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 601–700 of 1,000
On the Application of Developmental Cognitive Neuroscience in Educational Environments
Leisman, G. (2022) · Brain Sciences · doi:10.3390/brainsci12111501 · Cited by 25
Biological Basis of Intelligence
Haier, R. J. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511977244.019 · Cited by 19
Abnormal Brain Wiring as a Pathogenetic Mechanism in Schizophrenia
van den Heuvel, M. P., & Kahn, R. S. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.10.020 · Cited by 19
Self-citationHaring, L., Müürsepp, A., Mõttus, R., Ilves, P., Koch, K., Uppin, K., Tarnovskaja, J., Maron, E., Zharkovsky, A., Vasar, E., et al. (2016) · Psychological Medicine · doi:10.1017/s0033291716000684 · Cited by 19
Lang, X., Liu, H., Qin, W., Zhang, Y., Xuan, Y., & Yu, C. (2014) · Neuroreport · doi:10.1097/wnr.0000000000000294 · Cited by 18
Pijnenburg, M., Hosseini, S. M. H., Brumagne, S., Janssens, L., Goossens, N., & Caeyenberghs, K. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0401 · Cited by 18
Complex network analysis of brain functional connectivity under a multi-step cognitive task
Cai, S., Chen, W., Liu, D.-B., Tang, M., & Chen, X. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.09.058 · Cited by 19
Disrupted Topological Patterns of Large-Scale Network in Conduct Disorder
Jiang, Y., Liu, W., Ming, Q., Gao, Y., Ma, R., Zhang, X., Situ, W., Wang, X., Yao, S., & Huang, B. (2016) · Scientific Reports · doi:10.1038/srep37053 · Cited by 20
Marangolo, P., Fiori, V., Caltagirone, C., Incoccia, C., & Gili, T. (2019) · Brain Research · doi:10.1016/j.brainres.2019.146564 · Cited by 21
Brain function associated with reaction time after sport-related concussion
Churchill, N. W., Hutchison, M. G., Graham, S. J., & Schweizer, T. A. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00349-9 · Cited by 22
Gonzalez‐Burgos, L., Barroso, J., & Ferreira, D. (2020) · Aging · doi:10.18632/aging.202177 · Cited by 20
Redundancy circuits of the commissural pathways in human and rhesus macaque brains
Ghulam‐Jelani, Z., Barrios‐Martinez, J., Eguiluz‐Melendez, A., Gómez, R. J., Anania, Y., & Yeh, F.-C. F. (2021) · Human Brain Mapping · doi:10.1002/hbm.25363 · Cited by 17
van den Heuvel, M. P., & Pol, H. E. H. (2011) · Psiquiatría Biológica · doi:10.1016/j.psiq.2011.05.001 · Cited by 25
Li, M., Chen, H., Wang, J., Liu, F., Wang, Y., Lu, F., Yu, C., & Chen, H. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.11.033 · Cited by 19
Extending the Applicability of Graphlets to Directed Networks
Aparício, D., Ribeiro, P., & Silva, F. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2586046 · Cited by 19
Sreenivasan, K. R., Zhuang, X., Banks, S. J., Mishra, V. R., Yang, Z., Deshpande, G., & Cordes, D. (2017) · Brain Connectivity · doi:10.1089/brain.2016.0458 · Cited by 19
Mansoory, M. S., Oghabian, M. A., Jafari, A. H., & Shahbabaie, A. (2017) · Basic and Clinical Neuroscience Journal · doi:10.18869/nirp.bcn.8.5.371 · Cited by 21
Sex differences in brain and behavioral development
Beltz, A. M., Kelly, D. P., & Berenbaum, S. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814411-4.00027-5 · Cited by 21
Zhu, J., & Qiu, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119482 · Cited by 18
Structural-functional connectivity bandwidth of the human brain
Parsons, N., Ugon, J., Morgan, K. J., Shelyag, S., Hocking, A., Chan, S. Y., Poudel, G. R., D, J. F. D., & Caeyenberghs, K. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119659 · Cited by 20
