A geometric view of global signal confounds in resting-state functional MRI
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Resting-state functional magnetic resonance imaging (fMRI) is proving to be an effective tool for mapping the long-range functional connections of the brain in both health and disease. One of the primary measures of connectivity is the correlation between the blood oxygenation level dependent (BOLD) time series observed in different brain regions. The computation of the correlation is often dominated by the presence of a strong global component that can introduce significant variability across functional connectivity maps acquired from different experimental scans or subjects. To address this issue, a variety of global signal correction methods have been proposed, but there is currently a lack of a clear consensus on the best approach to use. Furthermore, there has been concern that some global signal correction methods, such as global signal regression, may produce significant negative bias in the correlation values. In this paper we introduce a framework for visualizing the signal structure of resting-state fMRI data and characterizing the properties of the global signal. Using this framework, we demonstrate that a portion of the global signal can be viewed as an additive confound that increases with the mean BOLD amplitude. An approach for minimizing the contribution of this additive confound is presented, and an initial comparison with existing global signal correction methods is provided.
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Outliers in whole-brain voxel-to-voxel BOLD-signal correlations were removed to control for motion-induced global signal confound.
“As an extra precautionary step, the voxel-to-voxel BOLD-signal correlations across the whole brain (including GM, WM, and CSF) was calculated and summed. Outliers in this measure likely originate in a motion-induced global signal confound capable of eluding conventional motion estimation70 and were removed.”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [70]
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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 · 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
Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems
Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L., & Raichle, M. E. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0604187103 · Cited by 2,101 · see citing works
Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets
Friston, K. J., Frith, C., Liddle, P., & Frackowiak, R. (1993) · Journal of Cerebral Blood Flow and Metabolism · doi:10.1038/jcbfm.1993.4 · Cited by 2,094 · 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
Van 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
Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations
Lowe, M. J., Mock, B. J., & Sorenson, J. A. (1998) · NeuroImage · doi:10.1006/nimg.1997.0315 · Cited by 1,394 · see citing works
Birn, R. M., Diamond, J. B., Smith, M. A., & Bandettini, P. A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.048 · Cited by 1,342 · 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
Mapping functionally related regions of brain with functional connectivity MR imaging.
Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., Quigley, M., & Meyerand, M. E. (2000) · PubMed Central · Cited by 908 · see citing works
Brain connectivity related to working memory performance
Hampson, M., Driesen, N. R., Skudlarski, P., Gore, J. C., & Constable, R. T. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3408-06.2006 · Cited by 853 · see citing works
Weissenbacher, 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
Intrinsic Fluctuations within Cortical Systems Account for Intertrial Variability in Human Behavior
Fox, M., Snyder, A. Z., Vincent, J. L., & Raichle, M. E. (2007) · Neuron · doi:10.1016/j.neuron.2007.08.023 · Cited by 753 · see citing works
Chang, C. E., & Glover, G. H. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.012 · Cited by 486 · see citing works
Hampson, M., Driesen, N. R., Roth, J. K., Gore, J. C., & Constable, R. T. (2010) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2010.03.021 · Cited by 363 · see citing works
Reduced Hippocampal Functional Connectivity in Alzheimer Disease
Allen, G., Barnard, H. D., McColl, R. W., Hester, A. L., Fields, J. A., Weiner, M., Ringe, W. K., Lipton, A. M., Brooker, M., McDonald, E. C., et al. (2007) · Archives of Neurology · doi:10.1001/archneur.64.10.1482 · Cited by 351 · see citing works
More accurate Talairach coordinates for neuroimaging using non-linear registration
Lacadie, C., Fulbright, R. K., Rajeevan, N., Constable, R. T., & Papademetris, X. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.240 · Cited by 351 · see citing works
Functional Topography: Multidimensional Scaling and Functional Connectivity in the Brain
Friston, K. J., Frith, C., Fletcher, P. C., Liddle, P. F., & Frackowiak, R. S. J. (1996) · Cerebral Cortex · doi:10.1093/cercor/6.2.156 · Cited by 276 · see citing works
Yan, L., Zhuo, Y., Ye, Y., Xie, S. X., An, J., Aguirre, G. K., & Wang, J. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21902 · Cited by 76 · see citing works
A Global Estimator Unbiased by Local Changes
Andersson, J., Ashburner, J., & Friston, K. J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0763 · Cited by 33 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
Yan, 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
Ciric, 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
Towards a consensus regarding global signal regression for resting state functional connectivity MRI
Murphy, K., & Fox, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.052 · Cited by 1,043 · see citing works
Palmqvist, S., Schöll, M., Strandberg, O., Mattsson, N., Stomrud, E., Zetterberg, H., Blennow, K., Landau, S., Jagust, W., & Hansson, O. (2017) · Nature Communications · doi:10.1038/s41467-017-01150-x · Cited by 911 · 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
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
The Convergence of Maturational Change and Structural Covariance in Human Cortical Networks
Alexander‐Bloch, A. F., Raznahan, A., Bullmore, E. T., & Giedd, J. N. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3554-12.2013 · Cited by 460 · see citing works
The global signal in fMRI: Nuisance or Information?
