The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index.
Topological organization of the human brain functional connectome across the lifespan
Open accessAbstract
Human brain function undergoes complex transformations across the lifespan. We employed resting-state functional MRI and graph-theory approaches to systematically chart the lifespan trajectory of the topological organization of human whole-brain functional networks in 126 healthy individuals ranging in age from 7 to 85 years. Brain networks were constructed by computing Pearson's correlations in blood-oxygenation-level-dependent temporal fluctuations among 1024 parcellation units followed by graph-based network analyses. We observed that the human brain functional connectome exhibited highly preserved non-random modular and rich club organization over the entire age range studied. Further quantitative analyses revealed linear decreases in modularity and inverted-U shaped trajectories of local efficiency and rich club architecture. Regionally heterogeneous age effects were mainly located in several hubs (e.g., default network, dorsal attention regions). Finally, we observed inverse trajectories of long- and short-distance functional connections, indicating that the reorganization of connectivity concentrates and distributes the brain's functional networks. Our results demonstrate topological changes in the whole-brain functional connectome across nearly the entire human lifespan, providing insights into the neural substrates underlying individual variations in behavior and cognition. These results have important implications for disease connectomics because they provide a baseline for evaluating network impairments in age-related neuropsychiatric disorders.
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?
In the citing paper
“high strength and betweenness hubs in adulthood are largely located within within the default mode, attentional, sensorimotor, and visual areas (Cao et al., 2014; Fransson et al., 2011; Grayson et al., 2014; Hwang et al., 2013)”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Cao et al., 2014
In the citing paper
“Among cortical areas, some have reported increasing hubness of frontal nodes (Cao et al., 2014; Wu et al., 2013) while in other lobes there have been mixed reports of increasing (Cao et al., 2014; Grayson et al., 2014) and decreasing (Wu et al., 2013; Zuo et al., 2012), or stable (Hwang et al., 2013) hubness.”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Cao et al., 2014
In the citing paper
“Returning to the MRI literature, there is convergent evidence that the locations of high-strength and high-betweenness hub regions appear to be stable after 5 years of age (Cao et al., 2014; Grayson et al., 2014; Hwang et al., 2013; Wang et al., 2012; Wu et al., 2013; Zuo et al., 2012).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Cao et al., 2014
In the citing paper
“More detailed network analyses have revealed that specific connections to cortical hub regions increase during childhood and adolescence (Grayson et al., 2014; Hwang et al., 2013) resulting in increased rich club coefficients in adulthood (Cao et al., 2014; Grayson et al., 2014).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Cao et al., 2014
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
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
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
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
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 organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Regression and time series model selection in small samples
HURVICH, C. M., & TSAI, C.-L. (1989) · Biometrika · doi:10.1093/biomet/76.2.297 · Cited by 6,586
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–300 of 440
Subcortico-Cortical Functional Connectivity in the Fetal Brain: A Cognitive Development Blueprint
Canini, M., Cavoretto, P. I., Scifo, P., Pozzoni, M., Petrini, A., Iadanza, A., Pontesilli, S., Scotti, R., Candiani, M., Falini, A., et al. (2020) · Cerebral Cortex Communications · doi:10.1093/texcom/tgaa008 · Cited by 22
