. E., Talukdar, T., Zamroziewicz, M. K., & Barbey, A. K. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.007 · Cited by 73
Yee, Y., Fernandes, D., French, L., Ellegood, J., Cahill, L. S., Vousden, D. A., Noakes, L. S., Scholz, J., van Eede, M. C., Nieman, B. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.028 · Cited by 71
Lu, J., Testa, N., Jordan, R., Elyan, R., Kanekar, S. G., Wang, J., Eslinger, P. J., Yang, Q. X., Zhang, B., & Karunanayaka, P. (2019) · Brain Sciences · doi:10.3390/brainsci9120338 · Cited by 66
Zhang, J., Huang, Z., Tumati, S., & Northoff, G. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000733 · Cited by 65
Cross, N. E., Paquola, C., Pomares, F. B., Perrault, A. A., Jegou, A., Nguyen, A., Aydın, Ü., Bernhardt, B. C., Grova, C., & Dang‐Vu, T. T. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117547 · Cited by 64
Cardenas, V. A., Bhat, J. V., Horwege, A. M., Ehrlich, T. J., Lavacot, J. A., Mathalon, D. H., Glover, G. H., Roach, B. J., Badran, B. W., Forman, S. D., et al. (2021) · Brain stimulation · doi:10.1016/j.brs.2021.11.008 · Cited by 69
Flodin, P., Martinsen, S., Mannerkorpi, K., Löfgren, M., Bilevičiūtė-Ljungar, I., Kosek, E., & Fransson, P. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.08.004 · Cited by 65
Cai, S., Chong, T., Zhang, Y., Li, J., von Deneen, K. M., Ren, J., Dong, M., & Huang, L. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00471 · Cited by 68
Heine, L., Castro, M., Martial, C., Tillmann, B., Laureys, S., & Perrin, F. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01704 · Cited by 67
Fullana, M. A., Zhu, X., Alonso, P., Cardoner, N., Real, E., López-Solà, C., Segalàs, C., Subirà, M., Galfalvy, H., Menchón, J. M., et al. (2017) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.160215 · Cited by 63
Disrupted neural variability during propofol‐induced sedation and unconsciousness
Huang, Z., Zhang, J., Wu, J., Liu, X., Xu, J., Zhang, J., Qin, P., Dai, R., Yang, Z. H., Mao, Y. Q., et al. (2018) · Human Brain Mapping · doi:10.1002/hbm.24304 · Cited by 62
Mishra, A., Rogers, B. P., Chen, L. M., & Gore, J. C. (2013) · Human Brain Mapping · doi:10.1002/hbm.22249 · Cited by 66
Baeken, C., Marinazzo, D., Schuerbeek, P. V., Wu, G., Mey, J. D., Luypaert, R., & Raedt, R. D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095740 · Cited by 68
Asymmetric Connectivity between the Anterior Temporal Lobe and the Language Network
Hurley, R. S. E., Bonakdarpour, B., Wang, X., & Mesulam, M. M. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00722 · Cited by 68
Characterising resting-state functional connectivity in a large sample of adults with ADHD
Mostert, J. C., Shumskaya, E., Mennes, M., Onnink, A. M. H., Hoogman, M., Kan, C. C., Vásquez, A. A., Buitelaar, J. K., Franke, B., & Norris, D. G. (2016) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2016.01.011 · Cited by 71
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Roy, A., Bernier, R. A., Wang, J., Benson, M. A., French, J. J., Good, D. C., & Hillary, F. G. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170541 · Cited by 67
Iidaka, T., Kogata, T., Mano, Y., & Komeda, H. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00252 · Cited by 66
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Naci, L., Haugg, A., MacDonald, A., Anello, M., Houldin, E., Naqshbandi, S., Gonzalez-Lara, L. E., Arango, M., Harle, C., Cusack, R., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-31525-z · Cited by 66
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McAvoy, M. P., Mitra, A., Coalson, R. S., d’Avossa, G., Keidel, J. L., Petersen, S. E., & Raichle, M. E. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv007 · Cited by 62
Robinson, P. A., Sarkar, S., Mehta-Pandejee, G., & Henderson, J. A. (2014) · Physical Review E · doi:10.1103/physreve.90.012707 · Cited by 64
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Foster, B. L., He, B. J., Honey, C. J., Jerbi, K., Maier, A., & Saalmann, Y. B. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00007 · Cited by 63
Xu, Y., Cao, M., Liao, X., Xia, M., Wang, X., Jeon, T., Ouyang, M., Chalak, L. F., Rollins, N. K., Huang, H., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy302 · Cited by 60
Hopman, H. J., Chan, S. S. M., Chu, C. W., Lu, H., Tse, C., Chau, S. W. H., LAM, L. C. W., Mak, A. D. P., & Neggers, S. F. W. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.04.081 · Cited by 65
Viviani, R., Messina, I., & Walter, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027050 · Cited by 62
Abnormal functional connectivity density in children with anisometropic amblyopia at resting-state
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Teipel, S. J., Wohlert, A., Metzger, C., Grimmer, T., Sorg, C., Ewers, M., Meisenzahl, E., Klöppel, S., Borchardt, V., Grothe, M. J., et al. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.01.018 · Cited by 62
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Banks, S. J., Zhuang, X., Bayram, E., Bird, C. M., Cordes, D., Caldwell, J., Cummings, J. L., & Initiative, A. D. N. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-180541 · Cited by 60
