Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

Human lifespan changes in the brain’s functional connectome

Nature Neuroscience volume 28, pages 891–901 (2025)Cite this article

Subjects

Abstract

Functional connectivity of the human brain changes through life. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals at 32 weeks of postmenstrual age to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a spatiotemporal cortical axis, transitioning from primary sensorimotor regions to higher-order association regions. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging and neuropsychiatric disorders.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Normative growth patterns of the functional connectome at a global level over the lifespan.
Fig. 2: Population-level and individual-level functional atlases throughout the lifespan.
Fig. 3: Lifespan normative growth patterns of brain system segregation.
Fig. 4: Lifespan normative growth patterns of regional FCS.

Similar content being viewed by others

Data availability

The MRI dataset listed in Supplementary Table 1 is partly available at the ABCD Study (https://nda.nih.gov/), the ABIDE Initiative (https://fcon_1000.projects.nitrc.org/indi/abide/), the ADNI (https://adni.loni.usc.edu/), the Age_ility Project (https://www.nitrc.org/projects/age-ility/), the BCP (https://nda.nih.gov/), the Brain Genomics Superstruct Project (https://doi.org/10.7910/DVN/25833), the Calgary Preschool MRI Dataset (https://osf.io/axz5r/), the Cambridge Centre for Ageing and Neuroscience dataset (https://www.cam-can.org/index.php?content=dataset/), the dHCP (http://www.developingconnectome.org/data-release/second-data-release/), the HCP (https://www.humanconnectome.org/), the Lifespan Human Connectome Project (https://nda.nih.gov/), the NKI-RS dataset (https://fcon_1000.projects.nitrc.org/indi/pro/nki.html), the NSPN dataset (https://nspn.org.uk/), the Pediatric Imaging, Neurocognition, and Genetics (PING) data repository (http://pingstudy.ucsd.edu/), the Pixar Dataset (https://openfmri.org/dataset/ds000228/), the Strategic Research Program for Brain Sciences MRI Dataset (https://bicr-resource.atr.jp/srpbsopen/), the Southwest University Adult Lifespan Dataset (http://fcon_1000.projects.nitrc.org/indi/retro/sald.html), the Southwest University Longitudinal Imaging Multimodal Brain data repository (http://fcon_1000.projects.nitrc.org/indi/retro/southwestuni_qiu_index.html) and the UK Biobank Brain Imaging Dataset (https://www.ukbiobank.ac.uk/). The dhcpSym surface atlases in ages from 32 to 44 postmenstrual weeks are available at https://brain-development.org/brain-atlases/atlases-from-the-dhcp-project/cortical-surface-template/. The UNC four-dimensional infant cortical surface atlases are available at https://bbm.web.unc.edu/tools/. The fs_LR_32k surface atlas is available at https://balsa.wustl.edu/. The subcortical atlases are available at https://github.com/yetianmed/subcortex/. The brain charts and lifespan developmental atlases are shared online via GitHub (https://github.com/sunlianglong/BrainChart-FC-Lifespan/). Source data are provided with this paper.

Code availability

The codes used in this paper are available on GitHub (https://github.com/sunlianglong/BrainChart-FC-Lifespan). Software packages used herein include MRIQC v0.15.0 (https://github.com/nipreps/mriqc/), QuNex v0.93.2 (https://gitlab.qunex.yale.edu/), HCP pipeline v4.4.0-rc-MOD-e7a6af9 (https://github.com/Washington-University/HCPpipelines/releases/), ABCD-HCP pipeline v1 (https://github.com/DCAN-Labs/abcd-hcp-pipeline/), dHCP structural pipeline v1 (https://github.com/BioMedIA/dhcp-structural-pipeline/), dHCP functional pipeline v1 (https://git.fmrib.ox.ac.uk/seanf/dhcp-neonatal-fmri-pipeline/), iBEAT pipeline v1.0.0 (https://github.com/iBEAT-V2/iBEAT-V2.0-Docker/), MSM v3.0 (https://github.com/ecr05/MSM_HOCR/), FreeSurfer v6.0.0 (https://surfer.nmr.mgh.harvard.edu/), FSL v6.0.5 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/), Connectome Workbench v1.5.0 (https://www.humanconnectome.org/software/connectome-workbench/), MATLAB R2018b (https://www.mathworks.com/products/matlab.html), SPM12 toolbox v6470 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/), GRETNA toolbox v2.0.0 (https://www.nitrc.org/projects/gretna/), BrainNet Viewer toolbox v20191031 (https://www.nitrc.org/projects/bnv/), cifti-matlab toolbox v2 (https://github.com/Washington-University/cifti-matlab/), HFR_ai toolbox v1.0-beta-20181108 (https://github.com/MeilingAva/Homologous-Functional-Regions//), System segregation code (https://github.com/mychan24/system-segregation-and-graph-tools), Python v3.8.3 (https://www.python.org/), neuroharmonize package v2.1.0 (https://github.com/rpomponio/neuroHarmonize/), scikit-learn package v1.1.3 (https://scikit-learn.org), R v4.2.0 (https://www.r-project.org/), GAMLSS package v5.4-3 (https://www.gamlss.com/) and ggplot2 package v3.4.2 (https://ggplot2.tidyverse.org/).

References

  1. Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995).

    Article  CAS  PubMed  Google Scholar 

  2. Sporns, O., Tononi, G. & Kotter, R. The human connectome: a structural description of the human brain. PLoS Comput Biol. 1, e42 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Smith, S. M. et al. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666–682 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Cao, M., Huang, H. & He, Y. Developmental connectomics from infancy through early childhood. Trends Neurosci. 40, 494–506 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zuo, X. N. et al. Human connectomics across the life span. Trends Cogn. Sci. 21, 32–45 (2017).

    Article  PubMed  Google Scholar 

  6. Di Martino, A. et al. Unraveling the miswired connectome: a developmental perspective. Neuron 83, 1335–1353 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Perovnik, M., Rus, T., Schindlbeck, K. A. & Eidelberg, D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat. Rev. Neurol. 19, 73–90 (2023).

    Article  PubMed  Google Scholar 

  8. Fornito, A., Zalesky, A. & Breakspear, M. The connectomics of brain disorders. Nat. Rev. Neurosci. 16, 159–172 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Gong, Q. & He, Y. Depression, neuroimaging and connectomics: a selective overview. Biol. Psychiatry 77, 223–235 (2015).

    Article  PubMed  Google Scholar 

  10. Bethlehem, R. A. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Rutherford, S. et al. Charting brain growth and aging at high spatial precision. eLife 11, e72904 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Edde, M., Leroux, G., Altena, E. & Chanraud, S. Functional brain connectivity changes across the human life span: from fetal development to old age. J. Neurosci. Res. 99, 236–262 (2021).

    Article  CAS  PubMed  Google Scholar 

  13. Biswal, B. B. et al. Toward discovery science of human brain function. Proc. Natl Acad. Sci. USA 107, 4734–4739 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Betzel, R. F. et al. Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage 102, 345–357 (2014).

    Article  PubMed  Google Scholar 

  15. Cao, M. et al. Topological organization of the human brain functional connectome across the lifespan. Dev. Cogn. Neurosci. 7, 76–93 (2014).

    Article  PubMed  Google Scholar 

  16. Chen, Y. et al. Age-related early/late variations of functional connectivity across the human lifespan. Neuroradiology 60, 403–412 (2018).

    Article  PubMed  Google Scholar 

  17. Petrican, R., Taylor, M. J. & Grady, C. L. Trajectories of brain system maturation from childhood to older adulthood: implications for lifespan cognitive functioning. Neuroimage 163, 125–149 (2017).

    Article  PubMed  Google Scholar 

  18. Vij, S. G., Nomi, J. S., Dajani, D. R. & Uddin, L. Q. Evolution of spatial and temporal features of functional brain networks across the lifespan. Neuroimage 173, 498–508 (2018).

