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International Journal of Alzheimer’s Disease
Volume 2011, Article ID 914085, 8 pages
Research Article

Morphological Factor Estimation via High-Dimensional Reduction: Prediction of MCI Conversion to Probable AD

1Départment de Radiologie, Faculté de Médecine, Université Laval, Québec, Canada G1K 7P4
2Centre de Recherche Université Laval Robert-Giffard, Québec, Canada G1J 2G3

Received 24 December 2010; Accepted 27 April 2011

Academic Editor: Katsuya Urakami

Copyright © 2011 Simon Duchesne and Abderazzak Mouiha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a novel morphological factor estimate from structural MRI for disease state evaluation. We tested this methodology in the context of Alzheimer's disease (AD) with 349 subjects. The method consisted in (a) creating a reference MRI feature eigenspace using intensity and local volume change data from 149 healthy, young subjects; (b) projecting MRI data from 75 probable AD, 76 controls (CTRL), and 49 Mild Cognitive Impairment (MCI) in that space; (c) extracting high-dimensional discriminant functions; (d) calculating a single morphological factor based on various models. We used this methodology in leave-one-out experiments to (1) confirm the superiority of an inverse-squared model over other approaches; (2) obtain accuracy estimates for the discrimination of probable AD from CTRL (90%) and the prediction of conversion of MCI subjects to probable AD (79.4%).