Quantitative EEG Spectral and Connectivity Analysis for Cognitive Decline in Amnestic Mild Cognitive Impairment.

Alzheimer’s disease electroencephalography mild cognitive impairment neurocognitive disorders spectrum analysis

Journal

Journal of Alzheimer's disease : JAD
ISSN: 1875-8908
Titre abrégé: J Alzheimers Dis
Pays: Netherlands
ID NLM: 9814863

Informations de publication

Date de publication:
08 Jan 2024
Historique:
medline: 13 1 2024
pubmed: 13 1 2024
entrez: 13 1 2024
Statut: aheadofprint

Résumé

Mild cognitive impairment (MCI) is considered to be the borderline of cognitive changes associated with aging and very early dementia. Cognitive functions in MCI can improve, remain stable or progress to clinically probable AD. Quantitative electroencephalography (qEEG) can become a useful tool for using the analytical techniques to quantify EEG patterns indicating cognitive impairment. The aim of our study was to assess spectral and connectivity analysis of the EEG resting state activity in amnestic MCI (aMCI) patients in comparison with healthy control group (CogN). 30 aMCI patients and 23 CogN group, matched by age and education, underwent equal neuropsychological assessment and EEG recording, according to the same protocol. qEEG spectral analysis revealed decrease of global relative beta band power and increase of global relative theta and delta power in aMCI patients. Whereas, decreased coherence in centroparietal right area considered to be an early qEEG biomarker of functional disconnection of the brain network in aMCI patients. In conclusion, the demonstrated changes in qEEG, especially, the coherence patterns are specific biomarkers of cognitive impairment in aMCI. Therefore, qEEG measurements appears to be a useful tool that complements neuropsychological diagnostics, assessing the risk of progression and provides a basis for possible interventions designed to improve cognitive functions or even inhibit the progression of the disease.

Sections du résumé

BACKGROUND BACKGROUND
Mild cognitive impairment (MCI) is considered to be the borderline of cognitive changes associated with aging and very early dementia. Cognitive functions in MCI can improve, remain stable or progress to clinically probable AD. Quantitative electroencephalography (qEEG) can become a useful tool for using the analytical techniques to quantify EEG patterns indicating cognitive impairment.
OBJECTIVE OBJECTIVE
The aim of our study was to assess spectral and connectivity analysis of the EEG resting state activity in amnestic MCI (aMCI) patients in comparison with healthy control group (CogN).
METHODS METHODS
30 aMCI patients and 23 CogN group, matched by age and education, underwent equal neuropsychological assessment and EEG recording, according to the same protocol.
RESULTS RESULTS
qEEG spectral analysis revealed decrease of global relative beta band power and increase of global relative theta and delta power in aMCI patients. Whereas, decreased coherence in centroparietal right area considered to be an early qEEG biomarker of functional disconnection of the brain network in aMCI patients. In conclusion, the demonstrated changes in qEEG, especially, the coherence patterns are specific biomarkers of cognitive impairment in aMCI.
CONCLUSIONS CONCLUSIONS
Therefore, qEEG measurements appears to be a useful tool that complements neuropsychological diagnostics, assessing the risk of progression and provides a basis for possible interventions designed to improve cognitive functions or even inhibit the progression of the disease.

Identifiants

pubmed: 38217593
pii: JAD230485
doi: 10.3233/JAD-230485
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Katarzyna Zawiślak-Fornagiel (K)

Department of Neurology, Prof. Kornel Gibiński Clinical University Center, Medical University of Silesia, Katowice, Poland.

Daniel Ledwoń (D)

Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland.

Monika Bugdol (M)

Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland.

Anna Grażyńska (A)

Department of Imaging Diagnostics and Interventional Radiology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland.

Maciej Ślot (M)

Department of Solid State Physics, Faculty of Physics and Applied Computer Science, University of Łódź, Łódź, Poland.

Justyna Tabaka-Pradela (J)

Department of Neurology, Prof. Kornel Gibiński Clinical University Center, Medical University of Silesia, Katowice, Poland.

Izabela Bieniek (I)

Department of Neurology, Prof. Kornel Gibiński Clinical University Center, Medical University of Silesia, Katowice, Poland.

Joanna Siuda (J)

Department of Neurology, Prof. Kornel Gibiński Clinical University Center, Medical University of Silesia, Katowice, Poland.
Department of Neurology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland.

Classifications MeSH