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      Electroencephalogram and Alzheimer's Disease: Clinical and Research Approaches

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          Abstract

          Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled “Cognitive Signal Processing Lab,” which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.

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          Diagnostic and statistical manual of mental disorders.

          Vijay A. Mittal, Elaine Walker (2011)
          Article 0 comments Cited 3519 times – based on 0 reviews     Review now
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            Über das Elektrenkephalogramm des Menschen

            Hans Berger (1929)
            Article 0 comments Cited 560 times – based on 0 reviews     Review now
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              EEG dynamics in patients with Alzheimer's disease.

              Jaeseung Jeong (2004)
              Alzheimer's disease (AD) is the most common neurodegenerative disorder characterized by cognitive and intellectual deficits and behavior disturbance. The electroencephalogram (EEG) has been used as a tool for diagnosing AD for several decades. The hallmark of EEG abnormalities in AD patients is a shift of the power spectrum to lower frequencies and a decrease in coherence of fast rhythms. These abnormalities are thought to be associated with functional disconnections among cortical areas resulting from death of cortical neurons, axonal pathology, cholinergic deficits, etc. This article reviews main findings of EEG abnormalities in AD patients obtained from conventional spectral analysis and nonlinear dynamical methods. In particular, nonlinear alterations in the EEG of AD patients, i.e. a decreased complexity of EEG patterns and reduced information transmission among cortical areas, and their clinical implications are discussed. For future studies, improvement of the accuracy of differential diagnosis and early detection of AD based on multimodal approaches, longitudinal studies on nonlinear dynamics of the EEG, drug effects on the EEG dynamics, and linear and nonlinear functional connectivity among cortical regions in AD are proposed to be investigated. EEG abnormalities of AD patients are characterized by slowed mean frequency, less complex activity, and reduced coherences among cortical regions. These abnormalities suggest that the EEG has utility as a valuable tool for differential and early diagnosis of AD.
              Article 0 comments Cited 327 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Int J Alzheimers Dis
                Journal ID (iso-abbrev): Int J Alzheimers Dis
                Journal ID (publisher-id): IJAD
                Title: International Journal of Alzheimer's Disease
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 2090-8024
                ISSN (Electronic): 2090-0252
                Publication date (Print): 2014
                Publication date (Electronic): 24 April 2014
                Volume: 2014
                Electronic Location Identifier: 349249
                Affiliations
                1Medical Physics Laboratory, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
                23rd Department of Neurology, Aristotle University of Thessaloniki, Exochi, 57010 Thessaloniki, Greece
                3Centre of Research and Technology, Information Technologies Institute, 6th Klm Charilaou-Thermi Road, P.O. Box 60361, Thermi, 57001 Thessaloniki, Greece
                Author notes

                Academic Editor: Francesco Panza

                Article
                DOI: 10.1155/2014/349249
                PMC ID: 4020452
                PubMed ID: 24868482
                SO-VID: 2023596f-81ac-4a1f-9f6d-f6eb80eec7cc
                Copyright © Copyright © 2014 Anthoula Tsolaki et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 15 January 2014
                Date accepted : 16 March 2014
                Funding
                Funded by: http://dx.doi.org/10.13039/501100000780 European Commission
                Award ID: FP7 2007–2013
                Categories
                Subject: Review Article

                ScienceOpen disciplines: Neurology
                Data availability:
                ScienceOpen disciplines: Neurology

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