Integrating Mri Multivariate Markers With Cognitive Neuropsychological Scores For An Optimal Decisional Space In Predicting Alzheimer'S Disease
PROCEEDINGS OF THE 2ND INTERNATIONAL CONGRESS ON PERSONALIZED MEDICINE (UPCP 2013)（2014）
Florida Int Univ
Abstract
This study proposes a statistics-based multidimensional approach to classify Alzheimer's disease (AD) and its prodromal stages using regional measures (cortical volume, cortical thickness and surface area) obtained from MRI scans and a neuropsychological test (MMSE). Normalization effect of different approaches on these measures is also studied and validated on 314 subjects. Results indicate neuropsychological test enhances classification and when combined with selected subcortical volumes yield a high classification accuracy of 92.3% for AD classification, 72.4% for amnestic mild cognitive impairment (aMCI) and 75.1% for non-aMCI, based on 2-fold cross validation using support vector machine (SVM) classifier. Also, normalization approaches and hierarchal models do not enhance performance significantly.
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