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Efficiency of Functional Brain Networks and Intellectual Performance
Open accessAbstract
Our brain is a complex network in which information is continuously processed and transported between spatially distributed but functionally linked regions. Recent studies have shown that the functional connections of the brain network are organized in a highly efficient small-world manner, indicating a high level of local neighborhood clustering, together with the existence of more long-distance connections that ensure a high level of global communication efficiency within the overall network. Such an efficient network architecture of our functional brain raises the question of a possible association between how efficiently the regions of our brain are functionally connected and our level of intelligence. Examining the overall organization of the brain network using graph analysis, we show a strong negative association between the normalized characteristic path length lambda of the resting-state brain network and intelligence quotient (IQ). This suggests that human intellectual performance is likely to be related to how efficiently our brain integrates information between multiple brain regions. Most pronounced effects between normalized path length and IQ were found in frontal and parietal regions. Our findings indicate a strong positive association between the global efficiency of functional brain networks and intellectual performance.
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“advancements in the study of structural and functional connectomics have provided substantial evidence of distinct patterns of brain activity organization, with segregated, yet integrated modules that dynamically interact during rest and task-based activity (van den Heuvel et al. 2009; Eickhoff et al. 2011; Laird et al. 2013; Raichle 2015; Margulies et al. 2016; Basile et al. 2022a)”
From Neuroanatomical correlates of peripersonal space: bridging the gap between perception, action, emotion and social cognition (2024) · cites this paper as van den Heuvel et al. 2009
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“the medial prefrontal cortex and precuneus (among other regions) were identified as having the greatest effect on network organization and global network efficiency.”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [98]
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“Experiments examining intelligence and network topology found that a higher IQ was negatively correlated with path length but not with clustering or overall connectivity.98”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [98]
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Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
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 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
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
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Efficient behavior of small-world networks
Latora, V., & Marchiori, M. (2001) · Physical Review Letters · doi:10.1103/physrevlett.87.198701 · Cited by 5,119
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Self-citationMapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Searching for a baseline: Functional imaging and the resting human brain
Gusnard, D. A., & Raichle, M. E. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094500 · Cited by 3,371
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Sengupta, A., Datta, S., Kar, S., & Routray, A. (2014) · IEEE India Conference · doi:10.1109/indicon.2014.7030422 · Cited by 5
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Wang, Y., Xu, M. H., Ren, L., Zhang, X., Wu, D., Yong He, A., Xu, N., & Yang, H. (2011) · doi:10.1109/iccad.2011.6105352 · Cited by 5
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Basten, U., & Fiebach, C. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.016 · Cited by 6
Lund, M. J., Alnæs, D., Rokicki, J., Schwab, S., Andreassen, O. A., Westlye, L. T., & Kaufmann, T. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276221 · Cited by 5
Silchenko, A. N., Hoffstaedter, F., & Eickhoff, S. B. (2023) · Human Brain Mapping · doi:10.1002/hbm.26481 · Cited by 4
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Mayorova, L. A., Samotaeva, I., Lebedeva, N. N., Лузин, Р. В., Gaskin, V., Rider, F. K., Михайловна, Т. А., Akzhigitov, R. G., & Guekht, A. B. (2017) · S S Korsakov Journal of Neurology and Psychiatry · doi:10.17116/jnevro2017117924-9 · Cited by 5
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Hubbard, N. A., Turner, M. P., Sitek, K. R., West, K. L., Kaczmarzyk, J., Himes, L. M., Thomas, B. P., Lu, H., & Rypma, B. (2021) · Human Brain Mapping · doi:10.1002/hbm.25352 · Cited by 4
