https://www.pure.ed.ac.uk/ws/files/139099155/Early_Diagnosis_of_Alzheimers_Disease.pdf

Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts

Alzheimer's disease (AD) is a frequently observed, irreversible brain function disorder among elderly individuals. Resting-state functional magnetic resonance imaging (rs-fMRI) has been introduced as an alternative approach to assessing brain functional abnormalities in AD patients. However, alterations in the brain rs-fMRI signal complexities in mild cognitive impairment (MCI) and AD patients remain unclear. Here, we described the novel application of permutation entropy (PE) to investigate the abnormal complexity of rs-fMRI signals in MCI and AD patients. The rs-fMRI signals of 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. After preprocessing, whole-brain entropy maps of the four groups were extracted and subjected to Gaussian smoothing. We performed a one-way analysis of variance (ANOVA) on the brain entropy maps of the four groups. The results after adjusting for age and sex differences together revealed that the patients with AD exhibited lower complexity than did the MCI and NC controls. We found five clusters that exhibited significant differences and were distributed primarily in the occipital, frontal, and temporal lobes. The average PE of the five clusters exhibited a decreasing trend from MCI to AD. The AD group exhibited the least complexity. Additionally, the average PE of the five clusters was significantly positively correlated with the Mini-Mental State Examination (MMSE) scores and significantly negatively correlated with Functional Assessment Questionnaire (FAQ) scores and global Clinical Dementia Rating (CDR) scores in the patient groups. Significant correlations were also found between the PE and regional homogeneity (ReHo) in the patient groups. These results indicated that declines in PE might be related to changes in regional functional homogeneity in AD. These findings suggested that complexity analyses using PE in rs-fMRI signals can provide important information about the fMRI characteristics of cognitive impairments in MCI and AD.

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Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping.

Mild cognitive impairment (MCI) is the preliminary stage of dementia, which may lead to Alzheimer’s disease (AD) in the elderly people. Therefore, early detection of MCI has the potential to minimize the risk of AD by ensuring the proper mental health care before it is too late. In this paper, we demonstrate a single-channel EEG-based MCI detection method, which is cost-effective and portable, and thus suitable for regular home-based patient monitoring. We collected the scalp EEG data from 23 subjects, while they were stimulated with five auditory speech signals. The cognitive state of the subjects was evaluated by the Montreal cognitive assessment test (MoCA). We extracted 590 features from the event-related potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with radial basis kernel (RBF) (sigma = 10/cost $= 10^{2}$ ). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI.

/pdf/a-single-channel-eeg-based-approach-to-detect-mild-cognitive-npqfbsrozc.pdf

A Single-Channel EEG-Based Approach to Detect Mild Cognitive Impairment via Speech-Evoked Brain Responses

The complexity change of brain activity in Alzheimer’s disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.

/pdf/multivariate-multi-scale-weighted-permutation-entropy-4h1ld6uypp.pdf

Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer's disease.

In this paper, weighted-permutation entropy (WPE) is applied to investigating the complexity abnormalities of Alzheimer's disease (AD) by analyzing 16-channel electroencephalograph (EEG) signals from 14 severe AD patients and 14 age-matched normal subjects. The WPE values are estimated in the delta, the theta, the alpha, and the beta sub-bands for each channel with an overlapped sliding window. WPE is modified from the permutation entropy (PE), which has been recently suggested as a measurement to extract the complexity of the EEG signals. The advantage of WPE over PE is verified by both the model simulated and the experimental EEG signals. Although the results show that both the average PE and WPE of AD patients are decreased in contrast with the normal group in these four sub-bands, especially in the theta band, WPE can exhibit a better performance in distinguishing the AD patients from the normal controls by the more significant differences in the four sub-bands, which may be attributed to the brain dysfunction. Thus, it suggests that WPE may become a probable useful tool to detect brain dysfunction in AD and it seems to be promising to disclose the abnormalities of brain activity for other neural disease.

Complexity extraction of electroencephalograms in Alzheimer's disease with weighted-permutation entropy

Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis.

Permutation Entropy (PE) statistic is a measure of self-similarity of the time series estimated from its ordinal patterns. This measure is used to detect the dynamical differences between patients with mild cognitive impairment (MCI) and normal controls. The comparison of PE values of Electroencephalograph (EEG) of the two groups in the resting eyes closed (EC) state and the short-term memory task (STM) state reveals altered efficiency of the different lobes of MCI brain in the compensational dynamical mechanism for task management. In resting EC state, PE values of MCI group is significantly (p∠0. 05) lower than that of controls in the frontal, temporal, and anterior parietal regions. In the STM task state, entropy levels of MCI group are significantly (p∠0. 05) lower than that of controls in the frontal region and the left parietal region. These findings suggest that nonlinear analysis of EEG using PE can provide important information about EEG characteristic of cognitively impaired condition that can lead to Alzheimer’s Disease(AD).

Permutation Entropy Analysis of EEG of Mild Cognitive Impairment Patients During Memory Activation Task

The work aims at classifying EEG of mild cognitive impairment (MCI) patients from that of normal control (NC) subjects using recurrence quantification analysis (RQA) and a simple visual memory task...

Recurrence quantification analysis of mci eeg under resting and visual memory task conditions

The present study is aimed at characterizing the EEG dynamics of the Mild cognitive impairment (MCI) subjects compared to that of normal controls using Recurrence Quantification Analysis (RQA) and Cross Recurrence Quantification Analysis (CRQA). EEG from MCI and control subjects are recorded under resting eyes closed (EC) condition. Recurrence rate of RQA and CRQA are calculated for the signals from both groups. RQA method quantifies the regularity/complexity signals and CRQA method quantifies the similarities between the two signals. The CRQA analysis of EEG is carried out for signals between the channels from different lobes. These two measures are combined in a feature space. The clear distinction of the two groups is obtained using this method of combined RQA and CRQA measures.

Combined recurrence and cross recurrence quantification of MCI EEG

This work investigates the effectiveness of the nonlinear method of cross recurrence quantification analysis (CRQA) for classifying EEG of subjects with mild cognitive impairment (MCI) based on their synchronization levels. The synchronization levels are quantified using the CRQA recurrence rate (RR) of EEG under memory activation state of picture recollection (PICREC) for both inter and intra lobar levels. In inter lobar level, MCI EEG shows higher RR values of CRQA with statistical significance on all regions when compared to the EEG of normal control group (NC). In intra lobar analysis also, RR values of MCI are significantly higher but only in the temporal and parietal regions. Receiver operating characteristics (ROC) is used for classification of EEG of these two groups. A fair classification is obtained between the EEG of MCI and NC by using ROC analysis under this memory activation task state.

Leena T. Timothy

Papers

Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis.

Permutation Entropy Analysis of EEG of Mild Cognitive Impairment Patients During Memory Activation Task

Recurrence quantification analysis of mci eeg under resting and visual memory task conditions

Combined recurrence and cross recurrence quantification of MCI EEG

Cross recurrence quantification analysis of mild cognitive impairment EEG under working memory condition