Journal
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Volume 8, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fncom.2014.00156
Keywords
Bayesian networks; AD diagnosis; spatial component analysis; magnetic resonance imaging; CAD systems
Funding
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XVVH-12-20012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Alzheimers Association
- Alzheimers Drug Discovery Foundation
- BioClinica, Inc.
- Biogen Idec Inc.
- Bristol-Myers Squibb Company
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- F. Hoffmann-La Roche Ltd
- Genentech, In c.
- GE Healthcare
- Innogenetics, N.V.
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Medpace, Inc.
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Synarc Inc.
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
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This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is used to model the dependencies between affected regions of AD. The structure of relations between affected regions allows to detect neurodegeneration with an estimated performance of 88% on more than 400 subjects and predict neurodegeneration with 80% accuracy, supporting the conclusion that modeling the dependencies between components increases the recognition of different patterns of brain degeneration in AD.
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