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Front Neurosci. 2016 Feb 23;10:47. doi: 10.3389/fnins.2016.00047. eCollection 2016.

Classification of Single Normal and Alzheimer's Disease Individuals from Cortical Sources of Resting State EEG Rhythms.

Author information

1
Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza"Rome, Italy; Department of Neuroscience, IRCCS San Raffaele PisanaRome, Italy.
2
Department of Clinical and Experimental Medicine, University of Foggia Foggia, Italy.
3
Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza" Rome, Italy.
4
Department of Electrical and Information Engineering, Polytechnic of Bari Bari, Italy.
5
Department of Integrated Imaging, IRCCS SDN - Istituto di Ricerca Diagnostica e NucleareNapoli, Italy; Department of Motor Sciences and Healthiness, University of Naples ParthenopeNaples, Italy.
6
Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging Troina, Italy.
7
Service of Clinical Neurophysiology (DiNOGMI; DipTeC), IRCCS Azienda Ospedaliera Universitaria San Martino - IST Genoa, Italy.
8
Dipartimento Emergenza e Trapianti d'Organi, University of Bari Bari, Italy.
9
Gerontology Research Group, Department of Medicine, Faculty of Health Sciences, University of A Coruña A Coruña, Spain.
10
Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. Panico Lecce, Italy.
11
Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. PanicoLecce, Italy; Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari "Aldo Moro"Bari, Italy.
12
Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. Panico Lecce, Italy.
13
Department of Imaging - Division of Radiology, Hospital "Di Venere" Bari, Italy.
14
Division of Neuroradiology, "F. Ferrari" Hospital Lecce, Italy.
15
Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari "Aldo Moro" Bari, Italy.
16
Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. PanicoLecce, Italy; Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari "Aldo Moro"Bari, Italy.
17
Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS Centro "S. Giovanni di Dio-F.B.F."Brescia, Italy; Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of GenevaGeneva, Switzerland.
18
Department of Integrated Imaging, IRCCS SDN - Istituto di Ricerca Diagnostica e Nucleare Napoli, Italy.

Abstract

Previous studies have shown abnormal power and functional connectivity of resting state electroencephalographic (EEG) rhythms in groups of Alzheimer's disease (AD) compared to healthy elderly (Nold) subjects. Here we tested the best classification rate of 120 AD patients and 100 matched Nold subjects using EEG markers based on cortical sources of power and functional connectivity of these rhythms. EEG data were recorded during resting state eyes-closed condition. Exact low-resolution brain electromagnetic tomography (eLORETA) estimated the power and functional connectivity of cortical sources in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz) were the frequency bands of interest. The classification rates of interest were those with an area under the receiver operating characteristic curve (AUROC) higher than 0.7 as a threshold for a moderate classification rate (i.e., 70%). Results showed that the following EEG markers overcame this threshold: (i) central, parietal, occipital, temporal, and limbic delta/alpha 1 current density; (ii) central, parietal, occipital temporal, and limbic delta/alpha 2 current density; (iii) frontal theta/alpha 1 current density; (iv) occipital delta/alpha 1 inter-hemispherical connectivity; (v) occipital-temporal theta/alpha 1 right and left intra-hemispherical connectivity; and (vi) parietal-limbic alpha 1 right intra-hemispherical connectivity. Occipital delta/alpha 1 current density showed the best classification rate (sensitivity of 73.3%, specificity of 78%, accuracy of 75.5%, and AUROC of 82%). These results suggest that EEG source markers can classify Nold and AD individuals with a moderate classification rate higher than 80%.

KEYWORDS:

Alzheimer's disease (AD); alpha rhythms; area under the receiver operating characteristic curve (AUROC); delta rhythms; electroencephalography (EEG); exact low-resolution brain electromagnetic tomography (eLORETA); lagged linear connectivity; spectral coherence

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