MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide
Vishnu M Bashyam, Guray Erus, Jimit Doshi, Mohamad Habes, Ilya Nasralah, Monica Truelove-Hill, Dhivya Srinivasan, Liz Mamourian, Raymond Pomponio, Yong Fan, Lenore J Launer, Colin L Masters, Paul Maruff, Chuanjun Zhuo, Henry Volzke, Sterling C Johnson, Jurgen Fripp, Nikolaos Koutsouleris, Theodore D Satterthwaite, Daniel Wolf Show all
Brain | OXFORD UNIV PRESS | Published : 2020
Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or d..View full abstract
Awarded by National Institute on Aging
Awarded by National Institute of Mental Health
Awarded by National Institutes of Health
This work was supported by the National Institute on Aging (grant number 1RF1AG054409) and the National Institute of Mental Health (grant number 5R01MH112070). M.H. was supported in part by The Allen H. and Selma W. Berkman Charitable Trust (Accelerating Research on Vascular Dementia) and the National Institutes of Health (grant number R01 HL127659-04S1, R01EB022573, and HHSN271201600059C). This work was also supported in part by the Intramural Research Program, National Institute on Aging, NIH. SHIP is part of the Community Medicine Research Net of the University Medicine Greifswald, which is supported by the German Federal State of MecklenburgWest Pomerania.