Cited by 1,334Functional Brain Connectivity StudiesComplex Network Analysis TechniquesAdvanced Neuroimaging Techniques and Applications

The DOI matches its Crossref record; 143 of the 143 references it cites were found in a registry or an index.

Modular Brain Networks

Open access

Sporns, Olaf, Betzel, Richard F.·2016·Annual Review of Psychology

DOI 10.1146/annurev-psych-122414-033634PMC PMC4782188

Open this paper in CoChat — free account Open PDF

Abstract

The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics.

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

Crossref · 10.1146/annurev-psych-122414-033634

Cites

143 of 143 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
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
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Self-citation
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
Self-citation
Community detection in graphs
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
The Modularity of Mind
Fodor, J. A. (1983) · doi:10.7551/mitpress/4737.001.0001 · Cited by 7,734

Show all 142 references

Research works printed but not linked: 3

MacMahon M, 2015, Phys. Rev. X, 5, 021006 [physics
Simon HA, 1962, Proc. Am. Philos. Soc., 106, 467
Zamora-Lopez G, 2010, Front. Neuroinform., 4, 1

Sorted by citations

Items 701–800 of 998

Graph theory analysis of a human body metabolic network: A systematic and organ‐specific study
Ruan, J., Wu, Y., Wang, H., Huang, Z., Liu, Z., Yang, X., Yang, Y., Zheng, H., Liang, D., Wang, M., et al. (2024) · Medical Physics · doi:10.1002/mp.17568 · Cited by 8
A Taxonomy of Neuroscientific Strategies Based on Interaction Orders
Neri, M., Brovelli, A., Castro, S., Fraisopi, F., Gatica, M., Herzog, R. A., Mediano, P. A. M., Mindlin, I., Petri, G., Bor, D., et al. (2025) · European Journal of Neuroscience · doi:10.1111/ejn.16676 · Cited by 11
Multi-Modal Brain Network Fusion for Intelligent Diagnostic Devices
Li, S., Zhu, Q., Sun, L., Ma, K., Ji, Y., Qi, S., & Zhang, D. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3563691 · Cited by 8
Brain Connectivity-Informed Regularization Methods for Regression
Karas, M., Brzyski, D., Džemidžić, M., Goñi, J., Kareken, D. A., Randolph, T. W., & Harezlak, J. (2017) · Statistics in Biosciences · doi:10.1007/s12561-017-9208-x · Cited by 8
Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room
Shafiei, S. B., Jing, Z., Attwood, K. M., Iqbal, U. H., Arman, S., Hussein, A. A., Durrani, M., & Guru, K. A. (2021) · Brain Sciences · doi:10.3390/brainsci11040468 · Cited by 8
Music to My Ears: Neural modularity and flexibility differ in response to real-world music stimuli
Bonomo, M. E., Brandt, A., Frazier, J. T., & Karmonik, C. (2022) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2021.12.007 · Cited by 10
Investigation of functional brain network reconfiguration during exposure to naturalistic stimuli using graph-theoretical analysis
Zhang, G., & Liu, X. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac20e7 · Cited by 8
Exploring dysconnectivity of the large-scale neurocognitive network across psychiatric disorders using spatiotemporal constrained nonnegative matrix factorization method
Li, Y., Zeng, W., Deng, J., Shi, Y., Nie, W., Luo, S., & Zhang, H. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab503 · Cited by 9
Changing Connectomes
Kaiser, M. (2020) · The MIT Press eBooks · doi:10.7551/mitpress/11980.001.0001 · Cited by 25
Bidirectionally connected cores in a mouse connectome: towards extracting the brain subnetworks essential for consciousness
Kitazono, J., Aoki, Y., & Oizumi, M. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac143 · Cited by 10
Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher’s Choice Paths
Dimitriadis, S. I. (2022) · Neuroinformatics · doi:10.1007/s12021-022-09610-6 · Cited by 6
Modularity and neuronal heterogeneity: Two properties that influence in vitro neuropharmacological experiments
Brofiga, M., Poggio, F., Callegari, F., Tedesco, M., & Massobrio, P. (2023) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2023.1147381 · Cited by 8
Plastic neural network with transmission delays promotes equivalence between function and structure
