Cited by 599Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesComplex Network Analysis Techniques

The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.

Brain Anatomical Network and Intelligence

Open access

Li, Yonghui, Liu, Yong, Li, Jun, Qin, Wen, Li, Kuncheng, Yu, Chunshui, Jiang, Tianzi·2009·PLoS Computational Biology

DOI 10.1371/journal.pcbi.1000395PMC PMC2683575

Open this paper in CoChat — free account Open PDF

Abstract

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1371/journal.pcbi.1000395

Cites

81 of 81 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

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

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

Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single subject brain
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
Exploring complex networks
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,236
Network motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
Efficient behavior of small-world networks
Latora, V., & Marchiori, M. (2001) · Physical Review Letters · doi:10.1103/physrevlett.87.198701 · Cited by 5,119
Mapping 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
The architecture of complex weighted networks.
Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2003) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0400087101 · Cited by 4,084
Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging
Mori, S., Crain, B. J., Chacko, V. P., & van Zijl, P. C. M. (1999) · Annals of Neurology · doi:10.1002/1531-8249(199902)45:2 · Cited by 3,513
Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471

Flagged first, then by citations

Show all 81 references

Sorted by citations

Items 501–599 of 599

Reorganization of brain networks in patients with temporal lobe epilepsy and comorbid headache
Yang, S., Wu, Y., Sun, L., Xiao, Y., & Wu, Y. (2023) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2023.109101 · 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
Supersizing Intelligence: How the Collective Mind Builds on Dual Networks
Checkley, M. S., & Galindo, M. F. A. (2018) · Strategic Change · doi:10.1002/jsc.2202 · Cited by 2
Neural Mechanisms of Expertise
Ullén, F., de Manzano, Ö., & Mosing, M. A. (2019) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198795872.013.6 · Cited by 2
Spatial Complex Brain Network
Dong, W., Wei, Z., Zhou, Y., Sun, Y., Li, F., & Li, J. (2019) · Springer eBooks · doi:10.1007/978-981-13-9113-2_13 · Cited by 2
Self-citation
Caracterización de la conectividad anatómica cerebral a partir de las neuroimágenes de la difusión y la teoría de grafos
Medina, I., & Yasser (2013) · Cited by 2
Atypical Cognitive Development
Pennington, B. F. (2015) · Wiley eBooks · doi:10.1002/9781118963418.childpsy223 · Cited by 2
The neural and psychological mechanisms of yoga and mindfulness meditation
Gard, T. (2015) · doi:10.26481/dis.20150320tg · Cited by 2
Research Consortia and Large-Scale Data Repositories for Studying Intelligence
Khundrakpam, B. S., Poline, J., & Evans, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.006 · Cited by 1
Cortical connectivity in the face of congenital structural changes—A case of homozygous LAMC3 mutation
Demirayak, P., Oğuz, K. K., Üstün, F. S., Ürgen, B. M., Topac, Y., Gilani, I., Kansu, T., Saygı, S., Özçelık, T., Boyacı, H., et al. (2021) · Brain and Behavior · doi:10.1002/brb3.2241 · Cited by 1
Effective connectivity analysis of brain networks of mathematically gifted adolescents using transfer entropy
Zhu, Y., Gong, W., Lu, X., & Wang, H. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-223819 · Cited by 1
Exploring the transmission of cognitive task information through optimal brain pathways
Wang, Z., Yang, Y., Huang, Z., Zhao, W., Su, K., Zhu, H., & Yin, D. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012870 · Cited by 1
Shared genetic architecture between the topology of brain white matter structural connectome and fluid intelligence
Dong, X., Huang, W., Chen, H., Zhang, Y., Liu, B., Zhang, D., Zhang, Z., Ma, G., & Shu, N. (2026) · Communications Biology · doi:10.1038/s42003-026-10131-0 · Cited by 1
Multi-network Topology Underlying Individual Language Learning Success
Song, P., Yang, S., Geng, X., Gan, Z., Wang, S., & Feng, G. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.2205-25.2026 · Cited by 1
Het verbonden brein
van den Heuvel, M. P. (2011) · Neuropraxis · doi:10.1007/s12474-011-0002-0 · Cited by 1
The Emergence of Structured, Living, and Conscious Matter in the Evolution of the Universe: A Theory of Structural Evolution and Interaction of Matter
