Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder
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Abstract
Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the whole brain transient connectivity patterns. In our study, we investigated how age and disease influence the dynamic changes in functional connectivity of TD and ASD. We used resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7-11 years), adolescents (12-17 years), and adults (18+ years) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures such as flexiblity, cohesion strength, and disjointness were explored for each subject to characterize the differences between ASD and TD. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We also found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor, and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.
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The only study to stratify participants into child, adolescent, and adult groups found evidence for greater hyper-variability of short-range functional connections that distinguished adolescents with ASD.
“The only study to stratify participants into child, adolescent, and adult groups found evidence for greater hyper-variability of short-range functional connections that distinguished adolescents with ASD (121).”
From Brain Mechanisms Supporting Flexible Cognition and Behavior in Adolescents With Autism Spectrum Disorder (2021) · cites this paper as [121]
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Cites
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works
DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI
Yan (2010) · Frontiers in System Neuroscience · doi:10.3389/fnsys.2010.00013 · Cited by 3,475 · see citing works
Exploring the brain network: A review on resting-state fMRI functional connectivity
van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2010.03.008 · Cited by 3,326 · see citing works
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 · see citing works
Network-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750 · 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
Time–frequency dynamics of resting-state brain connectivity measured with fMRI
Chang, C., & Glover, G. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.011 · Cited by 1,808 · see citing works
Dynamic reconfiguration of human brain networks during learning
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018985108 · Cited by 1,705 · see citing works
Multi-task connectivity reveals flexible hubs for adaptive task control
Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013) · Nature Neuroscience · doi:10.1038/nn.3470 · Cited by 1,704 · see citing works
The dynamic functional connectome: State-of-the-art and perspectives
Preti, M. G., Bolton, T. A. W., & Ville, D. V. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.061 · Cited by 1,425 · see citing works
Resting-State fMRI: A Review of Methods and Clinical Applications
Lee, M. H., Smyser, C. D., & Shimony, J. S. (2012) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3263 · Cited by 1,127 · see citing works
Damaraju, E., Allen, E. A., Belger, A., Ford, J. M., McEwen, S. C., Mathalon, D. H., Mueller, B. A., Pearlson, G. D., Potkin, S. G., Preda, A., et al. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.07.003 · Cited by 1,066 · see citing works
Structure and function of complex brain networks
Sporns, O. (2013) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2013.15.3/osporns · Cited by 931 · 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
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 · 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
Liu, X., & Duyn, J. H. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216856110 · Cited by 647 · see citing works
Deco, G., Ponce-Alvarez, A., Mantini, D., Romani, G. L., Hagmann, P., & Corbetta, M. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1091-13.2013 · Cited by 576 · see citing works
