EEG and Cognitive Biomarkers Based Mild Cognitive Impairment Diagnosis

Sharma, Neelam; Kolekar, Maheshkumar H.; Jha, Kamlesh; Kumar, Yogesh

doi:10.1016/j.irbm.2018.11.007

Cited by 71 publications

(40 citation statements)

References 28 publications

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“…The SpecEn results showed that the frequency distribution of the power spectrum changed. These findings confirmed results from previous studies: the EEG signals of patients with AD and MCI gradually slowed down [ 76 , 88 ]. However, the physiological interpretation of all these changes is uncertain.…”

Section: Resultssupporting

confidence: 91%

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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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: Resultssupporting

confidence: 91%

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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“…Whereas upper alpha power decreased (ERD) for cognitively healthy older adults, it increased (ERS) for people with aMCI. On the other hand, Sharma et al [74], (n = 15 dementia, 16 MCI, 13 OA), Kurt et al [122], (n = 22 aMCI, 21 OA), and Yener et al [123], (n = 18 aMCI, 18 OA); [124], (n = 30 aMCI, 28 OA) did not find significant differences between people with MCI and cognitively healthy older adults during tasks of attention. Sharma et al [74] did find a difference between people with MCI and people with dementia, but the direction of the difference was unclear, and dementia and MCI were not diagnosed according to widely accepted criteria.…”

Section: Alpha Power and Functional Connectivity During Task Performamentioning

confidence: 86%

“…On the other hand, Sharma et al [74], (n = 15 dementia, 16 MCI, 13 OA), Kurt et al [122], (n = 22 aMCI, 21 OA), and Yener et al [123], (n = 18 aMCI, 18 OA); [124], (n = 30 aMCI, 28 OA) did not find significant differences between people with MCI and cognitively healthy older adults during tasks of attention. Sharma et al [74] did find a difference between people with MCI and people with dementia, but the direction of the difference was unclear, and dementia and MCI were not diagnosed according to widely accepted criteria. The only study that focused on inhibition found that compared to cognitively healthy older adults, people with aMCI had lower ERD during the processing of go trials in the lower alpha band (Nguyen et al [125], n = 22 aMCI, 22 OA).…”

Section: Alpha Power and Functional Connectivity During Task Performamentioning

confidence: 86%

“…Three studies did not specify the MCI subtype or ascribe MCI to a particular dementia, but did compare people with MCI to people with AD [58,72,73]. Sharma et al [74] did not specify the MCI subtype or the type or dementia, and based their diagnoses solely on the Mini-Mental State Examination score. Jelic et al [75] included participants with "objective memory disturbances", but their diagnostic criteria were in line with those that were later developed for aMCI [6].…”

Section: Alpha Power and Functional Connectivity During Wakeful Restmentioning

confidence: 99%

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Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis

Lejko

Larabi

Herrmann

et al. 2020

JAD

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Background: Mild cognitive impairment (MCI) is a stage between expected age-related cognitive decline and dementia. Dementias have been associated with changes in neural oscillations across the frequency spectrum, including the alpha range. Alpha is the most prominent rhythm in human EEG and is best detected during awake resting state (RS). Though several studies measured alpha power and synchronization in MCI, findings have not yet been integrated. Objective: To consolidate findings on power and synchronization of alpha oscillations across stages of cognitive decline. Methods: We included studies published until January 2020 that compared power or functional connectivity between 1) people with MCI and cognitively healthy older adults (OA) or people with a neurodegenerative dementia, and 2) people with progressive and stable MCI. Random-effects meta-analyses were performed when enough data was available. Results: Sixty-eight studies were included in the review. Global RS alpha power was lower in AD than in MCI (ES = –0.30; 95% CI = –0.51, –0.10; k = 6), and in MCI than in OA (ES = –1.49; 95% CI = –2.69, –0.29; k = 5). However, the latter meta-analysis should be interpreted cautiously due to high heterogeneity. The review showed lower RS alpha power in progressive than in stable MCI, and lower task-related alpha reactivity in MCI than in OA. People with MCI had both lower and higher functional connectivity than OA. Publications lacked consistency in MCI diagnosis and EEG measures. Conclusion: Research indicates that RS alpha power decreases with increasing impairment, and could—combined with measures from other frequency bands—become a biomarker of early cognitive decline.

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“…Our goal is to compare normal subjects with those suffering from early mild cognitive impairment (Normal vs. EMCI). The most common strategy in the literature is to mix early and late phase (EMCI+LMCI cognitive impairment data) into a unique group (MCI) [ 39 , 40 , 41 , 42 , 43 ]. This is because the objective of this work is the early detection of the disease and determining which regions of the brain are involved in this phase.…”

Section: Discussionmentioning

confidence: 99%

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

Curado

Escolano

Lozano

et al. 2020

Entropy

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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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EEG and Cognitive Biomarkers Based Mild Cognitive Impairment Diagnosis

Cited by 71 publications

References 28 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

Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis

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

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