Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E
Dr Fabrizio Vecchio PhD
Brain Connectivity Laboratory, IRCCS San Raffaele Pisana
Search for more papers by this authorDr Francesca Miraglia PhD
Brain Connectivity Laboratory, IRCCS San Raffaele Pisana
Institute of Neurology, Area of Neuroscience, Catholic University of The Sacred Heart
Search for more papers by this authorDr Francesco Iberite
Brain Connectivity Laboratory, IRCCS San Raffaele Pisana
Search for more papers by this authorDr Giordano Lacidogna
Neuropsychological Center, Catholic University of The Sacred Heart
Search for more papers by this authorDr Valeria Guglielmi
Neuropsychological Center, Catholic University of The Sacred Heart
Search for more papers by this authorDr Camillo Marra
Institute of Neurology, Area of Neuroscience, Catholic University of The Sacred Heart
Neuropsychological Center, Catholic University of The Sacred Heart
Search for more papers by this authorDr Patrizio Pasqualetti
Service of Medical Statistics and Information Technology, Fatebenefratelli Foundation for Health Research and Education, AFaR Division
Search for more papers by this authorDr Francesco Danilo Tiziano
Institute of Medical Genetics, Catholic University, Policlinic A. Gemelli Foundation
Search for more papers by this authorCorresponding Author
Prof Paolo Maria Rossini
Institute of Neurology, Area of Neuroscience, Catholic University of The Sacred Heart
Fondazione Policlinico Universitario A.Gemelli IRCCS, Rome, Italy
Address correspondence to Dr Rossini, Institute of Neurology, Area of Neuroscience, Catholic University, Policlinic A. Gemelli Foundation, Rome, Italy. E-mail: [email protected]Search for more papers by this authorDr Fabrizio Vecchio PhD
Brain Connectivity Laboratory, IRCCS San Raffaele Pisana
Search for more papers by this authorDr Francesca Miraglia PhD
Brain Connectivity Laboratory, IRCCS San Raffaele Pisana
Institute of Neurology, Area of Neuroscience, Catholic University of The Sacred Heart
Search for more papers by this authorDr Francesco Iberite
Brain Connectivity Laboratory, IRCCS San Raffaele Pisana
Search for more papers by this authorDr Giordano Lacidogna
Neuropsychological Center, Catholic University of The Sacred Heart
Search for more papers by this authorDr Valeria Guglielmi
Neuropsychological Center, Catholic University of The Sacred Heart
Search for more papers by this authorDr Camillo Marra
Institute of Neurology, Area of Neuroscience, Catholic University of The Sacred Heart
Neuropsychological Center, Catholic University of The Sacred Heart
Search for more papers by this authorDr Patrizio Pasqualetti
Service of Medical Statistics and Information Technology, Fatebenefratelli Foundation for Health Research and Education, AFaR Division
Search for more papers by this authorDr Francesco Danilo Tiziano
Institute of Medical Genetics, Catholic University, Policlinic A. Gemelli Foundation
Search for more papers by this authorCorresponding Author
Prof Paolo Maria Rossini
Institute of Neurology, Area of Neuroscience, Catholic University of The Sacred Heart
Fondazione Policlinico Universitario A.Gemelli IRCCS, Rome, Italy
Address correspondence to Dr Rossini, Institute of Neurology, Area of Neuroscience, Catholic University, Policlinic A. Gemelli Foundation, Rome, Italy. E-mail: [email protected]Search for more papers by this authorAbstract
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
Potential Conflicts of Interest
Nothing to report.
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