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Hierarchical cognitive and psychosocial predictors of amnestic mild cognitive impairment

Abstract To identify neuropsychological and psychosocial factors predictive of amnestic Mild Cognitive Impairment (aMCI) among a group of 94 nondemented older adults, we employed a novel nonlinear multivariate classification statistical method called Optimal Data Analysis (ODA) in a dataset collected annually for 3 years. Performance on measures of memory and visuomotor processing speed or symptoms of depression in year 1 predicted aMCI status by year 2. Performance on a measure of learning at year 1 predicted aMCI status at year 3. No other measures significantly predicted incidence of aMCI at years 2 and 3. Results support the utility of multiple neuropsychological and psychosocial measures in the diagnosis of aMCI, and the present model may serve as a testable hypothesis for prospective investigations of the development of aMCI. (JINS, 2010, 16, 721–729.)