Journal
MEDICAL IMAGE ANALYSIS
Volume 16, Issue 4, Pages 819-830Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2011.12.003
Keywords
Manifold learning; Laplacian Eigenmaps; Classification; Metadata; Alzheimer's disease
Categories
Funding
- European Commission
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Abbott
- AstraZeneca AB
- Bayer Schering Pharma AG
- Bristol-Myers Squibb
- Eisai Global Clinical Development
- Elan Corporation
- Genentech
- GE Healthcare
- GlaxoSmithKline
- Innogenetics
- Johnson and Johnson
- Eli Lilly and Co.
- Medpace, Inc.
- Merck and Co., Inc.
- Novartis AG
- Pfizer Inc.
- F. Hoffman-La Roche
- Schering-Plough
- Synarc, Inc.
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- NIH [P30 AG010129, K01 AG030514]
- Dana Foundation
- Medical Research Council [MC_U120061309, MR/K006355/1] Funding Source: researchfish
- MRC [MR/K006355/1, MC_U120061309] Funding Source: UKRI
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We propose a framework for the extraction of biomarkers from low-dimensional manifolds representing inter-subject brain variation. Manifold coordinates of each image capture information about structural shape and appearance and, when a phenotype exists, about the subject's clinical state. Our framework incorporates subject meta-information into the manifold learning step. Apart from gender and age, information such as genotype or a derived biomarker is often available in clinical studies and can inform the classification of a query subject. Such information, whether discrete or continuous, is used as an additional input to manifold learning, extending the Laplacian Eigenmap objective function and enriching a similarity measure derived from pairwise image similarities. The biomarkers identified with the proposed method are data-driven in contrast to a priori defined biomarkers derived from, e.g., manual or automated segmentations. They form a unified representation of both the imaging and non-imaging measurements, providing a natural use for data analysis and visualization. We test the method to classify subjects with Alzheimer's Disease (AD), mild cognitive impairment (MCI) and healthy controls enrolled in the ADNI study. Non-imaging metadata used are ApoE genotype, a risk factor associated with AD. and the CSF-concentration of A beta(1-42), an established biomarker for AD. In addition, we use hippocampal volume as a derived imaging-biomarker to enrich the learned manifold. Our classification results compare favorably to what has been reported in a recent meta-analysis using established neuroimaging methods on the same database. (C) 2011 Elsevier B.V. All rights reserved.
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