Su Rui, Li Xin, Liu Yi, Cui Wei, Xie Ping, Han Ying
Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China.
Handan First Central Hospital, Handan, China.
Front Aging Neurosci. 2021 Jul 30;13:625081. doi: 10.3389/fnagi.2021.625081. eCollection 2021.
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 ( < 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 ( < 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.
轻度认知障碍（MCI）阶段在预防老年人发展为阿尔茨海默病方面起着至关重要的作用。在本研究中，应用神经反馈训练（NFT）来改善MCI患者的大脑认知功能。为了评估改善效果，提出了一种名为加权多重多尺度熵（WMMSE）的新算法，用于提取和分析MCI患者的脑电图（EEG）特征。为了克服传统多尺度熵（MSE）的信息丢失问题，WMMSE充分考虑了序列的相关性以及每个序列对总熵的贡献。由39名MCI患者组成的实验组在两个疗程中接受了10天的NFT训练。对照组包括21名未接受任何干预的MCI患者。使用莱姆尔 - 齐夫复杂度（LZC）进行初步评估，并使用WMMSE准确分析NFT的效果。结果表明，NFT训练后F4、C3、C4、O1和T5通道的WMMSE值高于训练前，且差异显著（<0.05）。此外，蒙特利尔认知评估（MoCA）结果的认知子量表显示，绝大多数MCI患者训练后的得分高于训练前，且差异显著（<0.05）。与对照组相比，实验组各通道的WMMSE值均有所增加。因此，NFT干预方法有助于大脑认知功能的恢复，并且WMMSE可以作为评估MCI大脑认知功能状态的生物标志物。
