Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition.

Brain / diagnostic imaging Cognition Humans Magnetic Resonance Imaging Models, Neurological Nerve Net / diagnostic imaging

Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
08 01 2021
Historique:
received: 14 04 2020
accepted: 08 12 2020
entrez: 9 1 2021
pubmed: 10 1 2021
medline: 10 8 2021
Statut: epublish

Résumé

Creative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

Identifiants

DOI: 10.1038/s41598-020-80293-2 PMID: 33420212 PMC: PMC7794287
pubmed: 33420212
doi: 10.1038/s41598-020-80293-2
pii: 10.1038/s41598-020-80293-2
pmc: PMC7794287
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

165

Références

Telesford, Q. K., Simpson, S. L., Burdette, J. H., Hayasaka, S. & Laurienti, P. J. The brain as a complex system: Using network science as a tool for understanding the brain. Brain Connect. 1, 295–308 (2011).
pubmed: 22432419 pmcid: 3621511 doi: 10.1089/brain.2011.0055
Turnbull, L. et al. Connectivity and complex systems: Learning from a multi-disciplinary perspective. Appl. Netw. Sci. https://doi.org/10.1007/s41109-018-0067- (2018).
doi: 10.1007/s41109-018-0067- pubmed: 30839779 pmcid: 6214298
Lee, U. & Mashour, G. A. Role of network science in the study of anesthetic state transitions. Anesthesiology 129, 1029–1044 (2018).
pubmed: 29683806 doi: 10.1097/ALN.0000000000002228
Bassett, D. S. & Bullmore, E. Small-world brain networks. Neuroscientist 12, 512–523 (2006).
pubmed: 17079517 doi: 10.1177/1073858406293182
Hojjati, S. H., Ebrahimzadeh, A. & Babajani-Feremi, A. Identification of the early stage of Alzheimer’s disease using structural MRI and resting-state fMRI. Front. Neurol. 10, 904 (2019).
pubmed: 31543860 pmcid: 6730495 doi: 10.3389/fneur.2019.00904
Bi, X., Jiang, Q., Sun, Q., Shu, Q. & Liu, Y. Analysis of Alzheimer’s disease based on the random neural network cluster in fMRI. Front. Neuroinform. 12, 60 (2018).
pubmed: 30245623 pmcid: 6137384 doi: 10.3389/fninf.2018.00060
Li, Y., Qin, Y., Chen, X. & Li, W. Exploring the functional brain network of Alzheimer’s disease: Based on the computational experiment. PLoS ONE 8, 1–7 (2013).
Mascali, D. et al. Disruption of semantic network in mild Alzheimer’s disease revealed by resting-state fMRI. Neuroscience 371, 38–48 (2018).
pubmed: 29197559 doi: 10.1016/j.neuroscience.2017.11.030
de Vos, F. et al. A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer’s disease. Neuroimage 167, 62–72 (2018).
pubmed: 29155080 doi: 10.1016/j.neuroimage.2017.11.025
Khambhati, A. N. et al. Dynamic network drivers of seizure generation, propagation and termination in human neocortical epilepsy. PLoS Comput. Biol. 11, 1–19 (2015).
doi: 10.1371/journal.pcbi.1004608
Van Diessen, E., Diederen, S. J. H., Braun, K. P. J., Jansen, F. E. & Stam, C. J. Functional and structural brain networks in epilepsy: What have we learned?. Epilepsia 54, 1855–1865 (2013).
pubmed: 24032627 doi: 10.1111/epi.12350
Lynall, M. E. et al. Functional connectivity and brain networks in schizophrenia. J. Neurosci. 30, 9477–9487 (2010).
pubmed: 20631176 pmcid: 2914251 doi: 10.1523/JNEUROSCI.0333-10.2010
Braun, U. et al. Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function. Proc. Natl. Acad. Sci. U.S.A. 113, 12568–12573 (2016).
pubmed: 27791105 pmcid: 5098640 doi: 10.1073/pnas.1608819113
Calhoun, V. D., Eichele, T. & Pearlson, G. D. Functional brain networks in schizophrenia: A review. Front. Hum. Neurosci. 3, 17 (2009).
pubmed: 19738925 pmcid: 2737438 doi: 10.3389/neuro.09.017.2009
Bassett, D. S. et al. Dynamic reconfiguration of human brain networks during learning. Proc. Natl. Acad. Sci. U.S.A. 108, 7641–7646 (2011).