Aur, D., & Tuszyński, J. A. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-811940-2.00010-1 · Cited by 1
Psychodiagnostics of the risk for developmental delay in preschool children
Nasledov, A., Александрович, М. С., Tkacheva, L. O., & Zashchirinskai, O. (2019) · doi:10.15862/43mnnpm19 · Cited by 1
Discernible interindividual patterns of global efficiency decline during theoretical brain surgery
Lin, Y., Dadario, N. B., Tang, S. J., Crawford, L. S., Tanglay, O., Dow, H.-K., Young, I. M., Ahsan, S. A., Doyen, S., & Sughrue, M. E. (2024) · Scientific Reports · doi:10.1038/s41598-024-64845-4 · Cited by 1
Review of the Neural Correlates of Intelligence and Convergence on a Holistic Approach
Smith, Q. (2023) · International journal of high school research · doi:10.36838/v5i7.26 · Cited by 1
Exploring Graph Theory Mechanisms of Fluid Intelligence in the DLPFC: Insights From Resting‐State fNIRS Across Various Time Windows
Wang, Y., Chen, Z., Cai, Z., Ao, W., Li, Q., Xu, M., & Zhou, S. (2025) · Brain and Behavior · doi:10.1002/brb3.70386 · Cited by 1
EEG Based Biomarker Identification Using Graph-Theoretic Concepts: Case Study in Alcoholism
Sakkalis, V., & Kostas Marias, A. (2012) · Fields Institute communications · doi:10.1007/978-1-4614-4133-5_9 · Cited by 1
Associating brain topological networks to cognitive performance.
Parente, F., & Colosimo, A. (2014) · Cited by 1
Altered structural brain network topology in infants with intrauterine growth restriction
Batallé, D., Eixarch, E., Muñoz‐Moreno, E., Figueras, F., Amat‐Roldan, I., & Gratacós, E. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235608 · Cited by 1
A Review of Research on the Maturation of Children and Adolescences’ Brain Structure and the Influence of Intelligence
조수현 (2017) · 인지과학 · doi:10.19066/cogsci.2017.28.4.004 · Cited by 1
Prediction of Behavioral Traits via Anatomical Connectivity Fingerprint
Wu, D., & Li, X. (2019) · International Conference on Biomedical Engineering and Technology (ICBET) · doi:10.1145/3326172.3326216 · Cited by 1
A Hierarchical Brain Network Model Based on the K-Shell Decomposition Algorithm
Peng, S., Zhou, W., & Han, Y. (2019) · International Conference on Computer Science & Education (ICCSE) · doi:10.1109/iccse.2019.8845399 · Cited by 1
Brain Network Nodes and Edges
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.003 · Cited by 1
A Vector Space Model for Neural Network Functions: Inspirations From Similarities Between the Theory of Connectivity and the Logarithmic Time Course of Word Production
Fromm, O., Klostermann, F., & Ehlen, F. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.00058 · Cited by 1
Multi-modal human brain mapping and Brainnetome study
Zuo, N., & Jiang, T. (2020) · Scientia Sinica Vitae · doi:10.1360/ssv-2019-0228 · Cited by 1
Self-citation
Different Approximation Methods for Calculation of Integrated Information Coefficient in the Brain during Instrumental Learning
Nazhestkin, I. A., & Svarnik, О. E. (2022) · Brain Sciences · doi:10.3390/brainsci12050596 · Cited by 1
Physics Clues on the Mind Substrate and Attributes
Torres, J. J., & Marro, J. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.836532 · Cited by 1
Neural network analysis and its application in neurosurgical planning
van Dellen, E., Douw, L., Bosma, I., Heimans, J. J., Stam, C. J., & Reijneveld, J. C. (2011) · Springer eBooks · doi:10.1007/978-3-7091-0723-2_29 · Cited by 1
Connectivity at a crossroads
Marshall, R. S., & Reijmer, Y. D. (2014) · Neurology · doi:10.1212/wnl.0000000000000627 · Cited by 1
A neural modelling approach to investigating general intelligence
Rasmussen, D. (2010) · UWSpace (University of Waterloo) · Cited by 1
The biology of schizophrenia as a neurodevelopmental disorder
van Berlekom, A. B. (2022) · Utrecht University Repository (Utrecht University) · doi:10.33540/1269 · Cited by 1
Functional and effective connectivity in MEG. Application to the study of epilepsy
Niso, G. (2013) · doi:10.20868/upm.thesis.23086 · Cited by 1
Efficiency in neural information processing
Knuutila, A. (2012) · Työväentutkimus Vuosikirja
Imagerie cérébrale : outils, principes et utilisations de la clinique aux sciences cognitives comparatives
Dhénain, M. (2015) · Bulletin de l Académie vétérinaire de France · doi:10.4267/2042/56869
A Graph-Theoretic Approach to Brain Networks Associated with Swallowing
Luan, B. (2013) · D-Scholarship@Pitt (University of Pittsburgh)
Bipolar disorder and cognitive functioning : a two-year naturalistic study
Arts, B. M. G. (2012) · Research Publications (Maastricht University) · doi:10.26481/dis.20120511ba