    Article  PubMed  Google Scholar 

  19. Wang, L., Su, L., Shen, H. & Hu, D. Decoding lifespan changes of the human brain using resting-state functional connectivity MRI. PLoS ONE 7, e44530 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rutherford, S. et al. Evidence for embracing normative modeling. eLife 12, e85082 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Fjell, A. M. et al. Relationship between structural and functional connectivity change across the adult lifespan: a longitudinal investigation. Hum. Brain Mapp. 38, 561–573 (2017).

    Article  PubMed  Google Scholar 

  22. Luo, A. C. et al. Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nat. Commun. 15, 3511 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wu, K. et al. Topological organization of functional brain networks in healthy children: differences in relation to age, sex, and intelligence. PLoS ONE 8, e55347 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Gu, S. et al. Emergence of system roles in normative neurodevelopment. Proc. Natl Acad. Sci. USA 112, 13681–13686 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sanders, A. F. et al. Age-related differences in resting-state functional connectivity from childhood to adolescence. Cereb. Cortex 33, 6928–6942 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Sherman, L. E. et al. Development of the default mode and central executive networks across early adolescence: a longitudinal study. Dev. Cogn Neurosci. 10, 148–159 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Marek, S., Hwang, K., Foran, W., Hallquist, M. N. & Luna, B. The contribution of network organization and integration to the development of cognitive control. PLoS Biol. 13, e1002328 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Borghi, E. et al. Construction of the World Health Organization child growth standards: selection of methods for attained growth curves. Stat. Med. 25, 247–265 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Stasinopoulos, D. M. & Rigby, R. A. Generalized additive models for location scale and shape (GAMLSS) in R. J. Stat. Softw. 23, 1–46 (2008).

    Google Scholar 

  30. Wang, D. et al. Parcellating cortical functional networks in individuals. Nat. Neurosci. 18, 1853–1860 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95, 791–807 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kong, R. et al. Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb. Cortex 29, 2533–2551 (2019).

    Article  PubMed  Google Scholar 

  33. Cui, Z. et al. Individual variation in functional topography of association networks in youth. Neuron 106, 340–353 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gilmore, J. H., Knickmeyer, R. C. & Gao, W. Imaging structural and functional brain development in early childhood. Nat. Rev. Neurosci. 19, 123–137 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).

    Article  PubMed  Google Scholar 

  36. Li, M. et al. Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients. Neuropsychopharmacology 48, 633–641 (2023).

    Article  CAS  PubMed  Google Scholar 

  37. Chan, M. Y., Park, D. C., Savalia, N. K., Petersen, S. E. & Wig, G. S. Decreased segregation of brain systems across the healthy adult lifespan. Proc. Natl Acad. Sci. USA 111, E4997–E5006 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Huntenburg, J. M., Bazin, P. L. & Margulies, D. S. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21–31 (2018).

    Article  PubMed  Google Scholar 

  39. Sydnor, V. J. et al. Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology. Neuron 109, 2820–2846 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Xia, Y. et al. Development of functional connectome gradients during childhood and adolescence. Sci. Bull. 67, 1049–1061 (2022).

    Article  Google Scholar 

  41. Pines, A. R. et al. Dissociable multi-scale patterns of development in personalized brain networks. Nat. Commun. 13, 2647 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 178, 540–551 (2018).

    Article  PubMed  Google Scholar 

  43. Kaczkurkin, A. N., Raznahan, A. & Satterthwaite, T. D. Sex differences in the developing brain: insights from multimodal neuroimaging. Neuropsychopharmacology 44, 71–85 (2019).

    Article  PubMed  Google Scholar 

  44. Cahill, L. Why sex matters for neuroscience. Nat. Rev. Neurosci. 7, 477–484 (2006).

    Article  CAS  PubMed  Google Scholar 

  45. Dorfschmidt, L. et al. Sexually divergent development of depression-related brain networks during healthy human adolescence. Sci. Adv. 8, eabm7825 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhang, C. et al. Sex and age effects of functional connectivity in early adulthood. Brain Connect. 6, 700–713 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Zhang, R., Rolls, E. T., Cheng, W. & Feng, J. Different cortical connectivities in human females and males relate to differences in strength and body composition, reward and emotional systems, and memory. Brain Struct. Funct. 229, 47–61 (2024).

    Article  PubMed  Google Scholar 

  48. Gao, W. et al. Evidence on the emergence of the brain’s default network from 2-week-old to 2-year-old healthy pediatric subjects. Proc. Natl Acad. Sci. USA 106, 6790–6795 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Jagust, W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat. Rev. Neurosci. 19, 687–700 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lotter, L. D. et al. Regional patterns of human cortex development correlate with underlying neurobiology. Nat. Commun. 15, 7987 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Brouwer, R. M. et al. Genetic variants associated with longitudinal changes in brain structure across the lifespan. Nat. Neurosci. 25, 421–432 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kopal, J., Uddin, L. Q. & Bzdok, D. The end game: respecting major sources of population diversity. Nat. Methods 20, 1122–1128 (2023).

    Article  CAS  PubMed  Google Scholar 

  53. Deichmann, R., Gottfried, J. A., Hutton, C. & Turner, R. Optimized EPI for fMRI studies of the orbitofrontal cortex. Neuroimage 19, 430–441 (2003).

    Article  CAS  PubMed  Google Scholar 

  54. Bayer, J. M. M. et al. Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models. Neuroimage 264, 119699 (2022).

    Article  PubMed  Google Scholar 

  55. Di Biase, M. A. et al. Mapping human brain charts cross-sectionally and longitudinally. Proc. Natl Acad. Sci. USA 120, e2216798120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Xia, M., Wang, J. & He, Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8, e68910 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Elyounssi, S. et al. Uncovering and mitigating bias in large, automated MRI analyses of brain development. Preprint at bioRxiv https://doi.org/10.1101/2023.02.28.530498 (2023).

  58. Wang, L. et al. Benchmark on automatic 6-month-old infant brain segmentation algorithms: the iSeg-2017 challenge. IEEE Trans. Med. Imaging 38, 2219–2230 (2019).

    Article  Google Scholar 

  59. Zeng, Z. et al. 3D-MASNet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old infant brain MR images. Hum. Brain Mapp. 44, 1779–1792 (2023).

    Article  PubMed  Google Scholar 

  60. Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).

    Article  PubMed  Google Scholar 

  61. Ji, J. L. et al. QuNex-an integrative platform for reproducible neuroimaging analytics. Front. Neuroinform. 17, 1104508 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Makropoulos, A. et al. The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 173, 88–112 (2018).

    Article  PubMed  Google Scholar 

  63. Wang, L. et al. iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction. Nat. Protoc. 18, 1488–1509 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Fitzgibbon, S. P. et al. The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants. Neuroimage 223, 117303 (2020).

    Article  PubMed  Google Scholar 

  65. Grayson, D. S. & Fair, D. A. Development of large-scale functional networks from birth to adulthood: a guide to the neuroimaging literature. Neuroimage 160, 15–31 (2017).

    Article  PubMed  Google Scholar 

  66. Eickhoff, S. B., Yeo, B. T. T. & Genon, S. Imaging-based parcellations of the human brain. Nat. Rev. Neurosci. 19, 672–686 (2018).

    Article  CAS  PubMed  Google Scholar 

  67. Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C. & Yeo, B. T. The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 2322–2345 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Li, M. et al. Performing group-level functional image analyses based on homologous functional regions mapped in individuals. PLoS Biol. 17, e2007032 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Cui, W. et al. Personalized fMRI delineates functional regions preserved within brain tumors. Ann. Neurol. 91, 353–366 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Brennan, B. P. et al. Use of an individual-level approach to identify cortical connectivity biomarkers in obsessive-compulsive disorder. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 4, 27–38 (2019).

    PubMed  Google Scholar 

  71. Mueller, S. et al. Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586–595 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Sun, L. et al. Structural insight into the individual variability architecture of the functional brain connectome. Neuroimage 259, 119387 (2022).

    Article  CAS  PubMed  Google Scholar 

  73. Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Craddock, R. C., James, G. A., Holtzheimer, P. E. 3rd, Hu, X. P. & Mayberg, H. S. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33, 1914–1928 (2012).

