1. Charting Brain Growth and Aging at High Spatial Precision
- Author
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Brenda W. Pennix, Laura K.M. Han, Lars T. Westlye, Phillip McGuire, S. Alexandra Burt, Roel J. T. Mocking, Andre F. Marquand, Luke W. Hyde, Roland Zahn, Seyed Mostafa Kia, Thomas Wolfers, Saige Rutherford, Mary M. Heitzeg, Amanda Worker, Chandra Sripada, Christine Wu Nordahl, Christian F. Beckmann, Richard Dinga, David G. Amaral, Soo Eun Chang, Johanna Bayer, Derek Sayre Andrews, Henricus G. Ruhé, Ivy F. Tso, Aaart Schene, Ole A. Andreasssen, Elizabeth R. Duval, Charlotte Fraza, Mariam Zabihi, Paola Dazzan, Pierre Berthet, and Serena Verdi
- Subjects
Relation (database) ,Computer science ,business.industry ,Interface (computing) ,Sample (statistics) ,Machine learning ,computer.software_genre ,Multiple disorders ,Brain growth ,Neuroimaging ,Normative ,Artificial intelligence ,business ,Reference model ,computer - Abstract
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 use 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=1,985), 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.
- Published
- 2021
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