pubmed: 21502525 pmcid: 3088578 doi: 10.1073/pnas.1018985108
Medaglia, J. D., Lynall, M.-E. & Bassett, D. S. Cognitive network neuroscience. J. Cogn. Neurosci. 27, 1471–1491 (2015).
pubmed: 25803596 pmcid: 4854276 doi: 10.1162/jocn_a_00810
Braun, U. et al. Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc. Natl. Acad. Sci. U.S.A. 112, 11678–11683 (2015).
pubmed: 26324898 pmcid: 4577153 doi: 10.1073/pnas.1422487112
Telesford, Q. K. et al. Detection of functional brain network reconfiguration during task-driven cognitive states. Neuroimage 142, 198–210 (2016).
pubmed: 27261162 doi: 10.1016/j.neuroimage.2016.05.078
Genon, S., Reid, A., Langner, R., Amunts, K. & Eickhoff, S. B. How to characterize the function of a brain region. Trends Cogn. Sci. 22, 350–364 (2018).
pubmed: 29501326 doi: 10.1016/j.tics.2018.01.010 pmcid: 7978486
Yoon, Y. B. et al. Brain structural networks associated with intelligence and visuomotor ability. Sci. Rep. 7, 1–9 (2017).
doi: 10.1038/s41598-017-02304-z
Assem, M., Glasser, M. F., Van Essen, D. C. & Duncan, J. A domain-general cognitive core defined in multimodally parcellated human cortex. Cereb. Cortex 00, 1–20 (2020).
Shohamy, D. & Turk-Browne, N. B. Mechanisms for widespread hippocampal involvement in cognition. J. Exp. Psychol. Gen. 142, 1159–1170 (2013).
pubmed: 24246058 pmcid: 4065494 doi: 10.1037/a0034461
Evelina, F. & Thompson-Schill, S. L. Reworking the language network. Trends Cogn. Sci. 18, 120–126 (2014).
doi: 10.1016/j.tics.2013.12.006
Smith, S. M. et al. Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl. Acad. Sci. U.S.A. 106, 13040–13045 (2009).
pubmed: 19620724 pmcid: 2722273 doi: 10.1073/pnas.0905267106
Biswal, B. B. et al. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. U.S.A. 107, 4734–4739 (2010).
pubmed: 20176931 pmcid: 2842060 doi: 10.1073/pnas.0911855107
Chen, J., Rubinov, M. & Chang, C. Methods and considerations for dynamic analysis of fMRI data. Neuroimaging Clin. 27, 547–560 (2017).
doi: 10.1016/j.nic.2017.06.009
Hutchison, R. M. et al. Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage 80, 360 (2013).
pubmed: 23707587 doi: 10.1016/j.neuroimage.2013.05.079
Siebenhühner, F., Weiss, S. A., Coppola, R., Weinberger, D. R. & Bassett, D. S. Intra- and inter-frequency brain network structure in health and schizophrenia. PLoS ONE 8, e72351 (2013).
pubmed: 23991097 pmcid: 3753323 doi: 10.1371/journal.pone.0072351
Gonzalez-Castillo, J. et al. The spatial structure of resting state connectivity stability on the scale of minutes. Front. Neurosci. 8, 1–19 (2014).
doi: 10.3389/fnins.2014.00138
De Zwart, J. A. et al. Temporal dynamics of the BOLD fMRI impulse response. Neuroimage 24, 667–677 (2005).
pubmed: 15652302 doi: 10.1016/j.neuroimage.2004.09.013
Abraham, A. The promises and perils of the neuroscience of creativity. Front. Hum. Neurosci. 7, 1–9 (2013).
doi: 10.3389/fnhum.2013.00246
Beaty, R. E. et al. Robust prediction of individual creative ability from brain functional connectivity. Proc. Natl. Acad. Sci. 115, 1087–1092 (2018).
pubmed: 29339474 doi: 10.1073/pnas.1713532115 pmcid: 5798342
Benedek, M., Christensen, A. P., Fink, A. & Beaty, R. E. Creativity assessment in neuroscience research. Psychol. Aesthet. Creat. Arts 13, 218–226 (2019).
doi: 10.1037/aca0000215
Beaty, R. E., Benedek, M., Silvia, P. & Schacter, D. L. Creative cognition and brain network dynamics. Trends Cogn. Sci. 20, 87–95 (2016).
pubmed: 26553223 doi: 10.1016/j.tics.2015.10.004
Beaty, R. E., Seli, P. & Schacter, D. L. Network neuroscience of creative cognition: Mapping cognitive mechanisms and individual differences in the creative brain. Curr. Opin. Behav. Sci. 27, 22–30 (2019).