    Article  PubMed  Google Scholar 

  75. Tian, Y. E. et al. Evaluation of brain-body health in individuals with common neuropsychiatric disorders. JAMA Psychiatry 80, 567–576 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Dunn, P. K. & Smyth, G. K. Randomized quantile residuals. J. Comput. Graph. Stat. 5, 236–244 (1996).

    Article  Google Scholar 

  77. Dinga, R. et al. Normative modeling of neuroimaging data using generalized additive models of location scale and shape. Preprint at bioRxiv https://doi.org/10.1101/2021.06.14.448106 (2021).

  78. Satterthwaite, T. D. et al. Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth. Neuroimage 83, 45–57 (2013).

    Article  PubMed  Google Scholar 

  79. Ciric, R. et al. Mitigating head motion artifact in functional connectivity MRI. Nat. Protoc. 13, 2801–2826 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).

    Article  PubMed  Google Scholar 

  81. Tian, Y. et al. Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat. Neurosci. 23, 1421–1432 (2020).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (82021004, 82327807 and 31830034 to Y. He), the scientific and technological innovation 2030 - the major project of the Brain Science and Brain-Inspired Intelligence Technology (2021ZD0200500 to Q.D. and 2022ZD0211500 to M.R.X.), the Changjiang Scholar Professorship Award (T2015027 to Y. He), the Beijing Natural Science Foundation (JQ23033 to M.R.X.), the National Natural Science Foundation of China (31521063 and 31221003 to Q.D.; 82071998 to M.R.X.; T2325006 to G.L.G.; 82202245 to Q.L.L.; 81971690 to X.H.L.; 32130045 to S.Z.Q.; 81571062 and 82172018 to Yong Liu; 81471120 to X.Z.; 61633018 to Y. Han; 81901101 to P.W.; 81400890 to D.W.W.; and 81920108019, 82330058, 91649117, 81771344 and 81471251 to S.J.Q.), the Beijing Brain Initiative of Beijing Municipal Science & Technology Commission (Z181100001518003 to S.T.), the Fund of Shenzhen Institute for Neuroscience Research (to S.T.), the Science and Technology Plan Project of Guangzhou (2018-1002-SF-0442 to S.J.Q.), the Guangzhou Key Laboratory (09002344 to S.J.Q.) and the Key R&D Program of Sichuan Province (2023YFS0076 to T.L.C.). We are grateful to the ABCD Study, the Autism Brain Imaging Data Exchange (ABIDE) Initiative, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Age_ility Project, the BCP, the Brain Genomics Superstruct Project, the Calgary Preschool MRI Dataset, the Cambridge Centre for Ageing and Neuroscience dataset, the dHCP, the HCP, the Lifespan Human Connectome Project (HCPA and HCPD), the Nathan Kline Institute-Rockland Sample (NKI-RS) dataset, the Neuroscience in Psychiatry Network (NSPN) dataset, the Pixar dataset, the Southwest University Adult Lifespan Dataset, the Southwest University Longitudinal Imaging Multimodal brain data repository, the UK Biobank brain imaging dataset, the Disease Imaging Data Archiving: Major Depressive Disorder (DIDA-MDD) Working Group and the Multi-center Alzheimer Disease Imaging (MCADI) Consortium. We thank the National Center for Protein Sciences at Peking University in Beijing, China, for assistance with MRI data acquisition. Data used in the preparation of this article were obtained from the ABCD Study website (https://abcdstudy.org/), held in the National Institute of Mental Health (NIMH) data archive. This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 years and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123 and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A list of participating sites and a complete list of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This paper reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report is available via https://nda.nih.gov/edit_collection.html?id=3165/, shared by the DCAN Labs ABCD-BIDS Community Collection (ABCC; collection investigator: D. Fair). For ABIDE I, primary support for the work by A. Di Martino was provided by the NIMH (K23MH087770) and the Leon Levy Foundation. Primary support for the work by M. P. Milham and the INDI team was provided by gifts from J. P. Healy and the Stavros Niarchos Foundation to the Child Mind Institute, as well as by an NIMH award to M. P. Milham (R03MH096321). For ABIDE II, primary support for the work by A. Di Martino and team was provided by the NIMH (5R21MH107045). Primary support for the work by M. P. Milham and his team provided by the NIMH (5R21MH107045); Nathan S. Kline Institute of Psychiatric Research). Additional support was provided by gifts from J. P. Healey, P. Green and R. Cowen to the Child Mind Institute. For ADNI, data used in the preparation of this article were obtained from the ADNI database (https://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this paper. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Data collection and sharing for this project was funded by the ADNI (NIH grant U01 AG024904) and the Department of Defense (ADNI award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica; Biogen; Bristol Myers Squibb; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research & Development; Johnson & Johnson Pharmaceutical Research & Development; Lumosity; Lundbeck; Merck & Co.; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the NIH (https://fnih.org/). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. For the BCP, data used herein is supported by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN BCP Consortium. Data were provided by the dHCP, KCL-Imperial-Oxford Consortium funded by the European Research Council under the European Union Seventh Framework Programme (FP/2007-2013/ERC grant agreement no. 319456). We are grateful to the families who generously supported this trial. Data were provided by the HCP, WU-Minn Consortium (principal investigators: D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH institutes and centers that support the NIH Blueprint for Neuroscience Research; and by the Mc-Donnell Center for Systems Neuroscience at Washington University. For HCP Lifespan, data were supported by the NIMH (award number U01MH109589) and by funds provided by the McDonnell Center for Systems Neuroscience at Washington University in St. Louis. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. For NKI-RS, funding for key personnel provided in part by the New York State Office of Mental Health and Research Foundation for Mental Hygiene. Additional project support provided by the NKI Center for Advanced Brain Imaging, the Brain Research Foundation (Chicago), the Stavros Niarchos Foundation and NIH grant P50 MH086385-S1. The NSPN study was funded by a Wellcome Trust award to the University of Cambridge and the University College London. This research has been conducted using data from the UK Biobank (https://www.ukbiobank.ac.uk/). The UK Biobank is generously supported by its founding funders the Wellcome Trust and UK Medical Research Council, as well as the Department of Health, Scottish Government, the Northwest Regional Development Agency, the British Heart Foundation and Cancer Research UK.

Author information

Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

    Lianglong Sun, Tengda Zhao, Xinyuan Liang, Mingrui Xia, Qiongling Li, Gaolang Gong, Qian Wang, Chenxuan Pang, Qian Yu, Yanchao Bi, Rui Chen, Zhengjia Dai, Yao Deng, Yuyin Ding, Qi Dong, Dingna Duan, Zaizhu Han, Ruiwang Huang, Qixiang Lin, Chao Liu, Ningyu Liu, Jing Lu, Leilei Ma, Shaozheng Qin, Sha Tao, Jiali Wang, Yanpei Wang, Yuehua Xu, Zhilei Xu, Liyuan Yang, Zilong Zeng, Haibo Zhang, Gai Zhao & Yong He

  2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

    Lianglong Sun, Tengda Zhao, Xinyuan Liang, Mingrui Xia, Qiongling Li, Gaolang Gong, Qian Wang, Chenxuan Pang, Qian Yu, Yanchao Bi, Zhengjia Dai, Dingna Duan, Qixiang Lin, Shaozheng Qin, Yuehua Xu, Zhilei Xu, Liyuan Yang, Zilong Zeng & Yong He

  3. IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Lianglong Sun, Tengda Zhao, Xinyuan Liang, Mingrui Xia, Qiongling Li, Gaolang Gong, Qian Wang, Chenxuan Pang, Qian Yu, Yanchao Bi, Zhengjia Dai, Dingna Duan, Zaizhu Han, Ruiwang Huang, Qixiang Lin, Chao Liu, Shaozheng Qin, Yuehua Xu, Zhilei Xu, Liyuan Yang, Zilong Zeng & Yong He

