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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
Predicting human resting-state functional connectivity from structural connectivity
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0811168106 · Cited by 3,307
Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.5062-08.2009 · Cited by 2,862
Wandering minds: the default network and stimulus-independent thought
Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007) · Science · doi:10.1126/science.1131295 · Cited by 2,792
Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3874-05.2006 · Cited by 2,393
Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network
Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn059 · Cited by 2,175
Organization, development and function of complex brain networks
SPORNS, O., CHIALVO, D., KAISER, M., & HILGETAG, C. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.07.008 · Cited by 2,055
The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence
Jung, R. E., & Haier, R. J. (2007) · Behavioral and Brain Sciences · doi:10.1017/s0140525x07001185 · Cited by 1,662
Intellectual ability and cortical development in children and adolescents
Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., Evans, A., Rapoport, J., & Giedd, J. (2006) · Nature · doi:10.1038/nature04513 · Cited by 1,543
Scale-Free Brain Functional Networks
Eguı́luz, V. M., Chialvo, D. R., Cecchi, G., Baliki, M. N., & Apkarian, A. V. (2005) · Physical Review Letters · doi:10.1103/physrevlett.94.018102 · Cited by 1,464
Neurophysiological Architecture of Functional Magnetic Resonance Images of Human Brain
Salvador, R. M., John Suckling, A., Coleman, M. R., Pickard, J. D., Menon, D. K., & Bullmore, E. T. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhi016 · Cited by 1,334
Neural mechanisms of general fluid intelligence
Gray, J. R., Chabris, C. F., & Braver, T. S. (2003) · Nature Neuroscience · doi:10.1038/nn1014 · Cited by 1,080
Dynamics of neuronal firing correlation: modulation of "effective connectivity"
Aertsen, A. M. H. J., Gerstein, G. L., Habib, M., & Palm, G. (1989) · Journal of Neurophysiology · doi:10.1152/jn.1989.61.5.900 · Cited by 1,073
A Neural Basis for General Intelligence
Duncan, J., Seitz, R. J., Kolodny, J., Bor, D., Herzog, H. R., Ahmed, A., Newell, F. N., & Emslie, H. C. (2000) · Science · doi:10.1126/science.289.5478.457 · Cited by 1,043
Graph theoretical analysis of complex networks in the brain
Stam, C. J., & Reijneveld, J. C. (2007) · Nonlinear Biomedical Physics · doi:10.1186/1753-4631-1-3 · Cited by 1,036
Self-citationGenetic influences on brain structure
Thompson, P. M., Cannon, T. D., Narr, K. L., van Erp, T. G. M., Poutanen, V.-P., HUTTUNEN, M. O., Lönnqvist, J. K., Standertskjöld-Nordenstam, C.-G., Kaprio, J. A., Khaledy, M., et al. (2001) · Nature Neuroscience · doi:10.1038/nn758 · Cited by 1,033
van den Heuvel, M. P., Mandl, R. C. W., Kahn, R. S., & Hulshoff Pol, H. E. (2009) · Human Brain Mapping · doi:10.1002/hbm.20737 · Cited by 987 · cited 2×
“…oftenreported functional default mode network (Raichle et al., 2001; Greicius et al., 2003; Damoiseaux et al., 2006; Buckner et al., 2008; van den Heuvel et al., 2008c, 2009), a dynamic resting-state network that is suggested to play a key role in processes of human cognition, like the…”
BackgroundSelf-citationvan den Heuvel, M. P., Stam, C. J., Boersma, M., & Pol, H. E. H. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.08.010 · Cited by 745
Self-citationKaiser, M., & Hilgetag, C. C. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020095 · Cited by 653
Wiring optimization can relate neuronal structure and function
Chen, B. L., Hall, D. H., & Chklovskii, D. B. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506806103 · Cited by 650
Structural brain variation and general intelligence
Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.04.025 · Cited by 564
Connectivity and complexity: the relationship between neuroanatomy and brain dynamics
Sporns, O., Tononi, G., & Edelman, G. M. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00053-8 · Cited by 553
Stam, C. J. (2003) · Neuroscience Letters · doi:10.1016/j.neulet.2003.10.063 · Cited by 550
Self-citationWiring Optimization in Cortical Circuits
Chklovskii, D. B., Schikorski, T., & Stevens, C. F. (2002) · Neuron · doi:10.1016/s0896-6273(02)00679-7 · Cited by 499
Normalized Cut Group Clustering of Resting-State fMRI Data
van den Heuvel, M. P., Mandl, R. C. W., & Pol, H. E. H. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002001 · Cited by 418
Genetics of brain fiber architecture and intellectual performance
Chiang, M.-C., Barysheva, M., Shattuck, D. W., Lee, A. D., Madsen, S. K., Avedissian, C., Klunder, A. D., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., et al. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.4184-08.2009 · Cited by 406
Brain spontaneous functional connectivity and intelligence
Song, M., Zhou, Y., Li, J., Liu, Y., Tian, L., Yu, C., & Jiang, T. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.036 · Cited by 353
van den Heuvel, M., Mandl, R., Luigjes, J., & Hulshoff Pol, H. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.2964-08.2008 · Cited by 349
Distributed brain sites for the g-factor of intelligence
Colom, R., Jung, R. E., & Haier, R. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.006 · Cited by 298
Genetic Contributions to Human Brain Morphology and Intelligence
Pol, H. E. H., Schnack, H. G., Posthuma, D., Mandl, R. C. W., Baaré, W. F., van Oel, C. J., van Haren, N. E. M., Collins, D. L., Evans, A. C., Amunts, K., et al. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.1312-06.2006 · Cited by 290
Self-citationSmit, D. J. A., Stam, C. J., Posthuma, D., Boomsma, D. I., & de Geus, E. J. C. (2007) · Human Brain Mapping · doi:10.1002/hbm.20468 · Cited by 275
Self-citationMultiple Bases of Human Intelligence Revealed by Cortical Thickness and Neural Activation
Choi, Y. Y., Shamosh, N. A., Cho, S. H., DeYoung, C. G., Lee, M.-J., Lee, J., Kim, S. I., Cho, Z., Kim, K., Gray, J. R., et al. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.3259-08.2008 · Cited by 220
Kaiser, M., & Hilgetag, C. C. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020095.eor · Cited by 48
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Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106
Exploring the brain network: A review on resting-state fMRI functional connectivity
van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2010.03.008 · Cited by 3,326
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284
Dynamic functional connectivity: Promise, issues, and interpretations
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962
Network-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750
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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