Rodrigo San-Martin, Francisco J. Fraga*, Claudio Del Percio, Roberta Lizio, Giuseppe Noce, Flavio Nobili , Dario Arnaldi , Fabrizia D'Antonio, Carlo De Lena, Bahar Güntekin , Lutfu Hanoğlu , John Paul Taylor, Ian McKeith, Fabrizio Stocchi, Raffaele Ferri, Marco Onofrj, Susanna Lopez , Laura Bonanni and Claudio Babiloni Pages 956 - 969 ( 14 )
<p>Background: Early differentiation between Alzheimer’s disease (AD) and Dementia with Lewy Bodies (DLB) is important for accurate prognosis, as DLB patients typically show faster disease progression. Cortical neural networks, necessary for human cognitive function, may be disrupted differently in DLB and AD patients, allowing diagnostic differentiation between AD and DLB. <p> Objective: This proof-of-concept study assessed whether the application of machine learning techniques to data derived from resting-state electroencephalographic (rsEEG) rhythms (discriminant sensor power, 19 electrodes) and source connectivity (between five cortical regions of interest) allowed differentiation between DLB and AD. <p> Methods: Clinical, demographic, and rsEEG datasets from DLB patients (N=30), AD patients (N=30), and control seniors (NOld, N=30), matched for age, sex, and education, were taken from our international database. Individual (delta, theta, alpha) and fixed (beta) rsEEG frequency bands were included. The rsEEG features for the classification task were computed at both sensor and source levels. The source level was based on eLORETA freeware toolboxes for estimating cortical source activity and linear lagged connectivity. Fluctuations of rsEEG recordings (band-pass waveform envelopes of each EEG rhythm) were also computed at both sensor and source levels. After blind feature reduction, rsEEG features served as input to support vector machine (SVM) classifiers. Discrimination of individuals from the three groups was measured with standard performance metrics (accuracy, sensitivity, and specificity). <p> Results: The trained SVM two-class classifiers showed classification accuracies of 97.6% for NOld vs. AD, 99.7% for NOld vs. DLB, and 97.8% for AD vs. DLB. Three-class classifiers (AD vs. DLB vs. NOld) showed classification accuracy of 94.79%. <p> Conclusion: These promising preliminary results should encourage future prospective and longitudinal cross-validation studies using higher resolution EEG techniques and harmonized clinical procedures to enable the clinical application of these machine learning techniques.</p>
Alzheimer’s disease, lewy body dementia, EEG source connectivity, LORETA, machine learning, feature selection.
Center for Mathematics, Computation and Cognition, Federal University of the ABC, São Bernardo do Campo, Engineering, Modeling and Applied Social Sciences Center, Federal University of the ABC, Santo André, Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome, IRCCS SDN, Napoli, IRCCS SDN, Napoli, Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Department of Human Neurosciences, Sapienza University of Rome, Department of Human Neurosciences, Sapienza University of Rome, Department of Biophysics, School of Medicine; REMER Research Center, Istanbul Medipol University, Istanbul, Department of Neurology, School of Medicine, Istanbul Medipol University, Istanbul, Translational and Clinical Research Institute, Newcastle University, Newcastle, Translational and Clinical Research Institute, Newcastle University, Newcastle, Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Oasi Research Institute - IRCCS, Troina, Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome, Department of Medicine and Aging Sciences, University G. d’Annunzio of Chieti-Pescara, Chieti, Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome