Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Sun, Jie; Wang, Bin; Niu, Yan; Tan, Yuan Sheng; Fan, Chanjuan; Zhang, Nan; Xue, Jiayue; Wei, Jing; Xiang, Jie

doi:10.3390/e22020239

Cited by 96 publications

(82 citation statements)

References 139 publications

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“…For resting-state EEG complexity before PS ( Figure 4 , left columns), at small scale factors, the Severe AD group presented lower resting-state MSE than the non-AD and Mild AD groups. Our results are in line with previous findings that EEG complexity at small time scales was lower in AD and MCI patients than in healthy controls, which may represent regular brain activity and reflect a local loss of complexity in AD patients [ 14 , 34 ]. At large scale factors, in contrast, AD subgroups presented higher resting-state MSE than non-AD groups.…”

Section: Discussionsupporting

confidence: 93%

“…Sample entropy (SE) [ 12 ], an information theory-based metric, was proposed to overcome the weaknesses of its precedence, the approximate entropy [ 13 ], including the bias of self-matches, relative inconsistency, and dependence on large data points and sample length. SE computes the degree of similarity between two sequences in order to characterize the uncertainty and unpredictability in physiological time-series signals [ 14 ]. As such, signals with repetitive structures (such as rhythmic oscillations) are more regular, predictable, and would yield low SE values; signals with random structures (such as random noise) are more irregular, unpredictable, and would yield high SE values.…”

Section: Introductionmentioning

confidence: 99%

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Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer’s Disease and Mild Cognitive Impairment

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Chen

et al. 2021

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Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer’s disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a “transitional change” between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer’s Disease and Mild Cognitive Impairment

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Chen

et al. 2021

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“…The human brain is a nonlinear and complex system. There has been increasing interest in analyzing the complexity of brain signals with technologies such as electroencephalogram (EEG) [1], magnetoencephalogram (MEG) [2], and functional magnetic resonance imaging (fMRI) [3]. Blood oxygenation level-dependent (BOLD) fMRI is a powerful noninvasive tool for whole-brain imaging [4].…”

Section: Introductionmentioning

confidence: 99%

Comparing Test-Retest Reliability of Entropy Methods: Complexity Analysis of Resting-State fMRI

et al. 2020

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In recent years, there has been growing interest in studying the complexity of resting-state functional magnetic resonance imaging (rs-fMRI) brain signals. As one of the most commonly used complexity methods, entropy measures have been used to quantitatively characterize abnormal brain activity in aged individuals and patients with psychopathic and neurological disorders, and most studies have analyzed brain signals from a single channel. The widely used entropy methods include approximate entropy (AE), sample entropy (SE), permutation entropy (PE), and fuzzy entropy (FE). However, the testretest reliability of different entropy methods remains to be explored. In this study, we investigated the distribution and test-retest reliability of four entropy measures and a new entropy algorithm we proposed, permutation fuzzy entropy (PFE), in three independent data sets at three levels, i.e., based on voxels, brain regions, and functional networks. Our results showed that analyzing fMRI signals with entropy showed strong tissue sensitivity. The highest reliability was achieved with PFE, and PE and FE were superior to AE and SE at all three levels. The percentage of nodes with good to excellent reliability in PFE, PE, FE, SE and AE was 94.31%, 52.65%, 18.56%, 11.36% and 0.76%, respectively. PFE and PE showed fair to good reliability in the visual network, auditory network, default-mode network, etc. In conclusion, characterizing brain entropy may provide an informative tool to assess the complexity of brain functions. Our results suggested that PFE and PE had better reliability and reflected more topological information related to normal and disordered functioning of the human brain.

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“…However, we decide to discard this modality because not every subject has information of all modalities and the number of patients with all modalities available is too small for reasonable classification. The second limitation is the lack of methods that separate MCI groups (EMCI and LMCI) with directed graphs in their experiments (see [ 14 , 40 , 45 ]). Moreover, other limitations include the cross-sectional nature of this database and the absence of longitudinal RS-fMRI data.…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Alzheimer’s Disease: Detecting Asymmetries with a Return Random Walk Link Predictor

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Lozano

et al. 2020

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Alzheimer’s disease has been extensively studied using undirected graphs to represent the correlations of BOLD signals in different anatomical regions through functional magnetic resonance imaging (fMRI). However, there has been relatively little analysis of this kind of data using directed graphs, which potentially offer the potential to capture asymmetries in the interactions between different anatomical brain regions. The detection of these asymmetries is relevant to detect the disease in an early stage. For this reason, in this paper, we analyze data extracted from fMRI images using the net4Lap algorithm to infer a directed graph from the available BOLD signals, and then seek to determine asymmetries between the left and right hemispheres of the brain using a directed version of the Return Random Walk (RRW). Experimental evaluation of this method reveals that it leads to the identification of anatomical brain regions known to be implicated in the early development of Alzheimer’s disease in clinical studies.

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Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Cited by 96 publications

References 139 publications

Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer’s Disease and Mild Cognitive Impairment

Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer’s Disease and Mild Cognitive Impairment

Comparing Test-Retest Reliability of Entropy Methods: Complexity Analysis of Resting-State fMRI

Early Detection of Alzheimer’s Disease: Detecting Asymmetries with a Return Random Walk Link Predictor

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