Hierarchical cognitive and psychosocial predictors of amnestic mild cognitive impairment.
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.
Han
S Duke
SD
Department of Behavioral Sciences, Rush University Medical Center, Chicago, Illinois, USA. Duke_Han@rush.edu
Suzuki
Hideo
H
Jak
Amy J
AJ
Chang
Yu-Ling
YL
Salmon
David P
DP
Bondi
Mark W
MW
D000368:Aged; D000369:Aged, 80 and over; D003072:Cognition Disorders / Q000145:classification / Q000175:diagnosis / Q000503:physiopathology / Q000523:psychology; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests; D012919:Social Behavior