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Quasi-periodic patterns (QPP): Large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity
Open accessAbstract
Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders.
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Cites
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Tracking whole-brain connectivity dynamics in the resting state
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs352 · Cited by 2,992
Steriade, M., Nunez, A., & Amzica, F. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-08-03252.1993 · Cited by 1,903
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
Emerging concepts for the dynamical organization of resting-state activity in the brain
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn2961 · Cited by 1,798
Intrinsic functional architecture in the anaesthetized monkey brain
Vincent, J. L., Patel, G. H., Fox, M. D., Snyder, A. Z., Baker, J. T., Van Essen, D. C., Zempel, J. M., Snyder, L. H., Corbetta, M., & Raichle, M. E. (2007) · Nature · doi:10.1038/nature05758 · Cited by 1,786
The neural bases of momentary lapses in attention
Weissman, D. H., Roberts, K. C., Visscher, K. M., & Woldorff, M. G. (2006) · Nature Neuroscience · doi:10.1038/nn1727 · Cited by 1,566
A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI
Allen, P. J., Josephs, O., & Turner, R. (2000) · NeuroImage · doi:10.1006/nimg.2000.0599 · Cited by 1,301
Competition between functional brain networks mediates behavioral variability
Kelly, A. M. C., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.08.008 · Cited by 1,287
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
Rombouts, S. A. R. B., Barkhof, F., Goekoop, R., Stam, C. J., & Scheltens, P. (2005) · Human Brain Mapping · doi:10.1002/hbm.20160 · Cited by 734
Shmuel, A., & Leopold, D. A. (2008) · Human Brain Mapping · doi:10.1002/hbm.20580 · Cited by 584
Baseline brain activity fluctuations predict somatosensory perception in humans
Boly, M., Balteau, E., Schnakers, C., Degueldre, C., Moonen, G., Luxen, A., Phillips, C., Peigneux, P., Maquet, P., & Laureys, S. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0611404104 · Cited by 529
The Resting Human Brain and Motor Learning
Albert, N. B., Robertson, E. M., & Miall, R. C. (2009) · Current Biology · doi:10.1016/j.cub.2009.04.028 · Cited by 510 · cited 2×
“, 2006) and is also sensitive to individual variations that are linked to behavioral performance (Albert et al., 2009; Boly et al., 2007; Hampson et al., 2006; Thompson et al., 2012; Waites et al., 2005; Weissman et al., 2006).”
BackgroundAlkire, M. T., Haier, R. J., & Fallon, J. H. (2000) · Consciousness and Cognition · doi:10.1006/ccog.1999.0423 · Cited by 391
Criteria for the Selection of Materials for Implanted Electrodes
Geddes, L. A., & Roeder, R. (2003) · Annals of Biomedical Engineering · doi:10.1114/1.1581292 · Cited by 240
Infraslow LFP correlates to resting-state fMRI BOLD signals
Pan, W.-J., Thompson, G. J., Magnuson, M. E., Jaeger, D., & Keilholz, S. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.035 · Cited by 231
Self-citationOn the relationship between slow cortical potentials and BOLD signal changes in humans
Khader, P., Schicke, T., Röder, B., & Rösler, F. (2008) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2007.05.018 · Cited by 99
Prado, J., Carp, J., & Weissman, D. H. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.022 · Cited by 79
Cited by
Questions and controversies in the study of time-varying functional connectivity in resting fMRI
Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., Kucyi, A., Liégeois, R., Lindquist, M. A., McIntosh, A. R., et al. (2020) · Network Neuroscience · doi:10.1162/netn_a_00116 · Cited by 648
Kiviniemi, V., Wang, X., Korhonen, V., Keinänen, T., Tuovinen, T., Autio, J., LeVan, P., Keilholz, S., Zang, Y.-F., Hennig, J., et al. (2016) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x15622047 · Cited by 453
Self-citationQuasi-periodic patterns contribute to functional connectivity in the brain
Abbas, A., Belloy, M., Kashyap, A., Billings, J., Nezafati, M., Schumacher, E. H., & Keilholz, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.076 · Cited by 81
Self-citationCognitive and Physiologic Impacts of the Infraslow Oscillation
Watson, B. O. (2018) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00044 · Cited by 74
Infraslow Electroencephalographic and Dynamic Resting State Network Activity
Grooms, J. K., Thompson, G. J., Pan, W.-J., Billings, J., Schumacher, E. H., Epstein, C. M., & Keilholz, S. D. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0492 · Cited by 74
Self-citation
Metadata
- Authors
- Thompson, G. J., Pan, W.-J., Magnuson, M. E., Jaeger, D., & Keilholz, S. D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 84
- Pages
- 1018-1031
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2075583786
- Cited by
- 158
- References
- 20
- FWCI
- 3.87
- Type
- journal article
- Language
- en
- Open access
- green
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