Toppi, J., Fallani, F. D. V., Petti, M., Vecchiato, G., Maglione, A. G., Cincotti, F., Salinari, S., Mattia, D., Babiloni, F., & Astolfi, L. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610154 · Cited by 7
Zhang, X., Braun, U., Harneit, A., Zang, Z., Geiger, L. S., Betzel, R. F., Chen, J., Schweiger, J. I., Schwarz, K., Reinwald, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/604322 · Cited by 5
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Wang, Y., Xu, M. H., Ren, L., Zhang, X., Wu, D., Yong He, A., Xu, N., & Yang, H. (2011) · International Conference on Computer Aided Design · doi:10.5555/2132325.2132413 · Cited by 4
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Adeshina, A. M., & Hashim, R. (2015) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-015-0274-9 · Cited by 4
Klimenko, O., Aristizábal, A., & Restrepo, C. F. (2019) · Dialnet (Universidad de la Rioja) · doi:10.25057/25005731.1258 · Cited by 16
Kawagoe, T. (2022) · Life · doi:10.3390/life12030416 · Cited by 6
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Xin, X., Duan, F., Kranz, G. S., Shu, D., Fan, R., Gao, Y., Zheng, Y. L., & Chang, J. (2022) · Neurorehabilitation · doi:10.3233/nre-220107 · Cited by 4
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Farrugia, C., Galdi, P., Irazu, I. A., Scerri, K., & Bajada, C. J. (2024) · Brain Structure and Function · doi:10.1007/s00429-023-02751-7 · Cited by 4
Hu, H., Coppola, P., Stamatakis, E. A., & Naçi, L. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf015 · Cited by 6
Agency performance modulates resting-state variation in prefrontal brain regions
de Bézenac, C. E., Sluming, V., Alhazmi, F. H., & Corcoran, R. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.12.035 · Cited by 4
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Guo, X., Yang, Y., Wang, Y., Gao, N., & Ma, H. T. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512460 · Cited by 4
(2016) · Elsevier eBooks · doi:10.1016/b978-0-12-407908-3.00009-1 · Cited by 5
Yeo, R. A., Ryman, S. G., Thompson, M. E., van den Heuvel, M. P., de Reus, M. A., Pommy, J. M., Seaman, B., & Jung, R. E. (2016) · Personality and Individual Differences · doi:10.1016/j.paid.2016.06.037 · Cited by 4
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Gkigkitzis, I., Haranas, I. I., & Kotsireas, I. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-56246-9_1 · Cited by 4
Turtles all the way down? Psychometric approaches to the reduction problem
Kievit, R. (2014) · UvA-DARE (University of Amsterdam) · Cited by 3
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Keunen, K. (2017) · Utrecht University Repository (Utrecht University) · Cited by 4
Zhao, K., Zheng, Q., Che, T., Martin, D., Li, Q., Ding, Y., Zheng, Y., Liu, Y., & Li, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.09.418509 · Cited by 4
Intelligence and academic performance: Is it all in your head?Preprint
Bottenhorn, K. L., Bartley, J. E., Riedel, M. C., Salo, T., Bravo, E. I., Odean, R., Nazareth, A., Laird, R. W., Musser, E. D., Pruden, S. M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.23.427928 · Cited by 4
Maliske, L., Lehmann, K., Schurz, M., Hildebrandt, M. K., Jauk, E., & Kanske, P. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/c6gsz · Cited by 4
Ng, J., Yu, J., Feusner, J. D., & Hawco, C. S. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00234 · Cited by 3
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Frischkorn, G. T., Hilger, K., Kretzschmar, A., & Schubert, A. (2022) · Psychologische Rundschau · doi:10.1026/0033-3042/a000598 · Cited by 6
Functional brain network analysis using electroencephalography in late-onset Lennox–Gastaut syndrome
Wang, Z. J., Park, S., Kim, H. D., Kang, H., Kim, N., & Hur, Y. J. (2024) · Epilepsy Research · doi:10.1016/j.eplepsyres.2024.107370 · Cited by 3
Whole-brain modular dynamics at rest predict sensorimotor learning performance
Standage, D., Gale, D. J., Nashed, J. Y., Flanagan, J. R., & Gallivan, J. P. (2024) · Network Neuroscience · doi:10.1162/netn_a_00420 · Cited by 3
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Taguchi, T., Kitazono, J., Sasai, S., & Oizumi, M. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0802-24.2025 · Cited by 3
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Lewis, C. (2015) · Cited by 3
An Integrated, Dynamic Functional Connectome Underlies Intelligence
Cohen, J. R., & D’Esposito, M. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.017 · Cited by 3
Evaluating the Weight of the Evidence
Euler, M. J., & McKinney, T. L. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.008 · Cited by 3
Heterogeneity Analysis on Multi-State Brain Functional Connectivity and Adolescent Neurocognition
Wang, S., Constable, R. T., Zhang, H., & Zhao, Y. (2024) · Journal of the American Statistical Association · doi:10.1080/01621459.2024.2311363 · Cited by 3
Behavior can be decoded across the cortex when individual differences are considered
Nakuci, J., Yeon, J., Kim, J. H., Kim, S.-P., & Rahnev, D. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00359 · Cited by 4
de Souza, E. A., Vieira, B. H., & Salmón, C. E. G. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae412 · Cited by 3
Understanding basic and social emotions in Alzheimer's disease and frontotemporal dementia
Sola, C., Zanelli, V., Molinari, M. A., Casadio, C., Ricci, F., Carpentiero, O., Tondelli, M., Lui, F., Nichelli, P., & Benuzzi, F. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1535722 · Cited by 5
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van Amelsvoort, T. (2013) · doi:10.26481/spe.20130118ta · Cited by 3
Benuwa, B., Ghansah, B., & Wornyo, D. K. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016909624 · Cited by 3
Relating Variations in Network Connectivity to Cognitive Function
Hampson, M. (2010) · Cited by 3
Computational Models of Dysconnectivity in Large-Scale Resting-State Networks
Demirtaş, M., & Deco, G. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809825-7.00004-3 · Cited by 3
Relational Network of People Constructed on the Basis of Similarity of Brain Activities
Shinkuma, R., Nishida, S., Kado, M., Maeda, N., & Nishimoto, S. (2019) · IEEE Access · doi:10.1109/access.2019.2933990 · Cited by 3
An eccentric perspective on brain networks
de Reus, M. A. (2015) · Utrecht University Repository (Utrecht University) · Cited by 3
Image-on-Scalar Regression via Deep Neural Networks.Preprint
Zhang, D., Li, L., Sripada, C., & Kang, J. (2020) · arXiv (Cornell University) · Cited by 3
Wang, R., Su, X., Chang, Z., Wu, Y., & Lin, P. (2020) · doi:10.21203/rs.3.rs-94060/v1 · Cited by 3
Cellular substrates of functional network integration and memory in temporal lobe epilepsyPreprint
Douw, L., Nissen, I. A., Fitzsimmons, S., Santos, F. A. N., Hillebrand, A., van Straaten, E. C. W., Stam, C. J., Hamer, P. C. D. W., Baayen, J. C., Klein, M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.31.428369 · Cited by 3
Self-citationReliability and Fault‐Tolerant and Delay‐Tolerant Routing
Misra, S., & Goswami, S. (2017) · doi:10.1002/9781119114864.ch14 · Cited by 3
Xiao, X., Hammond, C. J., Salmeron, B. J., Wang, D., Gu, H., Zhai, T., Murray, L., Quam, A., Hill, J., Nguyen, H., et al. (2024) · medRxiv · doi:10.1101/2024.06.13.24308906 · Cited by 3
NBS-Predict: An Easy-to-Use Toolbox for Connectome-Based Machine Learning
Serin, E., Vaidya, N., Walter, H., & Kruschwitz, J. (2024) · Neuromethods · doi:10.1007/978-1-0716-4260-3_13 · Cited by 2
Intellectual ability and cortical homotopy development in children and adolescents
Chen, L.-Z., & Zuo, X. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101596 · Cited by 3
Xiao, X., Hammond, C. J., Salmeron, B. J., Wang, D., Gu, H., Zhai, T., Murray, L., Quam, A., Hill, J., Nguyen, H., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.33136 · Cited by 2
Zhu, S., Li, Y., Fang, P., Wang, T., Liu, L., Li, Y., Bi, K., Ding, S., Zhu, J., & Zhu, D. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.121744 · Cited by 2
Deep learning based decomposition of brain networks
Lee, P., Choi, M., Kim, D., Lee, S., Jeong, H., & Han, C. E. (2019) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic.2019.8669055 · Cited by 2
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Barbey, A. K. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.009 · Cited by 2
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Willoughby, E. A., & Lee, J. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.022 · Cited by 2
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Xu, J., Wu, Z., Nurnberger, A., & Sabel, B. A. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630628 · Cited by 1
Farahani, H., Blagojević, M., Azadfallah, P., Watson, P., Esrafilian, F., & Saljoughi, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-31172-7_5 · Cited by 2
Evaluación de la Flexibilidad Cognitiva en la vejez: Evidencias de validez del Test CAMBIOS
Krzemien, D., Bario, D. A., Ferreyra, F. A., & Richard’s, M. M. (2023) · Revista Evaluar · doi:10.35670/1667-4545.v23.n3.43890 · Cited by 2
SuperAging functional connectomics from resting-state functional MRI
Diamond, B. R., Sridhar, J., Maier, J., Martersteck, A., & Rogalski, E. J. (2024) · Brain Communications · doi:10.1093/braincomms/fcae205 · Cited by 2
Dynamic adaptation to novelty in the brain is related to arousal and intelligence
Tanner, J., Faskowitz, J., Kennedy, D. P., & Betzel, R. F. (2026) · Cerebral Cortex · doi:10.1093/cercor/bhag025 · Cited by 3
Charlebois-Poirier, A.-R., Davoudi, S., Lalancette, È., Knoth, I. S., & Lippé, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-85635-6 · Cited by 2
