Evaluation of the Brain Function State During Mild Cognitive Impairment Based on Weighted Multiple Multiscale Entropy.

Yi Liu,Wei Cui,Rui Su,Ping Xie,Xin Li,Ying Han

doi:10.3389/fnagi.2021.625081

Abstract

The mild cognitive impairment (MCI) stage plays an essential role in preventing the progression of older adults to Alzheimer's disease. In this study, neurofeedback training (NFT) is applied to improve MCI brain cognitive function. To assess the improvement effect, a novel algorithm called Weighted Multiple Multiscale Entropy (WMMSE) is proposed to extract and analyze the electroencephalogram (EEG) features of patients with MCI. To overcome the information loss problem of traditional multiscale entropy (MSE), WMMSE fully considered the correlation of the sequence and the contribution of each sequence to the total entropy. The experimental group composed of 39 patients with MCI was subjected to NFT for 10 days during two sessions. The control group included 21 patients with MCI without any intervention. The Lempel-Ziv complexity (LZC) was used for primary assessment, and WMMSE was used to accurately analyze the effect of NFT. The results show that the WMMSE values of F4, C3, C4, O1, and T5 channels post-NFT are higher compared with pre-NFT and significant differences (P < 0.05). Moreover, the cognitive subscale of the Montreal Cognitive Assessment (MoCA) results shows that the post-NFT score is higher than the pre-NFT in the vast majority of the patients with MCI and significant differences (P < 0.05). When compared with the control group, the WMMSE values of the experimental group increased in each channel. Therefore, the NFT intervention method contributes to brain cognitive functional recovery, and WMMSE can be used as a biomarker to evaluate the state of MCI brain cognitive function.

Highlights

Mild cognitive impairment (MCI) is an early stage of Alzheimer’s disease (AD), and it is a critical target for preventing the progression to AD in older adults (Cheng et al, 2015; Li Y. et al, 2019)
The results indicated that neurofeedback training (NFT) and Weighted Multiple Multiscale Entropy (WMMSE) may improve the cognitive function of patients with mild cognitive impairment (MCI)
We proposed the WMMSE method to explore the characteristics of the EEG signal of patients with MCI after NFT

Summary

Introduction

Mild cognitive impairment (MCI) is an early stage of Alzheimer’s disease (AD), and it is a critical target for preventing the progression to AD in older adults (Cheng et al, 2015; Li Y. et al, 2019). In recent studies, increasing attention has been paid to neurofeedback training (NFT), which has improved brain dysfunction and clinical symptoms (Wang and Hsieh, 2013). Few studies have applied NFT in MCI rehabilitation. Patients with AD receiving NFT possess stable cognitive function and can enhance their information recognition and memory (Luijmes et al, 2016). The peak frequency of the alpha band of all subjects increased significantly after NFT, indicating that NFT could improve attention and cognitive function (Liu et al, 2014). NFT, as a training technique, succeeded in decreasing the ratio of theta/alpha power of patients with MCI and improving cognitive functions. This study demonstrated that NFT could be used as a rehabilitation training method to improve the memory and attention of patients with MCI (Jirayucharoensak et al, 2014).

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Results

Discussion

Conclusion