Protachevicz, P. R., Borges, F. S., Batista, A. M., Baptista, M. D. S., Caldas, I. L., Macau, E. E. N., & Lameu, E. L. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113480 · Cited by 8
Effect of antipsychotics on community structure in functional brain networks
Flanagan, R., Lacasa, L., Towlson, E. K., Lee, S. H., & Porter, M. A. (2019) · Journal of Complex Networks · doi:10.1093/comnet/cnz013 · Cited by 8
Combining Non-negative Matrix Factorization and Sparse Coding for Functional Brain Overlapping Community Detection
Li, X., Hu, Z., & Wang, H. (2018) · Cognitive Computation · doi:10.1007/s12559-018-9585-6 · Cited by 8
A graph representation of functional diversity of brain regions
Yin, D., Chen, X., Zeljic, K., Zhan, Y., Shen, X., Yan, G. F., & Wang, Z. (2019) · Brain and Behavior · doi:10.1002/brb3.1358 · Cited by 8
Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases
Yuan, Y., Liu, J., Zhao, P., Fu, X., Huo, H., & Fang, T. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00892 · Cited by 9
Dynamical Characteristics of State Transition Defined by Neural Activity of Phase in Alzheimer’s Disease
Nobukawa, S., Ikeda, T., Kikuchi, M., & Takahashi, T. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-92310-5_6 · Cited by 8
A Graph Signal Processing View on Functional Brain ImagingPreprint
Huang, W., Bolton, T. A. W., Medaglia, J. D., Bassett, D. S., Ribeiro, A., & Ville, D. V. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.01135 · Cited by 8
Fast embedding of multilayer networks: An algorithm and application to group fMRI.Preprint
Wilson, J. D., Baybay, M., Sankar, R., & Stillman, P. E. (2018) · arXiv (Cornell University) · Cited by 8
Dynamic large-scale connectivity of intrinsic cortical oscillations supports adaptive listening in challenging conditionsPreprint
Alavash, M., Tune, S., & Obleser, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.22.432315 · Cited by 8
Dynamic Network Connectivity: from monkeys to humans
Arnsten, A. F. T., Wang, M., & D’Esposito, M. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1353043 · Cited by 8
Topological disruption of low- and high-order functional networks in presbycusis
Xu, Y., Li, X., Qi, Y., Zhang, Y., Shang, S., Xing, C., Wu, Y., Guan, B., & Chen, Y. (2024) · Brain Communications · doi:10.1093/braincomms/fcae119 · Cited by 7
Attribute Diversity Aware Community Detection on Attributed Graphs Using Three-View Graph Attention Neural Networks
Zhang, Y., Yu, T., Chi, S., Wang, Z., Gao, Y., Zhang, J., & Zhou, T. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3672081 · Cited by 7
Network topology and metabolic alterations in early- and mid-stage Parkinson’s disease: insights from fluorodeoxyglucose PET imaging
Li, M., Liu, J., Lv, R., Liu, F., Wang, G., Wang, J., Cheng, J., Jia, M., Wang, N., & Liu, S. (2025) · Nuclear Medicine Communications · doi:10.1097/mnm.0000000000001951 · Cited by 7
Spatially bound functional heterogeneity drives modular organization in β-cell networks
Duh, M., Šterk, M., Bombek, L. K., MacDonald, P. E., Stožer, A., & Gosak, M. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2025.07.043 · Cited by 8
Cognitive and neural mechanisms of mental imagery supporting creative cognition
Gu, J., Wang, X., Liu, C., Yang, L., Fan, J., Sun, J., Kenett, Y. N., & Qiu, J. (2025) · Communications Biology · doi:10.1038/s42003-025-08513-x · Cited by 8
Graph neural networks for fMRI functional brain networks: A survey
Tang, J., Zhu, T., Zhou, W., & Zhao, W. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108137 · Cited by 7
Prenatal stress dysregulates resting-state functional connectivity and sensory motifs
Rezaei, Z., Jafari, Z., Afrashteh, N., Torabi, R., Singh, S., Kolb, B. E., Davidsen, J., & Mohajerani, M. H. (2021) · Neurobiology of Stress · doi:10.1016/j.ynstr.2021.100345 · Cited by 9
High activity and high functional connectivity are mutually exclusive in resting state zebrafish and human brains
Zarei, M., Xie, D., Jiang, F., Bagirov, A., Huang, B., Raj, A., Nagarajan, S. S., & Guo, S. (2022) · BMC Biology · doi:10.1186/s12915-022-01286-3 · Cited by 7
Abnormal dynamic reconfiguration of the large-scale functional network in schizophrenia during the episodic memory task
