The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
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.
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
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
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
Cited by
Sorted by citations
Items 401–500 of 1,000
Gao, Z., Xiao, Y., Zhu, F., Tao, B., Yu, W., & Lui, S. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105144 · Cited by 52
Arnold, A. E. G. F., Protzner, A. B., Bray, S., Levy, R., & Iaria, G. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00491 · Cited by 47
Behavioral and neural correlates of disrupted orienting attention in posttraumatic stress disorder
Block, S. R., King, A. P. J., Sripada, R. K., Weissman, D. H., Welsh, R. C., & Liberzon, I. (2016) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-016-0488-2 · Cited by 50
Test-retest reliability of brain responses to risk-taking during the balloon analogue risk task
Li, X., Pan, Y., Fang, Z., Lei, H., Zhang, X., Shi, H., Ma, N., Raine, P., Wetherill, R. R., Kim, J., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116495 · Cited by 48
Wang, Y., Wang, C., Miao, P., Liu, J., Wei, Y., Wu, L., Wang, K., & Cheng, J. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102507 · Cited by 45
Petrican, R., Taylor, M. J., & Grady, C. L. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.025 · Cited by 43
Distinct Frontoparietal Networks Underlying Attentional Effort and Cognitive Control
Berry, A. S., Sarter, M. F., & Lustig, C. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01112 · Cited by 45
Comparison between gradients and parcellations for functional connectivity prediction of behavior
Kong, R., Tan, Y. R., Wulan, N., Ooi, L. Q. R., Farahibozorg, S.-R., Harrison, S. J., Bijsterbosch, J. D., Bernhardt, B. C., Eickhoff, S. B., & Yeo, B. T. T. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120044 · Cited by 49
Tóth, B., Boha, R., Pósfai, M., Gaál, Z. A., Kónya, A., Stam, C. J., & Molnár, M. (2011) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2011.11.017 · Cited by 45
Self-citationJäncke, L., & Langer, N. (2011) · Journal of Neuropsychology · doi:10.1111/j.1748-6653.2011.02004.x · Cited by 48
Sun, X., Li, L., Ding, G., Wang, R., & Li, P. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.03.020 · Cited by 52
Du, H., Liao, X., Lin, Q., Li, G., Chi, Y., Liu, X., Yang, H., Wang, Y., & Xia, M. (2015) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.12431 · Cited by 45
Jung, W. H., Jang, J. H., Shin, N. Y., Kim, S. N., Choi, C., An, S. K., & Kwon, J. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051975 · Cited by 45
Large-scale Cortical Network Properties Predict Future Sound-to-Word Learning Success
Sheppard, J. P., Wang, J., & Wong, P. C. M. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00210 · Cited by 44
The topology of large Open Connectome networks for the human brain
Gastner, M. T., & Ódor, G. (2016) · Scientific Reports · doi:10.1038/srep27249 · Cited by 44
Koirala, N., Fleischer, V., Glaser, M. B., Zeuner, K. E., Deuschl, G., Volkmann, J., Muthuraman, M., & Groppa, S. (2017) · Brain Topography · doi:10.1007/s10548-017-0597-4 · Cited by 45
Shahriari, Y., Vaughan, T. M., McCane, L. M., Allison, B. Z., Wolpaw, J. R., & Krusienski, D. J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab22ea · Cited by 45
Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration
Khambhati, A. N., Mattar, M. G., Wymbs, N. F., Grafton, S. T., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.015 · Cited by 46
Understanding Human Intelligence by Imaging the Brain
Colom, R., & Thompson, P. M. (2011) · Wiley eBooks · doi:10.1002/9781444343120.ch12 · Cited by 44
Degree‐based statistic and center persistency for brain connectivity analysis
Yoo, K., Lee, P., Chung, M. K., Sohn, W. S., Chung, S. J., Na, D. L., Ju, D., & Jeong, Y. (2016) · Human Brain Mapping · doi:10.1002/hbm.23352 · Cited by 42
Spoormaker, V. I., Czisch, M., Maquet, P., & Jäncke, L. (2011) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2011.0078 · Cited by 49
Yeo, R. A., Ryman, S. G., van den Heuvel, M. P., de Reus, M. A., Jung, R. E., Pommy, J. M., Mayer, A. R., Ehrlich, S., Schulz, S. C., Morrow, E. M., et al. (2016) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617715000867 · Cited by 44
Beason‐Held, L. L. (2011) · Current Alzheimer Research · doi:10.2174/156720511795745294 · Cited by 43
Investigating Neural Efficiency in the Visuo-Spatial Domain: An fmri Study
Lipp, I., Benedek, M., Fink, A., Koschutnig, K., Reishofer, G., Bergner, S., Ischebeck, A. K., Ebner, F., & Neubauer, A. C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051316 · Cited by 45
Functional organization of intrinsic connectivity networks in Chinese-chess experts
Duan, X., Long, Z., Chen, H., Liang, D., Qiu, L., Huang, X., Liu, T. C., & Gong, Q. (2014) · Brain Research · doi:10.1016/j.brainres.2014.02.033 · Cited by 45
Functional Connectivity Architecture of the Human Brain
Wang, D., & Liu, H. (2014) · The Neuroscientist · doi:10.1177/1073858414543290 · Cited by 43
Cantou, P., Platel, H., Desgranges, B., & Groussard, M. (2017) · Journal of Chemical Neuroanatomy · doi:10.1016/j.jchemneu.2017.08.003 · Cited by 44
Cognitive Reserve Relates to Functional Network Efficiency in Alzheimer’s Disease
Weiler, M., Casseb, R. F., de Campos, B. M., Teixeira, C. V. L., Carletti‐Cassani, A. F. M. K., Vicentini, J. E., Magalhães, T. N. C., de Almeira, D. Q., Talib, L. L., Forlenza, O. V., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00255 · Cited by 45
Connectomics in Schizophrenia: From Early Pioneers to Recent Brain Network Findings
Collin, G., Turk, E., & van den Heuvel, M. P. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.01.002 · Cited by 45
Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach
Alaerts, K., Geerlings, F., Herremans, L., Swinnen, S. P., Verhoeven, J. S., Sunaert, S. G., & Wenderoth, N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137020 · Cited by 45
Differences in Aβ brain networks in Alzheimer's disease and healthy controls
Duan, H., Jiang, J., Xu, J. L., Zhou, H., Huang, Z., Yu, Z., & Yan, Z. (2016) · Brain Research · doi:10.1016/j.brainres.2016.11.019 · Cited by 42
White matter network connectivity deficits in developmental dyslexia
Lou, C., Duan, X., Altarelli, I., Sweeney, J. A., Ramus, F., & Zhao, J. (2018) · Human Brain Mapping · doi:10.1002/hbm.24390 · Cited by 43
High-Expanding Regions in Primate Cortical Brain Evolution Support Supramodal Cognitive Flexibility
Sneve, M. H., Grydeland, H., Rosa, M. G. P., Paus, T., Chaplin, T. A., Walhovd, K. B., & Fjell, A. M. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy268 · Cited by 41
Baene, W. D., Rutten, G.-J. M., & Sitskoorn, M. M. (2019) · European Journal of Neuroscience · doi:10.1111/ejn.14535 · Cited by 43
Functional Connectome Analyses Reveal the Human Olfactory Network Organization
Arnold, T. C., You, Y., Ding, M., Zuo, X., de Araújo, I., & Li, W. (2020) · eNeuro · doi:10.1523/eneuro.0551-19.2020 · Cited by 42
Barttfeld, P., Wicker, B., McAleer, P., Belin, P., Cojan, Y., Graziano, M., Leiguarda, R., & Sigman, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1301353110 · Cited by 41
Frequency Dependent Topological Patterns of Resting-State Brain Networks
Qian, L., Zhang, Y., Zheng, L., Shang, Y., Gao, J., & Liu, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124681 · Cited by 41
Altered resting-state functional connectivity in women with chronic fatigue syndrome
Kim, B.-H., Namkoong, K., Kim, J., Lee, S., Yoon, K. J., Choi, M., & Jung, Y. (2015) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2015.10.014 · Cited by 41
MRI correlates of general intelligence in neurotypical adults
Malpas, C. B., Genc, S., Saling, M. M., Velakoulis, D., Desmond, P. M., & O'Brien, T. J. (2015) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2015.07.012 · Cited by 45
van den Heuvel, M. P., Scholtens, L. H., Turk, E., Mantini, D., Vanduffel, W., & Barrett, L. F. (2016) · Human Brain Mapping · doi:10.1002/hbm.23229 · Cited by 40
Pezoulas, V. C., Zervakis, M. E., Michelogiannis, S., & Klados, M. A. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00189 · Cited by 38
Music and Brain Plasticity: How Sounds Trigger Neurogenerative Adaptations
Reybrouck, M., Vuust, P., & Brattico, E. (2018) · InTech eBooks · doi:10.5772/intechopen.74318 · Cited by 47
Efficient coding in the economics of human brain connectomics
Zhou, D., Lynn, C. W., Cui, Z., Ćirić, R., Baum, G. L., Moore, T. M., Roalf, D. R., Detre, J. A., Gur, R. C., Gur, R. E., et al. (2021) · Network Neuroscience · doi:10.1162/netn_a_00223 · Cited by 39
Weber, E. M. G., Hahn, T., Hilger, K., & Fiebach, C. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.006 · Cited by 44
SSVEP Response Is Related to Functional Brain Network Topology Entrained by the Flickering Stimulus
Zhang, Y., Xu, P., Huang, Y.-L., Cheng, K., & Yao, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072654 · Cited by 40
Medial Frontal White and Gray Matter Contributions to General Intelligence
Ohtani, T., Nestor, P. G., Sylvain Bouix, A., Saito, Y., Hosokawa, T., & Kubicki, M. R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112691 · Cited by 61
Tool-use practice induces changes in intrinsic functional connectivity of parietal areas
Yoo, K., Sohn, W. S., & Jeong, Y. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00049 · Cited by 39
Statistical network analysis for functional MRI: summary networks and group comparisons
Ginestet, C. E., Fournel, A., & Simmons, A. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00051 · Cited by 40
Attention in the heart of intelligence
Rueda, M. R. (2018) · Trends in Neuroscience and Education · doi:10.1016/j.tine.2018.11.003 · Cited by 42
Mitchell, B. L., Cuéllar-Partida, G., Grasby, K. L., Campos, A. I., Strike, L. T., Hwang, L., Okbay, A., Thompson, P. M., Medland, S. E., Martin, N. G., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116691 · Cited by 41
Predicting intelligence from brain gray matter volume
Hilger, K., Winter, N. R., Leenings, R., Sassenhagen, J., Hahn, T., Basten, U., & Fiebach, C. J. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02113-7 · Cited by 42
Bakshi, N. K., Pruitt, P. J., Radwan, J., Keshavan, M. S., Rajan, U., Zajac‐Benitez, C., & Diwadkar, V. A. (2011) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2011.01.002 · Cited by 41
Intrinsic Brain Connectivity Related to Age in Young and Middle Aged Adults
Hampson, M., Tokoglu, F., Shen, X., Scheinost, D., Papademetris, X., & Constable, R. T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044067 · Cited by 42
Identifying 22q11.2 Deletion Syndrome and Psychosis Using Resting-State Connectivity Patterns
Scariati, E., Schaer, M., Richiardi, J., Schneider, M., Debbané, M., Ville, D. V. D., & Eliez, S. (2014) · Brain Topography · doi:10.1007/s10548-014-0356-8 · Cited by 40
Keunen, K., Benders, M. J. N. L., Leemans, A., Stam, P. C. F.-V., Scholtens, L. H., Viergever, M. A., Kahn, R. S., Groenendaal, F., de Vries, L. S., & van den Heuvel, M. P. (2017) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.13487 · Cited by 39
Self-citationDevelopment of Brain Network in Children with Autism from Early Childhood to Late Childhood
Han, J., Zeng, K., Kang, J., Tong, Z., Cai, E., Chen, H., Ding, M., Gu, Y., Ouyang, G., & Li, X. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.10.015 · Cited by 40
Evaluating the granularity and statistical structure of lesions and behaviour in post-stroke aphasia
Zhao, Y., Halai, A. D., & Ralph, M. A. L. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa062 · Cited by 41
Barber, A. D., Hegarty, C. E., Lindquist, M. A., & Karlsgodt, K. H. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa391 · Cited by 35
Bio-inspired artificial synapses: Neuromorphic computing chip engineering with soft biomaterials
Ahmed, T. (2023) · Memories - Materials Devices Circuits and Systems · doi:10.1016/j.memori.2023.100088 · Cited by 38
Does sleep restore the topology of functional brain networks?
Koenis, M. M. G., Romeijn, N., Piantoni, G., Verweij, I. M., van der Werf, Y. D., Someren, E. J. W. V., & Stam, C. J. (2011) · Human Brain Mapping · doi:10.1002/hbm.21455 · Cited by 39
Self-citationA robust biomarker of large‐scale network failure in Alzheimer's disease
Wiepert, D. A., Lowe, V. J., Knopman, D. S., Boeve, B. F., Graff‐Radford, J., Petersen, R. C., Jack, C. R., & Jones, D. T. (2017) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2017.01.004 · Cited by 38
Communicability geometry captures traffic flows in cities
Akbarzadeh, M., & Estrada, E. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0407-3 · Cited by 35
Anderson, E. D., & Barbey, A. K. (2022) · Human Brain Mapping · doi:10.1002/hbm.26164 · Cited by 42
Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks
Rutter, L., Nadar, S. R., Holroyd, T., Carver, F. W., Apud, J. A., Weinberger, D. R., & Coppola, R. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00093 · Cited by 37
Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks
Hosseini, S. M. H., & Kesler, S. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067354 · Cited by 39
Magnuson, M. E., Thompson, G. J., Pan, W., & Keilholz, S. D. (2013) · Brain Connectivity · doi:10.1089/brain.2013.0167 · Cited by 37
Pisarchik, A. N., Andreev, A. V., Kurkin, S., Stoyanov, D., Badarin, A., Paunova, R., & Hramov, A. E. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0166148 · Cited by 36
Chen, S., Kang, J., Xing, Y., & Wang, G. (2015) · Human Brain Mapping · doi:10.1002/hbm.23007 · Cited by 38
Li, C., & Tian, L. (2014) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3850 · Cited by 34
BOLD cofluctuation ‘events’ are predicted from static functional connectivity
Ladwig, Z., Seitzman, B. A., Dworetsky, A., Yu, Y., Adeyemo, B., Smith, D. M., Petersen, S. E., & Gratton, C. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119476 · Cited by 32
Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition
Kunert-Graf, J. M., Eschenburg, K. M., Galas, D. J., Kutz, J. N., Rane, S. D., & Brunton, B. W. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00075 · Cited by 35
Fodor, Z., Horvath, A. A., Hidasi, Z., Gouw, A. A., Stam, C. J., & Csukly, G. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.680200 · Cited by 37
Self-citationMagnetoencephalography in Stroke Recovery and Rehabilitation
Paggiaro, A., Birbaumer, N., Cavinato, M., Turco, C., Formaggio, E., Felice, A. D., Masiero, S., & Piccione, F. (2016) · Frontiers in Neurology · doi:10.3389/fneur.2016.00035 · Cited by 38
Zhao, J. L., Liu, J., Jiang, X., Zhou, G., Chen, G., Ding, X. P., Fu, G., & Lee, K. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00452 · Cited by 39
Wang, B., Li, P., Li, D., Niu, Y., Yan, T., Li, T., Cao, R., Yan, P., Guo, Y., Yang, W., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00316 · Cited by 36
Hill, A. T., Hadas, I., Zomorrodi, R., Voineskos, D., Farzan, F., Fitzgerald, P. B., Blumberger, D. M., & Daskalakis, Z. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-74103-y · Cited by 37
Dissociated multimodal hubs and seizures in temporal lobe epilepsy
Douw, L., Desalvo, M. N., Tanaka, N., Cole, A. J., Liu, H., Reinsberger, C., & Stufflebeam, S. M. (2015) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.173 · Cited by 34
Dimond, D., Ishaque, A. H., Chenji, S., Mah, D., Chen, Z., Seres, P., Beaulieu, C., & Kalra, S. (2016) · Human Brain Mapping · doi:10.1002/hbm.23452 · Cited by 36
Inhibitory control in young adolescents: The role of sex, intelligence, and temperament.
Yücel, M., Fornito, A., Youssef, G. J., Dwyer, D., Whittle, S., Wood, S. J., Lubman, D. I., Simmons, J. G., Pantelis, C., & Allen, N. B. (2012) · Neuropsychology · doi:10.1037/a0027693 · Cited by 33
Pagani, M., Giuliani, A., Öberg, J., Chincarini, A., Morbelli, S. D., Brugnolo, A., Arnaldi, D., Picco, A., Bauckneht, M., Buschiazzo, A., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.043 · Cited by 33
Network attributes underlying intellectual giftedness in the developing brain
Ma, J., Kang, H. J., Kim, J., Jeong, H. S., Im, J. J., Namgung, E., Kim, M. J., Lee, S. L., Kim, T. D., Oh, J. K., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-11593-3 · Cited by 34
Marsman, A., Mandl, R. C. W., Klomp, D. W. J., Cahn, W., Kahn, R. S., Luijten, P. R., & Pol, H. E. H. (2017) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2017.00154 · Cited by 38
Self-citationYan, C.-Q., Huo, J., Wang, X., Zhou, P., Zhang, Y., Li, J., Kim, M., Shao, J., Hu, S., Wang, Y., et al. (2020) · Neural Plasticity · doi:10.1155/2020/5701042 · Cited by 33
Jiao, B., Zhang, D., Liang, A., Liang, B., Wang, Z., Li, J., Cai, Y., Gao, M., Gao, Z., Chang, S., et al. (2017) · Biological Psychology · doi:10.1016/j.biopsycho.2017.09.003 · Cited by 34
Multimodal Brain Connectomics-Based Prediction of Parkinson’s Disease Using Graph Attention Networks
Safai, A., Vakharia, N., Prasad, S., Saini, J. S., Shah, A., Lenka, A., Pal, P. K., & Ingalhalikar, M. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.741489 · Cited by 37
Touroutoglou, A., Andreano, J. M., Barrett, L. F., & Dickerson, B. C. (2015) · Hippocampus · doi:10.1002/hipo.22480 · Cited by 32
Qin, C., Liang, Y., Tan, X. L., Leng, X., Lin, H., Zeng, H., Zhang, C., Yang, J., Li, Y., Zheng, Y., et al. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00599 · Cited by 33
Leitgeb, E. P., Šterk, M., Petrijan, T., Gradišnik, P., & Gosak, M. (2020) · Epileptic Disorders · doi:10.1684/epd.2020.1203 · Cited by 34
Luo, L., Xu, L., Jung, R. E., Pearlson, G. D., Adalı, T., & Calhoun, V. D. (2012) · Brain Connectivity · doi:10.1089/brain.2011.0026 · Cited by 32
Delta-gamma coupling as a potential neurophysiological mechanism of fluid intelligence
Gągol, A., Magnuski, M., Kroczek, B., Kałamała, P., Ociepka, M., Santarnecchi, E., & Chuderski, A. (2017) · Intelligence · doi:10.1016/j.intell.2017.11.003 · Cited by 32
Hilger, K., & Fiebach, C. J. (2019) · Network Neuroscience · doi:10.1162/netn_a_00083 · Cited by 31
The Lesioned Brain: Still a Small-World?
Douw, L., van Dellen, E., Baayen, J. C., Klein, M., Velis, D. N., Alpherts, W. C. J., Heimans, J. J., Reijneveld, J. C., & Stam, C. J. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00174 · Cited by 32
Self-citationLee, J., Lee, A., Kim, H., Shin, M., Yun, S. M., Jung, Y., Chang, W. H., & Kim, Y. (2019) · Neural Plasticity · doi:10.1155/2019/3826495 · Cited by 31
Dhamala, E., Jamison, K. W., Jaywant, A., & Kuceyeski, A. F. (2021) · Human Brain Mapping · doi:10.1002/hbm.25709 · Cited by 31
Jiang, F., Jin, H., Gao, Y., Xie, X., Cummings, J., Raj, A., & Nagarajan, S. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119131 · Cited by 30
Homophilic wiring principles underpin neuronal network topology in vitro
Akarca, D., Dunn, A. W., Hornauer, P., Ronchi, S., Michele, F., Wang, C., Terrigno, M., Jagasia, R., Vértes, P. E., Mierau, S. B., et al. (2025) · eLife · doi:10.7554/elife.85300 · Cited by 31
Children's academic attainment is linked to the global organization of the white matter connectome
Bathelt, J., Gathercole, S. E., Butterfield, S., the CALM team, & Astle, D. E. (2018) · Developmental Science · doi:10.1111/desc.12662 · Cited by 35
Sex differences in the influence of body mass index on anatomical architecture of brain networks
Gupta, A., Mayer, E. A., Hamadani, K., Bhatt, R., Fling, C., Alaverdyan, M., Torgerson, C., Ashe-McNalley, C., Van Horn, J. D., Naliboff, B., et al. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.86 · Cited by 30
Kyeong, S.-H., Kim, J., & Kim, E. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182603 · Cited by 30
Linking brain network reconfiguration and intelligence: Are we there yet?
Girn, M., Mills, C., & Kalina Christoff, A. (2019) · Trends in Neuroscience and Education · doi:10.1016/j.tine.2019.04.001 · Cited by 29
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
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
