Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-30T19:40:32.176Z Has data issue: false hasContentIssue false
  • English
  • Français

EEG beta 2 power as surrogate marker for memory impairment: a pilot study

Published online by Cambridge University Press:  22 May 2017

Andreas K. Kaiser ,
Michael Doppelmayr  and
Bernhard Iglseder
Andreas K. Kaiser*
Affiliation:
Department of Geriatric Medicine, Salzburger Landeskliniken Betriebs GesmbH, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Salzburg, Austria Department of Clinical Psychology, Salzburger Landeskliniken Betriebs GesmbH, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Salzburg, Austria
Michael Doppelmayr
Affiliation:
Department for Sport Sciences, University Mainz, Mainz, Germany Center for Neurocognitive Research, University of Salzburg, Salzburg, Austria
Bernhard Iglseder
Affiliation:
Department of Geriatric Medicine, Salzburger Landeskliniken Betriebs GesmbH, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Salzburg, Austria
*
Correspondence should be addressed to: Andreas K. Kaiser, University Hospital Salzburg – Christian-Doppler-Klinik, Paracelsus Medical University, Ignaz-Harrer-Strasse 79, 5020 Salzburg, Austria. Phone: +43 (0)57255-33101. Email: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Background:

Memory deficits are dominant in dementia and are positively correlated with electroencephalographic (EEG) beta power. EEG beta power can predict the progression of Alzheimer´s (AD) as early as at the stage of mild cognitive impairment (MCI) and could possibly be used as surrogate marker for memory impairment. The objective of this study is to analyze the relationship between frontal and parietal EEG beta power and memory-test outcome. Frontal and parietal beta power is analyzed for a resting state and an eyes-closed backward counting condition and related to memory impairment parameters.

Methods:

A total of 28 right-handed female geriatric patients (mean age = 80.6) participated voluntarily in this study. Beta 1 (12.9–19.2 Hz) and beta 2 (19.2–32.4 Hz) EEG power at F3, F4, Fz, P3, P4, and Pz are correlated with immediate wordlist recall, delayed wordlist recall, recognition of learned words, and delayed figure recall. For classification between impaired and intact memory, we calculated a binary logistic regression model with memory impairment as a dependent variable and beta 2 power as an independent variable.

Results:

We found significant positive correlations between frontal and parietal beta power and delayed memory recall. A significant correlation (Bonferroni correction, p < 0.05) was found at F4 beta 2 during backward counting. The binary logistic regression model with F4 beta 2 power during the counting condition as a predictor yielded a sensitivity of 76.9% (95% CI) and a specificity of 73.3% (95% CI) for classifying patients into “verbal-memory impaired” and “intact.”

Conclusions:

EEG beta 2 power recorded during a backward counting condition with eyes closed can be used as surrogate marker for verbal memory impairment in geriatric patients. Antidepressant treatment was correlated with EEG data in resting state but not in counting condition. Further studies are necessary to verify the results of this pilot study.

Keywords

EEGbeta powerMCImemorygeriatric medicine
Type
Research Article
Information
International Psychogeriatrics , Volume 29 , Issue 9 , September 2017 , pp. 1515 - 1523
Copyright
Copyright © International Psychogeriatric Association 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Al-Qazzaz, N. K., Ali, S. H., Ahmad, S. A., Chellappan, K., Islam, M. S. and Escudero, J. (2014). Role of EEG as biomarker in the early detection and classification of dementia. ScientificWorldJournal, 2014, 906038.CrossRefGoogle ScholarPubMed
American Psychiatric Association (2000). Diagnostic and Statistical Manual-Text Revision (DSM-IV-TRim, 2000). Washington, DC: American Psychiatric Association.Google Scholar
Babiloni, C. et al. (2004). Cortical alpha rhythms in mild Alzheimer's disease. A multicentric EEG study. International Congress Series, 1270, 44–49.Google Scholar
Chandler, M. J. et al. (2005). A total score for the CERAD neuropsychological battery. Neurology, 65, 102–106.CrossRefGoogle ScholarPubMed
Claus, J. J. et al. (1998). Slowing on quantitative spectral EEG is a marker for rate of subsequent cognitive and functional decline in early Alzheimer disease. Alzheimer Disease and0 Associated Disorders, 12, 167–174.CrossRefGoogle ScholarPubMed
Dilling, H., Mombour, W., Schmidt, M. H. and World Health Organization. (1991). Internationale Klassifikation psychischer Störungen: ICD-10, Kapitel V (F, klinisch-diagnostische Leitlinien.Google Scholar
Folstein, M. F., Folstein, S. E. and McHugh, P. R. (1975). “Mini-mental state” : a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.Google Scholar
Gola, M., Kaminski, J., Brzezicka, A. and Wrobel, A. (2012). Beta band oscillations as a correlate of alertness–changes in aging. International Journal of Psychophysiology, 85, 62–67.CrossRefGoogle ScholarPubMed
Gu, Y., Chen, J., Lu, Y. and Pan, S. (2016). Integrative frequency power of EEG correlates with progression of mild cognitive impairment to dementia in Parkinson's disease. Clinical EEG and Neuroscience, 47 (2), 113–117.Google Scholar
Guntekin, B., Emek-Savas, D. D., Kurt, P., Yener, G. G. and Basar, E. (2013). Beta oscillatory responses in healthy subjects and subjects with mild cognitive impairment. Neuroimage Clinical, 3, 39–46.Google Scholar
Hanslmayr, S., Staudigl, T. and Fellner, M. C. (2012). Oscillatory power decreases and long-term memory: the information via desynchronization hypothesis. Frontiers in Human Neuroscience, 6, 74.CrossRefGoogle ScholarPubMed
Jobert, M., Wilson, F. J., Ruigt, G. S., Brunovsky, M., Prichep, L. S. and Drinkenburg, W. H. (2012). Guidelines for the recording and evaluation of pharmaco-EEG data in man: the International Pharmaco-EEG Society (IPEG). Neuropsychobiology, 66, 201–220.Google Scholar
Katz, R. (2004). Biomarkers and surrogate markers: an FDA perspective. NeuroRx, 1, 189–195.Google Scholar
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews, 29, 169–195.Google Scholar
Liu, J., Lai, T., Mu, K. J. and Zhou, Z. (2013). Electroencephalogram beta power assay: a promising diagnosis tool of cognitive impairment in early time after cerebral hemorrhage. Neurology India, 61, 472–477.Google Scholar
McBride, J. et al. (2014). Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease. Computer Methods and Programs in Biomedicine, 114, 153–163.Google Scholar
Moretti, D. V. et al. (2011). MCI patients’ EEGs show group differences between those who progress and those who do not progress to AD. Neurobiology of Aging, 32, 563–571.CrossRefGoogle Scholar
Moretti, D. V., Paternicò, D., Binetti, G., Zanetti, O. and Frisoni, G. B. (2012). EEG markers are associated to gray matter changes in thalamus and basal ganglia in subjects with mild cognitive impairment. NeuroImage, 60, 489–496.Google Scholar
Morris, J. C., Mohs, R. C., Rogers, H., Fillenbaum, G. and Heymann, A. (1988). Consortium to establish a registry for Alzheimer´s disease. Psychopharmacology Bulletin, 24, 641–652.Google Scholar
Olde Dubbelink, K. T. et al. (2014). Predicting dementia in Parkinson disease by combining neurophysiologic and cognitive markers. Neurology, 82, 263–270.Google Scholar
Petersen, R. C., Caracciolo, B., Brayne, C., Gauthier, S., Jelic, V. and Fratiglioni, L. (2014). Mild cognitive impairment: a concept in evolution. Journal of Internal Medicine, 275, 214–228.CrossRefGoogle ScholarPubMed
Petersen, R. C. et al. (2010). Alzheimer's disease neuroimaging initiative (ADNI): clinical characterization. Neurology, 74, 201–209.CrossRefGoogle Scholar
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G. and Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Archives of Neurology, 56, 303–308.CrossRefGoogle ScholarPubMed
Pijnenburg, Y. A., v d Made, Y., van Cappellen van Walsum, A. M., Knol, D. L., Scheltens, P. and Stam, C. J. (2004). EEG synchronization likelihood in mild cognitive impairment and Alzheimer's disease during a working memory task. Clinical Neurophysiology, 115, 1332–1339.Google Scholar
Poil, S. S., de Haan, W., van der Flier, W. M., Mansvelder, H. D., Scheltens, P. and Linkenkaer-Hansen, K. (2013). Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage. Frontiers in Aging Neuroscience, 5, 58.CrossRefGoogle ScholarPubMed
Sheikh, J. I. and Yesavage, J. A. (1986). Geriatric depression scale (GDS). Recent evidence and development of a shorter version. In Brink, T. L. (ed.), Clinical Gerontology: A Guide to Assessment and Intervention (pp. 165–173). NY: The Haworth Press, Inc.Google Scholar
Sollberger, M. and Monsch, A. (2007). Diagnostic manual for dementia. University Hospital Basel: Memory Clinic-Neuropsychology Center.Google Scholar
The Lancet, N. (2015). Societies can both grow old and lower dementia burden. Lancet Neurology, 14, 967.Google Scholar
van der Hiele, K. et al. (2007). EEG correlates in the spectrum of cognitive decline. Clinical Neurophysiology, 118, 1931–1939.Google Scholar
Vecchio, F. et al. (2013). Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review. Supplements to Clinical Neurophysiology, 62, 223–236.CrossRefGoogle ScholarPubMed
Wang, Y., Zhang, X., Huang, J., Zhu, M., Guan, Q. and Liu, C. (2013). Associations between EEG beta power abnormality and diagnosis in cognitive impairment post cerebral infarcts. Journal of Molecular Neuroscience, 49, 632–638.Google Scholar
Zigmond, A. S. and Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67, 361–370.Google Scholar