Wang, B., Pan, T., Guo, M., Li, Z., Yu, X., Li, D., Niu, Y., Cui, X., & Xiang, J. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac331 · Cited by 7
Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks
Pak, V., & Hashmi, J. A. (2023) · Network Neuroscience · doi:10.1162/netn_a_00328 · Cited by 10
Brain Network Modularity and Resilience Signaled by Betweenness Centrality Percolation Spiking
Kotlarz, P. L., Febo, M., Nino, J. C., & Initiative, O. B. O. T. A. D. N. (2024) · Applied Sciences · doi:10.3390/app14104197 · Cited by 8
Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2
Xu, Y., Liao, X., Lei, T., Cao, M., Zhao, J., Zhang, J., Zhao, T., Li, Q., Jeon, T., Ouyang, M., et al. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae204 · Cited by 7
Development of the whole-brain functional connectome explored via graph theory analysis
Hassett, J. D., Craig, B. T., Hilderley, A. J., Kinney‐Lang, E., Yeates, K. O., MacMaster, F. P., Miller, J. V., Noel, M. E., Brooks, B. L., Barlow, K. M., et al. (2024) · Aperture Neuro · doi:10.52294/001c.124565 · Cited by 10
Multiview Graph Learning With Consensus Graph
Karaaslanli, A., & Aviyente, S. (2025) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2025.3527888 · Cited by 7
Comprehensive analysis of slow-fast denatured Morris-Lecar neurons
Ghosh, I., Fatoyinbo, H. O., & Muni, S. S. (2025) · Physical review. E · doi:10.1103/physreve.111.044204 · Cited by 7
Increase in internetwork functional connectivity in the human brain with attention capture
Sun, H., Yue, Q., Sy, J. L., Godwin, D., Eaton, H. P., Raghavan, P., & Marois, R. (2020) · Journal of Neurophysiology · doi:10.1152/jn.00693.2019 · Cited by 6
Evaluation of eight-style Tai chi on cognitive function in patients with cognitive impairment of cerebral small vessel disease: study protocol for a randomised controlled trial
Zhong, X., Yan, X., Liang, H. Y., Xia, R., Chen, B., & Zhao, H. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-042177 · Cited by 7
Maximum Entropy Principle Underlies Wiring Length Distribution in Brain Networks
Song, Y., Zhou, D., & Li, S. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab110 · Cited by 8
Aging-Related Modular Architectural Reorganization of the Metabolic Brain Network
Huang, Q., Ren, S., Zhang, T., Li, J., Jiang, D., Xiao, J., Hua, F., Xie, F., & Guan, Y. (2021) · Brain Connectivity · doi:10.1089/brain.2021.0054 · Cited by 7
Nonlinear reconfiguration of network edges, topology and information content during an artificial learning task
Shine, J. M., Li, M., Koyejo, S., Fulcher, B., & Lizier, J. T. (2021) · Brain Informatics · doi:10.1186/s40708-021-00147-z · Cited by 9
Multi-layer analysis of multi-frequency brain networks as a new tool to study EEG topological organization
Puxeddu, M. G., Petti, M., & Laura Astolfi, A. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630173 · Cited by 7
Neurophysiological and Behavioral Differences in Human-Multiagent Tasks: An EEG Network Perspective
Bales, G. L., & Kong, Z. (2022) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3527928 · Cited by 7
Psychological resilience and neurodegenerative risk: A connectomics‐transcriptomics investigation in healthy adolescent and middle‐aged females
Petrican, R., Fornito, A., & Jones, N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119209 · Cited by 7
The Task-Dependent Modular Covariance Networks Unveiled by Multiple-Way Fusion-Based Analysis
Jiang, L., Li, F., Chen, B., Yi, C., Peng, Y., Zhang, T., Yao, D., & Xu, P. (2022) · International Journal of Neural Systems · doi:10.1142/s0129065722500356 · Cited by 7
Probabilistically Weighted Multilayer Networks disclose the link between default mode network instability and psychosis-like experiences in healthy adults
Plinio, S. D., & Ebisch, S. J. H. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119291 · Cited by 8
Sensory stimulation for upper limb amputations modulates adaptability of cortical large-scale systems and combination of somatosensory and visual inputs
Ding, K., Chen, Y., Bose, R., Osborn, L. E., Dragomir, A., & Thakor, N. V. (2022) · Scientific Reports · doi:10.1038/s41598-022-24368-2 · Cited by 7
Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses
Lewis, M., Santini, T., Theis, N. D., Muldoon, B., Dash, K., Rubin, J. E., Keshavan, M. S., & Prasad, K. M. R. (2023) · Scientific Reports · doi:10.1038/s41598-023-34210-y · Cited by 7
Evaluation of Module Dynamics in Functional Brain Networks After Stroke*
Wu, K., Fang, Q., Neville, K. L., & Jelfs, B. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340633 · Cited by 7
Toward neuroimaging‐based network biomarkers for transient ischemic attack
Lv, Y., Han, X., Song, Y., Han, Y., Zhou, C., Zhou, D., Zhang, F., Xue, Q., Liu, J., Zhao, L., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24602 · Cited by 7
Functional Network Dynamics on Functional MRI: A Primer on an Emerging Frontier in Neuroscience
Eijlers, A. J. C., Wink, A. M., Meijer, K. A., Douw, L., Geurts, J. J. G., & Schoonheim, M. M. (2019) · Radiology · doi:10.1148/radiol.2019194009 · Cited by 7
Untitled work
Hampshire (2020) · doi:10.1016/j.bpsc.2019.09.004 · Cited by 7
Modeling functional resting-state brain networks through neural message passing on the human connectome
Peraza, J. A., Martínez-Montes, E., Aubert, E., Valdes-Hernandez, P. A., & Mulet, R. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.11.014 · Cited by 8
BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks
Corsi, M., Chávez, M., Schwartz, D., George, N., Hugueville, L., Kahn, A. E., Dupont, S., Bassett, D. S., & Fallani, F. D. V. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abef39 · Cited by 8
Cytosolic PSD-95 interactor alters functional organization of neural circuits and AMPA receptor signaling independent of PSD-95 binding
Rodriguez, A. R., Anderson, E. D., O’Neill, K. M., McEwan, S. A., Vigilante, N. F., Kwon, M., Akum, B. F., Stawicki, T. M., Meaney, D. F., & Firestein, B. L. (2020) · Network Neuroscience · doi:10.1162/netn_a_00173 · Cited by 8
Towards understanding neurocognitive mechanisms of parenting: Maternal behaviors and structural brain network organization in late childhood
Richmond, S., Beare, R. J., Johnson, K. A., Allen, N. B., Seal, M. L., & Whittle, S. (2021) · Human Brain Mapping · doi:10.1002/hbm.25334 · Cited by 7
Network disruption based on multi-modal EEG-MRI in α-synucleinopathies
Wang, C., Hu, J., Li, P., Zhang, M., Zhou, L., Luo, N., Zhu, X., Yin, Q., Zhong, M., Zhou, X., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1442851 · Cited by 6
Trade-offs among brain structural network characteristics across the cognitive decline process in cerebral small vessel disease
Wang, Y., Liu, M., Chen, Y., Qiu, Y., Han, X., Xu, Q. J., Shen, D. G., & Zhou, Y. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1465181 · Cited by 6
Disrupted small-world architecture and altered default mode network topology of brain functional network in college students with subclinical depression
Zhang, B., Liu, S., Chen, S., Liu, X., Ke, Y., Qi, S., Wei, X., & Ming, D. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-06609-6 · Cited by 8
Mapping the psychopathic brain: Divergent neuroimaging findings converge onto a common brain network
Dugre, J. R., & Brito, S. A. D. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106272 · Cited by 8
Contrast subgraphs catch patterns of altered functional connectivity in autism spectrum disorder
Lanciano, T., Petri, G., Gili, T., & Bonchi, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-04932-2 · Cited by 7
Comparative evaluation of graph construction methods for individual brain metabolic network from FDG-PET images: an ADNI study in healthy subjects
Tuấn, P. M., Horowitz, T., Adel, M., Wojak, J., Trung, N. L., & Guedj, É. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07462-1 · Cited by 6
Oxytocin modulation of resting-state functional connectivity network topology in individuals with higher autistic traits
Hagan, A., Xu, L., Kou, J., Hu, Y., Klugah‐Brown, B., Li, J., Bore, M. C., Becker, B., Kendrick, K. M., & Jiang, X. (2025) · Psychoradiology · doi:10.1093/psyrad/kkaf021 · Cited by 6
dFCExpert: Learning Dynamic Functional Connectivity Patterns With Modularity and State Experts
Chen, T., Li, H., Zheng, H., & Yong Fan, A. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3617310 · Cited by 6
