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

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).

Keywords:

• Discriminant analysis
• Neuropsychological tests
• Diagnosis
• Alzheimer's disease
• Principal components analysis; Multivariate analyses; NINCDS-ADRDA criteria; Posterior probability

Maria Guillily is now at the Department of Pharmacology and Experimental Therapeutics at Boston University. Tiffany Sandoval is now at the San Diego State University/University of California at San Diego Joint Doctoral Program in Clinical Psychology. Elizabeth DeGrush is now at the Chicago College of Osteopathic Medicine at Midwestern University. Lindsey Reilly is now at the Springer Publishing Company.

We thank: the Geriatric Neurology and Psychiatry Clinic, University of Rochester Medical Center, Monroe Community Hospital, the Alzheimer's Disease Center, especially Paul Coleman, Charles Duffy, and Roger Kurlan, for their strong support of our research; Robert Emerson and William Vaughn for their technical contributions; Rafael Klorman for critical discussions; Susan E. Chapman for help in writing; Courtney Vargas, Dustina Holt, Jonathan DeRight, Cendrine Robinson, Kristen Morie, Anna Fagan, Michael Garber-Barron, Leon Tsao, and Brittany Huber for technical help; and the many voluntary participants in this research. This research was supported by the National Institute of Health Grants P30-AG08665, R01-AG018880, and P30-EY01319.