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

Highlights

• Quantitative EEG (qEEG) distinguishes patients with mild cognitive impairment and patients with dementia due to Alzheimer’s disease in the earliest stages of cognitive decline (i.e., MMSE > 23/30 in both groups).

• LORETA enables localization of defined areas differentiating between disease stages.

• qEEG may be a robust and objective marker for disease stage.

Abstract

Objective

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.

Methods

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.

Results

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.

Conclusions

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.

Significance

qEEG may help identifying patients who are candidates for possible benefit from future disease modifying treatments.

Introduction

According to the revised diagnostic criteria for Mild Cognitive Impairment (MCI; Albert et al., 2011) and dementia due to Alzheimer’s disease (AD; McKhann et al., 1984), the only distinctive clinical feature between these two entities consists in interference of cognitive deficits with activities of daily living (ADL). This differentiation depends on the appraisal of the patient’s social functioning rather than on biomedical facts and, therefore, it is subjective to a large extent. Moreover, MCI can be caused by various pathologies (Winblad et al., 2004) and only one third of MCI patients exhibit AD dementia in the near future (i.e., 3 years; Duara et al., 2011). With the possibility of disease modifying treatments for AD (Ballard et al., 2011, Bové et al., 2011, Wagner et al., 2012) correct identification of AD pathology as the cause of MCI becomes critical. Objective and easily available methods for early identification of AD are needed. Although the clinical diagnosis of dementia depends on the results of a comprehensive neuropsychological assessment, biomarkers are under investigation to facilitate and improve diagnostic accuracy. Atrophy of medial temporal structures is an example of a possible marker for early AD (Frisoni et al., 2010) and in combination with CSF-analysis (pospho-tau, Abeta 1–42), genetic load of ε4 alleles in the apolipoprotein E (Apo E4; Elias-Sonnenschein et al., 2011), and PET-imaging (Herholz et al., 2007) may increase diagnostic accuracy. Quantitative analysis of electroencephalography (qEEG) is another promising biomarker, as its alteration is associated with cognitive dysfunction and may differentiate between disease stages in AD (Babiloni et al., 2007, Roh et al., 2011, van der Hiele et al., 2007).

The present study aims to evaluate the diagnostic power of a high resolution qEEG system (257 electrodes) alone and in combination with genetic ApoE status regarding diagnosis of MCI versus early AD.