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

      research-article
      1 , 2 , * , 3 , 1 , 2 , 1 , 4 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 10 , 11 , 11 , 12 , 13 , 14 , 15 , 16 , 11 , 13 , 13 , 16 , 17 , 18 , 5
      Frontiers in Neuroscience
      Frontiers Media S.A.
      Alzheimer's disease (AD), electroencephalography (EEG), exact low-resolution brain electromagnetic tomography (eLORETA), spectral coherence, lagged linear connectivity, area under the receiver operating characteristic curve (AUROC), delta rhythms, alpha rhythms

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          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%.

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          Most cited references62

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          Development and validation of a geriatric depression screening scale: a preliminary report.

          A new Geriatric Depression Scale (GDS) designed specifically for rating depression in the elderly was tested for reliability and validity and compared with the Hamilton Rating Scale for Depression (HRS-D) and the Zung Self-Rating Depression Scale (SDS). In constructing the GDS a 100-item questionnaire was administered to normal and severely depressed subjects. The 30 questions most highly correlated with the total scores were then selected and readministered to new groups of elderly subjects. These subjects were classified as normal, mildly depressed or severely depressed on the basis of Research Diagnostic Criteria (RDC) for depression. The GDS, HRS-D and SDS were all found to be internally consistent measures, and each of the scales was correlated with the subject's number of RDC symptoms. However, the GDS and the HRS-D were significantly better correlated with RDC symptoms than was the SDS. The authors suggest that the GDS represents a reliable and valid self-rating depression screening scale for elderly populations.
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            EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis.

            Evidence is presented that EEG oscillations in the alpha and theta band reflect cognitive and memory performance in particular. Good performance is related to two types of EEG phenomena (i) a tonic increase in alpha but a decrease in theta power, and (ii) a large phasic (event-related) decrease in alpha but increase in theta, depending on the type of memory demands. Because alpha frequency shows large interindividual differences which are related to age and memory performance, this double dissociation between alpha vs. theta and tonic vs. phasic changes can be observed only if fixed frequency bands are abandoned. It is suggested to adjust the frequency windows of alpha and theta for each subject by using individual alpha frequency as an anchor point. Based on this procedure, a consistent interpretation of a variety of findings is made possible. As an example, in a similar way as brain volume does, upper alpha power increases (but theta power decreases) from early childhood to adulthood, whereas the opposite holds true for the late part of the lifespan. Alpha power is lowered and theta power enhanced in subjects with a variety of different neurological disorders. Furthermore, after sustained wakefulness and during the transition from waking to sleeping when the ability to respond to external stimuli ceases, upper alpha power decreases, whereas theta increases. Event-related changes indicate that the extent of upper alpha desynchronization is positively correlated with (semantic) long-term memory performance, whereas theta synchronization is positively correlated with the ability to encode new information. The reviewed findings are interpreted on the basis of brain oscillations. It is suggested that the encoding of new information is reflected by theta oscillations in hippocampo-cortical feedback loops, whereas search and retrieval processes in (semantic) long-term memory are reflected by upper alpha oscillations in thalamo-cortical feedback loops. Copyright 1999 Elsevier Science B.V.
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              Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain.

              This paper presents a new method for localizing the electric activity in the brain based on multichannel surface EEG recordings. In contrast to the models presented up to now the new method does not assume a limited number of dipolar point sources nor a distribution on a given known surface, but directly computes a current distribution throughout the full brain volume. In order to find a unique solution for the 3-dimensional distribution among the infinite set of different possible solutions, the method assumes that neighboring neurons are simultaneously and synchronously activated. The basic assumption rests on evidence from single cell recordings in the brain that demonstrates strong synchronization of adjacent neurons. In view of this physiological consideration the computational task is to select the smoothest of all possible 3-dimensional current distributions, a task that is a common procedure in generalized signal processing. The result is a true 3-dimensional tomography with the characteristic that localization is preserved with a certain amount of dispersion, i.e., it has a relatively low spatial resolution. The new method, which we call Low Resolution Electromagnetic Tomography (LORETA) is illustrated with two different sets of evoked potential data, the first showing the tomography of the P100 component to checkerboard stimulation of the left, right, upper and lower hemiretina, and the second showing the results for the auditory N100 component and the two cognitive components CNV and P300. A direct comparison of the tomography results with those obtained from fitting one and two dipoles illustrates that the new method provides physiologically meaningful results while dipolar solutions fail in many situations. In the case of the cognitive components, the method offers new hypotheses on the location of higher cognitive functions in the brain.

                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                23 February 2016
                2016
                : 10
                : 47
                Affiliations
                [1] 1Department of Physiology and Pharmacology “Vittorio Erspamer”, University of Rome “La Sapienza” Rome, Italy
                [2] 2Department of Neuroscience, IRCCS San Raffaele Pisana Rome, Italy
                [3] 3Department of Clinical and Experimental Medicine, University of Foggia Foggia, Italy
                [4] 4Department of Electrical and Information Engineering, Polytechnic of Bari Bari, Italy
                [5] 5Department of Integrated Imaging, IRCCS SDN - Istituto di Ricerca Diagnostica e Nucleare Napoli, Italy
                [6] 6Department of Motor Sciences and Healthiness, University of Naples Parthenope Naples, Italy
                [7] 7Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging Troina, Italy
                [8] 8Service of Clinical Neurophysiology (DiNOGMI; DipTeC), IRCCS Azienda Ospedaliera Universitaria San Martino - IST Genoa, Italy
                [9] 9Dipartimento Emergenza e Trapianti d'Organi, University of Bari Bari, Italy
                [10] 10Gerontology Research Group, Department of Medicine, Faculty of Health Sciences, University of A Coruña A Coruña, Spain
                [11] 11Department of Clinical Research in Neurology, University of Bari “Aldo Moro”, Pia Fondazione Cardinale G. Panico Lecce, Italy
                [12] 12Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari “Aldo Moro” Bari, Italy
                [13] 13Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari “Aldo Moro”, Pia Fondazione Cardinale G. Panico Lecce, Italy
                [14] 14Department of Imaging - Division of Radiology, Hospital “Di Venere” Bari, Italy
                [15] 15Division of Neuroradiology, “F. Ferrari” Hospital Lecce, Italy
                [16] 16Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro” Bari, Italy
                [17] 17Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS Centro “S. Giovanni di Dio-F.B.F.” Brescia, Italy
                [18] 18Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva Geneva, Switzerland
                Author notes

                Edited by: Fernando Maestú, Complutense University, Spain

                Reviewed by: José A. Pineda-Pardo, Center for Biomedical Technology, Spain; Emmanuel Chigozie Ifeachor, Plymouth University, UK

                *Correspondence: Claudio Babiloni claudio.babiloni@ 123456uniroma1.it

                This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2016.00047
                4763025
                26941594
                ed10088e-6160-44dd-86db-0b9d831417a0
                Copyright © 2016 Babiloni, Triggiani, Lizio, Cordone, Tattoli, Bevilacqua, Soricelli, Ferri, Nobili, Gesualdo, Millán-Calenti, Buján, Tortelli, Cardinali, Barulli, Giannini, Spagnolo, Armenise, Buenza, Scianatico, Logroscino, Frisoni and del Percio.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 August 2015
                : 02 February 2016
                Page count
                Figures: 7, Tables: 8, Equations: 0, References: 81, Pages: 18, Words: 12420
                Categories
                Psychiatry
                Original Research

                Neurosciences
                alzheimer's disease (ad),electroencephalography (eeg),exact low-resolution brain electromagnetic tomography (eloreta),spectral coherence,lagged linear connectivity,area under the receiver operating characteristic curve (auroc),delta rhythms,alpha rhythms

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