Journal
NEUROBIOLOGY OF AGING
Volume 36, Issue -, Pages S91-S102Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.neurobiolaging.2014.05.040
Keywords
Cortical thickness; Network properties; Fusion; Multiple kernel learning; Early detection; Mild cognitive impairment; Alzheimer
Categories
Funding
- Alzheimer Society of Canada Research Program (ASRP)
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Canadian Institutes of Health Research (CIHR)
- Michael Smith Foundation for Health Research (MSFHR)
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (from National Institutes of Health) [U01 AG024904, P30 AG010129, K01 AG030514]
- ADNI (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Alzheimer's Association
- Alzheimer Drug Discovery Foundation
- BioClinica, Inc
- Biogen Idec Inc
- Bristol-Myers Squibb Foundation
- Eisai
- Elan Pharmaceuticals, Inc
- Eli Lilly and Company
- F. Hoffmann-La Roche Ltd
- 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
- Piramal Imaging
- Servier
- Synarc Inc
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
- National Institutes of Health [P30 AG010129, K01 AG030514]
Ask authors/readers for more resources
Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer's disease but not its interregional covariation of thickness. We present novel features based on the inter-regional covariation of cortical thickness. Initially, the cortical labels of each subject are partitioned into small patches (graph nodes) by spatial k-means clustering. A graph is then constructed by establishing a link between 2 nodes if the difference in thickness between the nodes is below a certain threshold. From this binary graph, a thickness network is computed using nodal degree, betweenness, and clustering coefficient measures. Fusing them with multiple kernel learning, it is observed that thickness network features discriminate mild cognitive impairment (MCI) converters from controls (CN) with an area under curve (AUC) of 0.83, 74% sensitivity and 76% specificity on a large subset obtained from the Alzheimer's Disease Neuroimaging Initiative data set. A comparison of predictive utility in Alzheimer's disease and/or CN classification (AUC of 0.92, 80% sensitivity [SENS] and 90% specificity [SPEC]), in discriminating CN from MCI (converters and nonconverters combined; AUC of 0.75, SENS and SPEC of 64% and 73%, respectively) and in discriminating between MCI nonconverters and MCI converters (AUC of 0.68, SENS and SPEC of 65% and 64%) is also presented. ThickNet features as defined here are novel, can be derived from a single magnetic resonance imaging scan, and demonstrate the potential for the computer-aided prognostic applications. (C) 2015 Elsevier Inc. All rights reserved.
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