Date Published: December 31, 2015
Publisher: BioMed Central
Author(s): Florian Hatz, Martin Hardmeier, Nina Benz, Michael Ehrensperger, Ute Gschwandtner, Stephan Rüegg, Christian Schindler, Andreas U. Monsch, Peter Fuhr.
Electroencephalography (EEG) microstates and brain network are altered in patients with Alzheimer’s disease (AD) and discussed as potential biomarkers for AD. Microstates correspond to defined states of brain activity, and their connectivity patterns may change accordingly. Little is known about alteration of connectivity in microstates, especially in patients with amnestic mild cognitive impairment with stable or improving cognition within 30 months (aMCI).
Thirty-five outpatients with aMCI or mild dementia (mean age 77 ± 7 years, 47 % male, Mini Mental State Examination score ≥24) had comprehensive neuropsychological and clinical examinations. Subjects with cognitive decline over 30 months were allocated to the AD group, subjects with stable or improving cognition to the MCI-stable group. Results of neuropsychological testing at baseline were summarized in six domain scores. Resting state EEG was recorded with 256 electrodes and analyzed using TAPEEG. Five microstates were defined and individual data fitted. After phase transformation, the phase lag index (PLI) was calculated for the five microstates in every subject. Networks were reduced to 22 nodes for statistical analysis.
The domain score for verbal learning and memory and the microstate segmented PLI between the left centro-lateral and parieto-occipital regions in the theta band at baseline differentiated significantly between the groups. In the present sample, they separated in a logistic regression model with a 100 % positive predictive value, 60 % negative predictive value, 100 % specificity and 77 % sensitivity between AD and MCI-stable.
Combining neuropsychological and quantitative EEG test results allows differentiation between subjects with aMCI remaining stable and subjects with aMCI deteriorating over 30 months.
The online version of this article (doi:10.1186/s13195-015-0163-9) contains supplementary material, which is available to authorized users.
In the United Kingdom cognitive decline affects approximately 18 % in the elderly , and early classification of the underlying pathology and prognosis is difficult. As personalized medicine may gain importance in the future, the distinction between patients with a prodromal syndrome of neurodegenerative dementia and patients with other reasons for cognitive impairment, such as as depressive and sleep disorders or neurovascular diseases, becomes relevant. For early identification of dementia, the term mild cognitive impairment (MCI) was defined as a potential prodromal syndrome without significant impairment in activities of daily living. This term was replaced with mild neurocognitive disorder in the revised Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria . The term is unspecific as the disorder can be caused by various pathologies , and a considerable number of patients with MCI remain stable or improve over time . Diagnostic criteria for MCI due to Alzheimer’s disease were established [5, 6]. The rate of progression to AD in patients with MCI varies, depending on study design and definition of MCI. Maximally, 40 % of patients with amnestic (single or multiple domains) mild cognitive impairment (aMCI) at baseline progress to dementia within 2–3 years [7, 8]. However, novel treatment strategies require initiation of treatment at the earliest possible time ; therefore, the corroboration of the diagnosis and the identification of the cause of MCI are important.
A combination of qEEG with neuropsychological measures allows differentiation of patients with early AD from patients with aMCI who remain stable for 30 months with high sensitivity, specificity, and positive predictive value. For differentiation between beginning AD and other patients with MCI, the msPLI seemed to be superior to the PLI. This advantage is most probably explained by an increased signal-to-noise ratio. Microstates are believed to represent activity of different subnetworks of a global network [44, 45]. Using microstate segmentation, EEG periods reflecting the most active subnetworks were selectively included in the analysis, resulting in a more precise estimation of the averaged global network.
Integration of connectivity results and verbal learning and memory tests in a statistical model may allow for definition of cohorts of patients with MCI with an enhanced risk for AD, a stage at which clinical trials are most promising. The high positive predictive value of this model allows the definition of a patient cohort at great risk of fast cognitive deterioration at a time when they are only mildly affected.