pubmed: 30906824 doi: 10.1016/j.cobeha.2018.08.013
Buckner, R. L. & DiNicola, L. M. The brain’s default network: Updated anatomy, physiology and evolving insights. Nat. Rev. Neurosci. 20, 593–608 (2019).
pubmed: 31492945 doi: 10.1038/s41583-019-0212-7
Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–2356 (2007).
pubmed: 17329432 pmcid: 2680293 doi: 10.1523/JNEUROSCI.5587-06.2007
Ellamil, M., Dobson, C., Beeman, M. & Christoff, K. Evaluative and generative modes of thought during the creative process. Neuroimage 59, 1783–1794 (2012).
pubmed: 21854855 doi: 10.1016/j.neuroimage.2011.08.008
Jung, R. E., Mead, B. S., Carrasco, J. & Flores, R. A. The structure of creative cognition in the human brain. Hum. Neurosci. 7, 1–13 (2013).
Mayseless, N., Eran, A. & Shamay-Tsoory, S. G. Generating original ideas: The neural underpinning of originality. Neuroimage 116, 232–239 (2015).
pubmed: 26003860 doi: 10.1016/j.neuroimage.2015.05.030
Mok, L. W. The interplay between spontaneous and controlled processing in creative cognition. Front. Hum. Neurosci. 8, 2010–2014 (2014).
doi: 10.3389/fnhum.2014.00663
Mednick, S. A. The associative basis of the creative process. Psychol. Rev. 69, 220–232 (1962).
pubmed: 14472013 doi: 10.1037/h0048850
Heilman, K. M. Possible brain mechanisms of creativity. Arch. Clin. Neuropsychol. 31, 285–296 (2016).
pubmed: 27001974 doi: 10.1093/arclin/acw009
Fink, A. & Benedek, M. EEG alpha power and creative ideation. Neurosci. Biobehav. Rev. 44, 111–123 (2013).
doi: 10.1016/j.neubiorev.2012.12.002
Gonen-Yaacovi, G. et al. Rostral and caudal prefrontal contribution to creativity: A meta-analysis of functional imaging data. Front. Hum. Neurosci. 7, 1–22 (2013).
doi: 10.3389/fnhum.2013.00465
Abraham, A., Thybusch, K., Pieritz, K. & Hermann, C. Gender differences in creative thinking: Behavioral and fMRI findings. Brain Imaging Behav. 8, 39–51 (2014).
pubmed: 23807175 doi: 10.1007/s11682-013-9241-4
Abraham, A. et al. Creativity and the brain: Uncovering the neural signature of conceptual expansion. Neuropsychologia 50, 1906–1917 (2012).
pubmed: 22564480 doi: 10.1016/j.neuropsychologia.2012.04.015
Fink, A. et al. The creative brain: Investigation of brain activity during creative problem solving by means of EEG and fMRI. Hum. Brain Mapp. 748, 734–748 (2009).
doi: 10.1002/hbm.20538
Brown, A. S. An empirical verification of Mednick’s associative theory of creativity. Bull. Psychon. Soc. 2, 429–430 (1973).
doi: 10.3758/BF03334439
Benedek, M., Könen, T. & Neubauer, A. C. Associative abilities underlying creativity. Psychol. Aesthet. Creat. Arts 6, 273–281 (2012).
doi: 10.1037/a0027059
Benedek, M. et al. To create or to recall? Neural mechanisms underlying the generation of creative new ideas. Neuroimage 88, 125–133 (2014).
pubmed: 24269573 doi: 10.1016/j.neuroimage.2013.11.021
Ingegerd, C., Wendt, P. E. & Risberg, J. On the neurobiology of creativity. Differences in frontal activity between high and low creative subjects. Neuropsychologia 38, 873–885 (2000).
doi: 10.1016/S0028-3932(99)00128-1
Jaušovec, N. & Jaušovec, K. EEG activity during the performance of complex mental problems. Int. J. Psychophysiol. 36, 73–88 (2000).
pubmed: 10700625 doi: 10.1016/S0167-8760(99)00113-0
Arden, R., Chavez, R. S., Grazioplene, R. & Jung, R. E. Neuroimaging creativity: A psychometric view. Behav. Brain Res. 214, 143–156 (2010).
pubmed: 20488210 doi: 10.1016/j.bbr.2010.05.015
Dietrich, A. & Kanso, R. A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychol. Bull. 136, 822–848 (2010).
pubmed: 20804237 doi: 10.1037/a0019749
Kounios, J. et al. The origins of insight in resting-state brain activity. Neuropsychologia 46, 281–291 (2008).
pubmed: 17765273 doi: 10.1016/j.neuropsychologia.2007.07.013
Jung-Beeman, M. et al. Neural activity when people solve verbal problems with insight. PLoS Biol. 2, 500–510 (2004).
doi: 10.1371/journal.pbio.0020097
Shen, W. et al. Tracking the neurodynamics of insight: A meta-analysis of neuroimaging studies. Biol. Psychol. 138, 189–198 (2018).
pubmed: 30165082 doi: 10.1016/j.biopsycho.2018.08.018
Muldoon, S. F. & Bassett, D. S. Why network neuroscience? Compelling evidence and current frontiers. Phys. Life Rev. 11, 455–457 (2014).
pubmed: 24954730 doi: 10.1016/j.plrev.2014.06.006
van der Meer, J. N., Breakspear, M., Chang, L. J., Sonkusare, S. & Cocchi, L. Movie viewing elicits rich and reliable brain state dynamics. Nat. Commun. 11, 5004 (2020).
pubmed: 33020473 pmcid: 7536385 doi: 10.1038/s41467-020-18717-w
Holme, P. & Saramäki, J. Temporal networks. Phys. Rep. 519, 97–125 (2012).
doi: 10.1016/j.physrep.2012.03.001
Petersen, S. E. & Sporns, O. Brain networks and cognitive architectures. Physiol. Behav. 88, 207–219 (2015).
Fortunato, S. & Hric, D. Community detection in networks: A user guide. Phys. Rep. 659, 1–44 (2016).
doi: 10.1016/j.physrep.2016.09.002
Sporns, O. & Betzel, R. F. Modular brain networks. Annu. Rev. Psychol. 67, 613–640 (2016).
pubmed: 26393868 doi: 10.1146/annurev-psych-122414-033634
He, Y. et al. Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS ONE 4, 23–25 (2009).
doi: 10.1371/journal.pone.0005226
Chen, Z. J., He, Y., Rosa-Neto, P., Germann, J. & Evans, A. C. Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb. Cortex 18, 2374–2381 (2008).
pubmed: 18267952 pmcid: 2733312 doi: 10.1093/cercor/bhn003
Meunier, D., Lambiotte, R., Fornito, A., Ersche, K. D. & Bullmore, E. T. Hierarchical modularity in human brain functional networks. Front. Neuroinform. 3, 1–12 (2009).
doi: 10.3389/neuro.11.037.2009
Caterina, G. et al. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98, 439–452 (2018).
doi: 10.1016/j.neuron.2018.03.035
Michael, C., Bassett, D. S., Jonathan, P., Todd, B. & Petersen, S. E. Intrinsic and task-evoked network architectures of the human brain. NeuronNeuron 83, 238–251 (2014).
doi: 10.1016/j.neuron.2014.05.014
Mattar, M. G., Cole, M. W., Thompson-Schill, S. L. & Bassett, D. S. A functional cartography of cognitive systems. PLoS Comput. Biol. 11, 1–26 (2015).
doi: 10.1371/journal.pcbi.1004533
Shine, J. M. & Poldrack, R. A. Principles of dynamic network reconfiguration across diverse brain states. Neuroimage 180, 396–405 (2018).
pubmed: 28782684 doi: 10.1016/j.neuroimage.2017.08.010
Cohen, J. R. & D’Esposito, M. The segregation and integration of distinct brain networks and their relationship to cognition. J. Neurosci. 36, 12083–12094 (2016).
pubmed: 27903719 pmcid: 5148214 doi: 10.1523/JNEUROSCI.2965-15.2016
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A. & Onnela, J. P. Community structure in time-dependent, multiscale, and multiplex networks. Science 328, 876–878 (2010).
pubmed: 20466926 doi: 10.1126/science.1184819
Huang, P. S., Chen, H. C. & Liu, C. H. The development of Chinese word remote associates test for college students. Psychol. Test. 59, 581–607 (2012).
Shen, W. et al. Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study. NeuroImage Clin. 22, 101775 (2019).
pubmed: 30927604 pmcid: 6444301 doi: 10.1016/j.nicl.2019.101775
Porter, M. A., Onnela, J.-P. & Mucha, P. J. Communities in networks. Not. AMS 56, 1082–1097 (2009).
