Brain charts for the human lifespan

The PREVENT-AD Research Group, VETSA, 3R-BRAIN, AIBL, Alzheimer's Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, The Mayo Clinic Study of Aging, NSPN, POND

Research output: Contribution to journalArticlepeer-review

82 Scopus citations

Abstract

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Original languageEnglish (US)
Pages (from-to)525-533
Number of pages9
JournalNature
Volume604
Issue number7906
DOIs
StatePublished - Apr 21 2022

Bibliographical note

Funding Information:
R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NIH, NHS, the NIHR or the Department of Health and Social Care. We acknowledge the invaluable contribution to this effort made by several openly shared MRI datasets: OpenNeuro ( https://openneuro.org/ ), the Healthy Brain Network ( https://healthybrainnetwork.org/ ), UK BioBank ( https://www.ukbiobank.ac.uk/ ), ABCD ( https://abcdstudy.org/ ), the Laboratory of NeuroImaging ( https://loni.usc.edu/ ), data made available through the Open Science Framework ( https://osf.io/ ), COINS ( http://coins.mrn.org/dx ), the Developing Human Connectome Project ( http://www.developingconnectome.org/ ), the Human Connectome Project ( http://www.humanconnectomeproject.org/ ), the OpenPain project ( https://www.openpain.org ), the International Neuroimaging Datasharing Initiative (INDI) (https://fcon_1000.projects.nitrc.org/), and the NIMH Data Archive ( https://nda.nih.gov/ ). See Supplementary Information for further notes on the usage of open MRI data and data sharing. Data used in this article were provided by the brain consortium for reliability, reproducibility and replicability (3R-BRAIN) ( https://github.com/zuoxinian/3R-BRAIN ). Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database ( https://adni.loni.usc.edu/aibl-australian-imaging-biomarkers-and-lifestyle-study-of-ageing-18-month-data-now-released/ ). The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at https://www.aibl.csiro.au . Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( https://adni.loni.usc.edu/ ). 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 report. A complete listing of ADNI investigators can be found at https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf . More information on the ARWIBO consortium can be found at https://www.arwibo.it/ . More information on CALM team members can be found at https://calm.mrc-cbu.cam.ac.uk/team/ and in the Supplementary Information. Further information about the Cam-CAN corporate authorship membership can be found at https://www.cam-can.org/index.php?content=corpauth#12 . Data used in this article were obtained from the developmental component ‘Growing Up in China’ of the Chinese Color Nest Project ( http://deepneuro.bnu.edu.cn/?p=163 ). Data were downloaded from the COllaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS) ( https://coins.trendscenter.org/ ) and data collection was performed at the Mind Research Network. Data used in the preparation of this article were obtained from the IConsortium on Vulnerability to Externalizing Disorders and Addictions (c-VEDA), India ( https://cveda-project.org/ ). Details of The ENIGMA Developmental Brain Age working group can be found at https://github.com/ENIGMA-Developmental-BrainAge/main . Data used in the preparation of this article were obtained from the Harvard Aging Brain Study (HABS P01AG036694) ( https://habs.mgh.harvard.edu ). Data used in the preparation of this article were obtained from the IMAGEN consortium ( https://imagen-europe.com/ ). Data used in this article were obtained from the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD) ( https://recode.re.kr ). A full list of NSPN consortium members can be found at https://www.nspn.org.uk/nspn-team/ . The POND network ( https://pond-network.ca/ ) is a Canadian translational network in neurodevelopmental disorders, primarily funded by the Ontario Brain Institute.

Funding Information:
R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NIH, NHS, the NIHR or the Department of Health and Social Care. We acknowledge the invaluable contribution to this effort made by several openly shared MRI datasets: OpenNeuro (https://openneuro.org/), the Healthy Brain Network (https://healthybrainnetwork.org/), UK BioBank (https://www.ukbiobank.ac.uk/), ABCD (https://abcdstudy.org/), the Laboratory of NeuroImaging (https://loni.usc.edu/), data made available through the Open Science Framework (https://osf.io/), COINS (http://coins.mrn.org/dx), the Developing Human Connectome Project (http://www.developingconnectome.org/), the Human Connectome Project (http://www.humanconnectomeproject.org/), the OpenPain project (https://www.openpain.org), the International Neuroimaging Datasharing Initiative (INDI) (https://fcon_1000.projects.nitrc.org/), and the NIMH Data Archive (https://nda.nih.gov/). See Supplementary Information 21 for further notes on the usage of open MRI data and data sharing. Data used in this article were provided by the brain consortium for reliability, reproducibility and replicability (3R-BRAIN) (https://github.com/zuoxinian/3R-BRAIN). Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (https://adni.loni.usc.edu/aibl-australian-imaging-biomarkers-and-lifestyle-study-of-ageing-18-month-data-now-released/). The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at https://www.aibl.csiro.au. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/). 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 report. A complete listing of ADNI investigators can be found at https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. More information on the ARWIBO consortium can be found at https://www.arwibo.it/. More information on CALM team members can be found at https://calm.mrc-cbu.cam.ac.uk/team/ and in the Supplementary Information. Further information about the Cam-CAN corporate authorship membership can be found at https://www.cam-can.org/index.php?content=corpauth#12. Data used in this article were obtained from the developmental component ‘Growing Up in China’ of the Chinese Color Nest Project (http://deepneuro.bnu.edu.cn/?p=163). Data were downloaded from the COllaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS) (https://coins.trendscenter.org/) and data collection was performed at the Mind Research Network. Data used in the preparation of this article were obtained from the IConsortium on Vulnerability to Externalizing Disorders and Addictions (c-VEDA), India (https://cveda-project.org/). Details of The ENIGMA Developmental Brain Age working group can be found at https://github.com/ENIGMA-Developmental-BrainAge/main. Data used in the preparation of this article were obtained from the Harvard Aging Brain Study (HABS P01AG036694) (https://habs.mgh.harvard.edu). Data used in the preparation of this article were obtained from the IMAGEN consortium (https://imagen-europe.com/). Data used in this article were obtained from the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD) (https://recode.re.kr). A full list of NSPN consortium members can be found at https://www.nspn.org.uk/nspn-team/. The POND network (https://pond-network.ca/) is a Canadian translational network in neurodevelopmental disorders, primarily funded by the Ontario Brain Institute.

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Body Height
  • Brain/anatomy & histology
  • Humans
  • Longevity
  • Magnetic Resonance Imaging/methods
  • Neuroimaging

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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