Sustainable method for Alzheimer's prediction in Mild Cognitive Impairment: EEG connectivity and graph theory combined with ApoE

Camillo Marra, Francesco Danilo Tiziano, Fabrizio Vecchio, Francesca Miraglia, Giordano Lacidogna, Valeria Guglielmi, Patrizio Pasqualetti, Paolo Maria Rossini, Dr Fabrizio Vecchio, Dr Francesco Iberite, Dr Patrizio Pasqualetti

Risultato della ricerca: Contributo in rivistaArticolo in rivista

23 Citazioni (Scopus)

Abstract

Mild cognitive impairment (MCI) is a condition intermediate between physiological brain aging and dementia. Amnesic-MCI (aMCI) subjects progress to dementia (typically to Alzheimer-Dementia=AD) at an annual rate which is 20 times higher than that of cognitively intact elderly. The present study aims to investigate whether EEG network Small World properties (SW) combined with Apo-E genotyping, could reliably discriminate aMCI subjects who will convert to AD after approximately a year. 145 aMCI subjects were divided into two sub-groups and, according to the clinical follow-up, were classified as Converted to AD(C-MCI, 71) or Stable(S-MCI, 74). Results showed significant differences in SW in delta, alpha1, alpha2, beta2, gamma bands, with C-MCI in the baseline similar to AD. Receiver Operating Characteristic(ROC) curve, based on a first-order polynomial regression of SW, showed 57% sensitivity, 66% specificity and 61% accuracy(area under the curve: AUC=0.64). In 97 out of 145 MCI, Apo-E allele testing was also available. Combining this genetic risk factor with Small Word EEG, results showed: 96.7% sensitivity, 86% specificity and 91.7% accuracy(AUC=0.97). Moreover, using only the Small World values in these 97 subjects, the ROC showed an AUC of 0.63; the resulting classifier presented 50%sensitivity, 69%specificity and 59.6%accuracy. When different types of EEG analysis (power density spectrum) were tested, the accuracy levels were lower (68.86%). Concluding, this innovative EEG analysis, in combination with a genetic test (both low-cost and widely available), could evaluate on an individual basis with great precision the risk of MCI progression. This evaluation could then be used to screen large populations and quickly identify aMCI in a prodromal stage of dementia. This article is protected by copyright. All rights reserved.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
RivistaAnnals of Neurology
DOI
Stato di pubblicazionePubblicato - 2018

Keywords

  • Alzheimer
  • ApoE
  • EEG
  • MCI
  • alpha band
  • conversion
  • eLORETA
  • functional connectivity
  • graph theory
  • precision medicine

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