Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer’s Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis

Niu, Yan; Wang, Bin; Zhou, Mengni; Xue, Jiayue; Shapour, Habib; Cao, Rui; Cui, Xiaohong; Wu, Jinglong; Xiang, Jie

doi:10.3389/fnins.2018.00677

Cited by 42 publications

(43 citation statements)

References 77 publications

(111 reference statements)

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“…At the regional level, five clusters showed significant differences in En, as illustrated in Figure 7 . Niu et al [ 133 ] extracted the average MSEs of the whole brain, gray matter, white matter, and cerebrospinal fluid using corresponding masks on all time scales. Only the gray matter showed a trend toward an entropy difference between the groups at scale factor six.…”

Section: Resultsmentioning

confidence: 99%

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

Sun

Wang

Niu

et al. 2020

Entropy

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103

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Alzheimer’s disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000–2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.

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Section: Resultsmentioning

confidence: 99%

“… Brain regions with significant differences between groups on scale factors two, four, five, and six [ 133 ]. …”

Section: Figurementioning

confidence: 99%

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

Sun

Wang

Niu

et al. 2020

Entropy

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103

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“…It is important to multiply r by the standard deviation of x to account for amplitude variations across different signals [18,43]. In this study, we used the embedding dimension of m=2 and the tolerance parameter of r =0.15 for sample entropy analysis, as widely used options in the literature (see [49,50] ans examples). In addition, we used the tolerance parameter of r =0.5, as adapted by McDonough and Nashiro [1].…”

Section: Sample Entropymentioning

confidence: 99%

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

Omidvarnia

Zalesky

Mansour

et al. 2019

Preprint

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In 2014, McDonough and Nashiro [1] derived multiscale entropy -a marker of signal complexity-from resting state functional MRI data (rsfMRI), and found that functional brain networks displayed unique multiscale entropy fingerprints. This is a finding with potential impact as an imaging-based marker of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of this study was that rsfMRI data from only 20 healthy individuals was used for analysis. To overcome this limitation, we aimed to replicate McDonough and Nashiro's finding in a large cohort of healthy subjects. We used rsfMRI from the Human Connectome Project (HCP) comprising 936 gender-matched healthy young adults aged 22-35, each with 4 × 14.4-minute rsfMRI data from 100 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We also performed a test-retest analysis on the data of four recording sessions in 10 previously reported resting state networks (RSNs). Given that the morphology of multiscale entropy patterns is affected by the choice of the tolerance parameter (r ), we performed the analyses at two r values: 0.5, similar to the original study and 0.15, a commonly used option in the literature. Our results were similar to previous findings by McDonough and Nashiro emphasising high temporal complexity in the default mode network and fronto-parietal networks, and low temporal complexity in the cerebellum. We also investigated the effect of temporal resolution (determined by fMRI repetition time) by downsampling rsfMRI time series. At a low temporal resolution, we observed increased entropy and variance across datasets likely due to fewer data points in the multiscale entropy analysis. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially for r = 0.5. We also showed a positive relationship between temporal complexity of RSNs and fluid intelligence (people's capacity to reason and think flexibly), suggesting that complex dynamics is an important attribute of optimized brain function.

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“…MSE has been used to study changes in brain complexity that are related to aging [16,23,24], Alzheimer's disease [25,26], and schizophrenia [27,28]. SampEn was used to measure complexity within dFC in [28,29], the only two studies where SampEn was calculated using dFC between nodes and are therefore different from the other studies.…”

Section: Introductionmentioning

confidence: 99%

A Study of Brain Neuronal and Functional Complexities Estimated Using Multiscale Entropy in Healthy Young Adults

Menon

Krishnamurthy

2019

Entropy

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Brain complexity estimated using sample entropy and multiscale entropy (MSE) has recently gained much attention to compare brain function between diseased or neurologically impaired groups and healthy control groups. Using resting-state functional magnetic resonance imaging (rfMRI) blood oxygen-level dependent (BOLD) signals in a large cohort (n = 967) of healthy young adults, the present study maps neuronal and functional complexities estimated by using MSE of BOLD signals and BOLD phase coherence connectivity, respectively, at various levels of the brain’s organization. The functional complexity explores patterns in a higher dimension than neuronal complexity and may better discern changes in brain functioning. The leave-one-subject-out cross-validation method is used to predict fluid intelligence using neuronal and functional complexity MSE values as features. While a wide range of scales was selected with neuronal complexity, only the first three scales were selected with functional complexity. Fewer scales are advantageous as they preclude the need for long BOLD signals to calculate good estimates of MSE. The presented results corroborate with previous findings and provide a baseline for other studies exploring the use of MSE to examine changes in brain function related to aging, diseases, and clinical disorders.

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Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer’s Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis

Cited by 42 publications

References 77 publications

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

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

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

A Study of Brain Neuronal and Functional Complexities Estimated Using Multiscale Entropy in Healthy Young Adults

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