Charting brain growth and aging at high spatial precision

• Published in: eLife Vol. 11
• Main Authors: Rutherford, Saige, Fraza, Charlotte, Dinga, Richard, Kia, Seyed Mostafa, Wolfers, Thomas, Zabihi, Mariam, Berthet, Pierre, Worker, Amanda, Verdi, Serena, Andrews, Derek, Han, Laura KM, Bayer, Johanna MM, Dazzan, Paola, McGuire, Phillip, Mocking, Roel T, Schene, Aart, Sripada, Chandra, Tso, Ivy F, Duval, Elizabeth R, Chang, Soo-Eun, Penninx, Brenda WJH, Heitzeg, Mary M, Burt, S Alexandra, Hyde, Luke W, Amaral, David, Wu Nordahl, Christine, Andreasssen, Ole A, Westlye, Lars T, Zahn, Roland, Ruhe, Henricus G, Beckmann, Christian, Marquand, Andre F
• Format: Journal Article
• Language: English
• Published: England eLife Sciences Publications Ltd 01.02.2022; eLife Sciences Publications, Ltd
• Subjects: Adolescent; Adult; Age; Aged; Aged, 80 and over; Aging; Aging - physiology; Autism; Big Data; Bipolar disorder; Brain - diagnostic imaging; Brain - growth & development; brain chart; Child; Child, Preschool; Cohort Studies; Decision making; Female; Gender differences; growth chart; Humans; Hyperactivity; individual prediction; Life span; lifespan; Magnetic Resonance Imaging; Male; Medical imaging; Middle Aged; Models, Statistical; Neuroimaging; Neuroscience; normative model; Schizophrenia; Short Report; Software utilities; Young Adult; brain chart; individual prediction; lifespan; neuroscience; big data; normative model; growth chart; human
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.72904

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2–100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.