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A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease.

ABSTRACT: (1) Background: Growing evidence suggests that electroencephalography (EEG), recording the brain's electrical activity, can be a promising diagnostic tool for Alzheimer's disease (AD). The diagnostic biomarkers based on quantitative EEG (qEEG) have been extensively explored, but few of them helped clinicians in their everyday practice, and reliable qEEG markers are still lacking. The study aims to find robust EEG biomarkers and propose a systematic discrimination framework based on signal processing and computer-aided techniques to distinguish AD patients from normal elderly controls (NC). (2) Methods: In the proposed study, EEG signals were preprocessed firstly and Maximal overlap discrete wavelet transform (MODWT) was applied to the preprocessed signals. Variance, Pearson correlation coefficient, interquartile range, Hoeffding's D measure, and Permutation entropy were extracted as the input of the candidate classifiers. The AD vs. NC discriminant performance of each model was evaluated and an automatic diagnostic framework was eventually developed. (3) Results: A classification procedure based on the extracted EEG features and linear discriminant analysis based classifier achieved the accuracy of 93.18 ± 3.65 (%), the AUC of 97.92 ± 1.66 (%), the F-measure of 94.06 ± 4.04 (%), separately. (4) Conclusions: The developed discrimination framework can identify AD from NC with high performance in a systematic routine.

SUBMITTER: Ge Q

PROVIDER: S-EPMC7712949 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease.

Ge Qi Q Lin Zhuo-Chen ZC Gao Yong-Xiang YX Zhang Jin-Xin JX

Healthcare (Basel, Switzerland) 20201111 4

(1) Background: Growing evidence suggests that electroencephalography (EEG), recording the brain's electrical activity, can be a promising diagnostic tool for Alzheimer's disease (AD). The diagnostic biomarkers based on quantitative EEG (qEEG) have been extensively explored, but few of them helped clinicians in their everyday practice, and reliable qEEG markers are still lacking. The study aims to find robust EEG biomarkers and propose a systematic discrimination framework based on signal proces ...[more]

PMID: 33187374

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Project description:BACKGROUND:Although numerous electroencephalogram (EEG) studies have described differences in functional connectivity in Alzheimer's disease (AD) compared to healthy subjects, there is no general consensus on the methodology of estimating functional connectivity in AD. Inconsistent results are reported due to multiple methodological factors such as diagnostic criteria, small sample sizes and the use of functional connectivity measures sensitive to volume conduction. We aimed to investigate the reproducibility of the disease-associated effects described by commonly used functional connectivity measures with respect to the amyloid, tau and neurodegeneration (A/T/N) criteria. METHODS:Eyes-closed task-free 21-channel EEG was used from patients with probable AD and subjective cognitive decline (SCD), to form two cohorts. Artefact-free epochs were visually selected and several functional connectivity measures (AEC(-c), coherence, imaginary coherence, PLV, PLI, wPLI) were estimated in five frequency bands. Functional connectivity was compared between diagnoses using AN(C)OVA models correcting for sex, age and, additionally, relative power of the frequency band. Another model predicted the Mini-Mental State Exam (MMSE) score of AD patients by functional connectivity estimates. The analysis was repeated in a subpopulation fulfilling the A/T/N criteria, after correction for influencing factors. The analyses were repeated in the second cohort. RESULTS:Two large cohorts were formed (SCD/AD; n =?197/214 and n =?202/196). Reproducible effects were found for the AEC-c in the alpha and beta frequency bands (p =?6.20 × 10-7, Cohen's d =?-?0.53; p =?5.78 × 10-4, d =?-?0.37) and PLI and wPLI in the theta band (p =?3.81 × 10-8, d =?0.59; p =?1.62 × 10-8, d =?0.60, respectively). Only effects of the AEC-c remained significant after statistical correction for the relative power of the selected bandwidth. In addition, alpha band AEC-c correlated with disease severity represented by MMSE score. CONCLUSION:The choice of functional connectivity measure and frequency band can have a large impact on the outcome of EEG studies in AD. Our results indicate that in the alpha and beta frequency bands, the effects measured by the AEC-c are reproducible and the most valid in terms of influencing factors, correlation with disease severity and preferable properties such as correction for volume conduction. Phase-based measures with correction for volume conduction, such as the PLI, showed reproducible effects in the theta frequency band.

