AI Article Synopsis

  • Alzheimer's Disease (AD) is the most common form of dementia, posing significant health and economic challenges; understanding its neural dynamics is crucial for effective treatment.
  • The study analyzed resting-state EEG recordings from 111 subjects, including patients with AD, those with mild cognitive impairment (MCI), and healthy controls, focusing on patterns of brain connectivity using Cross-Approximate Entropy (Ca) and Cross-Sample Entropy (Cs).
  • Results indicated that Ca was more effective than Cs in revealing significant differences in brain connectivity, with AD patients showing lower connectivity in specific frequency bands, particularly the β band, suggesting its potential as an early biomarker for AD.

Article Abstract

Alzheimer's Disease (AD) represents the most prevalent form of dementia and is considered a major health problem due to its high prevalence and its economic costs. An accurate characterization of the underlying neural dynamics in AD is crucial in order to adopt effective treatments. In this regard, mild cognitive impairment (MCI) is an important clinical entity, since it is a risk-state for developing dementia. In the present study, coupling patterns of 111 resting-state electroencephalography (EEG) recordings were analyzed. Specifically, we computed Cross-Approximate Entropy () and Cross-Sample Entropy () of 37 patients with dementia due to AD, 37 subjects with MCI, and 37 healthy control (HC) subjects. Our results showed that outperformed , revealing higher number of significant connections among the three groups (Kruskal-Wallis test, FDR-corrected -values < 0.05). AD patients exhibited statistically significant lower similarity values at θ and β frequency bands compared to HC. MCI is also characterized by a global decrease of similarity in all bands, being only significant at β. These differences shows that β band might play a significant role in the identification of early stages of AD. Our results suggest that could increase the insight into brain dynamics at different AD stages. Consequently, it may contribute to develop early AD biomarkers, potentially useful as diagnostic information.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6232874PMC
http://dx.doi.org/10.3389/fninf.2018.00076DOI Listing

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