Craddock, R. C., Benhajali, Y., Chu, C., Chouinard-Decorte, F., Alan, E., András, J., Khundrakpam, B. S., John, L., Li, Q., Milham, M. P., et al. (2013) · Frontiers in Neuroinformatics · doi:10.3389/conf.fninf.2013.09.00041 · Cited by 485 · see citing works
The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder
Hahamy, A., Behrmann, M., & Malach, R. (2015) · Nature Neuroscience · doi:10.1038/nn.3919 · Cited by 416 · see citing works
Altered functional and structural brain network organization in autism
Rudie, J. D., Brown, J. A., Beck-Pancer, D., Hernandez, L. M., Dennis, E. L., Thompson, P. M., Bookheimer, S. Y., & Dapretto, M. (2013) · NeuroImage: Clinical · doi:10.1016/j.nicl.2012.11.006 · Cited by 411 · see citing works
DynamicBC : A MATLAB Toolbox for Dynamic Brain Connectome Analysis
Liao, W., Wu, G., Xu, Q., Ji, G., Zhang, Z., Zang, Y., & Lu, G. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0253 · Cited by 304 · see citing works
Song, J., Birn, R. M., Boly, M., Meier, T. B., Nair, V. A., Meyerand, M. E., & Prabhakaran, V. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0286 · Cited by 258 · see citing works
Betzel, R. F., Fukushima, M., He, Y., Zuo, X.-N., & Sporns, O. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.001 · Cited by 249 · see citing works
Rudie, J. D., Shehzad, Z. E., Hernandez, L. M., Colich, N. L., Bookheimer, S. Y., Iacoboni, M., & Dapretto, M. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr171 · Cited by 193 · see citing works
Mikuni, N., Nagamine, T., Ikeda, A., Terada, K., Taki, W., Kimura, J., Kikuchi, H., & Shibasaki, H. (1997) · NeuroImage · doi:10.1006/nimg.1997.0272 · Cited by 190 · see citing works
Braun, U., Schäfer, A., Bassett, D. S., Rausch, F., Schweiger, J. I., Bilek, E., Erk, S., Romanczuk‐Seiferth, N., Grimm, O., Geiger, L. S., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608819113 · Cited by 183 · see citing works
A Functional Cartography of Cognitive Systems
Mattar, M. G., Cole, M. W., Thompson‐Schill, S. L., & Bassett, D. S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004533 · Cited by 172 · see citing works
Brain network dynamics in high-functioning individuals with autism
Watanabe, T., & Rees, G. (2017) · Nature Communications · doi:10.1038/ncomms16048 · Cited by 165 · see citing works
Douw, L., Wakeman, D. G., Tanaka, N., Liu, H., & Stufflebeam, S. M. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.09.034 · Cited by 162 · see citing works
Yerys, B. E., Gordon, E. M., Abrams, D. N., Satterthwaite, T. D., Weinblatt, R., Jankowski, K. F., Strang, J. F., Kenworthy, L. E., Gaillard, W. D., & Vaidya, C. J. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.07.018 · Cited by 158 · see citing works
Positive affect, surprise, and fatigue are correlates of network flexibility
Betzel, R. F., Satterthwaite, T. D., Gold, J. I., & Bassett, D. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-00425-z · Cited by 158 · see citing works
Ye, M., Yang, T., Qing, P., Lei, X., Qiu, J., & Liu, G. (2015) · PLoS ONE · doi:10.1371/journal.pone.0133775 · Cited by 152 · see citing works
Intrinsic functional connectivity variance and state‐specific under‐connectivity in autism
Chen, H., Nomi, J. S., Uddin, L. Q., Duan, X., & Chen, H. (2017) · Human Brain Mapping · doi:10.1002/hbm.23764 · Cited by 137 · see citing works
Garcia, J. O., Ashourvan, A., Muldoon, S. F., Vettel, J. M., & Bassett, D. S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2786710 · Cited by 128 · see citing works
Rashid, B., Blanken, L. M. E., Muetzel, R. L., Miller, R., Damaraju, E., Arbabshirani, M. R., Erhardt, E. B., Verhulst, F. C., van der Lugt, A., Jaddoe, V. W. V., et al. (2018) · Human Brain Mapping · doi:10.1002/hbm.24064 · Cited by 120 · see citing works
de Lacy, N., Doherty, D., King, B., Rachakonda, S., & Calhoun, V. D. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.05.024 · Cited by 109 · see citing works
