Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning.
Autor: | Hwang G; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA., Abdulkadir A; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA., Erus G; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Habes M; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA., Pomponio R; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Shou H; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Doshi J; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Mamourian E; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Rashid T; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Bilgel M; Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA., Fan Y; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Sotiras A; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, Washington University in St Louis, St Louis, MO, USA., Srinivasan D; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Morris JC; Department of Neurology, Washington University in St Louis, St Louis, MO, USA., Albert MS; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Bryan NR; Department of Diagnostic Medicine, University of Texas, Austin, TX, USA., Resnick SM; Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA., Nasrallah IM; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Davatzikos C; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Wolk DA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.; Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA. |
---|---|
Jazyk: | angličtina |
Zdroj: | Brain communications [Brain Commun] 2022 May 07; Vol. 4 (3), pp. fcac117. Date of Electronic Publication: 2022 May 07 (Print Publication: 2022). |
DOI: | 10.1093/braincomms/fcac117 |
Abstrakt: | Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.) |
Databáze: | MEDLINE |
Externí odkaz: |