  4. School of Systems Science, Beijing Normal University, Beijing, China

    Xuhong Liao

  5. Chinese Institute for Brain Research, Beijing, China

    Gaolang Gong, Yanchao Bi, Zaixu Cui, Shaozheng Qin & Yong He

  6. Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Pindong Chen

  7. Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Yuan Chen & Jingliang Cheng

  8. Department of Radiology, Huaxi MR Research Center (HMRRC), Institute of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China

    Taolin Chen & Qiyong Gong

  9. Xiamen Key Laboratory of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China

    Taolin Chen & Qiyong Gong

  10. Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China

    Yuqi Cheng & Xiufeng Xu

  11. Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Jia-Hong Gao & Weiwei Men

  12. Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China

    Jia-Hong Gao & Weiwei Men

  13. IDG/McGovern Institute for Brain Research, Peking University, Beijing, China

    Jia-Hong Gao

  14. Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China

    Ying Han

  15. Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China

    Chu-Chung Huang

  16. Department of Radiology, Peking University Third Hospital, Beijing, China

    Ran Huo, Ying Liu & Huishu Yuan

  17. Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China

    Lingjiang Li & Bangshan Liu

  18. Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China

    Lingjiang Li & Bangshan Liu

  19. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Ching-Po Lin

  20. Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, China

    Ching-Po Lin

  21. Department of Education and Research, Taipei City Hospital, Taipei, China

    Ching-Po Lin

  22. Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China

    Yong Liu & Suyu Zhong

  23. Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China

    Jiang Qiu, Xiaoqin Wang & Dongtao Wei

  24. Department of Psychology, Southwest University, Chongqing, China

    Jiang Qiu, Xiaoqin Wang & Dongtao Wei

  25. Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China

    Shijun Qiu & Yanting Zheng

  26. Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China

    Tianmei Si & Yankun Wu

  27. Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China

    Shuping Tan

  28. Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China

    Yanqing Tang & Fei Wang

  29. Department of Radiology, Qilu Hospital of Shandong University, Ji’nan, China

    Dawei Wang

  30. Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China

    Pan Wang & Yuying Zhou

  31. Chongqing Key Laboratory of Neurobiology, Chongqing, China

    Peng Xie

  32. Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Peng Xie

  33. Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China

    Xi Zhang

  34. University of California, San Francisco, San Francisco, CA, USA

    Michael Weiner, Norbert Schuff, Howard J. Rosen & Bruce L. Miller

  35. University of California, San Diego, San Diego, CA, USA

    Paul Aisen, Ronald G. Thomas, Michael Donohue, Sarah Walter, Devon Gessert, Tamie Sather, Gus Jiminez, James Brewer & Helen Vanderswag

  36. Mayo Clinic, Rochester, NY, USA

    Ronald Petersen, Clifford R. Jack Jr., Matthew Bernstein, Bret Borowski, Jeff Gunter, Matt Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarci, Chad Ward, Sara S. Mason, Colleen S. Albers, David Knopman & Kris Johnson

  37. University of California, Berkeley, Berkeley, CA, USA

    William Jagust & Susan Landau

  38. University of Pennsylvania, Philadelphia, PA, USA

    John Q. Trojanowki, Leslie M. Shaw, Steven E. Arnold, Jason H. Karlawish & David Wolk

  39. University of Southern California, Los Angeles, CA, USA

    Arthur W. Toga, Karen Crawford, Scott Neu, Lon S. Schneider, Sonia Pawluczyk, Mauricio Beccera, Liberty Teodoro & Bryan M. Spann

  40. University of California, Davis, Davis, CA, USA

    Laurel Beckett, Danielle Harvey & Charles DeCArli

  41. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Robert C. Green, Reisa A. Sperling, Keith A. Johnson, Gad Marshall & Meghan Frey

  42. Indiana University, Bloomington, IN, USA

    Andrew J. Saykin, Tatiana M. Foroud, Li Shen, Faber Kelley, Sungeun Kim, Kwangsik Nho, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Scott Herring & Cynthia Hunt

  43. Washington University St. Louis, St. Louis, MO, USA

    John Morris, Nigel J. Cairns, Erin Householder & Lisa Taylor Reinwald

  44. Janssen Alzheimer Immunotherapy, South San Francisco, CA, USA

    Enchi Liu

  45. University of Washington, Seattle, WA, USA

    Tom Montine

  46. University of London, London, UK

    Nick Fox

  47. University of Southern California School of Medicine, Los Angeles, CA, USA

    Paul Thompson

  48. University of Michigan, Ann Arbor, MI, USA

    Robert A. Koeppe, Judith L. Heidebrink & Joanne L. Lord

  49. University of Utah, Salt Lake City, UT, USA

    Norm Foster

  50. Banner Alzheimer’s Institute, Phoenix, AZ, USA

    Eric M. Reiman, Kewei Chen, Adam Fleisher, Pierre Tariot & Stephanie Reeder

  51. University of Pittsburgh, Pittsburgh, PA, USA

    Chet Mathis, Oscar L. Lopez, MaryAnn Oakley & Donna M. Simpson

  52. University of Pennsylvania School of Medicine, Philadelphia, PA, USA

    Virginia Lee, Magdalena Korecka & Michal Figurski

  53. University of California, Irvine, Irvine, CA, USA

    Steven Potkin, Ruth A. Mulnard, Gaby Thai, Catherine McAdams Ortiz, Steven G. Potkin, Adrian Preda & Dana Nguyen

  54. Khachaturian, Radebaugh & Associates, Inc. and Alzheimer’s Association’s Ronald and Nancy Reagan’s Research Institute, Chicago, IL, USA

    Zaven Kachaturian

  55. General Electric, Boston, MA, USA

    Richard Frank

  56. Brown University, Providence, RI, USA

    Peter J. Snyder

  57. National Institute on Aging/National Institutes of Health, Bethesda, MD, USA

    Susan Molchan

  58. Oregon Health and Science University, Portland, OR, USA

    Jeffrey Kaye, Joseph Quinn, Betty Lind, Raina Carter & Sara Dolen

  59. Baylor College of Medicine, Houston, TX, USA

    Rachelle S. Doody, Javier Villanueva Meyer, Munir Chowdhury, Susan Rountree & Mimi Dang

  60. Columbia University Medical Center, New York, NY, USA

    Yaakov Stern, Lawrence S. Honig & Karen L. Bell

  61. Washington University, St. Louis, MO, USA

    Beau Ances, John C. Morris, Maria Carroll, Sue Leon, Mark A. Mintun & Stacy Schneider

  62. University of Alabama at Birmingham, Birmingham, MO, USA

    Angela Oliver, Randall Griffith, David Clark, David Geldmacher, John Brockington & Erik Roberson

  63. Mount Sinai School of Medicine, New York, NY, USA

    Hillel Grossman & Effie Mitsis

  64. Rush University Medical Center, Chicago, IL, USA

    Leyla deToledo-Morrell & Raj C. Shah

  65. Wien Center, Vienna, Austria

    Ranjan Duara, Daniel Varon, Maria T. Greig & Peggy Roberts

  66. Johns Hopkins University, Baltimore, MD, USA

    Marilyn Albert, Chiadi Onyike, Daniel D’Agostino II & Stephanie Kielb

  67. New York University, New York, NY, USA

    James E. Galvin, Dana M. Pogorelec, Brittany Cerbone, Christina A. Michel, Henry Rusinek, Mony J. de Leon, Lidia Glodzik & Susan De Santi

  68. Duke University Medical Center, Durham, NC, USA

    P. Murali Doraiswamy, Jeffrey R. Petrella & Terence Z. Wong

  69. University of Kentucky, Lexington, KY, USA

    Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates & Gary Conrad

  70. University of Rochester Medical Center, Rochester, NY, USA

    Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail & Connie Brand

  71. University of Texas Southwestern Medical School, Dallas, TX, USA

    Kyle Womack, Dana Mathews, Mary Quiceno, Ramon Diaz Arrastia, Richard King, Myron Weiner, Kristen Martin Cook & Michael DeVous

  72. Emory University, Atlanta, GA, USA

    Allan I. Levey, James J. Lah & Janet S. Cellar

  73. University of Kansas Medical Center, Lawrence, KS, USA

    Jeffrey M. Burns, Heather S. Anderson & Russell H. Swerdlow

  74. University of California, Los Angeles, Los Angeles, CA, USA

    Liana Apostolova, Kathleen Tingus, Ellen Woo, Daniel H. S. Silverman, Po H. Lu & George Bartzokis

  75. Mayo Clinic, Jacksonville, FL, USA

    Neill R. Graff Radford, Francine Parfitt, Tracy Kendall & Heather Johnson

  76. Yale University School of Medicine, New Haven, CT, USA

    Christopher H. van Dyck, Richard E. Carson & Martha G. MacAvoy

  77. McGill University, Montreal Jewish General Hospital, Montreal, Quebec, Canada

    Howard Chertkow, Howard Bergman & Chris Hosein

  78. Sunnybrook Health Sciences, Toronto, Ontario, Canada

    Sandra Black, Bojana Stefanovic & Curtis Caldwell

  79. University of British Columbia Clinic for AD & Related Disorders, Vancouver, British Columbia, Canada

    Ging Yuek Robin Hsiung, Howard Feldman, Benita Mudge & Michele Assaly Past

  80. Cognitive Neurology St. Joseph’s, Toronto, Ontario, Canada

    Andrew Kertesz, John Rogers & Dick Trost

  81. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA

    Charles Bernick & Donna Munic

  82. Northwestern University, Evanston, IL, USA

    Diana Kerwin, Marek Marsel Mesulam, Kristine Lipowski, Chuang Kuo Wu & Nancy Johnson