Baghernezhad, S., & Daliri, M. R. (2024) · GeroScience · doi:10.1007/s11357-024-01128-w · Cited by 23
Shafiei, G., Esper, N. B., Hoffmann, M. S., Ai, L., Chen, A. A., Cluce, J., Covitz, S. C., Giavasis, S., Lane, C., Mehta, K. P., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.026 · Cited by 32
Self-citationFunctional connectivity in aging
Liem, F., Geerligs, L., Damoiseaux, J. S., & Margulies, D. S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-816094-7.00010-6 · Cited by 24
Chen, K., Li, C., Sun, W., Tao, Y.-Y., Wang, R., Wen, H., & Liu, D. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab220 · Cited by 22
A mixed-modeling framework for analyzing multitask whole-brain network data
Simpson, S. L., Bahrami, M., & Laurienti, P. J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00065 · Cited by 21
Disrupted rich-club organization of brain structural networks in Parkinson’s disease
Liu, T., Yan, Y., Ai, J., Chen, D., Wu, J., Fang, B., & Yan, T. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02319-3 · Cited by 21
Colón-Pérez, L. M., Turner, S. M., Lubke, K. N., Pompilus, M., Febo, M., & Burke, S. N. (2019) · eNeuro · doi:10.1523/eneuro.0047-19.2019 · Cited by 21
Schizophrenia and Neurodevelopment: Insights From Connectome Perspective
Sun, X., & Xia, M. (2024) · Schizophrenia Bulletin · doi:10.1093/schbul/sbae148 · Cited by 20
Self-citationSchlemm, E., Frey, B. M., Mayer, C., Petersen, M., Fiehler, J., Hanning, U. M. P., Kühn, S., Twerenbold, R., Gallinat, J., Gerloff, C. P., et al. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.03.019 · Cited by 20
Age-Related Cognitive Effects of Videogame Playing Across the Adult Life span
Wang, P., Zhu, X.-T., Liu, H., Zhang, Y.-W., Hu, Y., Li, H.-J., & Zuo, X. (2017) · Games for Health Journal · doi:10.1089/g4h.2017.0005 · Cited by 24
Self-citationToward a Neuroscience of Adult Cognitive Developmental Theory
Girgis, F., Lee, D. J., Goodarzi, A., & Ditterich, J. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00004 · Cited by 25
Dajani, D. R., Burrows, C. A., Nebel, M. B., Mostofsky, S. H., Gates, K. M., & Uddin, L. Q. (2019) · Brain Connectivity · doi:10.1089/brain.2019.0669 · Cited by 23
Wang, Y., Zhang, Y., Zheng, W., Liu, X., Zhao, Z., Li, S., Chen, N., Yang, L., Fang, L., Yao, Z., et al. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28318 · Cited by 19
Functional brain networks in never-treated and treated long-term Ill schizophrenia patients
Li, Y., Li, F. Y., Liu, J., Liao, W., Li, X., Li, M., Meng, Y., Liang, S., Zhang, C., Yang, X., et al. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0428-2 · Cited by 21
Hou, X., Liu, P., Gu, H., Chan, M. Y., Li, Y., Peng, S., Wig, G. S., Yang, Y., Park, D. C., & Lu, H. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.11.028 · Cited by 19
Dopamine D1-signaling modulates maintenance of functional network segregation in aging
Pedersen, R., Johansson, J. J., & Salami, A. (2023) · Aging Brain · doi:10.1016/j.nbas.2023.100079 · Cited by 22
Wang, X., Huang, C., Tsai, S., Lin, C., & Cai, Q. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1331574 · Cited by 19
Ball, G., Beare, R. J., & Seal, M. L. (2017) · Human Brain Mapping · doi:10.1002/hbm.23656 · Cited by 18
Liu, X., Yang, H., Becker, B., Huang, X., Luo, C., Meng, C., & Biswal, B. B. (2021) · Human Brain Mapping · doi:10.1002/hbm.25403 · Cited by 19
Effect of rs1344706 in the ZNF804A gene on the brain network
Chen, X., Zhang, Z., Zhang, Q.-M., Zhao, W., Zhai, J., Chen, M., Du, B., Deng, X., Ji, F., Wang, C., et al. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.12.017 · Cited by 17
Self-citationAge-Associated Differences of Modules and Hubs in Brain Functional Networks
Zhang, Y., Wang, Y., Chen, N., Guo, M., Wang, X., Chen, G., Li, Y., Yang, L., Li, S., Yao, Z., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.607445 · Cited by 17
Zhang, H., Bai, X., & Diaz, M. T. (2021) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2021.107784 · Cited by 17
Networks of worry—towards a connectivity-based signature of late-life worry using higher criticism
Gerlach, A., Karim, H. T., Kazan, J., Aizenstein, H. J., Krafty, R. T., & Andreescu, C. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01648-5 · Cited by 17
The thalamo-cortical complex network correlates of chronic pain
Zippo, A. G., Valente, M., Caramenti, G. C., & Biella, G. E. M. (2016) · Scientific Reports · doi:10.1038/srep34763 · Cited by 17
Raj, A., Verma, P., & Nagarajan, S. S. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.959557 · Cited by 15
Cognitive stimulation has potential for brain activation in individuals with Rett syndrome
Migliorelli, C., Medina-Rivera, I., Bachiller, A., Tost, A., Alonso, J. F., Sala, A. L., Armstrong, J., O’Callaghan, M., Pineda, M., Mañanas, M. Á., et al. (2021) · Journal of Intellectual Disability Research · doi:10.1111/jir.12902 · Cited by 16
Reconfiguration of metabolic connectivity in ageing
Deery, H. A., Liang, E. X., Siddiqui, M. N., Murray, G., Voigt, K., Paolo, R. D., Moran, C., Egan, G. F., & Jamadar, S. D. (2024) · Communications Biology · doi:10.1038/s42003-024-07223-0 · Cited by 21
A Quantitative Study of Network Robustness in Resting-State fMRI in Young and Elder Adults
Gómez‐Ramírez, J., Li, Y., Wu, Q., & Wu, J. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00256 · Cited by 15
Longitudinally consistent estimates of intrinsic functional networks
Zhao, Q., Kwon, D., Müller‐Oehring, E. M., Berre, A. L., Adolf Pfefferbaum, A., Sullivan, E. V., & Pohl, K. M. (2019) · Human Brain Mapping · doi:10.1002/hbm.24541 · Cited by 14
Abellaneda‐Pérez, K., Vaqué‐Alcázar, L., Perellón‐Alfonso, R., Solé‐Padullés, C., Bargalló, N., Salvador, R., Ruffini, G., Nitsche, M. A., Pascual‐Leone, Á., & Bartrés‐Faz, D. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.725013 · Cited by 17
Chen, P., Chiou, J., Yang, Y.-F., Chen, Y., Hsieh, H.-L., Chang, Y., & Tseng, W. I. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162028 · 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
Alvand, A., Kuruvilla-Mathew, A., Kirk, I. J., Roberts, R. P., Pedersen, M., & Purdy, S. C. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103139 · Cited by 14
Di, X., Xu, T., Uddin, L. Q., & Biswal, B. B. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101280 · Cited by 18
Modeling the neurocognitive dynamics of language across the lifespan
Guichet, C., Banjac, S., Achard, S., Mermillod, M., & Baciu, M. (2024) · Human Brain Mapping · doi:10.1002/hbm.26650 · Cited by 16
Task-linked Diurnal Brain Network Reorganization in Older Adults: A Graph Theoretical Approach
Anderson, J. A. E., Sarraf, S., Amer, T., Bellana, B., Man, V. Y., Campbell, K. L., Hasher, L., & Grady, C. L. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01060 · Cited by 15
Luo, Y., Schulz, K. P., Alvarez, T. L., Halperin, J. M., & Li, X. (2018) · Cortex · doi:10.1016/j.cortex.2018.09.013 · Cited by 14
Lindbergh, C. A., Lv, J., Zhao, Y., Mewborn, C. M., Puente, A. N., Terry, D. P., Renzi‐Hammond, L. M., Hammond, B. R., Liu, T., & Miller, L. S. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-018-00034-y · Cited by 13
Ferré, P., Benhajali, Y., Steffener, J., Stern, Y., Joanette, Y., & Bellec, P. (2019) · Language Cognition and Neuroscience · doi:10.1080/23273798.2019.1608072 · Cited by 14
Li, R., Zhang, J., Wu, X., Wen, X., & Han, B. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02077-8 · Cited by 14
Adams, J. N., Chappel-Farley, M. G., Yaros, J. L., Taylor, L., Harris, A. L., Mikhail, A., McMillan, L. C., Keator, D. B., & Yassa, M. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-40092-x · Cited by 14
Mohan, A., Moreno, N. A., Song, J., Ridder, D. D., & Vanneste, S. (2017) · Brain Connectivity · doi:10.1089/brain.2016.0459 · Cited by 13
Makowski, C., Lewis, J. D., Khundrakpam, B. S., Tardif, C. L., Palaniyappan, L. K., Joober, R., MALLA, A. K., Shah, J. L., Bodnar, M. D., Chakravarty, M. M., et al. (2020) · Hippocampus · doi:10.1002/hipo.23215 · Cited by 16
Reorganization of the Neurobiology of Language After Sentence Overlearning
Skipper, J. I., Aliko, S., Brown, S., Jo, Y. J., Lo, S., Molimpakis, E., & Lametti, D. R. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab354 · Cited by 14
Koziol, L. F., Barker, L. A., Joyce, A. W., & Hrin, S. (2014) · Applied Neuropsychology Child · doi:10.1080/21622965.2014.946803 · Cited by 13
Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks
Petersen, A., Zhao, J., Carmichael, O. T., & Müller, H. (2016) · Brain Connectivity · doi:10.1089/brain.2016.0420 · Cited by 14
Wang, X., Jiao, Y., & Li, L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201243 · Cited by 13
Zuo, N., Salami, A., Liu, H., Yang, Z., & Jiang, T. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.09.006 · Cited by 13
Álvarez, D. M. H., Pacheco, L., Velasco-Segura, R., de la Mora, M. P., Romero, C. T., & García, N. G. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00209 · Cited by 13
Zheng, G., Zhang, L., Zhang, L. J., Li, Q., Pan, Z., Liang, X., Shi, D., & Lu, G. (2014) · BioMed Research International · doi:10.1155/2014/727452 · Cited by 12
Autologous Cord Blood Therapy for Infantile Cerebral Palsy: From Bench to Bedside
Jensen, A. (2014) · Obstetrics and Gynecology International · doi:10.1155/2014/976321 · Cited by 12
Lifespan Development of the Human Brain Revealed by Large-Scale Network Eigen-Entropy
Fan, Y., Zeng, L., Shen, H., Qin, J., Li, F., & Hu, D. (2017) · Entropy · doi:10.3390/e19090471 · Cited by 12
Pongpipat, E. E., Kennedy, K. M., Foster, C. M., Boylan, M. A., & Rodrigue, K. M. (2020) · Brain Connectivity · doi:10.1089/brain.2020.0791 · Cited by 12
Russo, A. G., Lührs, M., Salle, F. D., Esposito, F., & Goebel, R. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abecc3 · Cited by 13
Stability and dynamics of a spectral graph model of brain oscillations
Verma, P., Nagarajan, S. S., & Raj, A. (2022) · Network Neuroscience · doi:10.1162/netn_a_00263 · Cited by 11
Complex network analysis of resting-state fMRI of the brain
Anwar, A. R., Hashmy, M. Y., Imran, B., Riaz, M. H., Mehdi, S. M. M., Muthalib, M., Perrey, S., Deuschl, G., Groppa, S., & Muthuraman, M. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591506 · Cited by 12
Xu, J., Li, H., Li, C., Yao, J.-C., Hu, J., Wang, J., Hu, Q., Zhang, Y., & Zhang, J. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.06.050 · Cited by 12
Self-citationCommunity detection in the human connectome: Method types, differences and their impact on inference
Brooks, S. J., Jones, V. O., Wang, H., Deng, C., Golding, S. G. H., Lim, J., Gao, J., Daoutidis, P., & Stamoulis, C. (2024) · Human Brain Mapping · doi:10.1002/hbm.26669 · Cited by 12
The generalizability of cortical area parcellations across early childhood
Tu, J. C., Myers, M. J., Li, W., Li, J. J., Wang, X., Dierker, D. L., Day, T. K. M., Snyder, A. Z., Latham, A. R., Kenley, J. K., et al. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf116 · Cited by 12
Li, C., Qiao, K., Mu, Y., & Jiang, L. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.605158 · Cited by 14
Self-citationIntegrated structural and functional atlases of Asian children from infancy to childhood
Zhu, J., Zhang, H., Chong, Y. S., Shek, L. P., Gluckman, P. D., Meaney, M. J., Fortier, M. V., & Qiu, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118716 · Cited by 11
Trade-offs among cost, integration, and segregation in the human connectome
Ma, J., Chen, X., Gu, Y., Li, L., Cam-CAN, Lin, Y. L., & Dai, Z. (2022) · Network Neuroscience · doi:10.1162/netn_a_00291 · Cited by 11
Self-citationAltered structural connectome of children with auditory processing disorder: a diffusion MRI study
Alvand, A., Kuruvilla-Mathew, A., Roberts, R. P., Pedersen, M., Kirk, I. J., & Purdy, S. C. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad075 · Cited by 13
Developmental implications of children’s brain networks and learning
Chan, J. S. Y., Wang, Y., Yan, J. H., & Chen, H. (2016) · Reviews in the Neurosciences · doi:10.1515/revneuro-2016-0007 · Cited by 12
Volume entropy for modeling information flow in a brain graph
Lee, H., Kim, E., Ha, S., Kang, H., Huh, Y., Lee, Y., Lim, S., & Lee, D. S. (2019) · Scientific Reports · doi:10.1038/s41598-018-36339-7 · Cited by 12
