Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.8
2.7
Journals
Brain Sciences
Special Issues
M/EEG Analysis for Alzheimer’s Disease Diagnosis and Characterization
share announcement

M/EEG Analysis for Alzheimer’s Disease Diagnosis and Characterization

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 8703

Special Issue Editors

Prof. Dr. Jesús Poza

E-Mail Website
Guest Editor
Biomedical Engineering Group, University of Valladolid, C/Plaza de Santa Cruz, 8, 47002 Valladolid, Spain
Interests: Alzheimer’s disease; electroencephalography (EEG); magnetoencephalography (MEG); biomedical engineering; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carlos Gómez

E-Mail Website
Guest Editor
Biomedical Engineering Group, University of Valladolid, C/Plaza de Santa Cruz, 8, 47002 Valladolid, Spain
Interests: Alzheimer’s disease; electroencephalography (EEG); magnetoencephalography (MEG); biomedical engineering; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Dementia due to Alzheimer’s disease (AD) is a neurodegenerative disorder associated with cognitive impairment; behavior disorders; memory loss; and problems with abstract reasoning, planning, and decision making. As a consequence of the pathophysiological processes, neural activity from AD patients is progressively modified. In order to characterize these neural changes, electroencephalography (EEG) and magnetoencephalography (MEG) have gained prominence over the past few decades, mainly due to their ability to record the transient and rapid nature of brain activity.

The present Special Issue aims to gather original research studies that provide advances in the application of M/EEG for the characterization and classification of patients with AD and/or its prodromal stage, mild cognitive impairment (MCI).

Prof. Dr. Jesús Poza
Prof. Dr. Carlos Gómez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Alzheimer’s disease
  • Mild cognitive impairment
  • Electroencephalography
  • Magnetoencephalography
  • Spectral measures
  • Time-frequency analysis
  • Nonlinear methods
  • Synchronization measures
  • Graph theory parameters
  • Cross-frequency coupling
  • Classification techniques
  • Machine learning
  • Deep learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

11 pages, 969 KiB  
Article
Decreased Global EEG Synchronization in Amyloid Positive Mild Cognitive Impairment and Alzheimer’s Disease Patients—Relationship to APOE ε4
by Una Smailovic, Charlotte Johansson, Thomas Koenig, Ingemar Kåreholt, Caroline Graff and Vesna Jelic
Brain Sci. 2021, 11(10), 1359; https://doi.org/10.3390/brainsci11101359 - 16 Oct 2021
Cited by 6 | Viewed by 2488
Abstract
The apolipoprotein E (APOE) ε4 allele is a risk factor for Alzheimer’s disease (AD) that has been linked to changes in brain structure and function as well as to different biological subtypes of the disease. The present study aimed to investigate [...] Read more.
The apolipoprotein E (APOE) ε4 allele is a risk factor for Alzheimer’s disease (AD) that has been linked to changes in brain structure and function as well as to different biological subtypes of the disease. The present study aimed to investigate the association of APOE ε4 genotypes with brain functional impairment, as assessed by quantitative EEG (qEEG) in patients on the AD continuum. The study population included 101 amyloid positive patients diagnosed with mild cognitive impairment (MCI) (n = 50) and AD (n = 51) that underwent resting-state EEG recording and CSF Aβ42 analysis. In total, 31 patients were APOE ε4 non-carriers, 42 were carriers of one, and 28 were carriers of two APOE ε4 alleles. Quantitative EEG analysis included computation of the global field power (GFP) and global field synchronization (GFS) in conventional frequency bands. Amyloid positive patients who were carriers of APOE ε4 allele(s) had significantly higher GFP beta and significantly lower GFS in theta and beta bands compared to APOE ε4 non-carriers. Increased global EEG power in beta band in APOE ε4 carriers may represent a brain functional compensatory mechanism that offsets global EEG slowing in AD patients. Our findings suggest that decreased EEG measures of global synchronization in theta and beta bands reflect brain functional deficits related to the APOE ε4 genotype in patients that are on a biomarker-verified AD continuum. Full article
(This article belongs to the Special Issue M/EEG Analysis for Alzheimer’s Disease Diagnosis and Characterization)
►▼ Show Figures

Figure 1

31 pages, 2025 KiB  
Article
Robust EEG Based Biomarkers to Detect Alzheimer’s Disease
by Ali H. Al-Nuaimi, Marina Blūma, Shaymaa S. Al-Juboori, Chima S. Eke, Emmanuel Jammeh, Lingfen Sun and Emmanuel Ifeachor
Brain Sci. 2021, 11(8), 1026; https://doi.org/10.3390/brainsci11081026 - 31 Jul 2021
Cited by 21 | Viewed by 5211
Abstract
Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method [...] Read more.
Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity). Full article
(This article belongs to the Special Issue M/EEG Analysis for Alzheimer’s Disease Diagnosis and Characterization)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop