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Spectral characteristics of resting state networks
Abstract
Resting state networks (RSNs), as imaged by functional MRI, are distributed maps of areas believed to be involved in the function of the "resting" brain, which appear in both resting and task data. The current dominant view is that such networks are associated with slow (∼0.015Hz), spontaneous fluctuations in the BOLD signal. To date, limited work has investigated the frequency characteristics of RSNs; here we investigate a range of issues relating to their spectral and phase characteristics. Our results indicate that RSNs, although dominated by low frequencies in the raw BOLD signal, are in fact broadband processes that show temporal coherences across a wide frequency spectrum. In addition, we show that RSNs exhibit different levels of phase synchrony at different frequencies. These findings challenge the notion that FMRI resting signals are simple "low frequency" spontaneous signal fluctuations.
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“there is increasing evidence that there is useful signal at relatively high frequencies [47].”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [47]
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“Recent evidence [47] indicates that resting-state network signals occur up to at least 0.2Hz in good quality data;”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [47]
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Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,707
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
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
Self-citationFunctional 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
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,324
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
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
Self-citationConsistent 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
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Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962
Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., Eickhoff, S. B., Hakonarson, H. H., Gur, R. C., Gur, R. E., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.052 · Cited by 1,982
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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
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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
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Smith, D. V., Utevsky, A. V., Bland, A. R., Clement, N. J., Clithero, J. A., Harsch, A. E. W., Carter, R. M., & Huettel, S. A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.042 · Cited by 116
Liégeois, R., Santos, A., Matta, V., Ville, D. V. D., & Sayed, A. H. (2020) · Network Neuroscience · doi:10.1162/netn_a_00166 · Cited by 100
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Gili, T., Saxena, N. K., Diukova, A., Murphy, K., Hall, J. E., & Wise, R. G. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3480-12.2013 · Cited by 89
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Matthews, P. M., & Hampshire, A. (2016) · Neuron · doi:10.1016/j.neuron.2016.07.031 · Cited by 83
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Omata, K., Hanakawa, T., Morimoto, M., & Honda, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066869 · Cited by 74
Yuen, N. H., Osachoff, N., & Chen, J. J. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00900 · Cited by 65
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Gordon, E. M., Devaney, J. M., Bean, S. E., & Vaidya, C. J. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht229 · Cited by 63
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Huijbers, W., Schultz, A. P., Vannini, P., McLaren, D. G., Wigman, S. E., Ward, A. M., Hedden, T., & Sperling, R. A. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00366 · Cited by 62
Wang, Z., Zhang, Z., Liao, W., Xu, Q., Zhang, J., Lu, W., Jiao, Q., Chen, G., Feng, J., & Lu, G. (2014) · Epilepsy Research · doi:10.1016/j.eplepsyres.2014.03.003 · Cited by 59
Vergara, V. M., Mayer, A. R., Damaraju, E., Hutchison, K. E., & Calhoun, V. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.038 · Cited by 59
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Van Someren, E. J. W., Van Der Werf, Y. D., Roelfsema, P. R., Mansvelder, H. D., & Lopes da Silva, F. H. (2011) · Progress in Brain Research · doi:10.1016/b978-0-444-53839-0.00001-6 · Cited by 58
Adamo, N., Huo, L., Adelsberg, S., Petkova, E., Castellanos, F. X., & Di Martino, A. (2014) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-013-0428-4 · Cited by 55
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Li, J. M., Bentley, W. J., & Snyder, L. H. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419837112 · Cited by 53