Zhao, X., Li, Y., Chen, K., Li, K., Zhang, J., & Guo, X. (2019) · IEEE Access · doi:10.1109/access.2019.2923274 · Cited by 11
The impact of EEG electrode density on the mapping of cortical activity networks in infants
Asayesh, A., Vanhatalo, S., & Tokariev, A. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120932 · Cited by 10
Mei, Y., Qiu, D., Xiong, Z., Li, X., Zhang, P., Zhang, M., Zhang, X., Zhang, Y., Yu, X., Ge, Z., et al. (2024) · The Journal of Headache and Pain · doi:10.1186/s10194-024-01919-8 · Cited by 11
Mapping of Structure-Function Age-Related Connectivity Changes on Cognition Using Multimodal MRI
Pur, D. R., Preti, M. G., Ribaupierre, A. D., Ville, D. V. D., Eagleson, R. A., Mella, N., & de Ribaupierre, S. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.757861 · Cited by 10
Lin, J., Zhang, L., Guo, R., Jiao, S., Song, X., Feng, S., Wang, K., Li, M., Luo, Y., & Han, Z. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119722 · Cited by 11
Self-citationLi, J., Yang, Y., Viñas‐Guasch, N., Yang, Y., & Bi, H. (2022) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14943 · Cited by 10
Venkatesan, U. M., & Hillary, F. G. (2019) · Neuropsychology · doi:10.1037/neu0000553 · Cited by 10
Jiang, Y., Tian, Y., & Wang, Z. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00214 · Cited by 10
Has, A. C., Nolte, G., Pöttgen, J., Engel, A. K., Heesen, C., Gold, S. M., & Stellmann, J. (2022) · Journal of Neuroscience Research · doi:10.1002/jnr.25135 · Cited by 9
Brain topology underlying executive functions across the lifespan: focus on the default mode network
Menardi, A., Spoa, M., & Vallesi, A. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1441584 · Cited by 10
Network modeling and topology of aging
Li, F., Yang, D., Wu, S., Xue, C., Sang, M., Liu, X., Che, J., Wu, J., Gragnoli, C., Griffin, C. H., et al. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.10.006 · Cited by 9
Gogniat, M. A., Robinson, T. L., Jean, K. R., & Miller, L. S. (2022) · Aging Brain · doi:10.1016/j.nbas.2022.100036 · Cited by 11
Resting State Network Segregation Modulates Age-Related Differences in Language Production
Zhang, H., & Diaz, M. T. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00106 · Cited by 9
Computational Model of Functional Connectivity Distance Predicts Neural Alterations
Wadhera, T., & Mahmud, M. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3320243 · Cited by 9
Normative pathways in the functional connectome
Leming, M., Su, L., Chattopadhyay, S., & Suckling, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.028 · Cited by 11
Wang, X., Jiao, Y., & Li, L. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.11.003 · Cited by 9
A Novel Brain Network Construction Method for Exploring Age-Related Functional Reorganization
Li, W., Wang, M., Li, Y., Huang, Y., & Chen, X. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/2429691 · Cited by 8
Zhao, X., Chen, J., Xiao, P., Feng, J., Nie, Q. H., & Zhao, X. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa388 · Cited by 9
Structural Subnetwork Evolution Across the Life-Span: Rich-Club, Feeder, Seeder
Schirmer, M. D., & Chung, A. W. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00755-3_15 · Cited by 6
Mapping individual structural covariance network in development brain with dynamic time warping
Sun, H. G., Sun, Q., Li, Y., Zhang, J., Xing, H., & Wang, J. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae039 · Cited by 9
Self-citationSimilarity-guided multi-view functional brain network fusion
Li, Z., Liu, J., Sun, M., Zhang, F., Hu, B., & Dong, Q. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103632 · Cited by 10
Self-citationA Practical Introduction to Network Neuroscience for Communication Researchers
Fisher, J. T., Hopp, F. R., & Weber, R. (2020) · Communication Methods and Measures · doi:10.1080/19312458.2020.1860206 · Cited by 8
Hassanzadeh, Z., Bahrami, F., & Dortāj, F. (2024) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2024.1304378 · Cited by 10
Gu, Y., Wong, N. M. L., Chan, C. C. H., Wu, J., & Lee, T. M. C. (2025) · GeroScience · doi:10.1007/s11357-025-01515-x · Cited by 8
Multi-layer Large-Scale Functional Connectome Reveals Infant Brain Developmental Patterns
Zhang, H., Stanley, N., Mucha, P. J., Yin, W., Lin, W., & Shen, D. G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00931-1_16 · Cited by 8
Yang, L., Wei, A., Ouyang, T.-T., Cao, Z.-Z., Duan, A., & Zhang, H. (2021) · Annals of Translational Medicine · doi:10.21037/atm-21-367 · Cited by 8
A Neuroscience Primer for Integrating Geroscience With the Neurobiology of Aging
Hernandez, C. M., Hernandez, A. R., Hoffman, J. M., King, P. H., McMahon, L. L., Buford, T. W., Carter, C. S., Bizon, J. L., & Burke, S. N. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab301 · Cited by 10
Spatiotemporal dedifferentiation of the global brain signal topography along the adult lifespan
Ao, Y., Yang, C., Drewes, J., Jiang, M., Huang, L., Jing, X., Northoff, G., & Wang, Y. (2023) · Human Brain Mapping · doi:10.1002/hbm.26484 · Cited by 8
Luo, A. C., Sydnor, V. J., Pines, A. R., Larsen, B., Alexander‐Bloch, A. F., Cieslak, M., Covitz, S. C., Chen, A. A., Esper, N. B., Feczko, E., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.20.549090 · Cited by 8
Self-citationPan, Y., Bi, C., Kochunov, P. V., Shardell, M. D., Smith, J. C., McCoy, R. G., Ye, Z., Yu, J., Lu, T., Yang, Y., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00394 · Cited by 7
Task difficulty modulates age-related differences in functional connectivity during word production
Zhang, H., & Diaz, M. T. (2023) · Brain and Language · doi:10.1016/j.bandl.2023.105263 · Cited by 7
Cognitive advantages of sustainability concept mapping
Watson, M. K., Barrella, E. M., & Anderson, R. D. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.137427 · Cited by 7
Perez, D. C., Hernandez, J. J., Wulfekuhle, G. E., & Gratton, C. (2024) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2024.11.010 · Cited by 7
Aging-Related Modular Architectural Reorganization of the Metabolic Brain Network
Huang, Q., Ren, S., Zhang, T., Li, J., Jiang, D., Xiao, J., Hua, F., Xie, F., & Guan, Y. (2021) · Brain Connectivity · doi:10.1089/brain.2021.0054 · Cited by 7
Metadata
- Authors
- Cao, M., Wang, J.-H., Dai, Z.-J., Cao, X.-Y., Jiang, L.-L., Fan, F.-M., Song, X.-W., Xia, M.-R., Shu, N., Dong, Q., Milham, M. P., Castellanos, F. X., Zuo, X.-N., & He, Y.
- Affiliations
- Beijing Normal University (CN)
- Nathan Kline Institute for Psychiatric Research (US)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Zuo, Xi-Nian
- Venue
- Developmental Cognitive Neuroscience
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 7
- Pages
- 76-93
- ISSN
- 1878-9293 (ISSN-L)
- PMC
- PMC6987957
- OpenAlex
- W2093597642
- Cited by
- 440
- References
- 102
- Citations by year
- 2026: 18 · 2025: 30 · 2024: 39 · 2023: 28 · 2022: 33 · 2021: 41 · 2020: 38 · 2019: 51 · 2018: 41 · 2017: 41 · 2016: 32 · 2015: 30 · 2014: 17
- FWCI
- 14.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Developmental Cognitive Neuroscience (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Science Foundation (81030028, 31221003, 81171409)
- National Science Fund for Distinguished Young Scholars (81225012)
- Major Joint Fund for International Cooperation and Exchange of the National Natural Science Foundation (81220108014)
- Open Research Fund of the Key Laboratory of Behavioral Science, the Startup Foundation for Distinguished Research Professor (Y0CX492S03)
- Institute of Psychology, the Hundred Talents Program and the Key Research Program (KSZD-EW-TZ-002)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- lifespan development, network topology, modularity, rich club, local efficiency, brain network hubs, default mode network, dorsal attention network, resting-state fMRI, graph theory
- MeSH terms
- Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Aging, Brain, Brain Mapping, Child, Cognition, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Sex Factors, Connectome
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
