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Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E
Author(s) -
Vecchio Fabrizio,
Miraglia Francesca,
Iberite Francesco,
Lacidogna Giordano,
Guglielmi Valeria,
Marra Camillo,
Pasqualetti Patrizio,
Tiziano Francesco Danilo,
Rossini Paolo Maria
Publication year - 2018
Publication title -
annals of neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.764
H-Index - 296
eISSN - 1531-8249
pISSN - 0364-5134
DOI - 10.1002/ana.25289
Subject(s) - dementia , receiver operating characteristic , audiology , electroencephalography , psychology , cognitive impairment , area under the curve , medicine , neuropsychology , cognition , neuroscience , disease
Objective 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. Methods 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 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%). Interpretation 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. Ann Neurol 2018 Ann Neurol 2018;84:302–314