Good, B. H., De Montjoye, Y. A. & Clauset, A. Performance of modularity maximization in practical contexts. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 81, 1–20 (2010).
doi: 10.1103/PhysRevE.81.046106
Mumford, J. A. et al. Detecting network modules in fMRI time series: A weighted network analysis approach. Neuroimage 52, 1465–1476 (2010).
pubmed: 20553896 doi: 10.1016/j.neuroimage.2010.05.047
Clauset, A., Newman, M. E. J. & Moore, C. Finding community structure in very large networks. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscipl. Top. 70, 6 (2004).
Ferreira, L. N. & Zhao, L. Time series clustering via community detection in networks. Inf. Sci. 326, 227–242 (2016).
doi: 10.1016/j.ins.2015.07.046
Gilson, M. et al. Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability. Neuroimage 201, 25–27 (2019).
doi: 10.1016/j.neuroimage.2019.116007
Sanchez-Romero, R. et al. Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods. Netw. Neurosci. 2, 274–306 (2019).
doi: 10.1162/netn_a_00061
Zhang, L., Ye, Q., Shao, Y., Li, C. & Gao, H. An efficient hierarchy algorithm for community detection in complex networks. Math. Probl. Eng. 2014, 874217 (2014).
doi: 10.1155/2014/874217
Yang, Z., Algesheimer, R. & Tessone, C. J. A comparative analysis of community detection algorithms on artificial networks. Sci. Rep. 6, 30750 (2016).
pubmed: 27476470 pmcid: 4967864 doi: 10.1038/srep30750
Newman, M. E. J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. U.S.A. 103, 8577–8582 (2006).
pubmed: 16723398 pmcid: 1482622 doi: 10.1073/pnas.0601602103
Rosvall, M., Delvenne, J.-C., Schaub, M. T. & Lambiotte, R. Different approaches to community detection. (2017).
Garcia, J. O., Ashourvan, A., Muldoon, S. F., Vettel, J. M. & Danielle, S. Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function. Proc. IEEE Inst. Electr. Electron. Eng. 106, 846–867 (2018).
pubmed: 30559531 pmcid: 6294140 doi: 10.1109/JPROC.2017.2786710
Bassett, D. S. et al. Robust detection of dynamic community structure in networks. Chaos 23, 1–16 (2013).
doi: 10.1063/1.4790830
Van Dijk, K. R. A., Sabuncu, M. R. & Buckner, R. L. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59, 431–438 (2012).
pubmed: 21810475 doi: 10.1016/j.neuroimage.2011.07.044
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).
pubmed: 22019881 doi: 10.1016/j.neuroimage.2011.10.018
Shi, L. et al. Large-scale brain network connectivity underlying creativity in resting-state and task fMRI: Cooperation between default network and frontal-parietal network. Biol. Psychol. 135, 102–111 (2018).
pubmed: 29548807 doi: 10.1016/j.biopsycho.2018.03.005
Beaty, R. E., Benedek, M., Barry Kaufman, S. & Silvia, P. J. Default and executive network coupling supports creative idea production. Sci. Rep. 5, 1–14 (2015).
doi: 10.1038/srep10964
Liu, S. et al. Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process. Hum. Brain Mapp. 36, 3351–3372 (2015).
pubmed: 26015271 pmcid: 4581594 doi: 10.1002/hbm.22849
Beaty, R. E. et al. Creativity and the default network: A functional connectivity analysis of the creative brain at rest. Neuropsychologia 64, 92–98 (2014).
pubmed: 25245940 pmcid: 4410786 doi: 10.1016/j.neuropsychologia.2014.09.019
Wertz, C. J., Chohan, M. O., Flores, R. A. & Jung, R. E. Neuroanatomy of creative achievement. Neuroimage 209, 116487 (2020).
pubmed: 31874258 doi: 10.1016/j.neuroimage.2019.116487
Buckner, R. L. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron 80, 807–815 (2013).
pubmed: 24183029 doi: 10.1016/j.neuron.2013.10.044
Ito, M. Movement and thought: Identical control mechanisms by the cerebellum. Trends Neurosci. 16, 448–450 (1993).
pubmed: 7507615 doi: 10.1016/0166-2236(93)90073-U
