The DOI matches its Crossref record; 254 of the 254 references it cites were found in a registry or an index.
Exploring the network dynamics underlying brain activity during rest
Open accessAbstract
Since the mid 1990s, the intriguing dynamics of the brain at rest has been attracting a growing body of research in neuroscience. Neuroimaging studies have revealed distinct functional networks that slowly activate and deactivate, pointing to the existence of an underlying network dynamics emerging spontaneously during rest, with specific spatial, temporal and spectral characteristics. Several theoretical scenarios have been proposed and tested with the use of large-scale computational models of coupled brain areas. However, a mechanistic explanation that encompasses all the phenomena observed in the brain during rest is still to come. In this review, we provide an overview of the key findings of resting-state activity covering a range of neuroimaging modalities including fMRI, EEG and MEG. We describe how to best define and analyze anatomical and functional brain networks and how unbalancing these networks may lead to problems with mental health. Finally, we review existing large-scale models of resting-state dynamics in health and disease. An important common feature of resting-state models is that the emergence of resting-state functional networks is obtained when the model parameters are such that the system operates at the edge of a bifurcation. At this critical working point, the global network dynamics reveals correlation patterns that are spatially shaped by the underlying anatomical structure, leading to an optimal fit with the empirical BOLD functional connectivity. However, new insights coming from recent studies, including faster oscillatory dynamics and non-stationary functional connectivity, must be taken into account in future models to fully understand the network mechanisms leading to the resting-state activity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Like other studies focused on measures of functional connectivity, our conclusions are tempered by questions about the origins and significance of correlated fluctuations in the blood-oxygen-level dependent (BOLD) fMRI signal61-63.”
From Evolutionarily conserved prefrontal-amygdalar dysfunction in early-life anxiety (2014) · cites this paper as [62]
In the citing paper
“this conceptualization takes into account our increasing understanding of these whole brain networks as dynamic entities, with patterns of activity and connectivity (both within-network and with other networks) shifting as required by ever-changing situational demands (Cabral, Kringelbach, & Deco, 2014;Shine et al., 2016).”
From Mental Time Travel? A Neurocognitive Model of Event Simulation. (2020) · cites this paper as Cabral, Kringelbach, & Deco, 2014
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
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
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
Functional 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
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688
Risken, H. (1984) · Springer series in synergetics · doi:10.1007/978-3-642-61544-3 · Cited by 9,128
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Research works printed but not linked: 2
- Ghosh, 2008, Noise during rest enables the exploration of the brain's dynamic repertoire, PLoS Computational Biology, 4, e1000196, 10.1371/journal.pcbi.1000196 · DOI
- Milgram, 1967, Small-world problem, Psychology Today, 1, 61
Cited by
Sorted by citations
Items 101–200 of 351
Kang, J., Pae, C., & Park, H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.075 · Cited by 43
“Work has also been done on multistability during resting state (Cabral et al., 2014; Deco and Jirsa, 2012; Deco et al., 2015; Freyer et al., 2011; Freyer et al., 2012; Tognoli and Kelso, 2014) and task performance, (for example, in perception, Leopold and Logothetis, 1999; Sterzer et al., 2009).”
BackgroundInfluentialResting state network connectivity is attenuated by fMRI acoustic noise
Pellegrino, G., Schuler, A., Arcara, G., Pino, G. D., Piccione, F., & Kobayashi, E. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118791 · Cited by 40
Minati, L., Chiesa, P., Tabarelli, D., D’Incerti, L., & Jovicich, J. (2015) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4914938 · Cited by 40
Informational structures: A dynamical system approach for integrated information
Esteban, F. J., Galadí, J. A., Langa, J. A., Portillo, J. R., & Toscano, F. S. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006154 · Cited by 37
Neural dynamics differentially encode phrases and sentences during spoken language comprehension
Bai, F., Meyer, A., & Martin, A. E. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001713 · Cited by 40
Transient brain networks underlying interpersonal strategies during synchronized action
Heggli, O. A., Konvalinka, I., Cabral, J., Brattico, E., Kringelbach, M. L., & Vuust, P. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa056 · Cited by 38
“In this framework, cognition is thought to involve a coordinated integration of information between brain areas, through metastable networks that collectively form a repertoire of brain states (Cabral et al., 2014; Deco et al., 2015; Lord et al., 2019).”
BackgroundSelf-citationWein, S., Deco, G., Tomé, A. M., Goldhacker, M., Malloni, W. M., Greenlee, M. W., & Lang, E. W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5573740 · Cited by 42
“So far, models related to computational connectomics provided insights into different aspects of brain dynamics, emerging on various time scales [16, 83].”
BackgroundSelf-citationDynamics of cortical activity eigenmodes including standing, traveling, and rotating waves
Gabay, N. C., Babaie, T., & Robinson, P. A. (2018) · Physical review. E · doi:10.1103/physreve.98.042413 · Cited by 34
Perl, Y. S., Escrichs, A., Tagliazucchi, E., Kringelbach, M. L., & Deco, G. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010662 · Cited by 42
“Previous research has shown that the three different scenarios of noise [20,21], subcritical [16,21–23] and supercritical [24] are equally able to fit the empirical neuroimaging data in terms of functional connectivity.”
BackgroundSelf-citationGatica, M., Rosas, F. E., Mediano, P. A. M., Diez, I. P., Swinnen, S. P., Orio, P., Cofré, R., & Cortés, J. M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010431 · Cited by 36
Functional brain network identification and fMRI augmentation using a VAE-GAN framework
Qiang, N., Gao, J., Dong, Q., Yue, H., Liang, H., Liu, L., Yu, J., Hu, J., Zhang, S., Ge, B., et al. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107395 · Cited by 43
Doubly functional graphical models in high dimensions
Qiao, X., Qian, C., James, G. M., & Guo, S. (2019) · Biometrika · doi:10.1093/biomet/asz072 · Cited by 36
“Existing research has shown that the networks embedded in EEG data evolve over time, where 445 edges are bound to emerge and disappear (Cabral et al., 2014).”
BackgroundTopological Data Analysis for Multivariate Time Series Data
El-Yaagoubi, A. B., Chung, M. K., & Ombao, H. (2023) · Entropy · doi:10.3390/e25111509 · Cited by 45
“Traditionally, due to their stochastic nature, multichannel or multivariate brain signals have often been modeled using their underlying dependence network, i.e., dependence between brain regions or nodes in a brain network, see [11], [29], [41], [54].”
BackgroundSynchronization in Functional Networks of the Human Brain
Hövel, P., Viol, A., Loske, P., Merfort, L., & Vuksanović, V. (2018) · Journal of Nonlinear Science · doi:10.1007/s00332-018-9505-7 · Cited by 37
Toward a theory of coactivation patterns in excitable neural networks
Messé, A., Hütt, M. ‐., & Hilgetag, C. C. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006084 · Cited by 32
Quantitative evaluation of simulated functional brain networks in graph theoretical analysis
Lee, W. H., Bullmore, E. T., & Frangou, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.050 · Cited by 35
“…model (Kuramoto, 1984), which is considered the most representative model of coupled phase oscillators and is widely used in the neuroscience research (Cabral et al., 2012b, 2014b; Hellyer et al., 2014, 2015; Schmidt et al., 2014; Vasa et al., 2015; Vuksanovic and Hovel, 2014; Yan and Li, 2013).”
InfluentialKang, J., Pae, C., & Park, H. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222161 · Cited by 33
Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study
Chholak, P., Kurkin, S., Hramov, A. E., & Pisarchik, A. N. (2021) · Applied Sciences · doi:10.3390/app11010375 · Cited by 34
“Effective connectivity utilizes the functional connectivity information and additionally determines the direction of the dynamic information flow [9,10].”
BackgroundFundamental Activity Constraints Lead to Specific Interpretations of the Connectome
Schuecker, J., Schmidt, M., van Albada, S. J., Diesmann, M., & Helias, M. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005179 · Cited by 36
“Structures and mechanisms underlying large-scale interactions have been identified by means of dynamical models [10, 11] and graph theory [12, 13].”
BackgroundPagnoni, G., & Guareschi, F. T. (2015) · Mindfulness · doi:10.1007/s12671-015-0438-z · Cited by 33
“A large body of neuroimaging studies in the last decade has shown that even when we are not engaged in any specific task, an ongoing intrinsic and structurally organized activity characterizes virtually every cortical and subcortical structure (Power et al. 2014; Cabral et al. 2014).”
BackgroundDiagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
Ciprian, C., Masychev, K., Ravan, M., Manimaran, A., & Deshmukh, A. (2021) · Algorithms · doi:10.3390/a14050139 · Cited by 33
“However, resting-state electroencephalography (EEG) FC reflects the intrinsic inter-neuronal connections in specific circuits such as the default mode network (DMN) that are attenuated or interrupted during cognitive or sensorimotor tasks [7].”
BackgroundAbnormal dynamic community structure of the salience network in depression
Wei, M., Qin, J., Yan, R., Bi, K., Liu, C., Yao, Z., & Lü, Q. (2016) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25429 · Cited by 32
Charting moment-to-moment brain signal variability from early to late childhood
Miskovic, V., Owens, M., Kuntzelman, K. M., & Gibb, B. E. (2016) · Cortex · doi:10.1016/j.cortex.2016.07.006 · Cited by 33
“Unconstrained by sensory input or the execution of motor routines, ongoing large-scale neuronal activity is characterized by itinerant wandering along a set of linked, heteroclinic channels (Cabral et al., 2014; Deco et al., 2011, 2013).”
BackgroundModes of cognition: Evidence from metastable brain dynamics
Capouskova, K., Kringelbach, M. L., & Deco, G. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119489 · Cited by 32
“for the rest condition, when the parameters of the system are in a critical range, while the system is constantly repelled away from equilibrium (Breakspear, 2017; Cabral et al., 2014; Deco and Jirsa, 2012).”
Self-citationParviainen, T. M., Lyyra, P., & Nokia, M. S. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104908 · Cited by 39
Singleton, S. P., Luppi, A. I., Carhart-Harris, R. L., Cruzat, J., Roseman, L., Nutt, D. J., Deco, G., Kringelbach, M. L., Stamatakis, E. A., & Kuceyeski, A. F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.14.444193 · Cited by 30
Self-citationRemodeling of Sensorimotor Brain Connectivity in Gpr88 -Deficient Mice
Arefin, T. M., Mechling, A. E., Meirsman, A. C., Bienert, T., Hübner, N. S., Lee, H., Hamida, S. B., Ehrlich, A. T., Roquet, D., Hennig, J., et al. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0486 · Cited by 32
“During rest, endogenous fluctuations in low-frequency BOLD signals are synchronized between different regions— widely distributed throughout the brain, forming dynamic FC networks (Cabral et al., 2014).”
BackgroundMinati, L., Ito, H., Perinelli, A., Ricci, L., Faes, L., Yoshimura, N., Koike, Y., & Frasca, M. (2019) · IEEE Access · doi:10.1109/access.2019.2957014 · Cited by 30
Identifying Respiration-Related Aliasing Artifacts in the Rodent Resting-State fMRI
Pais‐Roldán, P., Biswal, B. B., Scheffler, K., & Yu, X. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00788 · Cited by 31
Determinants of Brain Rhythm Burst Statistics
Powanwe, A. S., & Longtin, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-54444-z · Cited by 30
Guan, S., Jiang, R., Bian, H., Yuan, J., Xu, P., Meng, C., & Biswal, B. B. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00493 · Cited by 27
“The majority of fMRI research to date has used a simplified model based on the assumptions of stationarity and linearity (de Pasquale et al., 2010; Cabral et al., 2014; Panerai, 2014).”
