Yorghos Tripodis – Boston University

Title: Predicting the cognitive status of an aging population. Abstract: Cognitive trajectories are characterized by tremendous heterogeneity in rates of change. We utilized a subset of the NACC dataset to estimate cognitive trajectories in order to investigate possible causes of differences in variability among normal controls. We analyzed data from 298 cases that were free from any cognitive impairment for at least 2 visits from the National Alzheimer Coordinating Center (NACC). 149 cases remained normal for at least 2 visits following our observation period, while 149 cases were diagnosed subsequently with Mild Cognitive Impairment (MCI). For all cases, we consider only time points when their cognitive status was normal. The groups were matched by age, sex, education and total number of visits. We used an innovative statistical method of dynamic factor models developed by the authors on the NACC neuropsychological battery. Based on a large array of test scores (MMSE, logical memory: immediate and delayed, digits backward and forward, animals, vegetables, TRAILS A and B, Boston naming test and WAIS), we estimated one latent composite trajectory for each individual. We then used linear mixed effect models to compare differences between groups in their rate of cognitive decline. We hypothesized that there will be differences in the cognitive trajectory between the two groups during their normal state. Factor analytic models are limited to cross-sectional datasets ignoring any longitudinal or dynamic analysis. The latent cognitive index is a weighted average of past and present scores of neuropsychological tests. These weights are a function of the between-subject variability as well as the correlation between tests. Measures that are highly correlated with other measures will get higher weight. Moreover, measures that show increased between- subject-variability will receive higher weight. Current factor analytic methods do not use any information from within- subject-variability over time. If we do not account for time variability we may over(under)inflate the weights. Past observations of measures that are stable over time will be discounted. Tests with rates of change that are highly correlated with other tests’ rates of change will receive more weight. The estimated cognitive trajectory shows significant differences in the rate of change (p-value=0.0003). The cases that remain in a normal cognitive status show significant improvement over time (estimate=-.06, p-value=0.01), indicating a probable learning effect. The cases that will convert to MCI show no improvement in their cognitive trajectory during the period, which are assigned with normal cognition (estimate=--.003, p-value=0.79).These data suggest that there is a probable learning effect in repeated testing only for those that remain in a normal cognitive status. For the cases that will convert to MCI in the future, there is no improvement in their cognitive trajectory. These differences may be used for a more timely diagnosis of MCI.