Falahpour, M., Thompson, W. K., Abbott, A. E., Jahedi, A., Mulvey, M. E., Datko, M., Liu, T. T., & Müller, R.-A. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0389 · Cited by 106 · see citing works
Dynamic Flexibility in Striatal-Cortical Circuits Supports Reinforcement Learning
Gerraty, R. T., Davidow, J. Y., Foerde, K., Galván, A., Bassett, D. S., & Daphna Shohamy, A. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2084-17.2018 · Cited by 102 · see citing works
Lin, P., Yang, Y., Jovicich, J., Pisapia, N. D., Wang, X., Zuo, C. S., & Levitt, J. J. (2015) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9384-6 · Cited by 91 · see citing works
Xu, Y., & Lindquist, M. A. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00285 · Cited by 83 · see citing works
Cohesive network reconfiguration accompanies extended training
Telesford, Q. K., Ashourvan, A., Wymbs, N. F., Grafton, S. T., Vettel, J. M., & Bassett, D. S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23699 · Cited by 65 · see citing works
Chen, G., Zhang, H., Xie, C., Chen, G., Zhang, Z., Teng, G., & Li, S. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00456 · Cited by 63 · see citing works
Cognitive impairment in epilepsy: the role of reduced network flexibility
Tailby, C., Kowalczyk, M. A., & Jackson, G. D. (2017) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.503 · Cited by 43 · see citing works
Age-dependent changes in task-based modular organization of the human brain
Schlesinger, K. J., Turner, B. O., Lopez, B. A., Miller, M. B., & Carlson, J. M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.001 · Cited by 42 · see citing works
Henry, T. R., Dichter, G. S., & Gates, K. M. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.10.006 · Cited by 38 · see citing works
Differences in dynamic and static functional connectivity between young and elderly healthy adults
Park, J. E., Jung, S. C., Ryu, K. H., Oh, J. Y., Kim, H. S., Choi, C.-G., Kim, S. J., & Shim, W. H. (2017) · Neuroradiology · doi:10.1007/s00234-017-1875-2 · Cited by 29 · see citing works
Douw, L., Leveroni, C. L., Tanaka, N., Emerton, B. C., Cole, A. J., Reinsberger, C., & Stufflebeam, S. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131209 · Cited by 25 · see citing works
Ramos-Nuñez, A. I., Fischer‐Baum, S., Martin, R. C., Yue, Q., Ye, F., & Deem, M. W. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00420 · Cited by 24 · see citing works
Harlalka, V., Bapi, R. S., Vinod, P. K. U., & Roy, D. (2018) · Brain Connectivity · doi:10.1089/brain.2018.0616 · Cited by 14 · see citing works
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- Harlalka V. Bapi Raju S. Vinod P. K. Roy D. (2018a). Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder. bioXriv 10.1101/387886 PMC6367662 30774589 · DOI
Cited by
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Tools of the trade: estimating time-varying connectivity patterns from fMRI data
Iraji, A., Faghiri, A., Lewis, N., Fu, Z., Rachakonda, S., & Calhoun, V. D. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa114 · Cited by 152 · see citing works
Uddin, L. Q. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.05.010 · Cited by 116 · see citing works
Li, Y., Zhu, Y., Nguchu, B. A., Wang, Y., Wang, H., Qiu, B., & Wang, X. (2020) · Autism Research · doi:10.1002/aur.2212 · Cited by 95 · see citing works
Low-Dimensional Spatiotemporal Dynamics Underlie Cortex-wide Neural Activity
MacDowell, C. J., & Buschman, T. J. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.090 · Cited by 94 · see citing works
Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics
Bolton, T. A. W., Morgenroth, E., Preti, M. G., & Ville, D. V. D. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.06.005 · Cited by 89 · see citing works
Xie, Y., Xu, Z., Xia, M., Liu, J., Shou, X., Cui, Z., Liao, X., & Yong He, A. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.12.004 · Cited by 81 · see citing works
