Artificial neural networks identify the predictive values of risk factors on the conversion of amnestic mild cognitive impairment

Massimo Tabaton, Patrizio Odetti, Sergio Cammarata, Roberta Borghi, Fiammetta Monacelli, Carlo Caltagirone, Paola Bossù, Massimo Buscema, Enzo Grossi

Research output: Contribution to journalArticle

Abstract

The search for markers that are able to predict the conversion of amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) is crucial for early mechanistic therapies. Using artificial neural networks (ANNs), 22 variables that are known risk factors of AD were analyzed in 80 patients with aMCI, for a period spanning at least 2 years. The cases were chosen from 195 aMCI subjects recruited by four Italian Alzheimer's disease units. The parameters of glucose metabolism disorder, female gender, and apolipoprotein E ε3/ε4 genotype were found to be the biological variables with high relevance for predicting the conversion of aMCI. The scores of attention and short term memory tests also were predictors. Surprisingly, the plasma concentration of amyloid-β42 had a low predictive value. The results support the utility of ANN analysis as a new tool in the interpretation of data from heterogeneous and distinct sources.

Original languageEnglish
Pages (from-to)1035-1040
Number of pages6
JournalJournal of Alzheimer's Disease
Volume19
Issue number3
DOIs
Publication statusPublished - 2010

Keywords

  • Alzheimer's disease
  • Artificial neural networks
  • Biological markers
  • Mild cognitive impairment

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Geriatrics and Gerontology
  • Clinical Psychology

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