Xiao, X., Hammond, C., Salmeron, B. J., Wang, D., Gu, H., Zhai, T., Nguyen, H., Lu, H., Ross, T. J., & Yang, Y. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.08.028 · Cited by 21
Sandini, C., Zöller, D., Scariati, E., Padula, M. C., Schneider, M., Schaer, M., Ville, D. V. D., & Eliez, S. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00327 · Cited by 18
Functional brain segregation changes during demanding mathematical task
Ghaderi, A. H., Nazari, M. A., & Darooneh, A. H. (2019) · International Journal of Neuroscience · doi:10.1080/00207454.2019.1586688 · Cited by 18
Resting-state network topology and planning ability in healthy adults
Vriend, C., Wagenmakers, M. J., van den Heuvel, O. A., & van der Werf, Y. D. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-02004-6 · Cited by 19
Intergenerational transmission of the patterns of functional and structural brain networks
Takagi, Y., Okada, N., Ando, S., Yahata, N., Morita, K., Koshiyama, D., Kawakami, S., Sawada, K., Koike, S., Endo, K., et al. (2021) · iScience · doi:10.1016/j.isci.2021.102708 · Cited by 17
Orso, B., Lorenzini, L., Arnaldi, D., Girtler, N. G., Brugnolo, A., Doglione, E., Mattioli, P., Biassoni, E., Massa, F., Peira, E., et al. (2022) · Biomedicines · doi:10.3390/biomedicines10030544 · Cited by 19
James, D. R. C., Leff, D. R., Orihuela‐Espina, F., Kwok, K., Mylonas, G. P., Athanasiou, T., Darzi, A. W., & Yang, G. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.056 · Cited by 18
Chen, Z., Beuckelaer, A. D., Wang, X., & Jia Liu, B. (2017) · Scientific Reports · doi:10.1038/s41598-017-16437-8 · Cited by 18
Network comparison using directed graphletsPreprint
Aparício, D., Ribeiro, P., & Silva, F. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.01964 · Cited by 18
Behavioral and brain dynamics of executive control in relation to children's fluid intelligence
Rico‐Picó, J., Hoyo, Á., Guerra, S., Conejero, Á., & Rueda, M. R. (2020) · Intelligence · doi:10.1016/j.intell.2020.101513 · Cited by 20
Sacci, R. L., Scott, H. L., Liu, Z., Bolmatov, D., Doughty, B., Katsaras, J., & Collier, C. P. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202200121 · Cited by 17
Drenthen, G. S., Fasen, F., Wald, E. L. A. F., Backes, W. H., Aldenkamp, A. P., Vermeulen, R. J., Hall, M. D.-V., Hendriksen, J. G. M., Klinkenberg, S., & Jansen, J. F. A. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102264 · Cited by 17
Bruzzone, S. E. P., Lumaca, M., Brattico, E., Vuust, P., Kringelbach, M. L., & Bonetti, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-08521-5 · Cited by 19
Nelson, M. C., Royer, J., Lu, W. D., Leppert, I. R., Campbell, J. S. W., Schiavi, S., Jin, H., Tavakol, S., de Wael, R. V., Rodríguez‐Cruces, R., et al. (2023) · Network Neuroscience · doi:10.1162/netn_a_00330 · Cited by 17
Maas, D. A., & Douw, L. (2023) · Neuro-Oncology Practice · doi:10.1093/nop/npad044 · Cited by 19
Frontoparietal network topology as a neural marker of musical perceptual abilities
Lumaca, M., Keller, P. E., Baggio, G., Pando‐Naude, V., Bajada, C. J., Martinez, M. A., Hansen, J. H., Ravignani, A., Joe, N., Vuust, P., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52479-z · Cited by 19
Topological isomorphisms of human brain and financial market networks
Vértes, P. E. (2011) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2011.00075 · Cited by 17
Yao, Z., Hu, B., Chen, X., Xie, Y., Gutknecht, J., & Majoe, D. (2017) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/1533317517731535 · Cited by 16