Single Volume Image Generator and Deep Learning-Based ASD Classification
Ahmed, M. R., Zhang, Y., Inan, O. T., & Liao, H. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2998603 · Cited by 80 · see citing works
Wang, M., Huang, J., Liu, M., & Zhang, D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102063 · Cited by 72 · see citing works
Long, Y., Liu, Z., Chan, C. K. Y., Wu, G., Xue, Z., Pan, Y., Chen, X., Huang, X., Li, D., & Pu, W. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00422 · Cited by 61 · see citing works
Fu, Z., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Turner, J. A., Du, Y., Assaf, M., Pearlson, G. D., & Calhoun, V. D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25205 · Cited by 60 · see citing works
Long, Y., Chen, C., Deng, M., Huang, X., Tan, W., Zhang, L., Fan, Z., & Liu, Z. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2019.12.45 · Cited by 53 · see citing works
Longitudinal functional brain network reconfiguration in healthy aging
Malagurski, B., Liem, F., Oschwald, J., Mérillat, S., & Jäncke, L. (2020) · Human Brain Mapping · doi:10.1002/hbm.25161 · Cited by 50 · see citing works
Huang, D., Liu, Z., Cao, H., Yang, J., Wu, Z., & Long, Y. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.04.061 · Cited by 50 · see citing works
Neural circuit pathology driven by Shank3 mutation disrupts social behaviors
Kim, S., Kim, Y.-E., Song, I., Ujihara, Y., Kim, N., Jiang, Y., Yin, H. H., Lee, T., & Kim, I. H. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110906 · Cited by 49 · see citing works
Cattarinussi, G., Giorgio, A. D., Moretti, F., Bondi, E., & Sambataro, F. (2023) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2023.110827 · Cited by 46 · see citing works
Henry, T. R., Dichter, G. S., & Gates, K. M. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.10.006 · Cited by 38 · see citing works
Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence
Lei, T., Liao, X., Chen, X., Zhao, T., Xu, Y., Xia, M., Zhang, J., Xia, Y., Sun, X., Wei, Y., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab263 · Cited by 36 · see citing works
Long, Y., Liu, X., & Liu, Z. (2023) · Brain Sciences · doi:10.3390/brainsci13030429 · Cited by 36 · see citing works
Gao, Y., Sun, J., Cheng, L. L., Yang, Q., Li, J., Hao, Z., Zhan, L., Shi, Y., Li, M., Jia, X., et al. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.06.011 · Cited by 34 · see citing works
A survey of brain functional network extraction methods using fMRI data
Du, Y., Fang, S., He, X., & Calhoun, V. D. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.05.011 · Cited by 32 · see citing works
Ma, L., Yuan, T., Li, W., Guo, L., Zhu, D., Wang, Z., Liu, Z., Xue, K., Wang, Y., Liu, J., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.794151 · Cited by 28 · see citing works
Abdallah, M., Zahr, N. M., Saranathan, M., Honnorat, N., Farrugia, N., Pfefferbaum, A., Sullivan, E. V., & Chanraud, S. (2021) · The Cerebellum · doi:10.1007/s12311-021-01241-y · Cited by 25 · see citing works
Choi, E. J., Vandewouw, M. M., Taylor, M. J., Arnold, P. D., Brian, J. A., Crosbie, J., Kelley, E. A., Lai, M., Liu, X., Schachar, R. J., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102476 · Cited by 25 · see citing works
Atypical core-periphery brain dynamics in autism
Roy, D., & Uddin, L. Q. (2021) · Network Neuroscience · doi:10.1162/netn_a_00181 · Cited by 22 · see citing works
Stability of sensorimotor network sculpts the dynamic repertoire of resting state over lifespan
Sastry, N. C., Roy, D., & Banerjee, A. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac133 · Cited by 24 · see citing works
Evoked and intrinsic brain network dynamics in children with autism spectrum disorder
