期刊
NEUROIMAGE
卷 97, 期 -, 页码 333-348出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2014.04.018
关键词
Brain morphology; Lifespan brain aging; Gaussian processes; Single case analysis; Bayesian inference
资金
- PostdocProgram of the German Academic Exchange Service (DAAD)
- BMBF [01EV0709]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Open Access Series of Imaging Studies (OASIS) [P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584]
- Medical Research Council [MR/J014257/1]
- Wellcome Trust [091593/Z/10/Z]
- Abbott
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Amorfix Life Sciences Ltd.
- AstraZeneca
- Bayer HealthCare
- BioClinica, Inc.
- Biogen Idec Inc.
- Bristol-Myers Squibb Company
- Eisai Inc.
- Elan Pharmaceuticals Inc.
- Eli Lilly and Company
- F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.
- GE Healthcare
- Innogenetics, N.V.
- Janssen Alzheimer Immunotherapy Research AMP
- Development, LLC
- Johnson AMP
- Johnson Pharmaceutical Research AMP
- Development LLC.
- Medpace, Inc.
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Servier
- Synarc Inc.
- Takeda Pharmaceutical Company
- Medical Research Council [MR/J014257/1] Funding Source: researchfish
- MRC [MR/J014257/1] Funding Source: UKRI
Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. (C) 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
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