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

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.

Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer’s disease?

“…Of special interest is a large group of measures that are based on the concept of information entropy ( Bandt & Pompe, 2002 ; Costa et al, 2002 , 2005 ; Courtiol et al, 2016 ; Dávalos et al, 2019 ; Fadlallah et al, 2013 ; Inouye et al, 1991 ; Keshmiri, 2020 ; Kosciessa et al, 2020 ; Richman & Moorman, 2000 ; Y. Yin et al, 2016 ). Entropy measures have been helpful in psychophysiological studies of healthy subjects ( Fekete et al, 2021 ; Grady & Garrett, 2018 ; M. Liu et al, 2019 ; Mahjoory et al, 2019 ; Miskovic et al, 2019 ; Waschke et al, 2019 ), and growing evidence suggests that the measures can also be used to demonstrate a significant, progressive loss of entropy of neural activity in AD patients ( Ando et al, 2021 ; Echegoyen et al, 2020 ; Gómez & Hornero, 2010 ; Maturana-Candelas et al, 2019 ; Shumbayawonda et al, 2020 ; Su et al, 2021 ; W. Y. Yu et al, 2021 ). This suggests that measures of complexity or entropy could have potential as biomarkers for AD.…”

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

“…Finally, although non-linear analysis is widely used in the characterisation of EEG changes throughout the process of cognitive decline [ 48 , 49 , 50 , 51 , 72 ], to the best of our knowledge, MSE has not been generally considered as an evaluation metric in previous NFT studies. That is, few NFT studies have applied MSE measurement in order to find NFT-induced EEG changes [ 52 , 75 ]. In fact, to our knowledge, there is only one study that reported the influence of classical NFT on EEG complexity in the elderly [ 52 ].…”

“…That is, few NFT studies have applied MSE measurement in order to find NFT-induced EEG changes [ 52 , 75 ]. In fact, to our knowledge, there is only one study that reported the influence of classical NFT on EEG complexity in the elderly [ 52 ]. In this study, an increase in EEG complexity located in fronto-central region was shown.…”

“…This metric has proven useful for exploring changes in EEG complexity [ 48 , 49 , 50 , 51 ], which has been shown to be related to cognition state [ 12 , 13 ]. Indeed, a recent study applied for the first time a variation of MSE algorithm to assess the effects of classical NFT on the subject’s EEG complexity, showing higher complexity values after training [ 52 ]. These results encourage further study of the NFT’s influence on brain signal complexity.…”

Identifying Amnestic Mild Cognitive Impairment With Convolutional Neural Network Adapted to the Spectral Entropy Heat Map of the Electroencephalogram

“…The data set used in this study is some of the clinical data reported by Su et al (2021) , including 26 subjects with aMCI with an average age of 65 years (60–70 years) and 20 HC subjects with an average age of 63.39 years (60–70 years). These aMCI patients were diagnosed by an experienced neurosurgeon and inclusion criteria included the Mini-Mental State Examination, Montreal Cognitive Assessment scores, and the Daily Living Test.…”