BackgroundKang, J., Jeong, S., Pae, C., & Park, H. (2021) · Human Brain Mapping · doi:10.1002/hbm.25442 · Cited by 24
“The human brain has been viewed as a dynamic complex system that possesses multiple stable states over a stable circuitry (Breakspear, 2017; J. Cabral, Kringelbach, & Deco, 2014; Deco & Jirsa, 2012; Deco, Tononi, Boly, & Kringelbach, 2015; Freyer et al., 2011; Freyer, Roberts, Ritter, & Breakspear,…”
BackgroundDynamic changes in network synchrony reveal resting-state functional networks
Vuksanović, V., & Hövel, P. (2015) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4913526 · Cited by 27
Moguilner, S., García, A. M., Mikulan, E., Hesse, E., Cordero, I. R. G., Melloni, M., Cervetto, S., Serrano, C. M., Herrera, E., Reyes, P. A., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-29538-9 · Cited by 28
Biophysical mechanism of the interaction between default mode network and working memory network
Yuan, Y., Pan, X., & Wang, R. (2021) · Cognitive Neurodynamics · doi:10.1007/s11571-021-09674-1 · Cited by 26
Qiang, N., Gao, J., Dong, Q., Li, J., Zhang, S., Liang, H., Sun, Y., Ge, B., Liu, Z., Wu, Z., et al. (2022) · Behavioural Brain Research · doi:10.1016/j.bbr.2023.114603 · Cited by 30
Comparing structure–function relationships in brain networks using EEG and fNIRS
Blanco, R., Preti, M. G., Koba, C., Ville, D. V. D., & Crimi, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-79817-x · Cited by 27
Chen, X., Liu, J., Wang, J., Xin, Z., Zhang, Q., Zhang, W., Xi, Y., Zhu, Y., Li, C., Li, J., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00241-1 · Cited by 27
Yu, M. (2020) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0018826 · Cited by 30
A meta-analysis of cranial electrotherapy stimulation in the treatment of depression
Price, L. R., Briley, J., Haltiwanger, S., & Hitching, R. (2020) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2020.12.043 · Cited by 28
“The understanding of brain regions and their associated function(s) has grown substantially; and with it the realization that the brain is organized via networks that continually monitor and adapt to each other (Sung et al., 2020; Ramírez-Barrantes et al., 2019; Cabral et al., 2014).”
BackgroundSafar, K., Zhang, J., Emami, Z., Gharehgazlou, A., Ibrahim, G. M., & Dunkley, B. T. (2021) · Brain Communications · doi:10.1093/braincomms/fcab044 · Cited by 26
Microsynt: Exploring the syntax of EEG microstates
Artoni, F., Maillard, J., Britz, J., Brunet, D., Lysakowski, C., Tramèr, M. R., & Michel, C. M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120196 · Cited by 27
“Alterations in the spatial and temporal organization of large scale resting state networks (RSNs) have been related to changes of brain functions in neurological and psychiatric diseases (Cabral et al., 2014; Fox and Greicius, 2010).”
Fukushima, M., & Sporns, O. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006497 · Cited by 25
Alterations in Cognition-Related Cerebello-Cerebral Networks in Multiple System Atrophy
Kawabata, K., Hara, K., Watanabe, H., Bagarinao, E., Ogura, A., Masuda, M., Yokoi, T., Kato, T., Ohdake, R., Ito, M., et al. (2019) · The Cerebellum · doi:10.1007/s12311-019-01031-7 · Cited by 25
Late-Life Depression: Modifications of Brain Resting State Activity
Cieri, F., Esposito, R., Cera, N., Pieramico, V., Tartaro, A., & Giannantonio, M. D. (2017) · Journal of Geriatric Psychiatry and Neurology · doi:10.1177/0891988717700509 · Cited by 25
Zhang, D., Johnstone, S. J., Roodenrys, S. J., Luo, X., Li, H., Wang, E., Zhao, Q., Song, Y., Liu, L., Qian, Q., et al. (2018) · Clinical Neurophysiology · doi:10.1016/j.clinph.2018.03.009 · Cited by 28
Kinney‐Lang, E., Yoong, M. M., Hunter, M. B., Tallur, K. K., Shetty, J., McLellan, A. E., Chin, R., & Escudero, J. (2018) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2018.11.011 · Cited by 25
Beyond the Epileptic Focus: Functional Epileptic Networks in Focal Epilepsy
Gil-López, F., Padilla, N., Soria‐Pastor, S., Setoaín, X., Boget, T., Rumià, J., Roldán, P., Reyes‐Leiva, D., Bargalló, N., Blanco, E. C., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz243 · Cited by 24
Brain dynamics: Synchronous peaks, functional connectivity, and its temporal variability
Rolls, E. T., Cheng, W., & Feng, J. (2021) · Human Brain Mapping · doi:10.1002/hbm.25404 · Cited by 23
Dreszer, J., Grochowski, M., Lewandowska, M., Nikadon, J., Gorgol, J., Bałaj, B., Finc, K., Duch, W., Kałamała, P., Chuderski, A., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25162 · Cited by 23
“…and desynchronized subsystems (Ibáñez-Molina & Iglesias-Parro,- 2016), the balance between functional segregation and integration processes (Tononi, Sporns, & Edelman, 1994; Deco, Ponce-Alvarez, et al., 2013), and the neural noise (Cabral et al., 2014; Cabral, Kringelbach, & Deco, 2017).”