Project description:Many factors are involved in Alzheimer's disease (AD) pathology including tau phosphorylation, amyloid β protein (Aβ) accumulation, lipid dysregulation, oxidative stress, and inflammation. The markers of these pathological processes in cerebral spinal fluid are used currently for AD diagnosis. However, peripheral biomarkers are the need of the hour for large population screening for AD. The main objective of the present study is to evaluate the peripheral levels of redox markers, lipid peroxidation (LPO) indicators, and pathological markers in AD patients. Blood was collected from AD patients (n = 45), controls (n = 45), and analyzed for pathological markers of AD including Aβ42 and tau, LPO, and redox indicators. Plasma Aβ42 was significantly (P < 0.001) elevated while total tau was decreased in AD compared to controls. Hydroxynonenal (HNE) and malondialdehyde (MDA) were higher (P < 0.001) in AD patients pointing the enhanced LPO in AD pathology. Receiver operating characteristic curve (ROC) analysis indicated that HNE is a better indicator of LPO compared to MDA. Plasma glutathione (GSH) level was significantly (P < 0.001) low while oxidized glutathione (GSSG) level was higher (P < 0.001) in AD patients with corresponding decrease in GSH/GSSG ratio (P < 0.001). ROC analysis indicated that GSH/GSSG ratio can be used as reliable indicator for redox imbalance in AD with a cutoff value of <8.73 (sensitivity 91.1%, specificity 97.8%). Correlation analysis revealed a positive correlation for both HNE and MDA with Aβ42 and a negative correlation with total tau. Negative correlation was observed between GSH/GSSG ratio and LPO markers. While oxidative stress has been implicated in pathology of various neurodegenerative disorders, the present study pinpoints the direct link between LPO and Aβ production in plasma of AD patients. Normally, at low amyloid concentration in body fluids, this peptide shown to function as a strong metal chelating antioxidant. However, when the Aβ production enhanced as in AD, through gain of functional transformation, Aβ evolves into prooxidant, thereby enhancing oxidative stress and LPO. Altered redox status with enhanced LPO observed in AD blood could contribute to the oxidation and S-glutathionylation proteins, which has to be addressed in future studies.

Project description:BackgroundWith demographic shifts toward older populations, the number of people with dementia is steadily increasing. Alzheimer's disease (AD) is the most common cause of dementia, and no curative treatment is available. The current best strategy is to delay disease progression and to practice early intervention to reduce the number of patients that ultimately develop AD. Therefore, promising novel biomarkers for early diagnosis are urgently required.MethodsTo identify blood-based biomarkers for early diagnosis of AD, we performed RNA sequencing (RNA-seq) analysis of 610 blood samples, representing 271 patients with AD, 91 cognitively normal (CN) adults, and 248 subjects with mild cognitive impairment (MCI). We first estimated cell-type proportions among AD, MCI, and CN samples from the bulk RNA-seq data using CIBERSORT and then examined the differentially expressed genes (DEGs) between AD and CN samples. To gain further insight into the biological functions of the DEGs, we performed gene set enrichment analysis (GSEA) and network-based meta-analysis.ResultsIn the cell-type distribution analysis, we found a significant association between the proportion of neutrophils and AD prognosis at a false discovery rate (FDR) < 0.05. Furthermore, a similar trend emerged in the results of routine blood tests from a large number of samples (n = 3,099: AD, 1,605; MCI, 994; CN, 500). In addition, GSEA and network-based meta-analysis based on DEGs between AD and CN samples revealed functional modules and important hub genes associated with the pathogenesis of AD. The risk prediction model constructed by using the proportion of neutrophils and the most important hub genes (EEF2 and RPL7) achieved a high AUC of 0.878 in a validation cohort; when further applied to a prospective cohort, the model achieved a high accuracy of 0.727.ConclusionsOur model was demonstrated to be effective in prospective AD risk prediction. These findings indicate the discovery of potential biomarkers for early diagnosis of AD, and their further improvement may lead to future practical clinical use.

Dataset Information

A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease.

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A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease.

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