Microstate connectivity alterations in patients with early Alzheimer's disease
Florian Hatz1, Martin Hardmeier2, Nina Benz3
1Department of Neurology, University Hospital of Basel, Petersgraben 4, 4031, Basel, Switzerland. florian.hatz@usb.ch.
Introduction:
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).
Methods:
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.
Results:
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.
Conclusions:
Combining neuropsychological and quantitative EEG test results allows differentiation between subjects with aMCI remaining stable and subjects with aMCI deteriorating over 30 months.
Insights
Electroencephalography (EEG) microstates and memory scores can differentiate Alzheimer's disease (AD) from stable mild cognitive impairment (MCI). This quantitative EEG analysis offers a promising biomarker for early AD detection and progression monitoring.
Area of Science:
- Neuroscience
- Biomarkers
- Cognitive Impairment
Background:
- Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) involve altered brain network connectivity.
- Electroencephalography (EEG) microstates are potential biomarkers for AD, but their connectivity changes in aMCI are understudied.
- Understanding microstate connectivity alterations is crucial for early diagnosis and prognosis in aMCI.
Purpose of the Study:
- To investigate alterations in EEG microstate connectivity in patients with aMCI.
- To identify potential biomarkers that differentiate between stable aMCI and aMCI progressing to AD.
- To assess the diagnostic and prognostic value of combining neuropsychological tests with quantitative EEG measures.
Main Methods:
- Recruited 35 outpatients with aMCI or mild dementia (MMSE ≥24).
- Classified subjects into AD or MCI-stable groups based on cognitive changes over 30 months.
- Analyzed resting-state EEG using TAPEEG, defined five microstates, and calculated the phase lag index (PLI) for microstate connectivity in the theta band.
Main Results:
- A specific verbal learning and memory domain score differentiated the groups.
- Microstate-segmented PLI in the theta band between left centro-lateral and parieto-occipital regions significantly differed between AD and MCI-stable groups.
- A logistic regression model combining these measures achieved 100% positive predictive value, 77% sensitivity, and 100% specificity in differentiating the groups.
Conclusions:
- Combining neuropsychological assessments with quantitative EEG microstate analysis can effectively differentiate between individuals with stable aMCI and those progressing towards AD.
- This approach holds promise as a non-invasive tool for early detection and monitoring of AD progression.
- Quantitative EEG microstate connectivity analysis represents a valuable biomarker for understanding cognitive decline in aMCI.
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