Vandervert, L. R. et al. How working memory and the cerebellum collaborate to produce creativity and innovation. Creat. Res. J. 19, 1–18 (2007).
doi: 10.1080/10400410709336873
Schmahmann, J. D. The cerebellum and cognition. Neurosci. Lett. 688, 62–75 (2019).
pubmed: 29997061 doi: 10.1016/j.neulet.2018.07.005
Schmahmann, J. D., Guell, X., Stoodley, C. J. & Halko, M. A. The theory and neuroscience of cerebellar cognition. Annu. Rev. Neurosci. 42, 337–364 (2019).
pubmed: 30939101 doi: 10.1146/annurev-neuro-070918-050258
Ito, M. Control of mental activities by internal models in the cerebellum. Nat. Rev. Neurosci. 9, 304–313 (2008).
pubmed: 18319727 doi: 10.1038/nrn2332
Neumann, N., Domin, M., Erhard, K. & Lotze, M. Voxel-based morphometry in creative writers: Grey matter increase in a prefronto-thalamic-cerebellar network. Eur. J. Neurosci. 48, 1647–1653 (2018).
doi: 10.1111/ejn.13952
Ogawa, T., Aihara, T., Shimokawa, T. & Yamashita, O. Large-scale brain network associated with creative insight: Combined voxel-based morphometry and resting-state functional connectivity analyses. Sci. Rep. 8, 1–11 (2018).
doi: 10.1038/s41598-018-24981-0
Sun, J. et al. Verbal creativity correlates with the temporal variability of brain networks during the resting state. Cereb. Cortex 29, 1047–1058 (2019).
pubmed: 29415253 doi: 10.1093/cercor/bhy010
Sunavsky, A. & Poppenk, J. Neuroimaging predictors of creativity in healthy adults. Neuroimage 206, 116292. https://doi.org/10.1016/j.neuroimage.2019.116292 (2019).
doi: 10.1016/j.neuroimage.2019.116292 pubmed: 31654758
Kenett, Y. N., Betzel, R. F. & Beaty, R. E. Community structure of the creative brain at rest. Neuroimage 210, 116578 (2020).
pubmed: 31982579 doi: 10.1016/j.neuroimage.2020.116578
Cole, M. W. et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16, 1348–1355 (2013).
pubmed: 23892552 pmcid: 3758404 doi: 10.1038/nn.3470
Fornito, A., Harrison, B. J., Zalesky, A. & Simons, J. S. Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proc. Natl. Acad. Sci. U.S.A. 109, 12788–12793 (2012).
pubmed: 22807481 pmcid: 3412011 doi: 10.1073/pnas.1204185109
Beaty, R. E. et al. Brain networks of the imaginative mind: Dynamic functional connectivity of default and cognitive control networks relates to openness to experience. Hum. Brain Mapp. 39, 811–821 (2018).
pubmed: 29136310 doi: 10.1002/hbm.23884
Folstein, M. F., Folstein, S. E. & McHugh, P. R. ‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975).
pubmed: 1202204 doi: 10.1016/0022-3956(75)90026-6
Wechsler, D. Wechsler Adult Intelligence Scale 3rd edn. (Psychological Corporation, San Antonio, 1997).
Carson, S., Peterson, J. B. & Higgins, D. M. Reliability, validity, and factor structure of the creative achievement questionnaire. Creat. Res. J. 17, 37–50 (2005).
doi: 10.1207/s15326934crj1701_4
Wechsler, D. Wechsler Memory Scale 3rd edn. (Psychological Corporation, San Antonio, 1997).
Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125–141 (2012).
pubmed: 22642651 doi: 10.1089/brain.2012.0073
Collignon, A. et al. Automated multi-modality image registration based on information theory. (1995).
Studholme, C., Hawkes, D. J. & Hill, D. L. G. Normalized entropy measure for multimodality image alignment. In Medical Imaging 1998: Image Processing (ed. Hanson, K. M.) Vol. 3338, 132–143 (SPIE, 1998).
Ashburner, J. & Friston, K. Unified segmentation. Neuroimage 26, 839–851 (2005).
pubmed: 15955494 doi: 10.1016/j.neuroimage.2005.02.018
Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90–101 (2007).
pubmed: 17560126 doi: 10.1016/j.neuroimage.2007.04.042
Chai, X. J., Castañón, A. N. & Dost Öngür, S.W.-G. Anticorrelations in resting state networks without global signal regression. Neuroimage 59, 1420–1428 (2012).
pubmed: 21889994 doi: 10.1016/j.neuroimage.2011.08.048
Friston, K. J., Williams, S., Howard, R. & Frackowiak, R. S. J. Movement-related effects in fMRI time-series. Magn. Reson. Med. 35, 346–355 (1996).
pubmed: 8699946 doi: 10.1002/mrm.1910350312
Power, J. D. et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320 (2014).
pubmed: 23994314 doi: 10.1016/j.neuroimage.2013.08.048
Wolak, T. et al. Altered functional connectivity in patients with sloping sensorineural hearing loss. Front. Hum. Neurosci. 13, 284 (2019).
pubmed: 31507391 pmcid: 6713935 doi: 10.3389/fnhum.2019.00284
Denkova, E., Nomi, J. S., Uddin, L. Q. & Jha, A. P. Dynamic brain network configurations during rest and an attention task with frequent occurrence of mind wandering. Hum. Brain Mapp. 40, 4564–4576 (2019).
pubmed: 31379120 pmcid: 6865814 doi: 10.1002/hbm.24721
Achard, S., Salvador, R., Whitcher, B., Suckling, J. & Bullmore, E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26, 63–72 (2006).
pubmed: 16399673 pmcid: 6674299 doi: 10.1523/JNEUROSCI.3874-05.2006
He, Y., Chen, Z. J. & Evans, A. C. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17, 2407–2419 (2007).
pubmed: 17204824 doi: 10.1093/cercor/bhl149
Allen, E. A. et al. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24, 663–676 (2014).
pubmed: 23146964 doi: 10.1093/cercor/bhs352
Ciric, R., Nomi, J. S., Uddin, L. Q. & Satpute, A. B. Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks. Sci. Rep. 7, 6537 (2017).
pubmed: 28747717 pmcid: 5529582 doi: 10.1038/s41598-017-06866-w
Hutchison, R. M. & Morton, J. B. Tracking the brain’s functional coupling dynamics over development. J. Neurosci. 35, 6849–6859 (2015).
pubmed: 25926460 pmcid: 6605187 doi: 10.1523/JNEUROSCI.4638-14.2015
Nomi, J. S. et al. Chronnectomic patterns and neural flexibility underlie executive function. Neuroimage 147, 861–871 (2017).
pubmed: 27777174 doi: 10.1016/j.neuroimage.2016.10.026
Nomi, J. S. et al. Dynamic functional network connectivity reveals unique and overlapping profiles of insula subdivisions. Hum. Brain Mapp. 37, 1770–1787 (2016).
pubmed: 26880689 pmcid: 4837017 doi: 10.1002/hbm.23135
Steimke, R. et al. Salience network dynamics underlying successful resistance of temptation. Soc. Cogn. Affect. Neurosci. 12, 1928–1939 (2017).
pubmed: 29048582 pmcid: 5716209 doi: 10.1093/scan/nsx123
Yang, Z., Craddock, R. C., Margulies, D. S., Yan, C.-G. & Milham, M. P. Common intrinsic connectivity states among posteromedial cortex subdivisions: Insights from analysis of temporal dynamics. Neuroimage 93, 124–137 (2014).
pubmed: 24560717 doi: 10.1016/j.neuroimage.2014.02.014
Hutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S. & Menon, R. S. Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum. Brain Mapp. 34, 2154–2177 (2013).
pubmed: 22438275 doi: 10.1002/hbm.22058
Rahman, N. A. A Course in Theoretical Statistics (Hafner Pub. Co., New York, 1968).
Ramos-Nuñez, A. I. et al. Static and dynamic measures of human brain connectivity predict complementary aspects of human cognitive performance. Front. Hum. Neurosci. 11, 1–13 (2017).
doi: 10.3389/fnhum.2017.00420
Reddy, P. G. et al. Brain state flexibility accompanies motor-skill acquisition. Neuroimage 171, 135–147 (2018).
pubmed: 29309897 doi: 10.1016/j.neuroimage.2017.12.093
Gerraty, R. T. et al. Dynamic flexibility in striatal-cortical circuits supports reinforcement learning. J. Neurosci. 38, 2442–2453 (2018).
pubmed: 29431652 pmcid: 5858591 doi: 10.1523/JNEUROSCI.2084-17.2018