Topological data analysis for revealing dynamic brain reconfiguration in MEG data
Duman, A. N., & Tatar, A. E. (2023) · PeerJ · doi:10.7717/peerj.15721 · Cited by 6
Static and dynamic interactions within the triple-network model in stroke patients with multidomain cognitive impairments
Wang, Y., Chen, H., Wang, C., Liu, J., Miao, P., Wei, Y., Wu, L., Wang, X., Wang, P., Zhang, Y., et al. (2024) · NeuroImage Clinical · doi:10.1016/j.nicl.2024.103655 · Cited by 6
A Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits
Duong-Tran, D. A., Nguyen, N., Mu, S., Chen, J., Bao, J., Xu, F. H., Garai, S., Pico, J. E. C., Kaplan, A. D., Chen, T., et al. (2024) · Mathematics · doi:10.3390/math12192967 · Cited by 6
Topological features of brain functional networks are reorganized during chronic tinnitus: A graph‐theoretical study
Han, S., Shen, Y., Wu, X., Dai, H., Li, Y., Liu, J., & Tao, D.-D. (2025) · European Journal of Neuroscience · doi:10.1111/ejn.16643 · Cited by 6
Single-cell resolution functional networks during unconsciousness are segregated into spatially intermixed modules
Kiyooka, D., Oomoto, I., Kitazono, J., Saito, Y., Kobayashi, M., Matsubara, C., Kobayashi, K., Murayama, M., & Oizumi, M. (2026) · Cell Reports · doi:10.1016/j.celrep.2025.116902 · Cited by 8
Stability of Network Communities as a Function of Task Complexity
Fuertinger, S., & Simonyan, K. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01026 · Cited by 6
The Fragmented Personality
Švrakić, D. M., & Jovanovic, M. D. (2018) · doi:10.1093/med/9780190884574.001.0001 · Cited by 7
Cohesive communities in dynamic brain functional networks
Fan, Y., Fan, Q., Zhou, L., Wang, R., Lin, P., & Wu, Y. (2021) · Physical review. E · doi:10.1103/physreve.104.014302 · Cited by 6
Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips
Ge, M., Ni, X., Xu, Q., Chen, S., Huang, J., Kang, Y., & Wu, F. H. (2021) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3480961 · Cited by 6
Toward an integrative neurovascular framework for studying brain networks
Guilbert, J., Légaré, A., Koninck, P. D., Desrosiers, P., & Desjardins, M. (2022) · Neurophotonics · doi:10.1117/1.nph.9.3.032211 · Cited by 9
Seeing the future: Connectome strength and network efficiency in visual network predict individual ability of episodic future thinking
Hu, B., Zhang, R., & Feng, T. (2022) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2022.108451 · Cited by 6
Enhanced intersubject similarity in functional connectivity by long-term abacus training
Zhang, Y., Xu, T., Han, X., Wang, Y., Liu, H., Zhou, C., & Chen, F. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad146 · Cited by 6
Disrupted topological properties of structural brain networks present a glutamatergic neuropathophysiology in people with narcolepsy
Chen, G., Wang, W., Wu, H., Zhao, X., Kang, X., Ren, J., Zhang, J., Sun, Y., He, J., Sun, S., et al. (2024) · SLEEP · doi:10.1093/sleep/zsae002 · Cited by 6
Fitting networks models for functional brain connectivity
Rajapakse, J. C., Gupta, S., & Sui, X. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950573 · Cited by 6
The intrinsic neural architecture of inhibitory control: The role of development and emotional experience
Petrican, R., & Grady, C. L. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.01.021 · Cited by 6
Configuring the Interval Target in a Multilayer Feedforward Neural Network on the Example of the Problem of Medical Diagnostics
Mirkin, E. L., & Savchenko, E. Y. (2020) · Springer proceedings in physics · doi:10.1007/978-3-030-58868-7_51 · Cited by 6
Does an Over-Connected Visual Cortex Undermine Efforts to Stay Sober After Treatment for Alcohol Use Disorder?
Muller, A. M., & Meyerhoff, D. J. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.536706 · Cited by 6
Brain’s Networks and Their Functional Significance in Cognition
Dragomir, A., & Omurtag, A. (2021) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_76-1 · Cited by 7
Cross-Sectional Characterization of Local Brain Network Connectivity Pre and Post Breast Cancer Treatment and Distinct Association With Subjective Cognitive and Psychological Function