Suprano, I., Delon‐Martin, C., Kocevar, G., Stamile, C., Hannoun, S., Achard, S., Badhwar, A., Fourneret, P., Revol, O., Nusbaum, F., et al. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00241 · Cited by 21
Decoding motor expertise from fine‐tuned oscillatory network organization
Amoruso, L., Pusil, S. A., García, A. M., & Ibáñez, A. M. (2022) · Human Brain Mapping · doi:10.1002/hbm.25818 · Cited by 13
Liu, H., Yu, H., Li, Y., Qin, W., Xu, L., Yu, C., & Liang, M. (2016) · Behavioural Brain Research · doi:10.1016/j.bbr.2016.08.046 · Cited by 16
Scholtens, L. H., Barrett, L. F., & van den Heuvel, M. P. (2018) · Brain Connectivity · doi:10.1089/brain.2018.0622 · Cited by 16
Luo, S., Zhu, Y., Fan, L., Gao, D., & Han, S. (2020) · Social Neuroscience · doi:10.1080/17470919.2020.1714718 · Cited by 16
Strongly reinforced Pólya urns with graph-based competition
van der Hofstad, R., Holmes, M., Kuznetsov, A., & Ruszel, W. M. (2014) · TU/e Research Portal · Cited by 16
Yang, Q., Qu, X., Sheng, C., Zhao, X. Q., Chen, G., Wang, X., Li, Y., Du, W., Wang, X., Sun, Y., et al. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121305 · Cited by 16
Information Processing in the Mental Workspace Is Fundamentally Distributed
Schlegel, A. A., Alexander, P. C., & Tse, P. U. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00899 · Cited by 15
Age-associated reorganization of metabolic brain connectivity in Chinese children
Huang, Q., Zhang, J., Zhang, T., Wang, H., & Yan, J. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04508-z · Cited by 14
Clayden, J. D., Stotesbury, H., Kawadler, J. M., Slee, A. E., Kölbel, M., Saunders, D. E., Hood, A., Wilkey, O., Layton, M., Inusa, B. P. D., et al. (2022) · Blood Advances · doi:10.1182/bloodadvances.2021006751 · Cited by 19
Kristanto, D., Burkhardt, M., Thiel, C. M., Debener, S., Gießing, C., & Hildebrandt, A. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105846 · Cited by 16
Sethi, M., Pedersen, M., & Jackson, G. D. (2016) · Epilepsia · doi:10.1111/epi.13304 · Cited by 15
Shaw, D. J., Mareček, R., Grosbras, M., Leonard, G. T., Pike, G. B., & Paus, T. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv138 · Cited by 15
Wagner, K., Schulze‐Bonhage, A., Urbach, H., Trippel, M., Spehl, T. S., Buschmann, F., Metternich, B., Ofer, I., Meyer, P. T., & Frings, L. (2017) · Epilepsia · doi:10.1111/epi.13757 · Cited by 15
Brain Development: The Effect of Interventions on Children and Adolescents
Grigorenko, E. L. (2017) · World Bank, Washington, DC eBooks · doi:10.1596/978-1-4648-0423-6_ch10 · Cited by 15
Chen, D., Lu, J., Zhou, H., Jiang, J., Wu, P., Guo, Q., Ge, J., Zhang, H., Shi, K., & Zuo, C. (2018) · Behavioural Neurology · doi:10.1155/2018/8420658 · Cited by 15
Ho, N. F., Tng, J. X. J., Wang, M., Chen, G., Subbaraju, V., Shukor, S., Ng, D., Tan, B., Puang, S. J., Kho, S.-H., et al. (2020) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2020.03.013 · Cited by 15
Zangrossi, A., Zanzotto, G., Lorenzoni, F., Indelicato, G., Aghedu, F. C., Cermelli, P., & Bisiacchi, P. S. (2021) · Behavioural Brain Research · doi:10.1016/j.bbr.2021.113130 · Cited by 15
Network Neuroscience Methods for Studying Intelligence
Hilger, K., & Sporns, O. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.004 · Cited by 14
Neuropsychological impacts of indirect revascularization for pediatric moyamoya disease
Kim, W., Lee, E., Park, S.-E., Park, E.-K., Kim, J.-S., Kim, D.-S., & Shim, K. (2018) · Child s Nervous System · doi:10.1007/s00381-018-3804-z · Cited by 15
Fedeli, D., Maschio, N. D., Caprioglio, C., Sulpizio, S., & Abutalebi, J. (2020) · Brain Connectivity · doi:10.1089/brain.2020.0751 · Cited by 15
Pindus, D. M., Zwilling, C. E., Jarret, J., Talukdar, T., Schwarb, H. D., Anderson, E. D., Cohen, N. J., Barbey, A. K., Kramer, A. F., & Hillman, C. H. (2020) · Scientific Reports · doi:10.1038/s41598-020-70679-7 · Cited by 14
Wu, X., Lin, F., Sun, W., Zhang, T., Sun, H., & Li, J. (2021) · Brain Sciences · doi:10.3390/brainsci11111467 · Cited by 13
Schellekens, W., Bakker, C., Ramsey, N. F., & Petridou, N. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009955 · Cited by 14
Brain Network Dynamics and Multiscale Modeling of Neurodegenerative Disorders: A Review
Shaheen, H., & Melnik, R. (2025) · IEEE Access · doi:10.1109/access.2025.3542634 · Cited by 19
Thinking as Analogy-Making: Toward a Neural Process Account of General Intelligence
Theves, S. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1555-24.2025 · Cited by 15
Predicting age across human lifespan based on structural connectivity from diffusion tensor imaging
Han, C. E., Peraza, L. R., Taylor, J.-P., & Kaiser, M. (2014) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2014.6981664 · Cited by 14
Zang, Z., Geiger, L. S., Braun, U., Cao, H., Zangl, M., Schäfer, A., Moessnang, C., Ruf, M., Reis, J., Schweiger, J. I., et al. (2018) · Network Neuroscience · doi:10.1162/netn_a_00045 · Cited by 15
Liu, S., Tijiang, Z., Liu, H., Wenjing, Z., Bo, T., Ganjun, S., Maoqiang, T., & Lui, S. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.01.004 · Cited by 14
Kanel, D., Counsell, S., & Nosarti, C. (2020) · Journal of Perinatology · doi:10.1038/s41372-020-00865-y · Cited by 14
Bohlken, M. M., Brouwer, R. M., Mandl, R. C. W., Hedman, A. M., van den Heuvel, M. P., van Haren, N. E. M., Kahn, R. S., & Pol, H. E. H. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.046 · Cited by 15
Self-citationPsychopathic traits are differentially associated with efficiency of neural communication
Tillem, S., van Dongen, J. D. M., Brazil, I. A., & Baskin-sommers, A. R. (2018) · Psychophysiology · doi:10.1111/psyp.13194 · Cited by 14
Gicas, K. M., Jones, A. A., Panenka, W. J., Giesbrecht, C. J., Lang, D. J., Vila‐Rodriguez, F., Leonova, O., Barr, A. M., Procyshyn, R. M., Su, W., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218201 · Cited by 14
An Introduction to Brain Networks
(2016) · Elsevier eBooks · doi:10.1016/b978-0-12-407908-3.00001-7 · Cited by 16
Invernizzi, A., Rechtman, E., Oluyemi, K., Renzetti, S., Curtin, P. C. P., Colicino, E., Ambrosi, C., Mascaro, L., Patrono, A., Corbo, D., et al. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1098441 · Cited by 14
Structural architecture and brain network efficiency link polygenic scores to intelligence
Genç, E., Metzen, D., Fraenz, C., Schlüter, C., Voelkle, M. C., Arning, L., Streit, F., Nguyen, H. P., Güntürkün, O., Ocklenburg, S., et al. (2023) · Human Brain Mapping · doi:10.1002/hbm.26286 · Cited by 15
Ren, X., & Libertus, M. E. (2023) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02008 · Cited by 13
Li, Y., Ma, X., Sunderraman, R., Ji, S., & Kundu, S. (2023) · Human Brain Mapping · doi:10.1002/hbm.26415 · Cited by 13
Sagues, E., Alfaro, F., Ramos-Rodríguez, R., & García‐Casares, N. (2025) · Brain Imaging and Behavior · doi:10.1007/s11682-025-01013-w · Cited by 13
Paldino, M. J., Chu, Z. D., Chapieski, M. L., Golriz, F., & Zhang, W. (2017) · British Journal of Radiology · doi:10.1259/bjr.20160656 · Cited by 13
Alterations of functional and structural connectivity in patients with brain metastases
Hua, B., Ding, X., Xiong, M., Zhang, F., Luo, Y., Ding, J., & Ding, Z. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233833 · Cited by 13
Pan, G., Xiao, L., Bai, Y., Wilson, T. W., Stephen, J. M., Calhoun, V. D., & Wang, Y. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3048594 · Cited by 14
Ictor, B. Y. V., Poormaker, I. S., Zisch, M. I. C., Aquet, P. I. M., & Äncke, L. U. J. (2015) · Cited by 13
Haartsen, R., Charman, T., Pasco, G., Johnson, M. H., Jones, E. J. H., The BASIS Team, Baron‐Cohen, S., Bedford, R., Blasi, A., Bolton, P., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-24870-7 · Cited by 16
Duan, F., Watanabe, K., Yoshimura, Y., Kikuchi, M., Minabe, Y., & Aihara, K. (2014) · Brain and Cognition · doi:10.1016/j.bandc.2014.01.011 · Cited by 13
Galli, G., Santarnecchi, E., Feurra, M., Bonifazi, M., Rossi, S., Paulus, M. P., & Rossi, A. (2015) · European Journal of Neuroscience · doi:10.1111/ejn.13125 · Cited by 13
Soldier Health Habits and the Metabolically Optimized Brain
Friedl, K. E., Breivik, T. J., Carter, R., Leyk, D., Opstad, P. K., Taverniers, J., & Trousselard, M. (2016) · Military Medicine · doi:10.7205/milmed-d-15-00464 · Cited by 15
Lang, S. (2016) · Medical Hypotheses · doi:10.1016/j.mehy.2016.11.010 · Cited by 13
Contextual and Developmental Differences in the Neural Architecture of Cognitive Control
Petrican, R., & Grady, C. L. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0667-17.2017 · Cited by 14
Paldino, M. J., Golriz, F., Zhang, W., & Chu, Z. D. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212901 · Cited by 13
Anomal, R. F., Brandão, D. S., Porto, S. B., de Oliveira, S. S., de Souza, R. F. L., Fiel, J., Gomes, B. D., Hazin, I., & Pereira, A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232660 · Cited by 16
Wang, Z., Zeljic, K., Jiang, Q., Gu, Y., Wang, W., & Wang, Z. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw347 · Cited by 14
Mellanby, J. H., & Theobald, K. (2014) · Education and Learning · doi:10.1002/9781119030461.ch5 · Cited by 13
Terstege, D. J., Durante, I. M., & Epp, J. R. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.907707 · Cited by 13
Nicotinic modulation of intrinsic brain networks in schizophrenia
Smucny, J., & Tregellas, J. R. (2013) · Biochemical Pharmacology · doi:10.1016/j.bcp.2013.06.011 · Cited by 13
A possible correlation between performance IQ, visuomotor adaptation ability and mu suppression
Anwar, M. N., Navid, M. S., Khan, M., & Kitajo, K. (2015) · Brain Research · doi:10.1016/j.brainres.2015.01.045 · Cited by 12
Nerve cells developmental processes and the dynamic role of cytokine signaling
Guidolin, D., Fede, C., & Tortorella, C. (2018) · International Journal of Developmental Neuroscience · doi:10.1016/j.ijdevneu.2018.11.003 · Cited by 13
The central role of the thalamus in language and cognition*
Theofanopoulou, C., & Boeckx, C. A. (2016) · Routledge eBooks · doi:10.4324/9781315709529-20 · Cited by 12
Peng, Y., Wang, Z., Wong, C. M., Nan, W., Rosa, A. C., Xu, P., Wan, F., & Hu, Y. S. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab933e · Cited by 12
Yoshiike, T., Kuriyama, K., Honma, M., Ikeda, H., & Kim, Y. (2014) · Psychophysiology · doi:10.1111/psyp.12180 · Cited by 12
Sripada, C., Angstadt, M., & Rutherford, S. E. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/412056 · Cited by 12
Archery under the (electroencephalography-)hood: Theta-lateralization as a marker for motor learning
Rampp, S., Spindler, K., Hartwigsen, G., Scheller, C., Simmermacher, S., Scheer, M., Strauß, C., & Prell, J. (2022) · Neuroscience · doi:10.1016/j.neuroscience.2022.07.019 · Cited by 11
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
