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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.
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“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
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“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
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“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
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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 101–200 of 440
Otte, W. M., Diessen, E. G. A. L. V., Paul, S., Ramaswamy, R., Rallabandi, V. P. S., Stam, C. J., & Roy, P. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.011 · Cited by 56
Developmental changes in functional brain networks from birth through adolescence
Gozdas, E., Holland, S. K., & Altaye, M. (2018) · Human Brain Mapping · doi:10.1002/hbm.24457 · Cited by 66
A Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms
Xiao, L., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2921207 · Cited by 54
Selective Vulnerability Related to Aging in Large-Scale Resting Brain Networks
Zhang, H., Chen, W., Jiao, Y., Xu, Y. Q., Zhang, X., & Wu, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108807 · Cited by 55
Perry, A., Wen, W. X., Kochan, N. A., Thalamuthu, A., Sachdev, P. S., & Breakspear, M. J. (2017) · Human Brain Mapping · doi:10.1002/hbm.23717 · Cited by 57
Bilingualism and aging: A focused neuroscientific review
Zhang, H., Wu, Y. J., & Thierry, G. (2020) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2020.100890 · Cited by 56
Fluctuations between high- and low-modularity topology in time-resolved functional connectivity
Fukushima, M., Betzel, R. F., He, Y., de Reus, M. A., van den Heuvel, M. P., Zuo, X., & Sporns, O. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.044 · Cited by 54
Self-citationConnectome-based models can predict processing speed in older adults
Gao, M., Wong, C. H. Y., Huang, H., Shao, R., Huang, R., Chan, C. C. H., & Lee, T. M. C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117290 · Cited by 53
Connectome-based models predict attentional control in aging adults
Fountain‐Zaragoza, S., Samimy, S., Rosenberg, M. D., & Prakash, R. S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.074 · Cited by 55
Dynamic functional connectivity profile of the salience network across the life span
Snyder, W. E., Uddin, L. Q., & Nomi, J. S. (2021) · Human Brain Mapping · doi:10.1002/hbm.25581 · Cited by 51
Kawagoe, T., Onoda, K., & Yamaguchi, S. (2017) · Scientific Reports · doi:10.1038/srep40107 · Cited by 59
Age-related changes in spatial and temporal features of resting state fMRI
Vij, S. G., Nomi, J. S., Dajani, D. R., & Uddin, L. Q. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.066 · Cited by 48
Spatial and spectral trajectories in typical neurodevelopment from childhood to middle age
Hunt, B. A. E., Wong, S. M., Vandewouw, M. M., Brookes, M. J., Dunkley, B. T., & Taylor, M. J. (2019) · Network Neuroscience · doi:10.1162/netn_a_00077 · Cited by 48
Longitudinal functional brain network reconfiguration in healthy aging
Malagurski, B., Liem, F., Oschwald, J., Mérillat, S., & Jäncke, L. (2020) · Human Brain Mapping · doi:10.1002/hbm.25161 · Cited by 54
Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club
Escrichs, A., Biarnés, C., Garre‐Olmo, J., Fernández‐Real, J. M., Ramos, R., Pamplona, R., Brugada, R., Serena, J. L., Ramió‐Torrentà, L., de Tuero, G. C., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa367 · Cited by 46
Subject identification using edge-centric functional connectivity
Jo, Y., Faskowitz, J., Esfahlani, F. Z., Sporns, O., & Betzel, R. F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118204 · Cited by 54
Reorganization of brain structural networks in aging: A longitudinal study
Coelho, A., Fernandes, H. M., Magalhães, R. J. D. S., Moreira, P. S., Marques, P., Soares, J. M., Amorim, L., Portugal‐Nunes, C., Castanho, T. C., Santos, N. C., et al. (2021) · Journal of Neuroscience Research · doi:10.1002/jnr.24795 · Cited by 49
Age-Related Decline in the Variation of Dynamic Functional Connectivity: A Resting State Analysis
Chen, Y., Wang, W., Zhao, X. W., Sha, M., Liu, Y., Zhang, X., Ma, J., Ni, H., & Ming, D. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00203 · Cited by 50
Jacobs, H. I. L., Müller‐Ehrenberg, L., Priovoulos, N., & Roebroeck, A. F. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.05.021 · Cited by 51