Auteurs

Abhishek Uday Patil (AU)

Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.
Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan.

Sejal Ghate (S)

Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Deepa Madathil (D)

Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Ovid J L Tzeng (OJL)

Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.
Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan.
Cognitive Neuroscience Laboratory, Institute of Linguistics, Academia Sinica, Taipei, Taiwan.
College of Humanities and Social Sciences, Taipei Medical University, Taipei, Taiwan.
Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei, Taiwan.
Hong Kong Institute for Advanced Study, City University of Hong Kong, Kowloon, Hong Kong.

Hsu-Wen Huang (HW)

Department of Linguistics and Translation, City University of Hong Kong, Kowloon, Hong Kong.

Chih-Mao Huang (CM)

Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. cmhuang@nctu.edu.tw.
Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan. cmhuang@nctu.edu.tw.
Cognitive Neuroscience Laboratory, Institute of Linguistics, Academia Sinica, Taipei, Taiwan. cmhuang@nctu.edu.tw.

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Classifications MeSH

questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Static and dynamic functional connectivity...
questionsmedicales.fr › Phénomènes psychologiques › Processus mentaux › Cognition › Static and dynamic functional connectivity...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Static and dynamic functional connectivity...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Imagerie par résonance magnétique › Static and dynamic functional connectivity...
questionsmedicales.fr › Techniques d'investigation › Modèles théoriques › Modèles biologiques › Modèles neurologiques › Static and dynamic functional connectivity...
questionsmedicales.fr › Système nerveux › Réseau nerveux › Static and dynamic functional connectivity...