Disentangling Alzheimer’s disease neurodegeneration from typical brain ageing using machine learning
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 02, 2022
- Source ID
- 10.1093/braincomms/fcac117
Entities
People
- Ahmed Abdulkadir
- Aristeidis Sotiras
- Christos Davatzikos
- David A. Wolk
- Dhivya Srinivasan
- Elizabeth Mamourian
- For The Adni
- From The Istaging Consortium
- Guray Erus
- Gyujoon Hwang
- Haochang Shou
- Ilya M. Nasrallah
- Jimit Doshi
- Marilyn S. Albert
- Mohamad Habes
- Murat Bilgel
- Nick R. Bryan
- Peter R Schofield
- Raymond Pomponio
- Susan M. Resnick
- Tanweer Rashid
- Yong Fan
Organizations
- Alzheimer's Disease Neuroimaging Initiative
- Johns Hopkins University
- National Institute on Aging
- National Institutes of Health
- University of Pennsylvania
- University of Texas Health Science Center at San Antonio
- University of Texas at Austin
- Washington University in St. Louis