Affiliations: [a] Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| [b] Department of Neurorehabilitation Sciences, Casa Cura Policlinico, Milano, Italy
| [c] Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di DioFatebenefratelli, Brescia, Italy
Correspondence:
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Correspondence to: Fabrizio Vecchio, PhD, Brain Connectivity Laboratory, IRCCS San Raffaele Pisana, Via Val Cannuta, 247, 00166 Rome, Italy. Tel.: +39 06 52253767; E-mails: fabrizio.vecchio@uniroma1.it, fabrizio.vecchio@sanraffaele.it.
Abstract: Background:Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer’s disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls cooperating with neuropsychological tests for an early diagnosis. Advanced network analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity and could be used in classification processes. If successfully reached, this goal would add a low-cost, easily accessible, and non-invasive technique with neuropsychological tests. Objective:To investigate the possibility to automatically classify physiological versus pathological aging from cortical sources’ connectivity based on a support vector machine (SVM) applied to EEG small-world parameter. Methods:A total of 295 subjects were recruited: 120 healthy volunteers and 175 AD. Graph theory functions were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA. A machine-learning classifier (SVM) was applied. EEG frequency bands were: delta (2–4 Hz), theta (4–8 Hz), alpha1 (8–10.5 Hz), alpha2 (10.5–13 Hz), beta1 (13–20 Hz), beta2 (20–30 Hz), and gamma (30–40 Hz). Results:The receiver operating characteristic curve showed AUC of 0.97±0.03 (indicating very high classification accuracy). The classifier showed 95% ±5% sensitivity, 96% ±3% specificity, and 95% ±3% accuracy for the classification. Conclusion:EEG connectivity analysis via a combination of source/connectivity biomarkers, highly correlating with neuropsychological AD diagnosis, could represent a promising tool in identification of AD patients. This approach represents a low-cost and non-invasive method, one that utilizes widely available techniques which, when combined, reach high sensitivity/specificity and optimal classification accuracy on an individual basis (0.97 of AUC).
Keywords: Alzheimer’s disease, delta and alpha bands, EEG, functional connectivity, graph theory, LORETA, machine learning classifier, small-world, support vector machine
