Hierarchical cognitive and psychosocial predictors of amnestic mild cognitive impairment

S. Duke Han, Hideo Suzuki, Amy J. Jak, Yu Ling Chang, David P. Salmon, Mark W. Bondi

Research output: Contribution to journalArticle

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)721-729
Number of pages9
JournalJournal of the International Neuropsychological Society
Volume16
Issue number4
DOIs
StatePublished - Jul 1 2010

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Keywords

  • Amnestic mild cognitive impairment
  • D-KEFS
  • Depression
  • MCI
  • Memory
  • Neuropsychology
  • Optimal data analysis
  • Visuomotor processing speed
  • aMCI

ASJC Scopus subject areas

  • Neuroscience(all)
  • Clinical Psychology
  • Clinical Neurology
  • Psychiatry and Mental health

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