BackgroundDetection of time-, frequency- and direction-resolved communication within brain networks
Crouch, B., Sommerlade, L., Veselcic, P., Riedel, G., Schelter, B. O., & Platt, B. (2018) · Scientific Reports · doi:10.1038/s41598-018-19707-1 · Cited by 25
Lee, T. H., Kim, M., Hwang, W. J., Kim, T., Kwak, Y. B., & Kwon, J. S. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.12.010 · Cited by 24
Muthuraman, M., Moliadze, V., Mideksa, K. G., Anwar, A. R., Stephani, U., Deuschl, G., Freitag, C. M., & Siniatchkin, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140832 · Cited by 23
“One more important point is the local magnetic fields which have the origin in sub-cortical regions may be distorted this could in turn have effects on the spatial accuracy in MEG[73].”
van Hartevelt, T. J., Cabral, J., Møller, A., FitzGerald, J. J., Green, A. L., Aziz, T. Z., Deco, G., & Kringelbach, M. L. (2015) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2015.00167 · Cited by 22
“As shown in previous studies (Cabral et al., 2014), this model accurately captures many features of the resting-state dynamics of FC. 3.”
ResultSelf-citationDefault Mode Network and Neural Phase Synchronization in Healthy Aging: A Resting State EEG Study
Chow, R., Rabi, R., Paracha, S., Hasher, L., Anderson, N. D., & Alain, C. (2022) · Neuroscience · doi:10.1016/j.neuroscience.2022.01.008 · Cited by 23
Whole animal modeling: piecing together nematode locomotion
Cohen, N., & Denham, J. E. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.12.002 · Cited by 21
Altered frontal connectivity as a mechanism for executive function deficits in fragile X syndrome
Schmitt, L., Li, J., Liu, R., Horn, P. S., Sweeney, J. A., Erickson, C. A., & Pedapati, E. V. (2022) · Molecular Autism · doi:10.1186/s13229-022-00527-0 · Cited by 21
“Specifically, gamma hyper-connectivity and alpha hypoconnectivity may reflect poor top-down regulation of local frontal circuits leading to hyperexcitability of local circuit function and subsequent cognitive and behavioral dysfunction [54, 55].”
BackgroundZeng, H., Su, D., Wang, P., Wang, M., Vollstädt‐Klein, S., Chen, Q., & Ye, H. (2018) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2018.00123 · Cited by 23
Dynamic properties of simulated brain network models and empirical resting-state data
Kashyap, A., & Keilholz, S. D. (2018) · Network Neuroscience · doi:10.1162/netn_a_00070 · Cited by 19
“We then thresholded the values at 0.3, based on literature search, and created recurrent plots (Cabral et al., 2014; Basset et al., 2012).”
BackgroundMethodInfluentialLiao, Y., Guo, N., Su, B.-Y., Chen, S.-J., Tsai, H.-F., & Lee, K.-Y. (2020) · Clinical EEG and Neuroscience · doi:10.1177/1550059420933142 · Cited by 21
“is increasingly considered informative when patients are engaged in neural tasks.(13,14) On the other hand, 3 levels of endophenotypes in ADHD have been proposed,(15) and the associations between behavioral symptoms, neuropsychological functions, neurophysiological technique, and neuropsychological mechanisms have bec…”
BackgroundBCI Use and Its Relation to Adaptation in Cortical Networks
Casimo, K., Weaver, K. E., Wander, J. D., & Ojemann, J. G. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2681963 · Cited by 21
“It is well established that motor learning generally, and more recently BCI learning, does involve distributed cortical resources [2].”
Azami, H., Mirjalili, M., Rajji, T. K., Wu, C., Humeau‐Heurtier, A., Jung, T., Wei, C.-S., Trinh, T.-T., & Liu, Y. (2025) · IEEE Journal of Selected Areas in Sensors · doi:10.1109/jsas.2025.3552546 · Cited by 21
“Resting-state EEGs allow for the measurement of spontaneous brain activity, which is thought to reflect the intrinsic functional organization and connectivity of the brain [52], [54], [55], [56], [57].”
Accelerometer time series augmentation through externally driving a non-linear dynamical system
Minati, L., Li, C., Bartels, J., Chakraborty, P., Li, Z., Yoshimura, N., Frasca, M., & Ito, H. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113100 · Cited by 20
Multivariate brain network graph identification in functional MRI
Aggarwal, P., Gupta, A., & Garg, A. K. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.08.007 · Cited by 20
Quantitative biomarkers to predict response to clozapine treatment using resting EEG data
Masychev, K., Ciprian, C., Ravan, M., Manimaran, A., & Deshmukh, A. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.08.017 · Cited by 20
The metastable brain associated with autistic-like traits of typically developing individuals
Sase, T., & Kitajo, K. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008929 · Cited by 20
“From a dynamical systems theory point of view, these two kinds of coupled oscillatory dynamics can be interpreted as being generated from coupled oscillatory attractors composed of the limit cycle [2] or its variant form, i.”
BackgroundZeng, Y., Lao, J., Wu, Z., Lin, G., Wang, Q., Yang, M., Zhang, S., Xu, D., Zhang, M., Liang, S., et al. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.10.157 · Cited by 19
Default Mode Network Changes in Moyamoya Disease Before and After Bypass Surgery: Preliminary Report
Sakamoto, Y., Okamoto, S., Maesawa, S., Bagarinao, E., Araki, Y., Izumi, T., Watanabe, H., Sobue, G., & Wakabayashi, T. (2018) · World Neurosurgery · doi:10.1016/j.wneu.2018.01.117 · Cited by 18
Shackman, A. J., Fox, A. S., & Seminowicz, D. A. (2015) · Behavioral and Brain Sciences · doi:10.1017/s0140525x14001010 · Cited by 22
Si, S., Wang, B., Liu, X., Yu, C., Ding, C., & Hai, Z. (2019) · Entropy · doi:10.3390/e21030300 · Cited by 21
Kaboodvand, N., van den Heuvel, M. P., & Fransson, P. (2019) · Network Neuroscience · doi:10.1162/netn_a_00104 · Cited by 18
“…local brain regions, each modeled by a local mean-field model, are interacting through the structural brain network architecture (Breakspear, 2017; Cabral et al., 2014; Deco & Jirsa, 2012; Deco, Jirsa, McIntosh, Sporns, & Kötter, 2009; Gollo, Zalesky, Hutchison, van den Heuvel, & Breakspear,…”
InfluentialRegional Patterns of Cortical Phase Synchrony in the Resting State
Casimo, K., Darvas, F., Wander, J. D., Ko, A. L., Grabowski, T. J., Novotny, E. J., Poliakov, A. V., Ojemann, J. G., & Weaver, K. E. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0362 · Cited by 19
“While resting state fMRI studies have substantially contributed to our understanding of the brain’s intrinsic and dynamic functional connectivity, the electrophysiological interactions that define resting state at a systems level are still far less understood (Cabral et al., 2014).”
BackgroundOchmann, S. L., Dyrba, M., Grothe, M. J., Kasper, E., Webel, S., Hauenstein, K., & Teipel, S. J. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-160773 · Cited by 18
Recent advances in functional neuroimaging analysis for cognitive neuroscience
Williams, N., & Henson, R. N. (2018) · Brain and Neuroscience Advances · doi:10.1177/2398212817752727 · Cited by 18
“There has also been a lot of work on simple neurobiological models that generate RSNs, in particular their graph-theoretic properties, such as small-worldness and presence of hubs (see Cabral et al., 2014, for a primer on different modelling approaches).”
BackgroundBillones, R. K. C., Bedruz, R. A., Caguicla, S. M. D., Ilagan, K. M. S., Monsale, K. R. C., Santos, A. G. G., Valenzuela, I. C., Villanueva, J. P., & Dadios, E. P. (2018) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem.2018.8666392 · Cited by 18
“This includes the resting-state activity of the brain which may provide a general perspective on the brain function [9].”
BackgroundEvidence for modulation of EEG microstates by mental workload levels and task types
Chen, J., Ke, Y., Ni, G., Liu, S., & Ming, D. (2023) · Human Brain Mapping · doi:10.1002/hbm.26552 · Cited by 16
“A variety of neuroimaging methods, such as fNIRS and fMRI, were used to reveal these networks (Cabral et al., 2014; Deco et al., 2011; Geng et al., 2017).”