Hughes, C. S., Faskowitz, J., Cassidy, B. S., Sporns, O., & Krendl, A. C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116521 · Cited by 53
Madden, D. J., Jain, S., Monge, Z. A., Cook, A. D., Lee, A., Huang, H., Howard, C. M., & Cohen, J. R. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.09.010 · Cited by 58
A posterior-to-anterior shift of brain functional dynamics in aging
Zhang, H., Lee, A., & Qiu, A. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1425-z · Cited by 48
Evidence for Accelerated Decline of Functional Brain Network Efficiency in Schizophrenia
Sheffield, J. M., Repovš, G., Harms, M. P., Carter, C. S., Gold, J. M., MacDonald, A. W., Ragland, J. D., Silverstein, S. M., Godwin, D., & Barch, D. M. (2015) · Schizophrenia Bulletin · doi:10.1093/schbul/sbv148 · Cited by 47
Functional hypergraph uncovers novel covariant structures over neurodevelopment
Gu, S., Yang, M., Medaglia, J. D., Gur, R. C., Gur, R. E., Satterthwaite, T. D., & Bassett, D. S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23631 · Cited by 48
Hughes, C. S., Cassidy, B. S., Faskowitz, J., Avena‐Koenigsberger, A., Sporns, O., & Krendl, A. C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.024 · Cited by 49
Jin, X., Liang, X., & Gong, G. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00932 · Cited by 46
Causal Interactions in Human Amygdala Cortical Networks across the Lifespan
Jiang, Y., Tian, Y., & Wang, Z. (2019) · Scientific Reports · doi:10.1038/s41598-019-42361-0 · Cited by 45
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
Self-citationA Comprehensive Analysis of Connectivity and Aging Over the Adult Life Span
Archer, J. A., Lee, A., Qiu, A., & Chen, S. A. (2015) · Brain Connectivity · doi:10.1089/brain.2015.0345 · Cited by 49
Bayesian exponential random graph modeling of whole-brain structural networks across lifespan
Sinke, M. R. T., Dijkhuizen, R. M., Caimo, A., Stam, C. J., & Otte, W. M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.066 · Cited by 46
Soman, S. M., Vijayakumar, N., Thomson, P., Ball, G., Hyde, C. E., & Silk, T. J. (2023) · Human Brain Mapping · doi:10.1002/hbm.26288 · Cited by 47
Data-driven brain network models differentiate variability across language tasks
Bansal, K., Medaglia, J. D., Bassett, D. S., Vettel, J. M., & Muldoon, S. F. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006487 · Cited by 42
Aydın, S. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09843-w · Cited by 46
Age-related reorganization of functional network architecture in semantic cognition
Martin, S., Williams, K. A., Saur, D., & Hartwigsen, G. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac387 · Cited by 39
A two-part mixed-effects modeling framework for analyzing whole-brain network data
Simpson, S. L., & Laurienti, P. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.021 · Cited by 44
Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks
Zhai, J., & Li, K. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00062 · Cited by 40
Yuan, B., Zhang, N., Yan, J., Cheng, J., Lu, J., & Wu, J. (2020) · Cortex · doi:10.1016/j.cortex.2020.04.015 · Cited by 42
Graph theory analysis of DTI tractography in children with traumatic injury
Watson, C. G., DeMaster, D., & Ewing‐Cobbs, L. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101673 · Cited by 42
Development of Network Topology and Functional Connectivity of the Prefrontal Cortex
Lopez, K. C., Kandala, S., Marek, S. A., & Barch, D. M. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz255 · Cited by 47
Rakesh, D., Allen, N. B., & Whittle, S. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100775 · Cited by 44
He, L., Wang, X., Zhuang, K., & Qiu, J. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.04.030 · Cited by 38
Cassady, K. E., Adams, J. N., Chen, X., Maaß, A., Harrison, T. M., Landau, S. M., Baker, S. L., & Jagust, W. J. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab122 · Cited by 46
Functional connectome of human cerebellum
Chen, Z., Zhang, R., Huo, H., Liu, P., Zhang, C., & Feng, T. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119015 · Cited by 43
Avila‐Varela, D. S., Hidalgo‐Lopez, E., Dagnino, P. C., Acero-Pousa, I., del Agua, E., Deco, G., Pletzer, B. A., & Escrichs, A. (2024) · npj Women s Health · doi:10.1038/s44294-024-00012-4 · Cited by 50
Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study
More, S., Eickhoff, S. B., Caspers, J., & Patil, K. R. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-67670-4_1 · Cited by 37
“This atlas has been utilized in several brain network analyses including for sex prediction [45, 5].”
