Quantitative EEG Analysis in Patients with Mild Cognitive Impairment

Öz

Aim: The aim of this study was to compare the electrophysiological results of 25 patients with Alzheimer Disease (AD) and 30 patients with Mild Cognitive Impairment (MCI) with 25 age-, gender- and educationmatched Healthy Control (HC) subjects using spectral power analysis method. Materials and Methods: Resting EEG recordings were obtained with eyes closed for 4 minutes and with eyes open for 4 minutes. Electrode locations F3, Fz, F4, F7, F8, C3, Cz, C4, P3, Pz, P4, O1, O2, FP1, FP2 were preferred for analyses. Absolute power values of delta, theta, alpha, beta frequency bands were obtained after the preprocessing steps of the data by Fast Fourier Transform. Mann-Whitney U test was used for AD-MCI, AD-HC, MCIHC pairwise comparisons of alpha and theta channel measurements. Kruskal-Wallis test was used for triple comparisons of AD, MCI, HC. Results: In this study, consistent with previous published studies, an increase in theta power of FZ channels was observed in the AD participant group compared to the other participant groups (MCI and HC), with AD > MCI > HC. Conclusion: The absolute power values of the frequency bands with differences detected between the groups are sensitive in distinguishing MCI-AD cases for the theta power value in the FZ channel. This result reveals the importance of resting QEEG absolute power value analysis in diagnosing AD disease and detecting MCI at an early stage.

Anahtar Kelimeler

• Mild Cognitive Impairment
• Alzheimer
• Resting EEG
• QEEG

Kaynakça

1. 1. Babiloni C, Lizio R, Marzano N, Capotosto P, Soricelli A, Triggiani AI, Cordone S, Gesualdo L, Percio CD. Brain neural synchronisation and functional coupling in Alzheimer's disease as revealed by resting state EEG rhythms. International Journal of Psychophysiology. 2016;103:88-102. doi:10.1016/j.ijpsycho.2015.02.008.
2. 2. Jones HR. Netter's Neurology. Translation: Börü ÜT. Istanbul: Nobel Tıp Kitabevleri. Chapter 32, 2012.
3. 3. Valero EP, Gordo MÁL, Gutiérrez CM, Muñoz IC, Carrillo RMV. A self-drive approach for multi-class discrimination in Alzheimer's disease based on on wearable EEG. Computer Methods and Programs in Biomedicine. 2022; 220, 10684. doi:10.1016/j.cmpb.2022.106841.
4. 4. Ponomareva NV, Korovaitseva GI, Rogaev EI. EEG Alterations in Non-Demented Individuals Related to Apolipoprotein E Genotype and to Risk of Alzheimer's Disease. Neurobiology of Aging. 2008;(29):819–827. doi:10.1016/j.neurobiolaging.2006.12.019.
5. 5. Kaya Ö. Resting EEG Findings in Amnestic Mild Cognitive Impairment and Alzheimer's Disease Cases Comparison. Expertise Thesis. Faculty of Medicine: Dokuz Eylül University; 2019.
6. 6.Ataseven S. Hippocampus Volumes and Spectral Spontaneous EEG in Mild Cognitive Impairment Patients Investigation of the Relationship Between Characteristics. Master Thesis. Institute of Health Sciences: Istanbul University; 2014.
7. 7.Oltu B. Electroencephalography Signals in Healthy, Mild Cognitive Impairment and Alzheimer's Disease Classification. Master Thesis. Institute of Science and Technology: Baskent University; 2020. 8.Cassani R, Falk TH, Fraga FJ, Kanda PAMK, Anghinah, A. The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis. Frontiers in Aging Neuroscience :. 2014;61-13. Doi: 10.3389/fnagi.2014.00055.
8. 9. Yumerhodzha SM. Clinical and Electrophysiological Correlation in Thalamic Stroke Patients, Quantitative EEG and MR Tractography. Expertise Thesis. Faculty of Medicine: Istanbul University; 2014.

