Literature DB >> 24214287

A promising method to distinguish vascular dementia from Alzheimer's disease with standardized low-resolution brain electromagnetic tomography and quantitative EEG.

Lei Wu, Lei Wu, Ying Chen, Jiong Zhou.   

Abstract

In clinical settings, it is difficult to distinguish Alzheimer's disease (AD) from vascular dementia (VD). The present study summarizes a clinical method to distinguish VD and AD at the early stage of the diseases. This study evaluated the possibility of differentiating 25 VD, 25 AD, and 25 healthy individuals (control, CN) by means of power spectral analysis and standardized low-resolution brain electromagnetic tomography (sLORETA) within alpha 1, alpha 2, beta 1, beta 2, delta, and theta frequency bands. Electroencephalogram (EEG) spectral analysis and sLORETA indicated that higher diffuse delta/theta and lower central/ posterior fast frequency bands were present in AD compared with CN. VD showed diffuse increased theta power compared with CN and lower delta than AD. AD also presented diffuse higher theta on spectral analysis and decreased alpha 2 and beta 1 values in central/temporal regions by sLORETA. Mini Mental State Examination (MMSE) scores were significantly associated with frontal alpha 1 sLORETA solutions (r = 0.91616, P < .001) and relative power (r = 0.87322, P < .01) in AD, but no correlations were found in VD. In conclusion, EEG spectral and sLORETA together could be a tool to distinguish the different EEG rhythmic activities in AD and VD.

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Year:  2014        PMID: 24214287     DOI: 10.1177/1550059413496779

Source DB:  PubMed          Journal:  Clin EEG Neurosci        ISSN: 1550-0594            Impact factor:   1.843


  3 in total

1.  Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment.

Authors:  Raymundo Cassani; Mar Estarellas; Rodrigo San-Martin; Francisco J Fraga; Tiago H Falk
Journal:  Dis Markers       Date:  2018-10-04       Impact factor: 3.434

2.  Mapping the Human Brain in Frequency Band Analysis of Brain Cortex Electroencephalographic Activity for Selected Psychiatric Disorders.

Authors:  Grzegorz M Wojcik; Jolanta Masiak; Andrzej Kawiak; Lukasz Kwasniewicz; Piotr Schneider; Nikodem Polak; Anna Gajos-Balinska
Journal:  Front Neuroinform       Date:  2018-10-24       Impact factor: 4.081

3.  Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal.

Authors:  Morteza Amini; MirMohsen Pedram; AliReza Moradi; Mahshad Ouchani
Journal:  Comput Math Methods Med       Date:  2021-04-23       Impact factor: 2.238

  3 in total

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