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The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity
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ABSTRACT Recent advances in neuroimaging methods and analysis have led to an expanding body of research that investigates how large‐scale brain network organization dynamically adapts to changes in one's environment, including both internal state changes and external stimulation. It is now possible to detect changes in functional connectivity that occur on the order of seconds, both during an unconstrained resting state and during the performance of constrained cognitive tasks. It is thought that these dynamic, time‐varying changes in functional connectivity, often referred to as dynamic functional connectivity (dFC), include features that are relevant to behavior and cognition. This review summarizes four aspects of the nascent literature directly testing that assumption: 1) how changes in functional network organization on the order of task blocks relate to differences in task demands and to cognitive ability; 2) how differences in dFC variability between different contexts relate to cognitive demands and behavioral performance; 3) how ongoing fluctuations in dFC impact perception and attention; and 4) how different patterns of dFC correspond to individual differences in cognition. The review ends by discussing promising directions for future research in this field. First, it comments on how dFC analyses can help to elucidate the mechanisms of healthy cognition. Next, it describes how dFC processes may be disrupted in disease, and how probing such dysfunction can increase understanding of neural etiology, as well as behavioral and cognitive impairments, observed in psychiatric and neurologic populations. Last, it considers the potential for computational models to uncover neuronal mechanisms of dFC, and how both healthy cognition and disease emerge from network dynamics. HighlightsA recent appreciation of dynamic functional connectivity (dFC) has emerged.This review probes recent examinations of the relationship between dFC and cognition.Promising directions include cognitive/clinical relevance and computational modeling.
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Cites
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284
Psychophysiological and Modulatory Interactions in Neuroimaging
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997) · NeuroImage · doi:10.1006/nimg.1997.0291 · Cited by 3,097
The functional role of cross-frequency coupling
Canolty, R. T., & Knight, R. T. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.09.001 · Cited by 2,101
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
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
Sonuga-Barke, E. J. S., & Castellanos, F. X. (2007) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2007.02.005 · Cited by 890
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.018 · Cited by 886
On spurious and real fluctuations of dynamic functional connectivity during rest
Leonardi, N., & Van De Ville, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.007 · Cited by 828
Dynamic reconfiguration of frontal brain networks during executive cognition in humans
Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., Schweiger, J. I., Grimm, O., Heinz, A., Tost, H., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422487112 · Cited by 791
Research works printed but not linked: 72
- Bargmann, 2013, From the connectome to brain function, Nat. Methods, 10, 483, 10.1038/nmeth.2451 · DOI · Paper page
- Bassett, 2015, Learning-induced autonomy of sensorimotor systems, Nat. Neurosci., 18, 744, 10.1038/nn.3993 · DOI · Paper page
- Breakspear, 2017, Dynamic models of large-scale brain activity, Nat. Neurosci., 20, 340, 10.1038/nn.4497 · DOI · Paper page