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Constable, R. T., Scheinost, D., Finn, E. S., Shen, X., Hampson, M., Winstanley, F. S., Spencer, D. D., & Papademetris, X. (2013) · Frontiers in Neurology · doi:10.3389/fneur.2013.00039 · Cited by 62
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Griffanti, L., Dipasquale, O., Laganà, M. M., Nemni, R., Clerici, M., Smith, S. M., Baselli, G., & Baglio, F. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00449 · Cited by 61
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Tierney, T. M., Croft, L. J., Centeno, M., Shamshiri, E., Perani, S., Baldeweg, T., Clark, C. A., & Carmichael, D. W. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.034 · Cited by 62
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Masterton, R. A. J., Carney, P. W., & Jackson, G. D. (2012) · Epilepsy Research · doi:10.1016/j.eplepsyres.2011.12.014 · Cited by 61
O’Neil, E. B., Hutchison, R. M., McLean, D. A., & Köhler, S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.02.005 · Cited by 64
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Lin, Q., Dai, Z., Xia, M., Han, Z., Huang, R., Gong, G., Liu, C., Bi, Y., & Yong He, A. (2015) · Scientific Data · doi:10.1038/sdata.2015.56 · Cited by 61
Choi, Y., Shin, E. Y., & Kim, S. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2003963117 · Cited by 59
Wagner, G., Li, M., Sacchet, M. D., Richard‐Devantoy, S., Turecki, G., Bär, K., Gotlib, I. H., Walter, M., & Jollant, F. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01232-x · Cited by 60
Tak, S., Polimeni, J. R., Wang, D. J. J., Yan, L., & Chen, J. J. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0299 · Cited by 60
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Bray, S., Arnold, A. E. G. F., Levy, R., & Iaria, G. (2014) · Human Brain Mapping · doi:10.1002/hbm.22646 · Cited by 60
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Li, Q., Cao, W., Liao, X., Chen, Z., Yang, T., Gong, Q., Zhou, D., Luo, C., & Yao, D. (2015) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2015.04.054 · Cited by 63
Gee, D. G., Biswal, B. B., Kelly, C. A. M., Stark, D. E., Margulies, D. S., Shehzad, Z. E., Uddin, L. Q., Klein, D. F., Banich, M. T., Castellanos, F. X., et al. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.073 · Cited by 60
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Takeuchi, H., Taki, Y., Nouchi, R., Sekiguchi, A., Hashizume, H., Sassa, Y., Kotozaki, Y., Miyauchi, C. M., Yokoyama, R., Iizuka, K., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.044 · Cited by 60
Orr, J. M., Turner, J. A., & Mittal, V. A. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.01.006 · Cited by 62
Keehn, R. J. J., Sanchez, S. S., Stewart, C. R., Zhao, W., Grenesko, E. L., Keehn, B., & Müller, R. (2016) · Autism Research · doi:10.1002/aur.1636 · Cited by 61
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Ashourvan, A., Gu, S., Mattar, M. G., Vettel, J. M., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.067 · Cited by 59
Wei, J., Chen, T., Li, C., Liu, G., Qiu, J., & Wei, D. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00422 · Cited by 60
Bottger, J., Schafer, A., Lohmann, G., Villringer, A., & Margulies, D. S. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.114 · Cited by 59
Gotts, S. J., Gilmore, A. W., & Martin, A. D. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116289 · Cited by 60
Wang, J., Liu, J., Wang, Z., Sun, P., Li, K., & Liang, P. (2019) · Aging · doi:10.18632/aging.102380 · Cited by 59
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Han, F., Liu, X., Mailman, R. B., Huang, X., Liu, X., & Initiative, T. A. D. N. (2023) · Nature Communications · doi:10.1038/s41467-023-43627-y · Cited by 68
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Gopinath, K., Krishnamurthy, V., Cabanban, R., & Crosson, B. (2015) · Brain Connectivity · doi:10.1089/brain.2014.0323 · Cited by 60
Liu, J., Qin, W., Yuan, K., Li, J., Wang, W., Li, Q., Wang, Y., Sun, J., von Deneen, K. M., Liu, Y., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023098 · Cited by 60
Metadata
- Authors
- Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Bandettini, Peter A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2009-02
- Volume
- 44
- Issue
- 3
- Pages
- 893-905
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2750906
- OpenAlex
- W2169787465
- Cited by
- 2,387
- References
- 64
- Citations by year
- 2026: 118 · 2025: 104 · 2024: 109 · 2023: 78 · 2022: 121 · 2021: 133 · 2020: 173 · 2019: 157 · 2018: 166 · 2017: 204 · 2016: 187 · 2015: 188 · 2014: 181 · 2013: 168 · 2012: 114
- FWCI
- 31.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (Z01MH002783)
- 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
- global signal regression, resting-state functional connectivity, default mode network, attentional networks, posterior cingulate cortex, phase difference, breath-holding, visual task, seed-based correlation analysis
- MeSH terms
- Biological Clocks, Brain, Computer Simulation, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Regression Analysis, Rest, Statistics as Topic, Young Adult
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