Predicting conversion from mild cognitive impairment to Alzheimer's disease using neuropsychological tests and multivariate methods

Abstract

Behavioral markers measured through neuropsychological testing in mild cognitive impairment (MCI) were analyzed and combined in multivariate ways to predict conversion to Alzheimer's disease (AD) in a longitudinal study of 43 MCI patients. The test measures taken at a baseline evaluation were first reduced to underlying components (principal component analysis, PCA), and then the component scores were used in discriminant analysis to classify MCI individuals as likely to convert or not. When empirically weighted and combined, episodic memory, speeded executive functioning, recognition memory (false and true positives), visuospatial memory processing speed, and visuospatial episodic memory were together strong predictors of conversion to AD. These multivariate combinations of the test measures achieved through the PCA were good, statistically significant predictors of MCI conversion to AD (84% accuracy, 86% sensitivity, and 83% specificity). Importantly, the posterior probabilities of group membership that accompanied the binary prediction for each participant indicated the confidence of the prediction. Most of the participants (81%) were in the highly confident probability bins (.70-1.00), where the obtained prediction accuracy was more than 90%. The strength and reliability of this multivariate prediction method were tested by cross-validation and randomized resampling.

Figure 1

The prediction accuracy for, and number of subjects in, each posterior probability bin based on the development set. Prediction accuracy is the percent of MCI individuals correctly predicted to convert to AD or remain stable. Each of the 43 MCI individuals was placed in a bin by the probability of group membership calculated through the discriminant function (placement was determined by the probability greater than 0.50).