Kupis, L. B., Romero, C., Dirks, B., Hoang, S., Parlade, M. V., Beaumont, A. L., Cardona, S. M., Alessandri, M., Chang, C., Nomi, J. S., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102396 · Cited by 22 · see citing works
Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer’s disease
Canal-García, A., Veréb, D., Mijalkov, M., Westman, E., Volpe, G., Pereira, J. B., & Initiative, A. D. N. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhad542 · Cited by 23 · see citing works
Restricted, Repetitive, and Stereotypical Patterns of Behavior in Autism—an fMRI Perspective
Noriega, G. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2912416 · Cited by 23 · see citing works
Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy
Ke, M., Wang, C., & Liu, G. (2023) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2023.1123534 · Cited by 20 · see citing works
Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning
Zhang, G., Li, Y., Zhang, X., Huang, L., Cheng, Y., & Shen, W. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.627062 · Cited by 20 · see citing works
Cui, X., Ding, C., Wei, J., Xue, J., Wang, X., Wang, B., & Xiang, J. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab133 · Cited by 20 · see citing works
Ding, C., Xiang, J., Cui, X., Wang, X., Li, D., Chen, C., & Wang, B. (2020) · Journal of Attention Disorders · doi:10.1177/1087054720959712 · Cited by 20 · see citing works
Dekhil, O., Shalaby, A. M., Soliman, A., Mahmoud, A. H., Kong, M., Barnes, G. N., Elmaghraby, A. S., & El-Baz, A. S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101899 · Cited by 19 · see citing works
Wang, Y., Xu, L., Fang, H., Wang, F., Gao, T., Zhu, Q., Jiao, G., & Ke, X. (2023) · Brain Sciences · doi:10.3390/brainsci13020280 · Cited by 18 · see citing works
Network controllability mediates the relationship between rigid structure and flexible dynamics
Gu, S., Fotiadis, P., Parkes, L., Xia, C. H., Gur, R. C., Gur, R. E., Roalf, D. R., Satterthwaite, T. D., & Bassett, D. S. (2021) · Network Neuroscience · doi:10.1162/netn_a_00225 · Cited by 17 · see citing works
Sigar, P., Uddin, L. Q., & Roy, D. (2022) · Autism Research · doi:10.1002/aur.2840 · Cited by 16 · see citing works
Karampasi, A. S., Savva, A. D., Korfiatis, V. C., Kakkos, I., & Matsopoulos, G. K. (2021) · Applied Sciences · doi:10.3390/app11136216 · Cited by 16 · see citing works
Liu, D., Tang, S., Wu, Z., Yang, J., Liu, Z., Wu, G., Sariah, A., Ouyang, X., & Long, Y. (2022) · Annals of Palliative Medicine · doi:10.21037/apm-21-2723 · Cited by 14 · see citing works
Wang, M., Wang, L., Yang, B., Yuan, L., Wang, X., Potenza, M. N., & Dong, G. (2022) · Brain Communications · doi:10.1093/braincomms/fcac177 · Cited by 12 · see citing works
Dynamic reconfiguration of human brain networks across altered states of consciousness
Liu, H., Hu, K., Peng, Y., Tian, X.-H., Wang, M., Ma, B., Wu, Y., Sun, W., Liu, B., Li, A., et al. (2021) · Behavioural Brain Research · doi:10.1016/j.bbr.2021.113685 · Cited by 13 · see citing works
Sheng, D., Pu, W., Linli, Z., Tian, G., Guo, S., & Yu, F. (2021) · Psychological Medicine · doi:10.1017/s0033291721003895 · Cited by 11 · see citing works
Li, W., Qiu, X., Chen, J., Chen, K., Chen, M., Wang, Y., Sun, W., Su, J., Chen, Y., Liu, X., et al. (2025) · Advanced Science · doi:10.1002/advs.202403801 · Cited by 11 · see citing works
Huang, X., Wu, Z., Liu, Z., Liu, D., Huang, D., & Long, Y. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.701420 · Cited by 11 · see citing works
Gao, L., Cao, Y., Zhang, Y., Liu, J., Zhang, T., Zhou, R., & Guo, X. (2024) · Brain Imaging and Behavior · doi:10.1007/s11682-024-00907-5 · Cited by 10 · see citing works
Zhu, G., Li, Y., Wan, L., Sun, C., Liu, X., Zhang, J., Liang, Y., Liu, G., Yan, H., Li, R., et al. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad413 · Cited by 10 · see citing works
Cao, B., Guo, Y. Q., Xia, F., Li, L., Ren, Z., Lü, M., Wang, J., & Huang, R. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf083 · Cited by 9 · see citing works
A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility
Chinichian, N., Kruschwitz, J., Reinhardt, P., Palm, M., Wellan, S. A., Erk, S., Heinz, A., Walter, H., & Veer, I. M. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1025428 · Cited by 9 · see citing works
Intracranial brain stimulation modulates fMRI-based network switching
Pedersen, M., & Zalesky, A. (2021) · Neurobiology of Disease · doi:10.1016/j.nbd.2021.105401 · Cited by 9 · see citing works
Ji, S., Ma, H., Yao, M., Guo, M., Li, S., Chen, N., Liu, X., Shao, X., Yao, Z., & Hu, B. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.06.024 · Cited by 9 · see citing works
Li, S., Zhang, Z., Jiang, A., Ma, X., Wang, M., Ni, H., Yang, B., Zheng, Y., Wang, L., & Dong, G. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.08.120 · Cited by 8 · see citing works
DySCo: A general framework for dynamic functional connectivity
de Alteriis, G., Sherwood, O., Ciaramella, A., Leech, R., Cabral, J., Turkheimer, F., & Expert, P. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012795 · Cited by 9 · see citing works
Li, S., Jiang, A., Ma, X., Zhang, Z., Ni, H., Wang, H., Liu, C., Song, X., & Dong, G.-H. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100485 · Cited by 9 · see citing works
Neurobiological and Behavioral Heterogeneity in Adolescents with Autism Spectrum Disorder
Leisman, G., Alfasi, R., & Melillo, R. J. (2025) · Brain Sciences · doi:10.3390/brainsci15101057 · Cited by 8 · see citing works
Cross-module switching diversity of brain network nodes in resting and cognitive states
Yi, C., Fan, Y., & Wu, Y. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09894-z · Cited by 8 · see citing works
Two-step clustering-based pipeline for big dynamic functional network connectivity data
Sendi, M. S. E., Salat, D. H., Miller, R. L., & Calhoun, V. D. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.895637 · Cited by 8 · see citing works
Resting-state functional connectivity in children cooled for neonatal encephalopathy
Spencer, A. P. C., Goodfellow, M., Chakkarapani, E., & Brooks, J. C. W. (2024) · Brain Communications · doi:10.1093/braincomms/fcae154 · Cited by 7 · see citing works
Yu, X., Mei, D., Wu, K., Li, Y., Chen, C., Chen, T., Shi, X., & Zou, Y. (2024) · Experimental Gerontology · doi:10.1016/j.exger.2024.112527 · Cited by 6 · see citing works
Bhavna, K., Ghosh, N., Banerjee, R., & Roy, D. (2025) · Network Neuroscience · doi:10.1162/netn.a.32 · Cited by 5 · see citing works
Wang, C., Lu, Y., Lin, Y., Wang, C., & Tian, P. (2023) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2022.922577 · Cited by 5 · see citing works
Hypergraph-based brain network switching rate and its application in epilepsy
Li, Z., Han, B., Wang, D., Yan, J., Zhang, X., & Yin, L. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117653 · Cited by 4 · see citing works
Probing the association between resting-state brain network dynamics and psychological resilience
Kraft, D., & Fiebach, C. J. (2021) · Network Neuroscience · doi:10.1162/netn_a_00216 · Cited by 4 · see citing works
Dynamic Reorganization Patterns of Brain Modules after Stroke Reflecting Motor Function
Yu, X., Wu, K., Li, Y., Chen, C., Chen, T., Shi, X., Tan, Z., & Zou, Y. (2024) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2310182 · Cited by 4 · see citing works
Aberrant Flexibility of Dynamic Brain Network in Patients with Autism Spectrum Disorder
Zhang, H., Peng, D., Tang, S., Bi, A., & Long, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11090882 · Cited by 5 · see citing works
González‐Escamilla, G., Chirumamilla, V. C., Koirala, N., Anwar, A. R., Tüscher, O., Vogt, J., Horstmann, P., Meyer, B., Bonanno, G. A., Groppa, S., et al. (2023) · Brain Communications · doi:10.1093/braincomms/fcad035 · Cited by 5 · see citing works
Chen, B. (2020) · International Journal of Neuroscience · doi:10.1080/00207454.2020.1806837 · Cited by 5 · see citing works
Su, C., Hu, Y., Liu, Y., Zhang, N., Tan, L., Zhang, S., Yi, A., & Xiao, Y. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0161-25.2025 · Cited by 3 · see citing works
Beyond networks: Toward adaptive models of biological complexity
Pessoa, L. (2025) · Physics of Life Reviews · doi:10.1016/j.plrev.2025.11.007 · Cited by 5 · see citing works
Bhavna, K., Ghosh, N., Banerjee, R., & Roy, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-72945-4 · Cited by 3 · see citing works
He, Y., Liang, Y., Tong, L., Cui, Y., & Yan, H. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.104912 · Cited by 3 · see citing works
Delineating a Pathway for the Discovery of Functional Connectome Biomarkers of Autism
Park, S., Thomson, P., Kiar, G., Castellanos, F. X., Milham, M. P., Bernhardt, B. C., & Martino, A. D. (2024) · Advances in neurobiology · doi:10.1007/978-3-031-69491-2_18 · Cited by 3 · see citing works
Learning Sparse Graphs with a Core-Periphery Structure
Gurugubelli, S., & Chepuri, S. P. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746825 · Cited by 3 · see citing works
Brain network analyses in clinical neuroscience
Sokolov, A. A., Granziera, C., Fischi-Gómez, E., Preti, M. G., Ryvlin, P., Ville, D. V. D., & Friston, K. J. (2019) · Swiss Archives of Neurology Psychiatry and Psychotherapy · doi:10.4414/sanp.2019.03074 · Cited by 3 · see citing works
Colloquium: Multiscale modeling of brain network organization
Presigny, C., & Fallani, F. D. V. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.031002 · Cited by 59 · see citing works
Dynamic structure-function coupling in macroscale neonatal brain networks
Zhang, Z., Zhang, C., Zhang, X., Xu, Y., Dou, W., Li, M., Zheng, W., & Li, B. (2025) · Communications Biology · doi:10.1038/s42003-025-09300-4 · Cited by 2 · see citing works
Bhavna, K., Sharma, S., & Rosado-Muñoz, A. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133145 · Cited by 2 · see citing works
Characterizing the Dynamic Reorganization in Healthy Ageing and Classification of Brain Age
Dash, A., Bapi, R. S., Roy, D., & Vinod, P. K. U. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891981 · Cited by 2 · see citing works
Ji, S., Zhang, H., Zhou, C., Liu, X., Liu, C., & Yu, H. (2024) · Neuroscience · doi:10.1016/j.neuroscience.2024.11.024 · Cited by 2 · see citing works
Unlocking the Mind: Artificial Intelligence Powered Insights Into Brain Signals and Human Behavior
Parui, S., & Diwan, A. (2025) · Studies in computational intelligence · doi:10.1007/978-981-96-5585-4_9 · Cited by 2 · see citing works
iSparse kmeans: a two-step clustering approach for big dynamic functional network connectivity dataPreprint
Sendi, M. S. E., Salat, D. H., Miller, R. L., & Calhoun, V. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.13.484193 · Cited by 2 · see citing works
Akbari, S., & Rajabioun, M. (2024) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237224500595 · Cited by 1 · see citing works
Jiang, X., Ma, J., Hou, C., & Li, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-04963-9 · Cited by 1 · see citing works
Effective neuromodulation in bipolar disorder brain based on whole-brain dynamic model
Wu, D., Fan, Y., Wang, Y., Wang, R., Han, Y., & Wu, Y. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-11610-5 · Cited by 1 · see citing works
Spatiotemporal transition of resting-state brain networks associates with human cognitive abilities
Zhou, L., Jiang, Z., Chang, Z., Wang, R., & Wu, Y. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10347-6 · Cited by 1 · see citing works
Ma, X., Belmonte, M. K., Zhao, Y., & Zhao, J. (2025) · Human Brain Mapping · doi:10.1002/hbm.70423 · Cited by 1 · see citing works
Dynamic Resting-State Network Markers of Disruptive Behavior Problems in Youth
Shappell, H. M., Liu, Z., Khodaei, M., He, G. S., Gee, D. G., Lindquist, M. A., Sukhodolsky, D. G., McCarthy, G., & Ibrahim, K. (2026) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2026.100689 · Cited by 2 · see citing works
Dynamic functional connectivity
França, L. G. S., Fenn‐Moltu, S., & Batallé, D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85280-7.00006-3 · Cited by 1 · see citing works
Dynamic functional connectivity analysis in individuals with Autism Spectrum Disorder
Prasad, P. K. C., Dadi, K., & Bapi, R. S. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191252 · Cited by 1 · see citing works
Multilayer network instability underlying persistent auditory verbal hallucinations in schizophrenia
Li, J., He, J., Ren, H., Li, Z., Ma, X., Yuan, L., Ouyang, L., Li, C., Hé, Y., Hé, Y., et al. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.116351 · Cited by 1 · see citing works
Lei, T., Liao, X., Chen, X., Zhao, T., Xu, Y., Xia, M., Zhang, J., Sun, X., Wei, Y., Men, W., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.03.433828 · Cited by 1 · see citing works
Sastry, N. C., Roy, D., & Banerjee, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.08.451590 · Cited by 1 · see citing works
Song, I., & Lee, T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.26.525765 · Cited by 2 · see citing works
Bhavna, K., Ghosh, N., Banerjee, R., & Roy, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.09.552564 · Cited by 1 · see citing works
Bhavna, K., Ghosh, N., Banerjee, R., & Roy, D. (2024) · medRxiv · doi:10.1101/2024.07.17.24310610 · Cited by 1 · see citing works
Floris, D. L., Saccaro, L. F., Delavari, F., Strzelczyk, D., Vieira, B. H., Elleaume, C., Pretzsch, C. M., Ecker, C., Banaschewski, T., Holt, R., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10317780/v1 · Cited by 1 · see citing works
Distinct profiles of spatio-temporal brain dynamics along symptoms dimensions in autismPreprint
Mullier, E., Kojovic, N., Denervaud, S., Vohryzek, J., Hagmann, P., & Schaer, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.11.434353
Dynamic Functional Connectome Harmonics
Taylor, H. P., & Yap, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_26
High modularity, more flexible and independent communication of brain networks after strokePreprint
Huai, Z., Yu, X., Wu, K., Li, Y., Chen, C., Zhu, C., & Yue, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3570871/v1
Wang, X., Sun, L., Liang, X., Zhao, T., Xia, M., Liao, X., & Yong He, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.21.576523
He, Y., Yang, F., & Grebogi, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4893535
Zhao, F., Song, L., Chen, Y., Wang, S., Wang, X., Zhai, Y., Xu, J., Zhang, Z., Lei, M., Cai, W., et al. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1477181
- Authors
- Harlalka, V., Bapi, R. S., Vinod, P. K., & Roy, D.
- Affiliations
- International Institute of Information Technology, Hyderabad (IN)
- University of Hyderabad (IN)
- National Brain Research Centre (IN)
- Corresponding author
- Vinod, P. K.
- Roy, Dipanjan
- Venue
- Frontiers in Human Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2019-02-01
- Volume
- 13
- Pages
- 6-6
- ISSN
- 1662-5161 (ISSN-L)
- PMC
- PMC6367662
- OpenAlex
- W2952965410
- Cited by
- 110
- References
- 51
- Citations by year
- 2026: 7 · 2025: 18 · 2024: 18 · 2023: 14 · 2022: 13 · 2021: 20 · 2020: 14 · 2019: 5 · 2017: 1
- FWCI
- 7.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Human Neuroscience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Department of Science and Technology, Ministry of Science and Technology, India (SR/CSRI/21/2016)
- Science and Engineering Research Board (ECR/2016/000488)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Keywords
- autism spectrum disorder, dynamic functional connectivity, network flexibility, symptom severity, brain modularity, dynamic variability, resting-state functional connectivity, sensorimotor regions, default mode network, age differences
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