9. 10. Blackburn DJ, Zhao Y, Marco MD, Bell SM, He F, Wei HL, Lawrence S;...Sarrigiannis, PG. A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronisation in People with Alzheimer's Disease and Healthy Controls. Brain Sci. 2018; 8(134):1-19. Doi: 10.3390/brainsci8070134.
10. 11. Jelic J, Johansson SE, Almkvist O, Shigeta M, Julin P, Nordberg A, ...Wahlund; LO. Quantitative electroencephalography in mild cognitive impairment: longitudinal changes and possible prediction of Alzheimer's disease. Neurobiology of Aging. 2000;21: 533-540. Doi: 10.1016/s0197-4580(00)00153-6.
11. 12. Smailovic U, Koenig T, Laukka EJ, Kalpouzos G, Andersson T, Winblad B, Jelic V. EEG time signature in Alzheimer´s disease: Functional brain networks falling apart. NeuroImage:Clinical. 2019;24: 1-12. Doi: 10.1016/j.nicl.2019.102046.
12. 13. Lejko N, Larabi DI, Herrmannd CS, Aleman A, Cur ´ ciˇ -Blake B. Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis. Journal of Alzheimer's Disease. 2020;78: 1047-1088. Doi: 10.3233/JAD-200962.
13. 14. HADIYOSO, S., CYNTHIA, LFAR., MENGKO, TLER., ZAKARIA, H. Early Detection of Mild Cognitive Impairment Using Quantitative Analysis of EEG Signals. 2nd International Conference on Bioinformatics, Biotechnology and Biomedical Engineering (BioMIC)- Bioinformatics and Biomedical Engineering, 2019.
14. 15. Meghdadi AH, Karić MS, McConnell M, Rupp G, Richard C, Hamilton J,....Berka C. Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment. PLoS ONE. 2021;16(2): 1-31. Doi: 10.1371/journal.pone.0244180.
15. 16. Miranda, P, Alexander, M, Daney, S, Lakey, J. (2019). Overview of current diagnostic, prognostic, and therapeutic use of EEG and EEG-based markers of cognition, mental, and brain health. Integr Mol Med, 6, 1-9. Doi: 10.15761/IMM.1000378
16. 17.Smailovic U. and Jelic V. Neurophysiological Markers of Alzheimer's Disease: Quantitative EEG Approach Neurol Ther. 2019; 8, 37-55. Doi: 10.1007/s40120-019-00169-0
17. 18. Lehmann C, Koenig T, Jelic V, Prichep L, John RE, Wahlund LO, Dodge Y, Dierks T. Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG). Journal of Neuroscience Methods. 2007;161: 342-350. Doi: 10.1016/j.jneumeth.2006.10.023
18. 19.Schmidt MT, Kanda PAM, Basile LFH, Lopes HFS, Baratho R, Demario JLC; ....Anghinah, R. Index of alpha/theta ratio of the electroencephalogram: a new marker for Alzheimer's disease. Frontiers in Aging Neuroscience. 2013;5(60): 1-6. Doi: 10.3389/fnagi.2013.00060.
19. 20. Roh JH, Park MH, Ko D, Park KW, Lee DH, Han C, Jo SA, Yang KS, Jung KY. Region and frequency specific changes of spectral power in Alzheimer's disease and mild cognitive impairment. Clinical Neurophysiology . 2011;122(11): 2169-2176. Doi: 10.1016/j.clinph.2011.03.023.
20. 21.Bayrak AG. Comparison of Spontaneous EEG Findings and Neuropsychological Profiles of Early-Onset Alzheimer's Patients and Late-Onset Alzheimer's Patients. Master's Thesis. Institute of Health Sciences: Dokuz Eylül University; 2017.
21. 22. Abuiyada H.HS. EEG Signalling through Artificial Neural Networks for Early Diagnosis of Alzheimer's Disease Classification. Master Thesis. Institute of Health Sciences: Üsküdar University; 2022