- Bullmore, 2011, Brain graphs: graphical models of the human brain connectome, Annu. Rev. Clin. Psychol., 7, 113, 10.1146/annurev-clinpsy-040510-143934 · DOI · Paper page
- Cabral, 2017, Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: models and mechanisms, Neuroimage, 160, 84, 10.1016/j.neuroimage.2017.03.045 · DOI · Paper page
- Calhoun, 2014, The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery, Neuron, 84, 262, 10.1016/j.neuron.2014.10.015 · DOI · Paper page
- Chang, 2013, EEG correlates of time-varying BOLD functional connectivity, Neuroimage, 72, 227, 10.1016/j.neuroimage.2013.01.049 · DOI · Paper page
- Chen, 2015, Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics, Neuroimage, 111, 476, 10.1016/j.neuroimage.2015.01.057 · DOI
- Choe, 2017, Comparing test-retest reliability of dynamic functional connectivity methods, Neuroimage, 158, 155, 10.1016/j.neuroimage.2017.07.005 · DOI
- Cohen, 2016, The segregation and integration of distinct brain networks and their relationship to cognition, J. Neurosci., 36, 12083, 10.1523/JNEUROSCI.2965-15.2016 · DOI · Paper page
- Cohen, 2014, Quantifying the reconfiguration of intrinsic networks during working memory, PLoS One, 9, 10.1371/journal.pone.0106636 · DOI
- Cole, 2014, Intrinsic and task-evoked network architectures of the human brain, Neuron, 83, 238, 10.1016/j.neuron.2014.05.014 · DOI · Paper page
- Cole, 2013, Multi-task connectivity reveals flexible hubs for adaptive task control, Nat. Neurosci., 16, 1348, 10.1038/nn.3470 · DOI · Paper page
- Cribben, 2012, Dynamic connectivity regression: determining state-related changes in brain connectivity, Neuroimage, 61, 907, 10.1016/j.neuroimage.2012.03.070 · DOI · Paper page
- Cribben, 2013, Detecting functional connectivity change points for single-subject fMRI data, Front. Comput. Neurosci., 7, 143, 10.3389/fncom.2013.00143 · DOI
- Damaraju, 2014, Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia, Neuroimage Clin., 5, 298, 10.1016/j.nicl.2014.07.003 · DOI · Paper page
- Davison, 2015, Brain network adaptability across task states, PLoS Comput. Biol., 11, e1004029, 10.1371/journal.pcbi.1004029 · DOI
- Deco, 2011, The dynamical balance of the brain at rest, Neuroscience, 17, 107, 10.1177/1073858409354384 · DOI · Paper page
- Deco, 2013, Resting brains never rest: computational insights into potential cognitive architectures, Trends Neurosci., 36, 268, 10.1016/j.tins.2013.03.001 · DOI · Paper page
- Deco, 2014, Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders, Neuron, 84, 892, 10.1016/j.neuron.2014.08.034 · DOI · Paper page
- D'Esposito, 2007, From cognitive to neural models of working memory, Philos. Trans. R. Soc. Lond. Series B Biol. Sci., 362, 761, 10.1098/rstb.2007.2086 · DOI · Paper page
- Eichele, 2008, Prediction of human errors by maladaptive changes in event-related brain networks, Proc. Natl. Acad. Sci. U. S. A., 105, 6173, 10.1073/pnas.0708965105 · DOI · Paper page
- Ekman, 2012, Predicting errors from reconfiguration patterns in human brain networks, Proc. Natl. Acad. Sci. U. S. A., 109, 16714, 10.1073/pnas.1207523109 · DOI
- Elton, 2015, Task-related modulation of functional connectivity variability and its behavioral correlations, Hum. Brain Mapp., 36, 3260, 10.1002/hbm.22847 · DOI
- Fatima, 2016, Dynamic functional connectivity shapes individual differences in associative learning, Hum. Brain Mapp., 37, 3911, 10.1002/hbm.23285 · DOI
- Finn, 2017, Can brain state be manipulated to emphasize individual differences in functional connectivity?, Neuroimage, 160, 40, 10.1016/j.neuroimage.2017.03.064 · DOI · Paper page
- Fornito, 2012, Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection, Proc. Natl. Acad. Sci. U. S. A., 109, 12788, 10.1073/pnas.1204185109 · DOI · Paper page
- Frank, 2015, How cognitive theory guides neuroscience, Cognition, 135, 14, 10.1016/j.cognition.2014.11.009 · DOI
- Ghosh, 2008, Noise during rest enables the exploration of the brain's dynamic repertoire, PLoS Comput. Biol., 4, 10.1371/journal.pcbi.1000196 · DOI · Paper page
- Gonzalez-Castillo, 2015, Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns, Proc. Natl. Acad. Sci. U. S. A., 112, 8762, 10.1073/pnas.1501242112 · DOI · Paper page
- Gratton, 2016, Evidence for two independent factors that modify brain networks to meet task goals, Cell Rep., 17, 1276, 10.1016/j.celrep.2016.10.002 · DOI
- Haimovici, 2017, On wakefulness fluctuations as a source of BOLD functional connectivity dynamics, Sci. Rep., 7, 5908, 10.1038/s41598-017-06389-4 · DOI
- Handwerker, 2012, Periodic changes in fMRI connectivity, Neuroimage, 63, 1712, 10.1016/j.neuroimage.2012.06.078 · DOI · Paper page
- Hindriks, 2016, Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?, Neuroimage, 127, 242, 10.1016/j.neuroimage.2015.11.055 · DOI · Paper page
- Hudetz, 2015, Dynamic repertoire of intrinsic brain states is reduced in propofol-induced unconsciousness, Brain Connect., 5, 10, 10.1089/brain.2014.0230 · DOI
- Hutchison, 2014, Isoflurane induces dose-dependent alterations in the cortical connectivity profiles and dynamic properties of the brain's functional architecture, Hum. Brain Mapp., 35, 5754, 10.1002/hbm.22583 · DOI
- Hutchison, 2015, Tracking the brain's functional coupling dynamics over development, J. Neurosci., 35, 6849, 10.1523/JNEUROSCI.4638-14.2015 · DOI
- Hutchison, 2013, Dynamic functional connectivity: promise, issues, and interpretations, Neuroimage, 80, 360, 10.1016/j.neuroimage.2013.05.079 · DOI · Paper page
- Keilholz, 2014, The neural basis of time-varying resting-state functional connectivity, Brain Connect., 4, 769, 10.1089/brain.2014.0250 · DOI
- Khambhati, 2018, Modeling and interpreting mesoscale network dynamics, Neuroimage, 180, 337, 10.1016/j.neuroimage.2017.06.029 · DOI
- Kitzbichler, 2011, Cognitive effort drives workspace configuration of human brain functional networks, J. Neurosci., 31, 8259, 10.1523/JNEUROSCI.0440-11.2011 · DOI · Paper page
- Ko, 2011, Quasi-periodic fluctuations in default mode network electrophysiology, J. Neurosci., 31, 11728, 10.1523/JNEUROSCI.5730-10.2011 · DOI
- Kopell, 2014, Beyond the connectome: the dynome, Neuron, 83, 1319, 10.1016/j.neuron.2014.08.016 · DOI · Paper page
- Kucyi, 2018, Just a thought: how mind-wandering is represented in dynamic brain connectivity, Neuroimage, 180, 505, 10.1016/j.neuroimage.2017.07.001 · DOI
- Kucyi, 2017, Dynamic brain network correlates of spontaneous fluctuations in attention, Cereb. Cortex, 27, 1831
- Laumann, 2017, On the stability of BOLD fMRI correlations, Cereb. Cortex, 27, 4719
- Leonardi, 2014, Disentangling dynamic networks: separated and joint expressions of functional connectivity patterns in time, Hum. Brain Mapp., 35, 5984, 10.1002/hbm.22599 · DOI
- Lindquist, 2014, Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach, Neuroimage, 101, 531, 10.1016/j.neuroimage.2014.06.052 · DOI · Paper page
- Liu, 2013, Time-varying functional network information extracted from brief instances of spontaneous brain activity, Proc. Natl. Acad. Sci. U. S. A., 110, 4392, 10.1073/pnas.1216856110 · DOI · Paper page
- Majeed, 2011, Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans, Neuroimage, 54, 1140, 10.1016/j.neuroimage.2010.08.030 · DOI · Paper page
- Monti, 2014, Estimating time-varying brain connectivity networks from functional MRI time series, Neuroimage, 103, 427, 10.1016/j.neuroimage.2014.07.033 · DOI
- Poldrack, 2015, Long-term neural and physiological phenotyping of a single human, Nat. Commun., 6, 8885, 10.1038/ncomms9885 · DOI · Paper page
- Preti, 2017, The dynamic functional connectome: state-of-the-art and perspectives, Neuroimage, 160, 41, 10.1016/j.neuroimage.2016.12.061 · DOI · Paper page
- Rack-Gomer, 2012, Caffeine increases the temporal variability of resting-state BOLD connectivity in the motor cortex, Neuroimage, 59, 2994, 10.1016/j.neuroimage.2011.10.001 · DOI
- Rashid, 2016, Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity, Neuroimage, 134, 645, 10.1016/j.neuroimage.2016.04.051 · DOI · Paper page
- Sadaghiani, 2013, Functional interactions between intrinsic brain activity and behavior, Neuroimage, 80, 379, 10.1016/j.neuroimage.2013.04.100 · DOI · Paper page
- Sadaghiani, 2015, Ongoing dynamics in large-scale functional connectivity predict perception, Proc. Natl. Acad. Sci. U. S. A., 112, 8463, 10.1073/pnas.1420687112 · DOI · Paper page
- Shine, 2016, Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention, Proc. Natl. Acad. Sci. U. S. A., 113, 9888, 10.1073/pnas.1604898113 · DOI
- Shirer, 2012, Decoding subject-driven cognitive states with whole-brain connectivity patterns, Cereb. Cortex, 22, 158, 10.1093/cercor/bhr099 · DOI · Paper page
- Siegel, 2017, Data quality influences observed links between functional connectivity and behavior, Cereb. Cortex, 27, 4492, 10.1093/cercor/bhw253 · DOI · Paper page
- Spadone, 2015, Dynamic reorganization of human resting-state networks during visuospatial attention, Proc. Natl. Acad. Sci. U. S. A., 112, 8112, 10.1073/pnas.1415439112 · DOI
- Tagliazucchi, 2015, Multimodal imaging of dynamic functional connectivity, Front. Neurol., 6, 10, 10.3389/fneur.2015.00010 · DOI
- Tagliazucchi, 2012, Dynamic BOLD functional connectivity in humans and its electrophysiological correlates, Front. Hum. Neurosci., 6, 339, 10.3389/fnhum.2012.00339 · DOI · Paper page
- Thompson, 2013, Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually, Hum. Brain Mapp., 34, 3280, 10.1002/hbm.22140 · DOI
- Thompson, 2013, Neural correlates of time-varying functional connectivity in the rat, Neuroimage, 83, 826, 10.1016/j.neuroimage.2013.07.036 · DOI
- Thompson, 2014, Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity, Neuroimage, 84, 1018, 10.1016/j.neuroimage.2013.09.029 · DOI · Paper page
- Uddin, 2015, Brain state differentiation and behavioral inflexibility in autism, Cereb. Cortex, 25, 4740, 10.1093/cercor/bhu161 · DOI
- Vaidya, 2013, Phenotypic variability in resting-state functional connectivity: current status, Brain Connect., 3, 99, 10.1089/brain.2012.0110 · DOI
- Wang, 2016, Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states, Proc. Natl. Acad. Sci. U. S. A., 113, 9653, 10.1073/pnas.1523980113 · DOI
- Xia, 2011, Magnetic resonance imaging and graph theoretical analysis of complex brain networks in neuropsychiatric disorders, Brain Connect., 1, 349, 10.1089/brain.2011.0062 · DOI
- Yang, 2014, Common intrinsic connectivity states among posteromedial cortex subdivisions: insights from analysis of temporal dynamics, Neuroimage, 93, 124, 10.1016/j.neuroimage.2014.02.014 · DOI
- Zalesky, 2015, Towards a statistical test for functional connectivity dynamics, Neuroimage, 114, 466, 10.1016/j.neuroimage.2015.03.047 · DOI · Paper page
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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
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Metadata
- Authors
- Cohen, J. R.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 180
- Issue
- Pt B
- Pages
- 515-525
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2759165456
- Cited by
- 298
- References
- 18
- FWCI
- 9.02
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- National Institute of Mental Health (R00MH102349)
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