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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
Published on: June 27, 2013
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Evaluation of the Brain Function State During Mild Cognitive Impairment Based on Weighted Multiple Multiscale Entropy
1Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China.
Frontiers in Aging Neuroscience
|August 16, 2021
Summary
Neurofeedback training (NFT) improved mild cognitive impairment (MCI) brain function. A new Weighted Multiple Multiscale Entropy (WMMSE) algorithm showed significant EEG changes, indicating NFT
Area of Science:
- Neuroscience
- Biomedical Engineering
- Gerontology
Background:
- Mild cognitive impairment (MCI) is a critical stage for preventing Alzheimer's disease progression in older adults.
- Existing methods for assessing cognitive function in MCI may not fully capture subtle neurological changes.
- Neurofeedback training (NFT) is a potential intervention to enhance cognitive function in individuals with MCI.
Purpose of the Study:
- To evaluate the efficacy of neurofeedback training (NFT) in improving brain cognitive function in patients with mild cognitive impairment (MCI).
- To introduce and validate a novel algorithm, Weighted Multiple Multiscale Entropy (WMMSE), for analyzing electroencephalogram (EEG) features in MCI patients.
- To establish WMMSE as a potential biomarker for assessing cognitive function recovery in MCI.
Main Methods:
- An experimental group of 39 MCI patients underwent 10 days of NFT (two sessions daily).
- A control group of 21 MCI patients received no intervention.
- Electroencephalogram (EEG) data were analyzed using Lempel-Ziv complexity (LZC) and the novel Weighted Multiple Multiscale Entropy (WMMSE) algorithm.
Main Results:
- WMMSE values in specific EEG channels (F4, C3, C4, O1, T5) significantly increased post-NFT compared to pre-NFT (P < 0.05).
- Montreal Cognitive Assessment (MoCA) scores improved significantly in the majority of NFT-treated MCI patients (P < 0.05).
- The experimental group showed increased WMMSE values across all channels compared to the control group.
Conclusions:
- Neurofeedback training (NFT) is an effective intervention for promoting cognitive functional recovery in patients with mild cognitive impairment (MCI).
- The Weighted Multiple Multiscale Entropy (WMMSE) algorithm is a sensitive and reliable biomarker for evaluating the effectiveness of NFT and the cognitive status of MCI patients.
- NFT offers a promising non-pharmacological approach to potentially delay or prevent the progression to Alzheimer's disease.