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Balsters, J. H., O’Connell, R. G., Galli, A., Nolan, H., Greco, E., Kilcullen, S. M., Bokde, A. L. W., Lai, R. Y. K., Upton, N., & Robertson, I. H. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.03.004 · Cited by 48
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Liu, X., Ward, B. D., Binder, J. R., Li, S., & Hudetz, A. G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092182 · Cited by 45
Simon, A. B., & Buxton, R. B. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.080 · Cited by 49
Chou, K., Yang, F., Fuh, J., Kuo, C.-Y., Wang, Y.-H., Lirng, J.-F., Lin, Y., Wang, S., & Lin, C. (2016) · Cephalalgia · doi:10.1177/0333102416668657 · Cited by 44
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Qian, L., Zhang, Y., Zheng, L., Shang, Y., Gao, J., & Liu, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124681 · Cited by 41
Cordes, D., Zhuang, X., Kaleem, M. F., Sreenivasan, K. R., Yang, Z., Mishra, V. R., Banks, S. J., Bluett, B., & Cummings, J. L. (2018) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1016/j.trci.2018.04.009 · Cited by 41
Faghiri, A., Iraji, A., Damaraju, E., Turner, J. A., & Calhoun, V. D. (2020) · Network Neuroscience · doi:10.1162/netn_a_00155 · Cited by 39
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Boyacıoğlu, R., Beckmann, C. F., & Barth, M. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00156 · Cited by 39
Self-citationJahanian, H., Holdsworth, S. J., Christen, T., Wu, H., Zhu, K., Kerr, A. B., Middione, M. J., Dougherty, R. F., Moseley, M. E., & Zaharchuk, G. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.09.033 · Cited by 39
Chen, J. E., Jahanian, H., & Glover, G. H. (2016) · Brain Connectivity · doi:10.1089/brain.2016.0441 · Cited by 35
Sala‐Llonch, R., Smith, S. M., Woolrich, M. W., & Duff, E. P. (2018) · Human Brain Mapping · doi:10.1002/hbm.24381 · Cited by 37
Self-citationVarikuti, D. P., Hoffstaedter, F., Genon, S., Schwender, H. R., Reid, A., & Eickhoff, S. B. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1286-x · Cited by 36
Chiang, S., Vankov, E. R., Yeh, H. J., Guindani, M., Vannucci, M., Haneef, Z., & Stern, J. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190220 · Cited by 38
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Bednarz, H. M., & Kana, R. K. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.03.025 · Cited by 36
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Li, Y., Yao, H., Lin, P., Zheng, L., Li, C., Zhou, B., Wang, P., Zhang, Z., Wang, L., An, N., et al. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00259 · Cited by 33
Wang, S., Li, Y., Qiu, S., Zhang, C., Wang, G., Xian, J., Li, T., & He, H. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102188 · Cited by 34
Mascali, D., Moraschi, M., DiNuzzo, M., Tommasin, S., Fratini, M., Gili, T., Wise, R. G., Mangia, S., Macaluso, E., & Giove, F. (2021) · Human Brain Mapping · doi:10.1002/hbm.25332 · Cited by 33
Wong, C. W., Olafsson, V. T., Plank, M., Snider, J., Halgren, E., Howard Poizner, A., & Liu, T. T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109622 · Cited by 33
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von Rhein, D. T., Oldehinkel, M., Beckmann, C. F., Oosterlaan, J., Heslenfeld, D. J., Hartman, C. A., Hoekstra, P. J., Franke, B., Cools, R., Buitelaar, J. K., et al. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12529 · Cited by 32
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Spisák, T., András, J., Kis, S. A., Opposits, G., Aranyi, C., Berényi, E. L., & Emri, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104947 · Cited by 26
Metadata
- Authors
- Niazy, R. K., Xie, J., Miller, K., Beckmann, C. F., & Smith, S. M.
- Affiliations
- King Faisal Specialist Hospital & Research Centre (SA)
- University of Oxford (GB)
- Corresponding author
- Smith, Stephen M.
- Venue
- Progress in brain research
- Publisher
- Elsevier
- Published
- 2011
- Volume
- 193
- Pages
- 259-276
- ISSN
- 0079-6123 (ISSN-L)
- OpenAlex
- W152769478
- Cited by
- 175
- References
- 33
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 3 · 2023: 4 · 2022: 9 · 2021: 12 · 2020: 12 · 2019: 6 · 2018: 11 · 2017: 11 · 2016: 20 · 2015: 19 · 2014: 25 · 2013: 27 · 2012: 6
- FWCI
- 40.45
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Progress in brain research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- resting-state networks, functional MRI, BOLD signal, spectral properties, phase synchronization, temporal coherence, spontaneous fluctuations, broadband oscillation, frequency spectrum
- MeSH terms
- Adult, Algorithms, Brain, Brain Mapping, Humans, Magnetic Resonance Imaging, Models, Neurological, Nerve Net, Young Adult
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