SSVEP Responses Reveal the Efficiency of Functional Brain Network Entrained by the Flickering Stimulus
Zhang, Y., Xu, P., & Yao, D. (2013) · IFAC Proceedings Volumes · doi:10.3182/20130902-3-cn-3020.00078
Degenerative processes in organotypic spinal slices: challenging pre-motor network with stress conditions
Medelin, M. (2014) · OpenstarTs (Univeristy of Trieste https://www.units.it/)
Padrões Emergentes em Redes Cerebrais e Redes Sociais e suas Influências na Cognição Humana
Silva, V. S. R., Motta, C. L. R., & Silva, J. O. P. E. (2014) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/cbie.sbie.2014.852
The Effect of COMT Gene Polymorphism on the Neural Substrate of Attention Control: A Meta-analysis of Imaging Genetics Studies
Liu, L., Li, J., Song, Y., & Liu, J. (2013) · Acta Psychologica Sinica · doi:10.3724/sp.j.1041.2012.00446
Self-citation
Visualizing the brain structure with a DT-MRI minimum spanning tree
Pakawanwong, P. (2015) · CEUR Workshop Proceedings
Consensus between networks based on streamline fiber tracking and probabilistic fiber tractography
Li, Q., & Jiang, T. (2016) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2016.7554847
Self-citation
Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders
Nakubulwa, M. A. (2016) · Aston Publications Explorer (Aston University) · doi:10.48780/publications.aston.ac.uk.00028813
Applied Advanced Error Control Coding for General Purpose Representation and Association Machine Systems
Dai, B. (2016) · Journal of International Crisis and Risk Communication Research
Development of Magnetic Resonance Based Spin Labeling Techniques for Functional Brain Imaging
Çavuşoğlu, M. (2012)
Recovering Missing Connections in Diffusion Weighted MRI Using Matrix Completion
Wang, C., Ng, B., Amir-Khalili, A., & Rafeef Abugharbieh, A. (2018) · Mathematics and visualization · doi:10.1007/978-3-319-73839-0_12
Brain—Through the Aeons
Chakravarthy, V. S. (2018) · Springer eBooks · doi:10.1007/978-981-13-3320-0_2
Neural Signaling and Communication
Jabeen, S. H., Ahmed, N., Sandhu, M. E., Chaudhry, N. R., & Raza, R. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.86318
Jumlah Sel Neuron Cerebrum pada Paparan Musik Mozart Lebih Tinggi Dibandingkan Dengan Paparan Musik Indonesia
Sholichah, A. M., Joewono, H. T., & Widjiati, W. (2019) · Jurnal Penelitian Kesehatan "SUARA FORIKES" (Journal of Health Research "Forikes Voice") · doi:10.33846/sf11104
Το νευροβιολογικό υπόστρωμα της νοημοσύνης: Ανατομικά και λειτουργικά χαρακτηριστικά του εγκεφάλου που συνδέονται με γνωστικές ικανότητες
Tomprou, D. M., Ntolka, E., & Παπαδάτου-Παστού, Μ. (2020) · Psychology the Journal of the Hellenic Psychological Society · doi:10.12681/psy_hps.23264
Relation between Functional Brain Networks and Fluid Intelligence Using Information Theory
Ashrafi, M., Ghazi, N., & Hamid Soltanian-zadeh, A. (2020) · Iranian Conference on Electrical Engineering · doi:10.1109/icee50131.2020.9260678
By Completing a Bestiary
Marro, J., & Torres, J. J. (2021) · doi:10.1063/9780735421769_006
Neural Network Dynamics and Efficiency Underlying Individual Variability in Spatial Orientation
Grant, A. E., & Arnold, F. A. (2012)
Combining local and global evolutionary trajectories of brain-behavior equilibrium through game theoryPreprint
Plinio, S. D., & Ebisch, S. J. H. (2020) · doi:10.31234/osf.io/2s6c9
Big Brain Network Data
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.006
P300 Characterization Through Granger Causal Connectivity in the Context of Brain-Computer Interface Technologies
Salazar, V., Changoluisa, V., & Rodrı́guez, F. B. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85030-2_21
Theories, Models, and Hypotheses
Barbey, A. K., Karama, S., & Haier, R. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.007
Statistical Preliminary
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.002
Series Expansion of Connectivity Matrices
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.013
Diffusions on Graphs
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.009
Dynamic Network Models
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.014
Bibliography
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.015
Preface
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.001
Network Simulations
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.007
Graph Theory
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.004
Sparse Networks
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.010
Persistent Homology
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.008
Brain Network Distances
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.011
Correlation Networks
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.005
Combinatorial Inferences for Networks
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.012
In a network state of mind
de Haan, W. (2012) · Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam)
Index
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610.016
References
(2016) · Elsevier eBooks · doi:10.1016/b978-0-12-407908-3.10000-7
Faculty Opinions recommendation of Intrinsic Functional Connectivity of the Brain in Adults with a Single Cerebral Hemisphere.
Clarke, S. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.736931120.793567802
Fundamental Issues
Barbey, A. K., Karama, S., & Haier, R. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.002
White Matter Alterations Following Childhood Trauma
Daniels, J. K. (2016) · Springer eBooks · doi:10.1007/978-3-319-08359-9_10
Neuroimaging Methods and Findings
(2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.013
Predictive Modeling Approaches
Barbey, A. K., Karama, S., & Haier, R. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.020
Investigating cognitive ability using action-based models of structural brain networks
Arora, V., Amico, E., Goñi, J., & Ventresca, M. (2022) · Journal of Complex Networks · doi:10.1093/comnet/cnac037
IQ: Where does it come from?
McCredie, H. (2019) · Assessment and Development Matters · doi:10.53841/bpsadm.2019.11.4.28
Understanding Individual Differences within Large-scale Brain Networks across Cognitive Contexts
Bottenhorn, K. L. (2021) · FIU - Digital Commons (Florida International University) · doi:10.25148/etd.fidc010186
The Convergence of Intelligence and LongevityPreprint
Gualtieri, C. (2024) · Qeios · doi:10.32388/wvwb0l
Controlling Alzheimer’s disease by deep brain stimulation based on a data-driven cortical network model
Yan, S., Yang, X., & Duan, Z. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-024-10148-3
Electrophysiological resting-state signatures link polygenic scores to general intelligence
Engler, R., Stammen, C., Arnau, S., Penate, J. S., Metzen, D., Digutsch, J., Gajewski, P. D., Getzmann, S., Fraenz, C., Reinders, J., et al. (2025) · Scientific Reports · doi:10.1101/2025.01.17.633388
The Convergence of Intelligence and LongevityPreprint
Gualtieri, C. (2024) · Qeios · doi:10.32388/wvwb0l.2
Rate of functional network maturity and the role of environmental factorsPreprint
Hughes-Small, B., Saljoughi, S., Stevenson, R. A., Soddu, A., & Stojanoski, B. B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.05.674401
Post mortem inference of the human brain microstructure using ultra-high field magnetic resonance imaging with strong gradients
Beaujoin, J. (2018) · theses.fr (ABES) · doi:10.70675/7199e303z83c8z4b55z9841z69bccf6064e6
Neuronaukowe podstawy inteligencji
Haier, R. J., Kabziński, P., & Mąka, S. (2026) · Wydawnictwo Uniwersytetu Łódzkiego eBooks · doi:10.18778/8331-892-9
Network‐Valued Random Vectors and Their Statistical Foundations: A Comparison of Inferential Techniques and Testing Frameworks Under Heterogeneity
Roy, A., Auer, P. L., & Banerjee, A. (2026) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.70075
Age of Language Acquisition Affects Topology of Brain White Matter Network: A Diffusion Tensor Magnetic Resonance Imaging Study
Zhang, Y., Tu, L., Omata, K., Chen, G., Liu, X., Takamura, T., Zhang, L., Wakasugi, N., Zou, C., Togo, H., et al. (2026) · Brain Sciences · doi:10.3390/brainsci16080878
Atypical development of white matter structural networks in children and adolescents with autism spectrum disorder: a graph theory study
Li, M., Kurita, K., Yamada, T., Wang, Y., Izumoto, M., Iwatani, Y., Mohri, I., Shou, Q., Yamashita, M., Hamatani, S., et al. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1844698
Concurrent alterations in brain hemispheric asymmetry of structural and functional networks in single-sided deafness
Jiao, S., Qiao, Y., Liu, Q., Zhu, M., Guo, H., & Shang, Y. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122252
FAIRsharing record for: Brainstem Connectome
FAIRsharing Team, & Schmitt, O. (2018) · FAIRsharing.org · doi:10.25504/fairsharing.hd7sxq
Resting-State EEG Graph Analysis Reveals Enhanced Alpha-Band Integration in High-IQ Individuals: The MENSA-Connect Project
Marco, M. D., Zauli, A., Iacovelli, C., Reale, G., Garbuglia, M., Cacciatore, E. L., Grandis, M. S. D., Tomaino, A., Rossi, S., & Caliandro, P. (2026) · Journal of Integrative Neuroscience · doi:10.31083/jin56100
Kardinaltugenden und Werte: Fälle für das Forschungslabor?
Wahl, K. (2014) · Springer eBooks · doi:10.1007/978-3-642-55407-0_6

