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Mohammad Javad Sedghizadeh1, Hamid Aghajan2, Zahra Vahabi3,4
1Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
Abstract:
Mild cognitive impairment (MCI) is known as an early stage of cognitive decline. Amnestic MCI (aMCI) is considered as the preliminary stage of dementia which may progress to Alzheimer's disease (AD). While some aMCI patients may stay in this condition for years, others might develop dementia associated with AD. Early detection of MCI allows for potential treatments to prevent or decelerate the process of developing dementia. Standard methods of diagnosing MCI and AD employ structural (imaging), behavioral (cognitive tests), and genetic or molecular (blood or CSF tests) techniques. Our study proposes network-level neural synchronization parameters as topographical markers for diagnosing aMCI and AD. We conducted a pilot study based on EEG data recorded during an olfactory task from a group of elderly participants consisting of healthy individuals and patients of aMCI and AD to assess the value of different indicators of network-level phase and amplitude synchronization in differentiating the three groups. Significant differences were observed in the percent phase locking value, theta-gamma phase-amplitude coupling, and amplitude coherence between the groups, and classifiers were developed to differentiate the three groups based on these parameters. The observed differences in these indicators of network-level functionality of the brain can help explain the underlying processes involved in aMCI and AD.
Insights
Researchers identified new brainwave synchronization markers for early detection of mild cognitive impairment (MCI) and Alzheimer
Area of Science:
- Neuroscience
- Cognitive Science
- Biomedical Engineering
Background:
- Mild cognitive impairment (MCI) represents an early stage of cognitive decline.
- Amnestic MCI (aMCI) is a precursor to Alzheimer's disease (AD), necessitating early diagnostic methods.
- Current diagnostic tools for MCI and AD include imaging, cognitive tests, and molecular analyses.
Purpose of the Study:
- To explore network-level neural synchronization parameters as potential topographical markers for diagnosing aMCI and AD.
- To assess the efficacy of phase and amplitude synchronization indicators in differentiating between healthy individuals, aMCI patients, and AD patients.
Main Methods:
- A pilot study utilized EEG data recorded during an olfactory task.
- Participants included healthy elderly individuals, aMCI patients, and AD patients.
- Analysis focused on network-level phase and amplitude synchronization parameters.
Main Results:
- Significant differences were found in percent phase locking value, theta-gamma phase-amplitude coupling, and amplitude coherence between the groups.
- Classifiers were successfully developed to distinguish between healthy, aMCI, and AD participants based on these neural synchronization parameters.
Conclusions:
- Network-level neural synchronization parameters show promise as objective biomarkers for diagnosing aMCI and AD.
- These findings offer insights into the neural underpinnings of cognitive decline in aMCI and AD.
- This approach may lead to improved early detection and management strategies for dementia.
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