Brain charts for the human lifespan
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 built an interactive resource to benchmark brain morphology, www.brainchart.io, derived from any current or future sample of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional 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 individual centile scores over longitudinal assessments; and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of individual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of diverse developmental and clinical effects on human brain structure. Furthermore, we provide open resources to support future advances towards adoption of brain charts as standards for quantitative benchmarking of typical or atypical brain MRI scans.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵† jointly supervised the work
↵* Data used in this article were obtained from the brain consortium for reproducibility, reliability and replicability (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 (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 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 online.
↵***** Data used in this article were collected and processed by the UMN Baby Connectome Project (BCP) consortium and the Masonic Institute for the Developing Brain (MIDB) Informatics group. The groups are comprised of the following individuals: Amanda Ruetger, Audrey Houghton, Ben Lynch, Thomas Pengo, Jim Wilgenbusch, Tim Hendrickson, Trevor Day, Sooyeon Sung, Kathy Snider, Lucille Moore, Alice Graham, Damien Fair, Eric Feczko and Jed Elison
↵****** 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 to the radiographers 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’ of Chinese Color Nest Project (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 the preparation of this article were obtained from the IMAGEN consortium.
↵************* 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.
Extensive new analyses to include QC, harmonisation, increased validation of out of sample estimation and regional specifity
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