MethodDagnino, P. C., Escrichs, A., López‐González, A., Gosseries, O., Annen, J., Perl, Y. S., Kringelbach, M. L., Laureys, S., & Deco, G. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011350 · Cited by 18
Self-citationFunctional neuroanatomy of human cortex cerebri in relation to wanting sex and having it
Georgiadis, J. R. (2015) · Clinical Anatomy · doi:10.1002/ca.22528 · Cited by 17
“…is that the brain does not possess an isolated or dedicated “sex cortex”: the human cortex cerebri is organized in several functional modules (“networks” comprising several cortical areas) that differ in the degrees of functional connectivity within and among them (see e.g., Cabral et al., 2014).”
BackgroundNeurocomputational Model of EEG Complexity during Mind Wandering
Ibáñez-Molina, A. J., & Iglesias‐Parro, S. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00020 · Cited by 18
“The computational model we used (a variant of Kuramoto model to take into account conduction delays) has showed rather relevant research results (Breakspear et al., 2010; Cabral et al., 2011, 2014).”
BackgroundMethodInfluentialRaj, A., Verma, P., & Nagarajan, S. S. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.959557 · Cited by 15
“Node-level Kuramoto oscillators were coupled using realistic brain connectivity and time delays determined by fiber lengths (Cabral et al., 2014; Finger et al., 2016).”
BackgroundMethodDifferential Effects of Simulated Cortical Network Lesions on Synchrony and EEG Complexity
Ibáñez-Molina, A. J., Iglesias‐Parro, S., & Escudero, J. (2018) · International Journal of Neural Systems · doi:10.1142/s0129065718500247 · Cited by 15
Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics
Jeong, S., Kang, J., Pae, C., Eo, J., Park, S., Son, J., & Park, H. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118618 · Cited by 14
Tau trajectory in Alzheimer’s disease: Evidence from the connectome-based computational models
Bitra, V. R., Challa, S. R., Adiukwu, P. C., & Rapaka, D. (2023) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2023.110777 · Cited by 15
Learning the effective order of a hypergraph dynamical system
Neuhäuser, L., Scholkemper, M., Tudisco, F., & Schaub, M. T. (2024) · Science Advances · doi:10.1126/sciadv.adh4053 · Cited by 16
“Kuramoto oscillator dynamics have been applied to various synchronization phenomena of phase oscillators 36 , ranging from power networks 37 to brain activity 38 .”
Zarghami, T. S., Hossein‐Zadeh, G., & Bahrami, F. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00214 · Cited by 14
Co-evolving dynamics and topology in a coupled oscillator model of resting brain function
Pope, M., Seguin, C., Varley, T. F., Faskowitz, J., & Sporns, O. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120266 · Cited by 14
Examining Neural Connectivity in Schizophrenia Using Task-Based EEG: A Graph Theory Approach
Iglesias‐Parro, S., Soriano, M. F., Ibáñez-Molina, A. J., Pérez-Matres, A. V., & de Miras, J. R. (2023) · Sensors · doi:10.3390/s23218722 · Cited by 13
Rosso, M., Fernández‐Rubio, G., Keller, P. E., Brattico, E., Vuust, P., Kringelbach, M. L., & Bonetti, L. (2025) · Advanced Science · doi:10.1002/advs.202413195 · Cited by 19
Self-citationTiesinga, P. H. E., Bakker, R., Hill, S., & Bjaalie, J. G. (2015) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.02.003 · Cited by 15
“An alternative strategy for characterizing neural circuits is to record neural activity using imaging methods or electrodes, as reviewed in the other papers in this issue, and to infer connectivity by various means [62-64].”
BackgroundNeuroscience and Whitehead I: Neuro-ecological Model of Brain
Northoff, G. (2016) · Axiomathes · doi:10.1007/s10516-016-9286-2 · Cited by 14
Connection strength of the macaque connectome augments topological and functional network attributes
de Lange, S. C., Ardesch, D. J., & van den Heuvel, M. P. (2019) · Network Neuroscience · doi:10.1162/netn_a_00101 · Cited by 12
“according to structural pathways and functional dynamics estimated from the synchronization between oscillators (Cabral et al., 2014; Kuramoto, 1975; Rodrigues et al., 2016; Vuksanović & Hövel, 2014).”
Ahn, J. S., Heo, J., Oh, J., Lee, D., Jhung, K., Kim, J., & Park, J. Y. (2022) · Brain Sciences · doi:10.3390/brainsci12020274 · Cited by 15
Predicting resting-state functional connectivity with efficient structural connectivity
Chen, X., & Wang, Y. (2018) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2017.7510880 · Cited by 14
“In consideration of the spontaneous dynamics of interacting brain areas, neural mass models coupled with anatomical architecture are developed to understand the emergence of resting-state fluctuations [5].”
BackgroundBayesian inference of a spectral graph model for brain oscillations
Jin, H., Verma, P., Jiang, F., Nagarajan, S. S., & Raj, A. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120278 · Cited by 12
“It is, therefore, necessary to capture their variability and find out all possible parameter settings compatible with the observed phenomena (Gonçalves et al., 2020).”
Stability and dynamics of a spectral graph model of brain oscillations
Verma, P., Nagarajan, S. S., & Raj, A. (2022) · Network Neuroscience · doi:10.1162/netn_a_00263 · Cited by 11
“Numerous evidences have pointed toward the brain being multistable (Kelso, 2012) and several modeling approaches have indicated that the brain functional activity exhibits multistability by operating close to a bifurcation point (Cabral et al., 2014; Deco & Jirsa, 2012; Deco et al., 2017; Freyer et al., 2011; Golos, J…”
BackgroundHämäläinen, M., Huang, M., & Bowyer, S. M. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.02.001 · Cited by 12
State-Dependent Effective Connectivity in Resting-State fMRI
Park, H., Eo, J., Pae, C., Son, J., Park, S., & Kang, J. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.719364 · Cited by 12
“However, according to the nonlinear network theory, the brain transits spontaneously across multiple metastable states (Freyer et al., 2011, 2012; Rabinovich and Varona, 2011; Deco and Jirsa, 2012; Kelso, 2012; Cabral et al., 2014; Tognoli and Kelso, 2014; Deco et al., 2015; Breakspear, 2017).”
BackgroundArvin, S., Glud, A. N., & Yonehara, K. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.783474 · Cited by 14
“…equations, however, simulations have previously been applied to macaque (Honey and Sporns, 2008), and human brain research (Kitzbichler et al., 2009; Cabral et al., 2014), showing high congruence between simulation data and resting-state activity (Cabral et al., 2014; Vuksanovi´c and Hövel, 2014).”
MethodTowards an efficient validation of dynamical whole-brain models
Wischnewski, K. J., Eickhoff, S. B., Jirsa, V., & Popovych, O. V. (2022) · Scientific Reports · doi:10.1038/s41598-022-07860-7 · Cited by 15
“This procedure, often termed grid search , has been applied in a variety of model-based studies 16,35,37,38 .”
MethodPaths to Oblivion: Common Neural Mechanisms of Anaesthesia and Disorders of ConsciousnessPreprint
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Allanson, J. E., Pickard, J. D., Williams, G. B., Craig, M. M., Finoia, P., Peattie, A. R. D., Coppola, P., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.14.431140 · Cited by 12
Metadata
- Authors
- Cabral, J., Kringelbach, M. L., & Deco, G.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Aarhus University (DK)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Corresponding author
- Cabral, Joana
- Venue
- Progress in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 114
- Pages
- 102-131
- ISSN
- 0301-0082 (ISSN-L)
- OpenAlex
- W1973272857
- Cited by
- 351
- References
- 254
- Citations by year
- 2026: 14 · 2025: 24 · 2024: 35 · 2023: 25 · 2022: 29 · 2021: 28 · 2020: 28 · 2019: 25 · 2018: 33 · 2017: 34 · 2016: 27 · 2015: 35 · 2014: 14
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Progress in Neurobiology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, other-oa)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc-nd)
- Funders
- ERC Advanced Grant DYSTRUCTURE (295129)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- resting-state brain activity, network dynamics, functional brain networks, bifurcation, criticality, EEG, MEG, functional MRI, oscillatory dynamics, mental health
- MeSH terms
- Animals, Brain, Humans, Neural Pathways, Rest, Nonlinear Dynamics, Neuroimaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
