Diagnosis of Alzheimer's disease using neuropsychological testing improved by multivariate analyses

Brain and Cognitive Sciences and Center for Visual Science, University of Rochester, Rochester, NY 14627-0270, USA.
Journal of Clinical and Experimental Neuropsychology (Impact Factor: 2.16). 03/2010; 32(8):793-808. DOI: 10.1080/13803390903540315
Source: PubMed

ABSTRACT Neuropsychological assessment aids in the diagnosis of Alzheimer's disease (AD) by objectively establishing cognitive impairment from standardized tests. We present new criteria for diagnosis that use weighted combined scores from multiple tests. Our method employs two multivariate analyses: principal components analysis (PCA) and discriminant analysis. PCA (N = 216 participants) created more interpretable cognitive dimensions by resolving 49 test measures in our neuropsychological battery to 13 component scores for each participant. The component scores were used to build discriminant functions that classified each participant as either an early-stage AD (N = 55) or normal elderly (N = 78). Our discriminant function performed with high accuracy, sensitivity, and specificity (nearly all >90%) in the development, a cross-validation, and a new-subjects validation. When contrasted to two different traditional empirical methods for diagnosis (using cutscores and defining AD as falling below 5% on two or more test domains), our results suggested that the multivariate method was superior in classification (approximately 20% more accurate).

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