MethodFunctional connectome through the human life span
Sun, L., Zhao, T., Liang, X., Xia, M., Li, Q., Liao, X., Gong, G., Wang, Q., Pang, C., Yu, Q., et al. (2024) · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · doi:10.58530/2024/1294 · Cited by 42
Self-citationHemisphere and Gender Differences in the Rich-Club Organization of Structural Networks
Wang, B., Zhan, Q., Yan, T., Imtiaz, S., Xiang, J., Niu, Y., Liu, M., Wang, G., Cao, R., & Li, D. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz027 · Cited by 37
Wang, Y., Zuo, C., Xu, Q., Liao, S., Kanji, M., & Wang, D. (2019) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2019.109796 · Cited by 37
Aging, brain plasticity, and motor learning
Gooijers, J., Pauwels, L., Hehl, M., Seer, C., Cuypers, K., & Swinnen, S. P. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102569 · Cited by 45
Sun, X., Liu, Z., & Perc, M. (2019) · Nonlinear Dynamics · doi:10.1007/s11071-019-04914-w · Cited by 35
Cai, L., Dong, Q., Wang, M., & Niu, H. (2019) · Neurophotonics · doi:10.1117/1.nph.6.2.025005 · Cited by 37
Self-citationIdentifying the neural network for neuromodulation in epilepsy through connectomics and graphs
Vetkas, A., Germann, J., Elias, G. J. B., Loh, A., Boutet, A., Yamamoto, K., Sarica, C., Samuel, N., Milano, V. C., Fomenko, A., et al. (2022) · Brain Communications · doi:10.1093/braincomms/fcac092 · Cited by 36
Sappok, T., Hassiotis, A., Bertelli, M. O., Dziobek, I., & Sterkenburg, P. S. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192013109 · Cited by 42
Age-related vulnerability of the human brain connectome
Filippi, M., Cividini, C., Basaia, S., Spinelli, E. G., Castelnovo, V., Leocadi, M., Canu, E., & Agosta, F. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02157-1 · Cited by 36
Kolskår, K. K., Alnæs, D., Kaufmann, T., Richard, G., Sanders, A., Ulrichsen, K. M., Moberget, T., Andreassen, O. A., Nordvik, J. E., & Westlye, L. T. (2018) · eNeuro · doi:10.1523/eneuro.0092-18.2018 · Cited by 33
Wang, S., Li, Y., Qiu, S., Zhang, C., Wang, G., Xian, J., Li, T., & He, H. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102188 · Cited by 34
Eating behavior associated with gray matter volume alternations: A voxel based morphometry study
Yao, L., Li, W., Dai, Z., & Dong, C. (2015) · Appetite · doi:10.1016/j.appet.2015.10.017 · Cited by 38
Self-citationJiao, 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
Vieira, B. H., Rondinoni, C., & Salmón, C. E. G. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116662 · Cited by 33
Strategies and cognitive reserve to preserve lexical production in aging
Baciu, M., Banjac, S., Roger, É., Haldin, C., Perrone‐Bertolotti, M., Lœvenbruck, H., & Démonet, J. (2021) · GeroScience · doi:10.1007/s11357-021-00367-5 · Cited by 38
Structure and Function of Large-Scale Brain Systems
Koziol, L. F., Barker, L. A., Joyce, A. W., & Hrin, S. (2014) · Applied Neuropsychology Child · doi:10.1080/21622965.2014.946797 · Cited by 32
The functional brain connectome of the child and autism spectrum disorders
Mevel, K., & Fransson, P. (2016) · Acta Paediatrica · doi:10.1111/apa.13484 · Cited by 32
Protracted Development of the Proprioceptive Brain Network During and Beyond Adolescence
Cignetti, F., Fontan, A., Menant, J. C., Nazarian, B., Anton, J., Vaugoyeau, M., & Assaiante, C. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv323 · Cited by 35
A Graph Theory Approach to Clarifying Aging and Disease Related Changes in Cognitive Networks
Wright, L. M., Marco, M. D., & Venneri, A. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.676618 · Cited by 32
Brain Atrophy and Reorganization of Structural Network in Parkinson's Disease With Hemiparkinsonism
Xu, X., Guan, X., Guo, T., Zeng, Q., Ye, R., Wang, J., Zhong, J., Xuan, M., Gu, Q., Huang, P., et al. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00117 · Cited by 30
Self-citationSporns, O. (2020) · doi:10.1093/med/9780198713005.003.0013 · Cited by 29
Brain Networks and Adolescent Alcohol Use
Yip, S. W., Lichenstein, S. D., Liang, Q., Chaarani, B., Dager, A. D., Pearlson, G. D., Banaschewski, T., Bokde, A. L. W., Desrivières, S., Flor, H., et al. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.2949 · Cited by 28
Lamar, M., Ajilore, O., Leow, A., Charlton, R. A., Cohen, J., GadElkarim, J., Yang, S., Zhang, A., Davis, R., Penney, D. L., et al. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.03.034 · Cited by 29
Functional modular architecture underlying attentional control in aging
Monge, Z. A., Geib, B. R., Siciliano, R. E., Packard, L. E., Tallman, C. W., & Madden, D. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.002 · Cited by 28
Hrybouski, S., Cribben, I., McGonigle, J., Olsen, F., Carter, R., Seres, P., Madan, C. R., & Malykhin, N. V. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02226-7 · Cited by 28
Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods
Venkadesh, S., & Horn, J. D. V. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.752332 · Cited by 25
Single‐subject cortical morphological brain networks across the adult lifespan
Ruan, J., Wang, N., Li, J., Wang, J., Zou, Q., Lv, Y., Zhang, H., & Wang, J. (2023) · Human Brain Mapping · doi:10.1002/hbm.26450 · Cited by 28
Self-citationThe effect of external stimulation on functional networks in the aging healthy human brain
Escrichs, A., Perl, Y. S., Martínez‐Molina, N., Biarnés, C., Garre‐Olmo, J., Fernández‐Real, J. M., Ramos, R., Martí‐Lluch, R., Pamplona, R., Brugada, R., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac064 · Cited by 26
Multimodal multilayer network centrality relates to executive functioning
Breedt, L. C., Santos, F. A. N., Hillebrand, A., Reneman, L., van Rootselaar, A., Schoonheim, M. M., Stam, C. J., Ticheler, A., Tijms, B. M., Veltman, D. J., et al. (2022) · Network Neuroscience · doi:10.1162/netn_a_00284 · Cited by 24
Brain network connectivity associated with anticipatory postural control in children and adults
Cignetti, F., Vaugoyeau, M., Decker, L. M., Grosbras, M., Girard, N. J., Chaix, Y., Péran, P., & Assaiante, C. (2018) · Cortex · doi:10.1016/j.cortex.2018.08.013 · Cited by 29
Emergence of canonical functional networks from the structural connectome
Xie, X., Cai, C., Damasceno, P. F., Nagarajan, S. S., & Raj, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118190 · Cited by 24
Functional brain modules reconfigure at multiple scales across the human lifespanPreprint
Betzel, R. F., Mišić, B., He, Y., Rumschlag, J. A., Zuo, X., & Sporns, O. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.08045 · Cited by 35
Self-citationThe overlapping modular organization of human brain functional networks across the adult lifespan
Gu, Y., Li, L., Zhang, Y., Ma, J., Yang, C., Xiao, Y., Shu, N., Can, C., Lin, Y., & Dai, Z. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119125 · Cited by 24
Self-citationAge-related early/late variations of functional connectivity across the human lifespan
Chen, Y., Zhao, X. W., Zhang, X., Liu, Y., Zhou, P., Ni, H., Ma, J., & Ming, D. (2018) · Neuroradiology · doi:10.1007/s00234-017-1973-1 · Cited by 26
Shah, C., Liu, J., Lv, P., Sun, H., Xiao, Y., Liu, J., Zhao, Y., Zhang, W., Li, Y., Gong, Q., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00318 · Cited by 27
Brain hubs in lesion models: Predicting functional network topology with lesion patterns in patients
Yuan, B., Fang, Y., Han, Z., Song, L., Yong He, A., & Bi, Y. (2017) · Scientific Reports · doi:10.1038/s41598-017-17886-x · Cited by 24
Adolescent sex differences in cortico-subcortical functional connectivity during response inhibition
Chung, Y. S., Calhoun, V. D., & Stevens, M. C. G. (2019) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-019-00718-y · Cited by 27
Zhang, H., Gertel, V. H., Cosgrove, A. L., & Diaz, M. T. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.10.025 · Cited by 26
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Lei, D., Ma, J., Zhang, J., Wang, M., Zhang, K., Chen, F., Suo, X., Gong, Q., & Du, X. (2015) · BioMed Research International · doi:10.1155/2015/463708 · Cited by 27
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Ferré, P., Jarret, J., Brambati, S. M., Bellec, P., & Joanette, Y. (2020) · Neurobiology of Language · doi:10.1162/nol_a_00007 · Cited by 25
Stability of sensorimotor network sculpts the dynamic repertoire of resting state over lifespan
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Age differences in functional network reconfiguration with working memory training
Iordan, A. D., Moored, K., Katz, B., Cooke, K. A., Buschkuehl, M., Jaeggi, S. M., Polk, T. A., Peltier, S., Jonides, J., & Reuter‐Lorenz, P. A. (2021) · Human Brain Mapping · doi:10.1002/hbm.25337 · Cited by 26
Langella, S., Mucha, P. J., Giovanello, K. S., & Dayan, E. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.09.002 · Cited by 21
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Stumme, J., Krämer, C., Miller, T., Schreiber, J., Caspers, S., & Jockwitz, C. (2022) · Human Brain Mapping · doi:10.1002/hbm.26030 · Cited by 21
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Tang, Y., Long, J., Wang, W., Liao, J., Xie, H., Zhao, G., & Zhang, H. (2016) · Scientific Reports · doi:10.1038/srep26209 · Cited by 24
Wang, X., Zhu, Q., Song, Y., & Liu, J. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx217 · Cited by 22
Abellaneda‐Pérez, K., Vaqué‐Alcázar, L., Solé‐Padullés, C., & Bartrés‐Faz, D. (2019) · Journal of Neuroscience Research · doi:10.1002/jnr.24514 · Cited by 22
Shumbayawonda, E., Tosun, P. D., Fernández, A., Hughes, M. P., & Abásolo, D. (2018) · Entropy · doi:10.3390/e20070506 · Cited by 22
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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
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