  83. Premiere Research Institute Palm Beach Neurology, West Palm Beach, FL, USA

    Carl Sadowsky, Walter Martinez & Teresa Villena

  84. Georgetown University Medical Center, Washington, DC, USA

    Raymond Scott Turner, Kathleen Johnson & Brigid Reynolds

  85. Stanford University, Stanford, CA, USA

    Jerome Yesavage, Joy L. Taylor, Barton Lane, Allyson Rosen & Jared Tinklenberg

  86. Banner Sun Health Research Institute, Sun City, AZ, USA

    Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson & Sherye A. Sirrel

  87. Boston University, Boston, MA, USA

    Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash & Patricia Lynn Johnson

  88. Howard University, Washington, DC, USA

    Thomas O. Obisesan, Saba Wolday & Joanne Allard

  89. Case Western Reserve University, Cleveland, OH, USA

    Alan Lerner, Paula Ogrocki & Leon Hudson

  90. University of California, Davis Medical Center, Sacramento, CA, USA

    Evan Fletcher, Owen Carmichael, John Olichney & Charles DeCarli

  91. Neurological Care of CNY, New York, NY, USA

    Smita Kittur

  92. Parkwood Hospital, Parkwood, CA, USA

    Michael Borrie, T. Y. Lee & Rob Bartha

  93. University of Wisconsin, Madison, WI, USA

    Sterling Johnson, Sanjay Asthana & Cynthia M. Carlsson

  94. Dent Neurologic Institute, Amherst, NY, USA

    Vernice Bates, Horacio Capote & Michelle Rainka

  95. Ohio State University, Columbus, OH, USA

    Douglas W. Scharre, Maria Kataki & Anahita Adeli

  96. Albany Medical College, Albany, NY, USA

    Earl A. Zimmerman, Dzintra Celmins & Alice D. Brown

  97. Hartford Hospital, Olin Neuropsychiatry Research Center, Hartford, CT, USA

    Godfrey D. Pearlson, Karen Blank & Karen Anderson

  98. Dartmouth Hitchcock Medical Center, Albany, NY, USA

    Robert B. Santulli, Tamar J. Kitzmiller & Eben S. Schwartz

  99. Wake Forest University Health Sciences, Winston-Salem, NC, USA

    Kaycee M. Sink, Jeff D. Williamson, Pradeep Garg & Franklin Watkins

  100. Rhode Island Hospital, Providence, RI, USA

    Brian R. Ott, Henry Querfurth & Geoffrey Tremont

  101. Butler Hospital, Providence, RI, USA

    Stephen Salloway, Paul Malloy & Stephen Correia

  102. Medical University of South Carolina, Charleston, SC, USA

    Jacobo Mintzer, Kenneth Spicer & David Bachman

  103. St Joseph’s Health Care, Toronto, Ontario, Canada

    Elizabether Finger, Stephen Pasternak, Irina Rachinsky & Dick Drost

  104. Nathan Kline Institute, Orangeburg, SC, USA

    Nunzio Pomara, Raymundo Hernando & Antero Sarrael

  105. University of Iowa College of Medicine, Iowa City, IA, USA

    Susan K. Schultz, Laura L. Boles Ponto, Hyungsub Shim & Karen Elizabeth Smith

  106. Cornell University, Ithaca, NY, USA

    Norman Relkin, Gloria Chaing & Lisa Raudin

  107. University of South Florida Health/Byrd Alzheimer’s Institute, Tampa, FL, USA

    Amanda Smith, Kristin Fargher & Balebail Ashok Raj

Authors
  1. Lianglong Sun
    View author publications

    Search author on:PubMed Google Scholar

  2. Tengda Zhao
    View author publications

    Search author on:PubMed Google Scholar

  3. Xinyuan Liang
    View author publications

    Search author on:PubMed Google Scholar

  4. Mingrui Xia
    View author publications

    Search author on:PubMed Google Scholar

  5. Qiongling Li
    View author publications

    Search author on:PubMed Google Scholar

  6. Xuhong Liao
    View author publications

    Search author on:PubMed Google Scholar

  7. Gaolang Gong
    View author publications

    Search author on:PubMed Google Scholar

  8. Qian Wang
    View author publications

    Search author on:PubMed Google Scholar

  9. Chenxuan Pang
    View author publications

    Search author on:PubMed Google Scholar

  10. Qian Yu
    View author publications

    Search author on:PubMed Google Scholar

  11. Yanchao Bi
    View author publications

    Search author on:PubMed Google Scholar

  12. Pindong Chen
    View author publications

    Search author on:PubMed Google Scholar

  13. Rui Chen
    View author publications

    Search author on:PubMed Google Scholar

  14. Yuan Chen
    View author publications

    Search author on:PubMed Google Scholar

  15. Taolin Chen
    View author publications

    Search author on:PubMed Google Scholar

  16. Jingliang Cheng
    View author publications

    Search author on:PubMed Google Scholar

  17. Yuqi Cheng
    View author publications

    Search author on:PubMed Google Scholar

  18. Zaixu Cui
    View author publications

    Search author on:PubMed Google Scholar

  19. Zhengjia Dai
    View author publications

    Search author on:PubMed Google Scholar

  20. Yao Deng
    View author publications

    Search author on:PubMed Google Scholar

  21. Yuyin Ding
    View author publications

    Search author on:PubMed Google Scholar

  22. Qi Dong
    View author publications

    Search author on:PubMed Google Scholar

  23. Dingna Duan
    View author publications

    Search author on:PubMed Google Scholar

  24. Jia-Hong Gao
    View author publications

    Search author on:PubMed Google Scholar

  25. Qiyong Gong
    View author publications

    Search author on:PubMed Google Scholar

  26. Ying Han
    View author publications

    Search author on:PubMed Google Scholar

  27. Zaizhu Han
    View author publications

    Search author on:PubMed Google Scholar

  28. Chu-Chung Huang
    View author publications

    Search author on:PubMed Google Scholar

  29. Ruiwang Huang
    View author publications

    Search author on:PubMed Google Scholar

  30. Ran Huo
    View author publications

    Search author on:PubMed Google Scholar

  31. Lingjiang Li
    View author publications

    Search author on:PubMed Google Scholar

  32. Ching-Po Lin
    View author publications

    Search author on:PubMed Google Scholar

  33. Qixiang Lin
    View author publications

    Search author on:PubMed Google Scholar

  34. Bangshan Liu
    View author publications

    Search author on:PubMed Google Scholar

  35. Chao Liu
    View author publications

    Search author on:PubMed Google Scholar

  36. Ningyu Liu
    View author publications

    Search author on:PubMed Google Scholar

  37. Ying Liu
    View author publications

    Search author on:PubMed Google Scholar

  38. Yong Liu
    View author publications

    Search author on:PubMed Google Scholar

  39. Jing Lu
    View author publications

    Search author on:PubMed Google Scholar

  40. Leilei Ma
    View author publications

    Search author on:PubMed Google Scholar

  41. Weiwei Men
    View author publications

    Search author on:PubMed Google Scholar

  42. Shaozheng Qin
    View author publications

    Search author on:PubMed Google Scholar

  43. Jiang Qiu
    View author publications

    Search author on:PubMed Google Scholar

  44. Shijun Qiu
    View author publications

    Search author on:PubMed Google Scholar

  45. Tianmei Si
    View author publications

    Search author on:PubMed Google Scholar

  46. Shuping Tan
    View author publications

    Search author on:PubMed Google Scholar

  47. Yanqing Tang
    View author publications

    Search author on:PubMed Google Scholar

  48. Sha Tao
    View author publications

    Search author on:PubMed Google Scholar

  49. Dawei Wang
    View author publications

    Search author on:PubMed Google Scholar

  50. Fei Wang
    View author publications

    Search author on:PubMed Google Scholar

  51. Jiali Wang
    View author publications

    Search author on:PubMed Google Scholar

  52. Pan Wang
    View author publications

    Search author on:PubMed Google Scholar

  53. Xiaoqin Wang
    View author publications

    Search author on:PubMed Google Scholar

  54. Yanpei Wang
    View author publications

    Search author on:PubMed Google Scholar

  55. Dongtao Wei
    View author publications

    Search author on:PubMed Google Scholar

  56. Yankun Wu
    View author publications

    Search author on:PubMed Google Scholar

  57. Peng Xie
    View author publications

    Search author on:PubMed Google Scholar

  58. Xiufeng Xu
    View author publications

    Search author on:PubMed Google Scholar

  59. Yuehua Xu
    View author publications

    Search author on:PubMed Google Scholar

  60. Zhilei Xu
    View author publications

    Search author on:PubMed Google Scholar

  61. Liyuan Yang
    View author publications

    Search author on:PubMed Google Scholar

  62. Huishu Yuan
    View author publications

    Search author on:PubMed Google Scholar

  63. Zilong Zeng
    View author publications

    Search author on:PubMed Google Scholar

  64. Haibo Zhang
    View author publications

    Search author on:PubMed Google Scholar

  65. Xi Zhang
    View author publications

    Search author on:PubMed Google Scholar

  66. Gai Zhao
    View author publications

    Search author on:PubMed Google Scholar

  67. Yanting Zheng
    View author publications

    Search author on:PubMed Google Scholar

  68. Suyu Zhong
    View author publications

    Search author on:PubMed Google Scholar

  69. Yong He
    View author publications

    Search author on:PubMed Google Scholar

Consortia

Alzheimer’s Disease Neuroimaging Initiative

  • Michael Weiner
  • , Paul Aisen
  • , Ronald Petersen
  • , Clifford R. Jack Jr.
  • , William Jagust
  • , John Q. Trojanowki
  • , Arthur W. Toga
  • , Laurel Beckett
  • , Robert C. Green
  • , Andrew J. Saykin
  • , John Morris
  • , Leslie M. Shaw
  • , Enchi Liu
  • , Tom Montine
  • , Ronald G. Thomas
  • , Michael Donohue
  • , Sarah Walter
  • , Devon Gessert
  • , Tamie Sather
  • , Gus Jiminez
  • , Danielle Harvey
  • , Matthew Bernstein
  • , Nick Fox
  • , Paul Thompson
  • , Norbert Schuff
  • , Charles DeCArli
  • , Bret Borowski
  • , Jeff Gunter
  • , Matt Senjem
  • , Prashanthi Vemuri
  • , David Jones
  • , Kejal Kantarci
  • , Chad Ward
  • , Robert A. Koeppe
  • , Norm Foster
  • , Eric M. Reiman
  • , Kewei Chen
  • , Chet Mathis
  • , Susan Landau
  • , Nigel J. Cairns
  • , Erin Householder
  • , Lisa Taylor Reinwald
  • , Virginia Lee
  • , Magdalena Korecka
  • , Michal Figurski
  • , Karen Crawford
  • , Scott Neu
  • , Tatiana M. Foroud
  • , Steven Potkin
  • , Li Shen
  • , Faber Kelley
  • , Sungeun Kim
  • , Kwangsik Nho
  • , Zaven Kachaturian
  • , Richard Frank
  • , Peter J. Snyder
  • , Susan Molchan
  • , Jeffrey Kaye
  • , Joseph Quinn
  • , Betty Lind
  • , Raina Carter
  • , Sara Dolen
  • , Lon S. Schneider
  • , Sonia Pawluczyk
  • , Mauricio Beccera
  • , Liberty Teodoro
  • , Bryan M. Spann
  • , James Brewer
  • , Helen Vanderswag
  • , Adam Fleisher
  • , Judith L. Heidebrink
  • , Joanne L. Lord
  • , Sara S. Mason
  • , Colleen S. Albers
  • , David Knopman
  • , Kris Johnson
  • , Rachelle S. Doody
  • , Javier Villanueva Meyer
  • , Munir Chowdhury
  • , Susan Rountree
  • , Mimi Dang
  • , Yaakov Stern
  • , Lawrence S. Honig
  • , Karen L. Bell
  • , Beau Ances
  • , John C. Morris
  • , Maria Carroll
  • , Sue Leon
  • , Mark A. Mintun
  • , Stacy Schneider
  • , Angela Oliver
  • , Randall Griffith
  • , David Clark
  • , David Geldmacher
  • , John Brockington
  • , Erik Roberson
  • , Hillel Grossman
  • , Effie Mitsis
  • , Leyla deToledo-Morrell
  • , Raj C. Shah
  • , Ranjan Duara
  • , Daniel Varon
  • , Maria T. Greig
  • , Peggy Roberts
  • , Marilyn Albert
  • , Chiadi Onyike
  • , Daniel D’Agostino II
  • , Stephanie Kielb
  • , James E. Galvin
  • , Dana M. Pogorelec
  • , Brittany Cerbone
  • , Christina A. Michel
  • , Henry Rusinek
  • , Mony J. de Leon
  • , Lidia Glodzik
  • , Susan De Santi
  • , P. Murali Doraiswamy
  • , Jeffrey R. Petrella
  • , Terence Z. Wong
  • , Steven E. Arnold
  • , Jason H. Karlawish
  • , David Wolk
  • , Charles D. Smith
  • , Greg Jicha
  • , Peter Hardy
  • , Partha Sinha
  • , Elizabeth Oates
  • , Gary Conrad
  • , Oscar L. Lopez
  • , MaryAnn Oakley
  • , Donna M. Simpson
  • , Anton P. Porsteinsson
  • , Bonnie S. Goldstein
  • , Kim Martin
  • , Kelly M. Makino
  • , M. Saleem Ismail
  • , Connie Brand
  • , Ruth A. Mulnard
  • , Gaby Thai
  • , Catherine McAdams Ortiz
  • , Kyle Womack
  • , Dana Mathews
  • , Mary Quiceno
  • , Ramon Diaz Arrastia
  • , Richard King
  • , Myron Weiner
  • , Kristen Martin Cook
  • , Michael DeVous
  • , Allan I. Levey
  • , James J. Lah
  • , Janet S. Cellar
  • , Jeffrey M. Burns
  • , Heather S. Anderson
  • , Russell H. Swerdlow
  • , Liana Apostolova
  • , Kathleen Tingus
  • , Ellen Woo
  • , Daniel H. S. Silverman
  • , Po H. Lu
  • , George Bartzokis
  • , Neill R. Graff Radford
  • , Francine Parfitt
  • , Tracy Kendall
  • , Heather Johnson
  • , Martin R. Farlow
  • , Ann Marie Hake
  • , Brandy R. Matthews
  • , Scott Herring
  • , Cynthia Hunt
  • , Christopher H. van Dyck
  • , Richard E. Carson
  • , Martha G. MacAvoy
  • , Howard Chertkow
  • , Howard Bergman
  • , Chris Hosein
  • , Sandra Black
  • , Bojana Stefanovic
  • , Curtis Caldwell
  • , Ging Yuek Robin Hsiung
  • , Howard Feldman
  • , Benita Mudge
  • , Michele Assaly Past
  • , Andrew Kertesz
  • , John Rogers
  • , Dick Trost
  • , Charles Bernick
  • , Donna Munic
  • , Diana Kerwin
  • , Marek Marsel Mesulam
  • , Kristine Lipowski
  • , Chuang Kuo Wu
  • , Nancy Johnson
  • , Carl Sadowsky
  • , Walter Martinez
  • , Teresa Villena
  • , Raymond Scott Turner
  • , Kathleen Johnson
  • , Brigid Reynolds
  • , Reisa A. Sperling
  • , Keith A. Johnson
  • , Gad Marshall
  • , Meghan Frey
  • , Jerome Yesavage
  • , Joy L. Taylor
  • , Barton Lane
  • , Allyson Rosen
  • , Jared Tinklenberg
  • , Marwan N. Sabbagh
  • , Christine M. Belden
  • , Sandra A. Jacobson
  • , Sherye A. Sirrel
  • , Neil Kowall
  • , Ronald Killiany
  • , Andrew E. Budson
  • , Alexander Norbash
  • , Patricia Lynn Johnson
  • , Thomas O. Obisesan
  • , Saba Wolday
  • , Joanne Allard
  • , Alan Lerner
  • , Paula Ogrocki
  • , Leon Hudson
  • , Evan Fletcher
  • , Owen Carmichael
  • , John Olichney
  • , Charles DeCarli
  • , Smita Kittur
  • , Michael Borrie
  • , T. Y. Lee
  • , Rob Bartha
  • , Sterling Johnson
  • , Sanjay Asthana
  • , Cynthia M. Carlsson
  • , Steven G. Potkin
  • , Adrian Preda
  • , Dana Nguyen
  • , Pierre Tariot
  • , Stephanie Reeder
  • , Vernice Bates
  • , Horacio Capote
  • , Michelle Rainka
  • , Douglas W. Scharre
  • , Maria Kataki
  • , Anahita Adeli
  • , Earl A. Zimmerman
  • , Dzintra Celmins
  • , Alice D. Brown
  • , Godfrey D. Pearlson
  • , Karen Blank
  • , Karen Anderson
  • , Robert B. Santulli
  • , Tamar J. Kitzmiller
  • , Eben S. Schwartz
  • , Kaycee M. Sink
  • , Jeff D. Williamson
  • , Pradeep Garg
  • , Franklin Watkins
  • , Brian R. Ott
  • , Henry Querfurth
  • , Geoffrey Tremont
  • , Stephen Salloway
  • , Paul Malloy
  • , Stephen Correia
  • , Howard J. Rosen
  • , Bruce L. Miller
  • , Jacobo Mintzer
  • , Kenneth Spicer
  • , David Bachman
  • , Elizabether Finger
  • , Stephen Pasternak
  • , Irina Rachinsky
  • , Dick Drost
  • , Nunzio Pomara
  • , Raymundo Hernando
  • , Antero Sarrael
  • , Susan K. Schultz
  • , Laura L. Boles Ponto
  • , Hyungsub Shim
  • , Karen Elizabeth Smith
  • , Norman Relkin
  • , Gloria Chaing
  • , Lisa Raudin
  • , Amanda Smith
  • , Kristin Fargher
  •  & Balebail Ashok Raj

DIDA-MDD Working Group

  • Yong He
  • , Lingjiang Li
  • , Jingliang Cheng
  • , Qiyong Gong
  • , Ching-Po Lin
  • , Jiang Qiu
  • , Shijun Qiu
  • , Tianmei Si
  • , Yanqing Tang
  • , Fei Wang
  • , Peng Xie
  • , Xiufeng Xu
  •  & Mingrui Xia

MCADI

  • Yong Liu
  • , Xi Zhang
  • , Ying Han
  • , Dawei Wang
  • , Yuying Zhou
  •  & Pan Wang

Contributions

L.L.S. and Y. He conceptualized the study. Y. He supervised the project. L.L.S., T.D.Z., X.Y.L., M.R.X. and Y. He designed the methodology. L.L.S. developed visualizations. Q.L.L., X.H.L., D.N.D., Z.L.Z., Z.L.X. and Z.X.C. provided guidance on data analysis and interpretation of the results. L.L.S., X.Y.L., Q.W., C.X.P., Q.Y., Q.L.L., Y.H.X., R.H., H.S.Y., Ying Liu and M.R.X. performed data quality control. G.L.G., Y.C.B., P.D.C., R.C., Y.C., T.L.C., J.L.C., Y.Q.C., Z.J.D., Y.D., Y.Y.D., Q.D., J.-H.G., Q.Y.G., Y. Han, Z.Z.H., C.-C.H., R.W.H., L.J.L., C.-P.L., Q.X.L., B.S.L., C.L., N.Y.L., Yong Liu, J.L., L.L.M., W.W.M., S.Z.Q., J.Q., S.J.Q., T.M.S., S.P.T., Y.Q.T., S.T., D.W.W., F.W., J.L.W., P.W., X.Q.W., Y.P.W., D.T.W., Y.K.W., P.X., X.F.X., L.Y.Y., H.B.Z., X.Z., G.Z., Y.T.Z. and S.Y.Z. collected a subset of the data for this study. L.L.S. and Y. He wrote the manuscript. All authors reviewed the final manuscript.

Corresponding author

Correspondence to Yong He.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Theodore Satterthwaite and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Distance-related lifespan growth patterns of the global connectome.

Normative growth curves and growth rates of the global mean of short-range (a), middle-range (b), and long-range (c) functional connectome. In the upper panel, the median (50th) centile of each curve is represented by a solid line, while the 5th, 25th, 75th, and 95th centiles are indicated by dotted lines. In the lower panel, the growth rate of each curve is characterized by the first derivative of the median centile line. The gray shaded areas represent the 95% confidence interval, which were estimated by bootstrapping 1,000 times (see Methods for details). yr, year.

Source data

Extended Data Fig. 2 Lifespan growth patterns in interindividual variability of the functional connectome.

a, Lifespan growth curves and growth rates of interindividual variability of the global mean of the connectome (left panel), global variance of the connectome (middle panel), and global system segregation (right panel). b, Lifespan growth curves (showed as centile lines) and growth rates (showed as the central line) of interindividual variability system segregation in each network. The gray shaded areas represent the 95% confidence interval, which were estimated by bootstrapping 1,000 times. VIS, visual; SM, somatomotor; DA, dorsal attention; VA, ventral attention; LIM, limbic; FP, frontoparietal; DM, default mode; yr, year.

Source data

Extended Data Fig. 3 Statistical differences in functional homogeneity among three atlases.

a, One-way repeated analysis of variance (RANOVA) of global homogeneity for each age group. Within each age interval, for each participant we calculated the within-system homogeneity of three parcellation atlases, namely the adult-based group atlas, the age-specific group atlas, and the individual-specific atlas. The within-system homogeneity was quantified by averaging Pearson’s correlations between the time series of all vertex pairs within each system. Given that the iterative processes for both the age-specific group atlas and the individual-specific atlas were based on the finer 18-network parcellation, we calculated within-system homogeneity using the 18 networks. To summarize an overall system homogeneity, we averaged the homogeneity values across systems. The RANOVA revealed significant differences in the global homogeneity among three atlases for any given age group (all F > 267, p < 10−26, two-sided). The gray lines connect three atlases for the same participant. b, The post hoc analyses revealed group differences (all p < 10−8, two-sided, Bonferroni-corrected) in functional homogeneity between any pairs of atlases. The bars for each age group represent the mean difference in global homogeneity between two atlases for all participants in that group. Notably, for the 14 age intervals from 32 postmenstrual weeks to 7 years and from 75 to 80 years, the number of participants included in each interval was fewer than 300. Therefore, all these participants were involved in the construction of the age-specific group atlases (Supplementary Table 11). For the 12 age intervals from 7 to 70 years, the number of participants included in each interval was more than 300. Therefore, for the age range of 7 to 70 years, we compared functional homogeneity across atlases using independent participants who were not involved in the atlas construction. wk, week; mon, month; yr, year.

Source data

Extended Data Fig. 4 Hierarchical clustering analysis of 26 age-specific group atlases.

a, Hierarchical clustering of the 26 × 26 atlas similarity matrix. The atlas similarity was defined as the degree of vertex label overlap between two atlases. For instance, if there were 4,000 vertices with the same label in two atlases, the atlas similarity was 4,000/4,609 = 0.868. b, The reference atlas was derived from the average of eight adult-like atlases, identified as a homogeneous cluster of 18- to 80-year-old atlases. For each vertex, we assigned the label as the system that exhibited the highest occurrence probability across the eight atlases, generating the 7-network reference atlas. VIS, visual; SM, somatomotor; DA, dorsal attention; VA, ventral attention; LIM, limbic; FP, frontoparietal; DM, default mode; w, week; m, month; y, year.

Source data

Extended Data Fig. 5 Lifespan growth patterns of within-system and between-system functional connectivity.

The lower triangular matrix (shown in black) represents the normative growth curves for within-system and between-system FC, while the upper triangular matrix (shown in blue) represents the growth rates for these FC measures. The diagonal of the matrix shows the growth curves and growth rates of within-system FC; the off-diagonal elements represent the growth curves and growth rates of between-system FC. For the growth curve, the median (50th) centile is shown as a solid line, and the 5th, 25th, 75th, and 95th centiles are represented by dotted lines. The growth rate is characterized by the first derivative of the median centile. The gray shaded areas denote the 95% confidence interval, estimated through bootstrapping 1,000 times. VIS, visual; SM, somatomotor; DA, dorsal attention; VA, ventral attention; LIM, limbic; FP, frontoparietal; DM, default mode; FC, functional connectivity.

Source data

Extended Data Fig. 6 Sex differences in the normative growth curves of the functional connectome at global, system, and regional levels.

a, Sex-stratified growth curves for global functional metrics. The solid line represents the 50th centile, with the two surrounding dotted lines denoting the 95% confidence interval, which were estimated by bootstrapping 1,000 times. The subplots from left to right represent the global mean of the connectome, global variance of the connectome, and global system segregation, respectively. b, Sex-specific growth curves for system segregation in each network. The solid line represents the 50th centile, with the two surrounding dotted lines denoting the 95% confidence interval. c, Sex differences in the growth curves of regional-level FCS, where red colors indicate that the values of males are significantly higher than those of females, and blue colors denote that the values of females are significantly higher than those of males. Among the 4,609 vertices, 3,872 exhibited significant sex differences (p < 0.05, Benjamini-Hochberg FDR corrected). FCS, functional connectivity strength; VIS, visual; SM, somatomotor; DA, dorsal attention; VA, ventral attention; LIM, limbic; FP, frontoparietal; DM, default mode. M, male; F, female. **, p < 0.01, ***, p < 0.001, Benjamini-Hochberg FDR corrected. The exact p-values is provided in Supplementary Table 3.

Source data

Extended Data Fig. 7 A series of sensitivity analyses for the validation of lifespan normative growth curves and growth rates of the functional connectome.

a, Global mean of the connectome. b, Global variance of the connectome. c, Global system segregation. d, System segregation in each network. These sensitive analyses included the validation of the potential effects of head motion using more strict head motion threshold (mean framewise displacement (FD) < 0.2 mm, N = 24,494), the impact of uneven sample and site distributions across ages using a balanced sampling strategy that ensures uniformity in participant and site numbers (N = 6,770, resampling 1,000 times), the reproducibility of the results using a split half approach (Nsubgroup1 = 16,663, Nsubgroup2 = 16,587), the potential effects of data samples using a bootstrap resampling analysis (1,000 times), and the potential effects of specific sites using a leave-one-site-out (LOSO) analysis. For the balanced resampling analysis, the figure showed the average of the 1,000 resampled median centile lines. For the bootstrapping resampling analysis, the figure showed the average of the 1,000 bootstrapped median centile lines. For the LOSO analysis, the figure displays the average of the 132 median centile lines. VIS, visual; SM, somatomotor; DA, dorsal attention; VA, ventral attention; LIM, limbic; FP, frontoparietal; DM, default mode; yr, year.

Source data

Extended Data Fig. 8 Lifespan normative growth patterns of negative functional connectivity.

a, Global mean of negative functional connectivity by calculating averaged negative connectivity across all edges. The left panel shows the averaged median (50th) centile as a solid line, surrounded by the averaged 5th, 25th, 75th, and 95th centiles as dotted lines. In the right panel, the solid line illustrates the growth rate of the averaged median centile, with its 95% confidence interval highlighted by gray shaded areas. b, Global mean of negative connectivity by calculating averaged negative connectivity across only non-zero edges. The left panel shows the averaged median (50th) centile as a solid line, surrounded by the averaged 5th, 25th, 75th, and 95th centiles as dotted lines. In the right panel, the solid line illustrates the growth rate of the averaged median centile, with its 95% confidence interval highlighted by gray shaded areas. c, The median centiles (top panel) and their growth rates (bottom panel) for all vertices at several key age points. d, The lifespan growth axis of negative functional connectivity, represented by the first principal component (accounting for 53.5% of the variance) from a PCA on regional-level FCS curve. e, Based on the lifespan principal axis, all vertices across the brain were equally divided into 20 bins. The zero-centered curves of all vertices within each bin were averaged. The first vigintile (depicted in darkest blue) represents one pole of the axis, while the twentieth vigintile represents the opposite pole (depicted in darkest yellow). f, A strong negative correlation was observed between the lifespan principal growth axis and the sensorimotor-association (S-A) axis (r = -0.50, pspin < 0.0001, one-sided) (linear association shown with a 95% confidence interval). FCS, functional connectivity strength; PCA, principal component analysis; wk, week; yr, year.

Source data

Extended Data Fig. 9 Lifespan normative growth patterns of regional system segregation of subcortical structures.

a, The subcortical parcellation (scale I) proposed by Tian et al.81, including eight regions: Hippocampus (HIP), Amygdala (AMY), Posterior thalamus (pTHA), Anterior thalamus (aTHA), Nucleus accumbens (NAc), Globus pallidus (GP), Putamen (PUT), Caudate nucleus (CAU). b, The left panel showed the normative growth curves of mean system segregation in the whole-subcortex. The median (50th) centile is represented by a solid line, while the 5th, 25th, 75th, and 95th centiles are indicated by dotted lines. The right panel showed the growth rates of mean system segregation in the whole-subcortex. The growth peak occurred at the third decade of life (32.1 years, 95% bootstrap confidence interval 31.0–33.2). The gray shaded areas represent the 95% confidence interval, which were estimated by bootstrapping 1,000 times. c, Normative growth curve (top panel, showed as centile lines) and growth rate (bottom panel, showed as the central line) of system segregation for each subcortical region. The gray shaded areas represent the 95% confidence interval. yr, year.

Source data

Supplementary information

Supplementary Information

Supplementary Methods, Results 1–9, Figs. 1–25 and Tables 3–12 and names and affiliations of the consortium authors.

Reporting Summary

Supplementary Tables 1 and 2

The demographics and image acquisition of each scanner site.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, L., Zhao, T., Liang, X. et al. Human lifespan changes in the brain’s functional connectome. Nat Neurosci 28, 891–901 (2025). https://doi.org/10.1038/s41593-025-01907-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41593-025-01907-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing