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Quantitative EEG and apolipoprotein E-genotype improve classification of patients with suspected Alzheimer's disease

Hatz, F. and Benz, N. and Hardmeier, M. and Zimmermann, R. and Rueegg, S. and Schindler, C. and Miserez, A. R. and Gschwandtner, U. and Monsch, A. U. and Fuhr, P.. (2013) Quantitative EEG and apolipoprotein E-genotype improve classification of patients with suspected Alzheimer's disease. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, Vol. 124, H. 11. pp. 2146-2152.

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Official URL: http://edoc.unibas.ch/dok/A6194550

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Abstract

To establish a model for better identification of patients in very early stages of Alzheimer's disease, AD (including patients with amnestic MCI) using high-resolution EEG and genetic data.; A total of 26 patients in early stages of probable AD and 12 patients with amnestic MCI were included. Both groups were similar in age and education. All patients had a comprehensive neuropsychological examination and a high resolution EEG. Relative band power characteristics were calculated in source space (LORETA inverse solution for spectral data) and compared between groups. A logistic regression model was calculated including relative band-power at the most significant location, ApoE status, age, education and gender.; Differences in the delta band at 34 temporo-posterior source locations (p>.01) between AD and MCI groups were detected after correction for multiple comparisons. Classification slightly increased when ApoE status was added (p=.06 maximum likelihood test). Adjustment of analyses for the confounding factors age, gender and education did not alter results.; Quantitative EEG (qEEG) separates between patients with amnestic MCI and patients in early stages of probable AD. Adding information about Apo ε4 allele frequency slightly enhances diagnostic accuracy.; qEEG may help identifying patients who are candidates for possible benefit from future disease modifying treatments.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Biostatistics > Biostatistics Frequentist Modelling
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
UniBasel Contributors:Schindler, Christian
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Elsevier
ISSN:1388-2457
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:23 May 2014 08:34
Deposited On:23 May 2014 08:34

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