Molloy, M. F., Taxali, A., Angstadt, M., Greathouse, T., Toda-Thorne, K., McCurry, K. L., Weigard, A. S., Kardan, O., Burchell, L., Dziubinski, M., et al. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf074 · Cited by 2
Hysteresis in anesthesia and recovery: Experimental observation and dynamical mechanism
Su, C.-W., Zheng, L., Li, Y., Zhou, H., Wang, J., Huang, Z., & Lai, Y. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023289 · Cited by 2
Predicting Treatment Response from Resting State fMRI Data: Comparison of Parcellation Approaches
Ghosh, S., Keshavan, A., & Langs, G. (2013) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2013.64 · Cited by 2
Intelligence and embodiment: A statistical mechanics approach
Chinea, A., & Korutcheva, E. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.01.007 · Cited by 2
Strongly reinforced Pólya urns with graph-based competition
van der Hofstad, R., Holmes, M., Kuznetsov, A., & Ruszel, W. M. (2016) · The Annals of Applied Probability · doi:10.1214/16-aap1153 · Cited by 2
Topological analysis of the brain functional networks
Shijo, Y., Arakawa, S., & Murata, M. (2015) · International Conference on Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS) · doi:10.4108/icst.bict.2014.257881 · Cited by 2
Howard, N. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-45114-0_35 · Cited by 2
Anatomical and Functional Brain Network Architecture in Schizophrenia
Collin, G., & van den Heuvel, M. P. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801829-3.00026-4 · Cited by 2
Neuroergonomic assessment of the robotic enhancement of surgery
James, D. R. C. (2012) · doi:10.25560/9652 · Cited by 2
Aging and Efficiency of Brain Functional Networks: Preliminary Study in Korean Women
Kim, H., & Chey, J. (2016) · 한국심리학회지: 인지 및 생물 · doi:10.22172/cogbio.2016.28.4.004 · Cited by 2
Functional and Anatomical Connectivity of the Brain in Poststroke Aphasia
Mayorova, L. A., Вадимовна, А. В., Kuptsova, S. V., & Shklovsky, V. M. (2019) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-019-00787-z · Cited by 2
Shinkuma, R., Nishida, S., Maeda, N., Kado, M., & Nishimoto, S. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3074069 · Cited by 2
Normal development of the brain: a survey of joint structural–functional brain studies
Namiranian, R., Malakshan, S. R., Moghaddam, H. A., Khadem, A. A., & Jafari, R. (2022) · Reviews in the Neurosciences · doi:10.1515/revneuro-2022-0017 · Cited by 4
Neurobiological Basis of Personal Wisdom
Sanders, J. D., & Jeste, D. V. (2012) · Springer eBooks · doi:10.1007/978-90-481-9231-1_5 · Cited by 2
Bukhari, H., Su, C., Dhamala, E., Gu, Z., Jamison, K. W., & Kuceyeski, A. F. (2023) · Human Brain Mapping · doi:10.1002/hbm.26296 · Cited by 2
Topology Identification via Growing a Chow-Liu Tree Network
Hassan-Moghaddam, S., & Jovanović, M. R. (2018) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2018.8619207 · Cited by 2
ConnectViz: Accelerated approach for brain structural connectivity using Delaunay triangulation
Adeshina, A. M., & Hashim, R. (2015) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-014-0227-8 · Cited by 2
Neurobiological heterogeneity in ADHD
de Zeeuw, P. (2008) · Utrecht University Repository (Utrecht University) · Cited by 2
Atypical Cognitive Development
Pennington, B. F. (2015) · Wiley eBooks · doi:10.1002/9781118963418.childpsy223 · Cited by 2
Correction: Sculpting the Intrinsic Modular Organization of Spontaneous Brain Activity by Art
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Metadata
- Authors
- van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E.
- Affiliations
- University Medical Center Utrecht (NL)
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2009-06-10
- Volume
- 29
- Issue
- 23
- Pages
- 7619-7624
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6665421
- OpenAlex
- W2165985389
- Cited by
- 1,078
- References
- 42
- Citations by year
- 2026: 33 · 2025: 39 · 2024: 42 · 2023: 40 · 2022: 51 · 2021: 69 · 2020: 74 · 2019: 68 · 2018: 61 · 2017: 85 · 2016: 85 · 2015: 94 · 2014: 72 · 2013: 96 · 2012: 74
- FWCI
- 27.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion, other-oa)
- Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- 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
- functional brain networks, small-world networks, global efficiency, average path length, intelligence quotient, resting-state functional connectivity, graph theory, frontal cortex, parietal cortex, information integration
- MeSH terms
- Adult, Brain, Female, Humans, Intelligence, Intelligence Tests, Magnetic Resonance Imaging, Male, Neural Pathways, Cluster Analysis, Linear Models
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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.
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