Brain charts for the human lifespan

Abstract
Over the past 25 years, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, there are no reference standards against which to anchor measures of individual differences in brain morphology, in contrast to growth charts for traits such as height and weight. Here, we built an interactive online resource (www.brainchart.io) to quantify individual differences in brain structure from any current or future magnetic resonance imaging (MRI) study, against models of expected age-related trends. With the goal of basing these on the largest and most inclusive dataset, we aggregated MRI data spanning 115 days post-conception through 100 postnatal years, totaling 122,123 scans from 100,071 individuals in over 100 studies across 6 continents. When quantified as centile scores relative to the reference models, individual differences show high validity with non-MRI brain growth estimates and high stability across longitudinal assessment. Centile scores helped identify previously unreported brain developmental milestones and demonstrated increased genetic heritability compared to non-centiled MRI phenotypes. Crucially for the study of brain disorders, centile scores provide a standardised and interpretable measure of deviation that reveals new patterns of neuroanatomical differences across neurological and psychiatric disorders emerging during development and ageing. In sum, brain charts for the human lifespan are an essential first step towards robust, standardised quantification of individual variation and for characterizing deviation from age-related trends. Our global collaborative study provides such an anchorpoint for basic neuroimaging research and will facilitate implementation of research-based standards in clinical studies.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵* 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://www.loni.usc.edu/ADNI). 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 (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 report. 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
↵*** A complete listing of ARWiBo researchers can be found in the Supplementary Materials
↵**** The Centre for Attention Learning and Memory (CALM) research clinic is based at and supported by funding from the Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, as well as funding from the EU Horizon 2020 Personalised Medicine “LifeBrain” project (H2020-SC1-2016-2017, Topic SC1-PM-04-2016). The lead investigators are Duncan Astle, Kate Baker, Susan E. Gathercole, Joni Holmes, Rogier A. Kievit, and Tom Manly. Data collection was assisted by a team of researchers and PhD students that includes Danyla Akarca, Joe Bathelt, Giacomo Bignardi, Sarah Bishop, Erica Botanic, Lara Bridge, Diandra Bkric, Annie Bryant, Sally Butterfield, Elizabeth Byrne, Gemma Crickmore, Edwin Dalmaijer; Fánchea Daly, Tina Emery, Grace Franckel, Laura Forde, Delia Fuhrmann, Andrew Gadie, Sara Gharooni, Jacalyn Guy, Erin Hawkins, Agniezska Jaroslawska, Sara Joeghan, Amy Johnson, Jonathan Jones, Elise Ng-Cordell, Sinéad O’Brien, Cliodhna O’Leary, Joseph Rennie, Ivan Simpson-Kent, Roma Siugzdaite, Tess Smith, Stepheni Uh, Francesca Woolgar, Mengya Zhang, and Natalia Zdorovtsova. We thank the many professionals working in children’s services in the southeast and east of England for their support and to the children and their families for giving up their time to visit the clinic, and the radiographer for facilitating pediatric scanning.
↵***** The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) research was supported by the Biotechnology and Biological Sciences Research Council (BB/H008217/1), as well as funding from the Medical Research Council Cognition and Brain Sciences Unit, and the EU Horizon 2020 Personalised Medicine “LifeBrain” project (H2020-SC1-2016-2017, Topic SC1-PM-04-2016). We thank the Cam-CAN respondents and their primary care teams in Cambridge for their participation in this study. Further information about the Cam-CAN corporate authorship membership can be found at http://www.cam-can.org/index.php?content=corpauth#12.
↵****** Data used in this article were obtained from the developmental component ‘Growing Up in China’ and the standardization component ‘3R-BRAIN’ of Chinese Color Nest Project (https://github.com/zuoxinian/CCNP). More information of the CCNP team can be found at the DeepNeuro Lab (http://deepneuro.bnu.edu.cn/?p=163).
↵******* Data was downloaded from the COllaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx) and data collection was performed at the Mind Research Network, and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to Dr. Vince Calhoun.
↵******** The ENIGMA Developmental Brain Age working group principally consists of Drs. James Cole, Niall Bourke, Heather Whalley, David Glahn, Laura Han, Francesca Biondo, Katherine Karlsgodt, Carrie Bearden, Jakob Seidlitz, Richard Bethlehem, Eileen Xu, Marieke Bos, Sam Mathia, Sophia Frangou, Miruna Carmen Barbu, Yoonho Chung, and Aaron Alexander-Bloch
↵********* Data used in the preparation of this article were obtained from the Harvard Aging Brain Study (HABS - P01AG036694; https://habs.mgh.harvard.edu). The HABS study was launched in 2010, funded by the National Institute on Aging. and is led by principal investigators Reisa A. Sperling MD and Keith A. Johnson MD at Massachusetts General Hospital/Harvard Medical School in Boston, MA.
↵********** 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 is a Canadian translational network in neurodevelopmental disorders, primarily funded by the Ontario Brain Institute.
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