A pattern of components from brain event-related potentials (ERPs) (cognitive non-invasive electrical brain measures) performed well in separating early-stage Alzheimer's disease (AD) subjects from normal-aging control subjects and shows promise for developing a clinical diagnostic for probable AD. A Number-Letter task elicited brain activity related to cognitive processes. In response to the task stimuli, brain activity was recorded as ERPs, whose components were measured by principal components analysis (PCA). The ERP component scores to relevant and irrelevant stimuli were used in discriminant analyses to develop functions that successfully classified individuals as belonging to an early-stage Alzheimer's disease group or a like-aged Control group, with probabilities of an individual belonging to each group. Applying the discriminant function to the developmental half of the data showed 92% of the subjects were correctly classified into either the AD group or the Control group with a sensitivity of 1.00. The two crossvalidation results were good with sensitivities of 0.83 and classification accuracies of 0.75-0.79. P3 and CNV components, as well as other, earlier ERP components, e.g. C145 and the memory "Storage" component, were useful in the discriminant functions.

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 Components 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 subjects (81%) were in the highly confident probability bins (0.70 – 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.

Predicting which individuals will progress to Alzheimer’s disease (AD) is important in both clinical and research settings. We used brain Event-Related Potentials (ERPs) obtained in a perceptual/cognitive paradigm with various processing demands to predict which individual Mild Cognitive Impairment (MCI) subjects will develop AD versus which will not. ERP components, including P3, memory “storage” component, and other earlier and later components, were identified and measured by Principal Components Analysis. When measured for particular task conditions, a weighted set of eight ERP component_conditions performed well in discriminant analysis at predicting later AD progression with good accuracy, sensitivity, and specificity. The predictions for most individuals (79%) had high posterior probabilities and were accurate (88%). This method, supported by a cross-validation where the prediction accuracy was 70–78%, features the posterior probability for each individual as a method of determining the likelihood of progression to AD. Empirically obtained prediction accuracies rose to 94% when the computed posterior probabilities for individuals were 0.90 or higher (which was found for 40% of our MCI sample).

We analyzed verbal episodic memory learning and recall using the Logical Memory (LM) subtest of the Wechsler Memory Scale-III in order to determine how gender differences in AD compare to those seen in normal elderly and whether or not these differences impact assessment of AD. We administered the LM to both an AD and a Control group, each comprised of 21 men and 21 women, and found a large drop in performance from normal elders to AD. Of interest was a gender interaction whereby the women’s scores dropped 1.6 times more than the men’s did. Control women on average outperformed Control men on every aspect of the test, including immediate recall, delayed recall, and learning. Conversely, AD women tended to perform worse than AD men. Additionally, the LM achieved perfect diagnostic accuracy in discriminant analysis of AD vs. Control women, a statistically significantly higher result than for men. The results indicate the LM is a more powerful and reliable tool in detecting AD in women than in men.

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 subjects) created more interpretable cognitive dimensions by resolving 49 test measures in our neuropsychological battery to 13 component scores for each subject. 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).