Kesler, S. R., Tang, T. T., Henneghan, A. M., Wright, M. L., Gaber, M. W., & Palesh, O. G. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.746493 · Cited by 6
Measuring and Fostering Diversity in Affective Computing Research
Hupont, I., Tolan, S., Frau, P., Porcaro, L., & Gómez, E. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3244041 · Cited by 6
Altered Regional Brain Communities during High Order Cognitive Processes: Relation to Vigilance Decrement
Abbasi, N. I., Bose, R., Kumar, Y., Bodala, I. P., Bezerianos, A., Thakor, N. V., & Dragomir, A. (2019) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2019.8717003 · Cited by 6
Module partitioning for multilayer brain functional network using weighted clustering ensemble
Jiao, Z., Ming, X., Cao, Y., Cheng, C., & Wang‎, S. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01535-4 · Cited by 6
iWordNet: A New Approach to Cognitive Science and Artificial Intelligence
Chang, M., & Chang, M. (2017) · Advances in Artificial Intelligence · doi:10.1155/2017/1948317 · Cited by 6
Autonomic Control of Heart Rate in the Neonatal Rhesus Monkey
Goldberg, J. M., & Moberg, G. P. (1985) · Journal of Medical Primatology · doi:10.1111/j.1600-0684.1985.tb00167.x · Cited by 6
A contraction based, singular perturbation approach to near-decomposability in complex systemsPreprint
Bousquet, G. D., & Slotine, J.-J. E. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.08464 · Cited by 6
Brain Modularity Mediates the Relation between Task Complexity and PerformancePreprint
Yue, Q., Martin, R. C., Fischer‐Baum, S., Ramos-Nuñez, A. I., Ye, F., & Deem, M. W. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/224949 · Cited by 6
System-level high-amplitude co-fluctuationsPreprint
Betzel, R. F., Chumin, E. J., Esfahlani, F. Z., Tanner, J., & Faskowitz, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.26.501262 · Cited by 7
Self-citation
Spontaneous pathology in PS19 tauopathy mice progresses via brain networks
Ramirez, D. M. O., Whitesell, J. D., Bhagwat, N., Thomas, T. L., Ajay, A. D., Nawaby, A., Delatour, B., Bay, S., LaFaye, P., Harris, J. A., et al. (2025) · Neurobiology of Disease · doi:10.1016/j.nbd.2025.107072 · Cited by 5
Functional Segregation‐Integration Preference Configures the Cognitive Decline Against Cerebral Small Vessel Disease: An MRI Study
Hu, W., Wang, Y., Xie, Z., Liu, M., Han, X., Hu, Y., Wang, X., Dai, Y., Xu, Q. J., & Zhou, Y. F. (2024) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.70162 · Cited by 5
A Machine learning classification framework using fused fractal property feature vectors for Alzheimer’s disease diagnosis
Sun, S., Cui, C., Li, Y., Meng, Y., Pan, W., & Li, D. (2024) · Brain Research · doi:10.1016/j.brainres.2024.149373 · Cited by 5
Neural basis of adolescent THC-induced potentiation of opioid responses later in life
Hubbard, E., Derdeyn, P., Galinato, V. M., Wu, A., Bartas, K., Mahler, S. V., & Beier, K. T. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-02033-8 · Cited by 5
The balance between integration and segregation drives network dynamics maximizing multistability and metastability
Palma-Espinosa, J., Villouta, S. O., Coronel‐Oliveros, C., Maidana, J. P., & Orio, P. (2025) · Scientific Reports · doi:10.1038/s41598-025-01612-z · Cited by 5
Multimodal Imaging Markers of Cognitive Resilience and Molecular Mechanisms of Brain Resilience in Alzheimer’s Disease
Yang, Z. X., Sheng, J., Zhang, Q., Zhao, X., Song, Y., Dong, G., Zhang, R., Zhao, H., Wang, J., Pan, R., et al. (2025) · Journal of Integrative Neuroscience · doi:10.31083/jin26584 · Cited by 7
Modular architecture confers robustness to damage and facilitates recovery in spiking neural networks modeling in vitro neurons
Sumi, T., Houben, A. M., Yamamoto, H., Kato, H., Katori, Y., Soriano, J., & Hirano‐Iwata, A. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1570783 · Cited by 5
Controlling the human connectome with spatially diffuse input signals
Betzel, R. F., Puxeddu, M. G., Seguin, C., Bazinet, V., Luppi, A. I., Podschun, A. N., Singleton, S. P., Faskowitz, J., Parakkattu, V., Mišić, B., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-09560-8 · Cited by 5
Self-citation

Authors: Sporns, O., & Betzel, R. F.
Affiliations: Indiana University Bloomington (US)
ORCID: Sporns, Olaf
Betzel, Richard F.
Venue: Annual Review of Psychology
Publisher: Annual Reviews
Published: 2016-01-04
Volume: 67
Issue: 1
Pages: 613-640
ISSN: 0066-4308 (ISSN-L)
1545-2085
DOI: 10.1146/annurev-psych-122414-033634
PMC: PMC4782188
OpenAlex: W2143502460
Cited by: 1,334
References: 142
Citations by year: 2026: 102 · 2025: 147 · 2024: 138 · 2023: 133 · 2022: 137 · 2021: 145 · 2020: 135 · 2019: 113 · 2018: 129 · 2017: 93 · 2016: 53
FWCI: 9.31
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Annual Review of Psychology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Science Foundation (0903495)
James S. McDonnell Foundation (#22002082)
National Institutes of Health
Division of Graduate Education
National Center for Complementary and Integrative Health (R01AT009036)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords: brain networks, network modules, community detection, structural connectivity, functional connectivity, graph theory, network neuroscience, brain evolution, functional specialization, complex dynamics
MeSH terms: Brain, Brain Mapping, Humans, Nerve Net

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

Modular Brain Networks

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks