Age-related changes in the topological organization of the white matter structural connectome across the human lifespan
Open accessAbstract
Lifespan is a dynamic process with remarkable changes in brain structure and function. Previous neuroimaging studies have indicated age‐related microstructural changes in specific white matter tracts during development and aging. However, the age‐related alterations in the topological architecture of the white matter structural connectome across the human lifespan remain largely unknown. Here, a cohort of 113 healthy individuals (ages 9–85) with both diffusion and structural MRI acquisitions were examined. For each participant, the high‐resolution white matter structural networks were constructed by deterministic fiber tractography among 1024 parcellation units and were quantified with graph theoretical analyses. The global network properties, including network strength, cost, topological efficiency, and robustness, followed an inverted U‐shaped trajectory with a peak age around the third decade. The brain areas with the most significantly nonlinear changes were located in the prefrontal and temporal cortices. Different brain regions exhibited heterogeneous trajectories: the posterior cingulate and lateral temporal cortices displayed prolonged maturation/degeneration compared with the prefrontal cortices. Rich‐club organization was evident across the lifespan, whereas hub integration decreased linearly with age, especially accompanied by the loss of frontal hubs and their connections. Additionally, age‐related changes in structural connections were predominantly located within and between the prefrontal and temporal modules. Finally, based on the graph metrics of structural connectome, accurate predictions of individual age were obtained (r = 0.77). Together, the data indicated a dynamic topological organization of the brain structural connectome across human lifespan, which may provide possible structural substrates underlying functional and cognitive changes with age. Hum Brain Mapp 36:3777–3792, 2015. © 2015 Wiley Periodicals, Inc.
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Lifespan changes of the connectome show good consistency with cognitive maturation.
“Lifespan changes of the connectome also show good consistency with cognitive maturation (Zhao et al., 2015).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Zhao et al., 2015
Loss of frontal hubs with aging may be driven by vulnerable prefrontal tracts.
“and loss of frontal hubs that may be driven by the vulnerable prefrontal tracts (Zhao et al., 2015).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Zhao et al., 2015
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Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works
Efficiency and Cost of Economical Brain Functional Networks
Achard, S., & Bullmore, E. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030017 · Cited by 2,553 · see citing works
Network hubs in the human brain
van den Heuvel, M. P., & Sporns, O. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.012 · Cited by 2,123 · cited 4× · see citing works
“One of the most important findings was an inverted Ushaped trajectory of the topological efficiency of the WM network across lifespan, which is consistent with the hypothesis proposed by Collin and van den Heuvel (2013).”
BackgroundResultInfluentialExecutive Function and the Frontal Lobes: A Meta-Analytic Review
Alvarez, J. A., & Emory, E. (2006) · Neuropsychology Review · doi:10.1007/s11065-006-9002-x · Cited by 1,981 · see citing works
The maturing architecture of the brain's default network
Fair, D. A., Cohen, A. L., Dosenbach, N. U. F., Church, J. A., Miezin, F. M., Barch, D. M., Raichle, M. E., Petersen, S. E., & Schlaggar, B. L. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0800376105 · Cited by 1,314 · see citing works
Topological organization of the human brain functional connectome across the lifespan
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., et al. (2014) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2013.11.004 · Cited by 440 · see citing works
Self-citationThe Neonatal Connectome During Preterm Brain Development
van den Heuvel, M. P., Kersbergen, K. J., de Reus, M. A., Keunen, K., Kahn, R. S., Groenendaal, F., de Vries, L. S., & Benders, M. J. N. L. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu095 · Cited by 386 · see citing works
Rich-club organization of the newborn human brain
Ball, G., Aljabar, P., Zebari, S., Tusor, N., Arichi, T., Merchant, N., Robinson, E. C., Ogundipe, E., Rueckert, D., Edwards, A. D., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1324118111 · Cited by 360 · see citing works
The Rich Club of the<i>C. elegans</i>Neuronal Connectome
Towlson, E. K., Vértes, P. E., Ahnert, S. E., Schafer, W. R., & Bullmore, E. T. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3784-12.2013 · Cited by 343 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Akaike H (1974): A new look at statistical-model identification. IEEE Trans Automat Control 19:716-723.
- Mori S, van Zijl PC (2002): Fiber tracking: Principles and strategies-A technical review. NMR Biomed 15:468-480.
- Raz, N. (2000) Aging of the brain and its impact on cognitive performance: Integration of structural and functional findings. In: Craik FIM, Salthouse TA, editors. The Handbook of Aging and Cognition, 2nd ed. New Jersey, USA, Lawrence Erlbaum Associates Publishers. pp. 1-90.
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Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302 · see citing works
“hubs that may be driven by the vulnerable prefrontal tracts [546].”
BackgroundSelf-citationNaik, S., Banerjee, A., Bapi, R. S., Deco, G., & Roy, D. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.04.007 · Cited by 89 · see citing works
Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis
Kim, D.-J., & Min, B.-K. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.039 · Cited by 73 · see citing works
- Authors
- Zhao, T., Cao, M., Niu, H., Zuo, X., Evans, A., He, Y., Dong, Q., & Shu, N.
- Affiliations
- Beijing Normal University (CN)
- Chinese Academy of Sciences (CN)
- Montreal Neurological Institute and Hospital (CA)
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University Beijing 100875 China
- Corresponding author
- Shu, Ni
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2015-07-14
- Volume
- 36
- Issue
- 10
- Pages
- 3777-3792
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6869038
- OpenAlex
- W2152573143
- Cited by
- 209
- References
- 9
- Citations by year
- 2026: 15 · 2025: 21 · 2024: 14 · 2023: 21 · 2022: 24 · 2021: 16 · 2020: 26 · 2019: 24 · 2018: 19 · 2017: 18 · 2016: 7 · 2015: 4
- FWCI
- 12.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion)
- Funders
- 973 program (2013CB837300)
- Natural Science Foundation of China (81471732, 31221003, 81030028, 81201122, 81171409, 81220108014)
- Beijing New Medical Discipline Based Group (100270569)
- Fundamental Research Funds for the Central Universities (2013YB28)
- National Science Fund for Distinguished Young Scholars of China (81225012)
- Key Research Program and the Hundred Talents Program of the Chinese Academy of Sciences (KSZD-EW-TZ-002)
- 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
- brain aging, human lifespan, network topology, graph theory, rich club, network hubs, prefrontal cortex, temporal cortex, posterior cingulate cortex
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Aging, Algorithms, Brain, Child, Female, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Fibers, Neural Pathways, Sex Characteristics, Temporal Lobe, Reproducibility of Results, Prefrontal Cortex, Young Adult, Diffusion Tensor Imaging, Connectome, White Matter
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