Liu, T. T., Nalci, A., & Falahpour, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.036 · Cited by 415 · see citing works
Self-citationGabard-Durnam, L. J., Flannery, J., Goff, B., Gee, D. G., Humphreys, K. L., Telzer, E., Hare, T., & Tottenham, N. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.038 · Cited by 372 · see citing works
The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures
Wong, C. W., Olafsson, V. T., Tal, O., & Liu, T. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.057 · Cited by 281 · see citing works
Self-citationNoise contributions to the fMRI signal: An overview
Liu, T. T. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.008 · Cited by 279 · see citing works
Self-citationStudying Brain Organization via Spontaneous fMRI Signal
Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2014) · Neuron · doi:10.1016/j.neuron.2014.09.007 · Cited by 250 · see citing works
Glasser, M. F., Coalson, T. S., Bijsterbosch, J. D., Harrison, S. J., Harms, M. P., Anticevic, A., Essen, D. C. V., & Smith, S. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.076 · Cited by 253 · see citing works
Characterizing variation in the functional connectome: promise and pitfalls
Kelly, C. A. M., Biswal, B. B., Craddock, R. C., Castellanos, F. X., & Milham, M. P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.02.001 · Cited by 257 · see citing works
Subcortical evidence for a contribution of arousal to fMRI studies of brain activity
Liu, X., de Zwart, J. A., Schölvinck, M. L., Chang, C., Ye, F. Q., Leopold, D. A., & Duyn, J. H. (2018) · Nature Communications · doi:10.1038/s41467-017-02815-3 · Cited by 235 · see citing works
Spreng, R. N., Stevens, W. D., Viviano, J. D., & Schacter, D. L. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.05.020 · Cited by 234 · see citing works
Co-activation patterns in resting-state fMRI signals
Liu, X., Zhang, N., Chang, C. E., & Duyn, J. H. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.041 · Cited by 232 · see citing works
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 · see citing works
Lag structure in resting-state fMRI
Mitra, A., Snyder, A. Z., Hacker, C. D., & Raichle, M. E. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00804.2013 · Cited by 185 · see citing works
Bring the Noise: Reconceptualizing Spontaneous Neural Activity
Uddin, L. Q. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.06.003 · Cited by 160 · see citing works
The Extrinsic and Intrinsic Functional Architectures of the Human Brain Are Not Equivalent
Mennes, M., Kelly, C. A. M., Colcombe, S. J., Castellanos, F. X., & Milham, M. P. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs010 · Cited by 160 · see citing works
Roberts, G. M. P., Lord, A., Frankland, A., Wright, A., Lau, P., Lévy, F., Lenroot, R., Mitchell, P. B., & Breakspear, M. J. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.08.018 · Cited by 157 · see citing works
Wong, C. W., Olafsson, V. T., Tal, O., & Liu, T. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.035 · Cited by 130 · see citing works
Self-citationEigenmodes of brain activity: Neural field theory predictions and comparison with experiment
Robinson, P. A., Zhao, X., Aquino, K. M., Griffiths, J. D., Sarkar, S., & Mehta-Pandejee, G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.050 · Cited by 127 · see citing works
Neurovascular factors in resting-state functional MRI
Liu, T. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.071 · Cited by 125 · see citing works
Self-citationBeall, E. B., & Lowe, M. J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.038 · Cited by 105 · see citing works
Englot, D. J., D’Haese, P.-F., Konrad, P. E., Jacobs, M. L., Gore, J. C., Abou-Khalil, B. W., & Morgan, V. L. (2017) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2017-315732 · Cited by 79 · see citing works
Alterations in resting functional connectivity due to recent motor task
Tung, K.-C., Uh, J., Mao, D., Xu, F., Xiao, G., & Lu, H. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.006 · Cited by 76 · see citing works
Ning, L., Makris, N., Camprodon, J. A., & Yogesh Rathi, A. (2018) · Brain stimulation · doi:10.1016/j.brs.2018.10.004 · Cited by 71 · see citing works
An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI
Churchill, N. W., Spring, R., Afshin‐Pour, B., Dong, F., & Strother, S. C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131520 · Cited by 64 · see citing works
Unmasking Language Lateralization in Human Brain Intrinsic Activity
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 63 · see citing works
Robinson, P. A., Sarkar, S., Mehta-Pandejee, G., & Henderson, J. A. (2014) · Physical Review E · doi:10.1103/physreve.90.012707 · Cited by 64 · see citing works
Hahn, A., Strandberg, T. O., Stomrud, E., Nilsson, M., van Westen, D., Palmqvist, S., Ossenkoppele, R., & Hansson, O. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz020 · Cited by 61 · see citing works
Clinical applications of magnetic resonance imaging based functional and structural connectivity
Wu, C., Ferreira, F., Fox, M., Harel, N., Hattangadi-Gluth, J. A., Horn, A., Jbabdi, S., Kahan, J., Oswal, A., Sheth, S. A., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118649 · Cited by 59 · see citing works
An orderly sequence of autonomic and neural events at transient arousal changes
Gu, Y., Han, F., Sainburg, L. E., Schade, M. M., Buxton, O. M., Duyn, J. H., & Liu, X. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119720 · Cited by 56 · see citing works
A ventral salience network in the macaque brain
Touroutoglou, A., Bliss‐Moreau, E., Zhang, J., Mantini, D., Vanduffel, W., Dickerson, B. C., & Barrett, L. F. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.029 · Cited by 54 · see citing works
Global signal regression acts as a temporal downweighting process in resting-state fMRI
Nalci, A., Rao, B. D., & Liu, T. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.015 · Cited by 54 · see citing works
Self-citationBeyond noise to function: reframing the global brain activity and its dynamic topography
Zhang, J., & Northoff, G. (2022) · Communications Biology · doi:10.1038/s42003-022-04297-6 · Cited by 50 · see citing works
Churchill, N. W., & Strother, S. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.102 · Cited by 52 · see citing works
Zhao, Z., Tang, C., Yin, D., Wu, J., Gong, J., Sun, L., Jia, J., Xu, D., & Fan, M. (2018) · Human Brain Mapping · doi:10.1002/hbm.24277 · Cited by 49 · see citing works
Alonso, B. D. C., Hidalgo-Tobón, S., Suárez, P. D., Flores, J. G., Carrillo, B. D. C., & Barragán-Pérez, E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099119 · Cited by 47 · see citing works
Owens‐Walton, C., Jakabek, D., Li, X., Wilkes, F., Walterfang, M., Velakoulis, D., van Westen, D., Looi, J. C., & Hansson, O. (2018) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2018.03.004 · Cited by 45 · see citing works
Nystedt, J., Mannfolk, P., Jönsen, A., Nilsson, P., Strandberg, T. O., & Sundgren, P. C. (2019) · The Journal of Comparative Neurology · doi:10.1002/cne.24656 · Cited by 45 · see citing works
Carbonell, F., Bellec, P., & Shmuel, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.013 · Cited by 44 · see citing works
Functional connectivity networks with and without global signal correction
Hayasaka, S. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00880 · Cited by 43 · see citing works
A Precuneal Causal Loop Mediates External and Internal Information Integration in the Human Brain
Lyu, D., Pappas, I., Menon, D. K., & Stamatakis, E. A. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0647-21.2021 · Cited by 41 · see citing works
Qiu, M., Scheinost, D., Ramani, R., & Constable, R. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.080 · Cited by 36 · see citing works
Tal, O., Diwakar, M., Wong, C. W., Olafsson, V. T., Lee, R., Huang, M., & Liu, T. T. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00063 · Cited by 39 · see citing works
Self-citationZhang, H., Qiu, Y., Luo, Y., Xu, P., Li, Z., Zhu, W., Wu, Q., Tao, W., Guan, Q., & Chen, F. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.10.364 · Cited by 39 · see citing works
He, N., Palaniyappan, L. K., Linli, Z., & Guo, S. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00476-x · Cited by 36 · see citing works
Reduced Complexity in Stroke with Motor Deficits: A Resting-State fMRI Study
Liang, L., Hu, R., Luo, X.-M., Feng, B., Long, W., & Song, R. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.03.020 · Cited by 30 · see citing works
Functional Connectivity Changes in Systemic Lupus Erythematosus: A Resting-State Study
Nystedt, J., Mannfolk, P., Jönsen, A., Bengtsson, A. A., Nilsson, P., Sundgren, P. C., & Strandberg, T. O. (2018) · Brain Connectivity · doi:10.1089/brain.2017.0557 · Cited by 29 · see citing works
Gu, Y., Han, F., Sainburg, L. E., & Liu, X. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa096 · Cited by 29 · see citing works
Optimizing fMRI preprocessing pipelines for block-design tasks as a function of age
Churchill, N. W., Raamana, P. R., Spring, R., & Strother, S. C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.028 · Cited by 27 · see citing works
Hua, M., Peng, Y., Zhou, Y., Qin, W., Yu, C., & Liang, M. (2019) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2019.109837 · Cited by 27 · see citing works
Resting state FMRI research in child psychiatric disorders
Oldehinkel, M., Francx, W., Beckmann, C. F., Buitelaar, J. K., & Mennes, M. (2013) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-013-0480-0 · Cited by 24 · see citing works
Arousal Contributions to Resting-State fMRI Connectivity and Dynamics
Gu, Y., Han, F., & Liu, X. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01190 · Cited by 23 · see citing works
Chen, S., Li, H., Wang, L., Du, X., & Dong, G. (2019) · Addictive Behaviors · doi:10.1016/j.addbeh.2019.106202 · Cited by 23 · see citing works
Chen, S., Dong, D., Jackson, T., Su, Y., & Chen, H. (2015) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2015.06.036 · Cited by 22 · see citing works
Nuisance effects and the limitations of nuisance regression in dynamic functional connectivity fMRI
Nalci, A., Rao, B. D., & Liu, T. T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.024 · Cited by 22 · see citing works
Self-citationMehta-Pandejee, G., Robinson, P. A., Henderson, J. A., Aquino, K. M., & Sarkar, S. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.03.014 · Cited by 17 · see citing works
Liu, W., Hua, M., Qin, J., Tang, Q., Han, Y., Tian, H., Lian, D., Zhang, Z., Wang, W., Wang, C., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00333-3 · Cited by 16 · see citing works
Reprint of ‘Noise contributions to the fMRI signal: An Overview'
Liu, T. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.031 · Cited by 16 · see citing works
Self-citationMoradi, N., Dousty, M., & Sotero, R. C. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00736 · Cited by 11 · see citing works
Guan, X., Zheng, W., Fan, K., Xu, H., Li, X., Yan, Z., Lü, Z., & Gong, J. (2022) · BMC Pediatrics · doi:10.1186/s12887-022-03348-5 · Cited by 9 · see citing works
The diagnostic potential of resting state functional MRI: Statistical concerns
Doubovikov, E. D., & Aksenov, D. P. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121334 · Cited by 9 · see citing works
Bennett, M. R., Farnell, L., Gibson, W. G., & Lagopoulos, J. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/1/016012 · Cited by 7 · see citing works
Hu, B., Guan, X., Zhai, H., Xu, H., Hu, C., & Gong, J. F. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1509899 · Cited by 5 · see citing works
Brain Dynamics: Global Pulse and Brain State Switching
Ville, D. V. D. (2019) · Current Biology · doi:10.1016/j.cub.2019.06.006 · Cited by 5 · see citing works
Liu, W., Qin, J., Tang, Q., Han, Y., Fang, T., Zhang, Z., Wang, C., Lin, X., Tian, H., Zhuo, C., et al. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2020.113436 · Cited by 5 · see citing works
Kung, Y., Li, C., Hsu, A., Liu, C.-Y., Wu, C. W., Chang, W., & Lin, C. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120535 · Cited by 4 · see citing works
Zhang, H., Sachdev, P. S., Thalamuthu, A., Yong He, A., Xia, M., Kochan, N. A., Crawford, J. D., Trollor, J. N., Brodaty, H., & Wen, W. X. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9843-y · Cited by 4 · see citing works
Robert E. Kelly, Jr., Hoptman, M. J., Lee, S., Alexopoulos, G. S., Gunning, F. M., & McKeown, M. J. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109410 · Cited by 3 · see citing works
Guan, X., Zheng, W., Fan, K., Xu, H., Hu, B., Li, X., Yan, Z., Lü, Z., & Gong, J. F. (2024) · Brain Imaging and Behavior · doi:10.1007/s11682-023-00799-x · Cited by 3 · see citing works
Guan, X., Hu, B., Zheng, W., Zhai, H., Xu, H., Hu, C., Yan, Z., Chen, N., Xiang, L., Lü, Z., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-73088-2 · Cited by 3 · see citing works
No evidence for motor recovery-related cortical reorganization after stroke using resting-state fMRIPreprint
Branscheidt, M., Ejaz, N., Xu, J., Widmer, M., Harran, M. D., Cortés, J. C., Kitago, T., Celnik, P. A., Hernandez-Castillo, C. R., Diedrichsen, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/681320 · Cited by 3 · see citing works
Nalci, A., Luo, W., & Liu, T. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.07.018 · Cited by 2 · see citing works
Self-citationAnteraper, S. A., Nieto-Castañón, A., & Whitfield, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-35206-6_5 · Cited by 2 · see citing works
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Buxton, R. B., Prisk, G. K., & Hopkins, S. R. (2022) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00715.2021 · Cited by 2 · see citing works
Gu, Y., Han, F., Sainburg, L. E., & Liu, X. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/444463 · Cited by 2 · see citing works
Nanda, A., & Rubinov, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120110 · Cited by 1 · see citing works
Guan, X., Zheng, W., Hu, B., Zhai, H., Ni, J., Han, X., Hu, C., Wu, J., Wang, J., Xu, F., et al. (2025) · Neurosurgery · doi:10.1227/neu.0000000000003823 · Cited by 1 · see citing works
Wavelet Analysis of Big Data Contaminated by Large Noise in an fMRI Study of Neuroplasticity
Efromovich, S., & Wu, J. (2018) · Methodology And Computing In Applied Probability · doi:10.1007/s11009-018-9626-3 · Cited by 1 · see citing works
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Efromovich, S. (2019) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1467 · Cited by 1 · see citing works
Yan, N., Hu, B., Zhai, H., Xu, H., Hu, C., Guan, X., & Gong, J. F. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1568945 · Cited by 1 · see citing works
Guan, X., Zheng, W., Fan, K., Han, X., Li, X., Yan, Z., Lü, Z., & Gong, J. (2022) · Research Square · doi:10.21203/rs.3.rs-1270992/v1
Guan, X., Zheng, W., Fan, K., Han, X., Li, X., Yan, Z., Lu, Z., & Gong, J. F. (2022) · Figshare · doi:10.6084/m9.figshare.19959919
Assessing functional connectivity: the role of functional MRI and electrophysiological techniques
Liu, T. T. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-19099-5.00012-x
Self-citationGuan, X.-Y., Zheng, W.-J., Fan, K. F. M., Han, X., Li, X., Yan, Z., Lu, Z., & Gong, J. (2022) · Figshare · doi:10.6084/m9.figshare.19959922
Cerebrum functional network alterations following posterior fossa surgery in Children
Zhu, R., Hu, B., Zhai, H., Hu, C., Han, X., Lu, Q., Ni, J., Guan, X., & Gong, J. (2026) · Child s Nervous System · doi:10.1007/s00381-026-07187-y
Li, Q., Xing, Y., Shan, Y., Yin, Y., Tang, Y., & Lu, J. (2026) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/15333175261491804
Yang, X., Xia, H., Hu, Y., Xu, C., Wang, X., Deng, Q., Xie, Q., Dai, L., Yang, Q., Li, C., et al. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.10.031
- Authors
- He, H., & Liu, T. T.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Liu, Thomas T.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 59
- Issue
- 3
- Pages
- 2339-2348
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3254803
- OpenAlex
- W2055720818
- Cited by
- 93
- References
- 20
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 4 · 2023: 1 · 2022: 6 · 2021: 4 · 2020: 7 · 2019: 10 · 2018: 12 · 2017: 9 · 2016: 7 · 2015: 4 · 2014: 6 · 2013: 12 · 2012: 4
- FWCI
- 3.57
- 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
- China Scholarship Council
- 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
- resting-state fMRI, functional connectivity, global signal regression, BOLD time series
- MeSH terms
- Algorithms, Brain, Brain Mapping, Data Interpretation, Statistical, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neural Pathways, Oxygen, Signal Processing, Computer-Assisted, Reproducibility of Results, Models, Statistical, Principal Component Analysis
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