Authors: Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T.
Affiliations: Chinese Academy of Sciences (CN)
Capital Medical University (CN)
Corresponding author: Yu, Chunshui
Jiang, Tianzi
ORCID: Li, Yonghui
Liu, Yong
Li, Jun
Qin, Wen
Li, Kuncheng
Yu, Chunshui
Jiang, Tianzi
Venue: PLoS Computational Biology
Publisher: Public Library of Science (PLoS)
Published: 2009-05-29
Volume: 5
Issue: 5
Pages: e1000395
ISSN: 1553-734X (ISSN-L)
1553-7358
DOI: 10.1371/journal.pcbi.1000395
PMC: PMC2683575
OpenAlex: W2073017605
Cited by: 597
References: 81
Citations by year: 2026: 9 · 2025: 9 · 2024: 14 · 2023: 18 · 2022: 26 · 2021: 41 · 2020: 29 · 2019: 53 · 2018: 27 · 2017: 40 · 2016: 56 · 2015: 54 · 2014: 43 · 2013: 59 · 2012: 50
FWCI: 29.97
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Computational Biology (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Figshare (submittedVersion, cc-by)
Funders: National Natural Science Foundation of China (30730035, 60675033, 30670601)
Chinese Academy of Sciences
Program for New Century Excellent Talents in University (NCET-07-0568)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords: intelligence, global efficiency, average path length, individual differences in intelligence, structural connectivity, diffusion tensor tractography, graph theory, information transmission efficiency, IQ
MeSH terms: Adolescent, Adult, Brain, Brain Mapping, Female, Humans, Intelligence, Intelligence Tests, Male, Models, Neurological, Nerve Net, Neural Pathways, Cluster Analysis, Diffusion Magnetic Resonance Imaging

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

Brain Anatomical Network and Intelligence

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks