Measuring Alterations of Spontaneous EEG Neural Coupling in Alzheimer's Disease and Mild Cognitive Impairment by Means of Cross-Entropy Metrics

Alzheimer's Disease (AD) represents the most prevalent form of dementia and is considered a major health problem due to its high prevalence and its economic costs. An accurate characterization of the underlying neural dynamics in AD is crucial in order to adopt effective treatments. In this regard, mild cognitive impairment (MCI) is an important clinical entity, since it is a risk-state for developing dementia. In the present study, coupling patterns of 111 resting-state electroencephalography (EEG) recordings were analyzed. Specifically, we computed Cross-Approximate Entropy (Cross-ApEn) and Cross-Sample Entropy (Cross-SampEn) of 37 patients with dementia due to AD, 37 subjects with MCI, and 37 healthy control (HC) subjects. Our results showed that Cross-SampEn outperformed Cross-ApEn, revealing higher number of significant connections among the three groups (Kruskal-Wallis test, FDR-corrected p-values < 0.05). AD patients exhibited statistically significant lower similarity values at θ and β1 frequency bands compared to HC. MCI is also characterized by a global decrease of similarity in all bands, being only significant at β1. These differences shows that β band might play a significant role in the identification of early stages of AD. Our results suggest that Cross-SampEn could increase the insight into brain dynamics at different AD stages. Consequently, it may contribute to develop early AD biomarkers, potentially useful as diagnostic information

Differentiating Epileptic from Psychogenic Nonepileptic EEG Signals using Time Frequency and Information Theoretic Measures of Connectivity

Differentiating psychogenic nonepileptic seizures from epileptic seizures is a difficult task that requires timely recording of psychogenic events using video electroencephalography (EEG). Interpretation of video EEG to distinguish epileptic features from signal artifacts is error prone and can lead to misdiagnosis of psychogenic seizures as epileptic seizures resulting in undue stress and ineffective treatment with antiepileptic drugs. In this study, an automated surface EEG analysis was implemented to investigate differences between patients classified as having psychogenic or epileptic seizures. Surface EEG signals were grouped corresponding to the anatomical lobes of the brain (frontal, parietal, temporal, and occipital) and central coronal plane of the skull. To determine if differences were present between psychogenic and epileptic groups, magnitude squared coherence (MSC) and cross approximate entropy (C-ApEn) were used as measures of neural connectivity. MSC was computed within each neural frequency band (delta: 0.5Hz-4Hz, theta: 4-8Hz, alpha: 8-13Hz, beta: 13-30Hz, and gamma: 30-100Hz) between all brain regions. C-ApEn was computed bidirectionally between all brain regions. Independent samples t-tests were used to compare groups. The statistical analysis revealed significant differences between psychogenic and epileptic groups for both connectivity measures with the psychogenic group showing higher average connectivity. Average MSC was found to be lower for the epileptic group between the frontal/central, parietal/central, and temporal/occipital regions in the delta band and between the temporal/occipital regions in the theta band. Average C-ApEn was found to be greater for the epileptic group between the frontal/parietal, parietal/frontal, parietal/occipital, and parietal/central region pairs. These results suggest that differences in neural connectivity exist between psychogenic and epileptic patient groups

Measuring Alterations of Spontaneous EEG Neural Coupling in Alzheimer's Disease and Mild Cognitive Impairment by Means of Cross-Entropy Metrics

[EN] Alzheimer's Disease (AD) represents the most prevalent form of dementia and is considered a major health problem due to its high prevalence and its economic costs. An accurate characterization of the underlying neural dynamics in AD is crucial in order to adopt effective treatments. In this regard, mild cognitive impairment (MCI) is an important clinical entity, since it is a risk-state for developing dementia. In the present study, coupling patterns of 111 resting-state electroencephalography (EEG) recordings were analyzed. Specifically, we computed Cross-Approximate Entropy (Cross-ApEn) and Cross-Sample Entropy (Cross-SampEn) of 37 patients with dementia due to AD, 37 subjects with MCI, and 37 healthy control (HC) subjects. Our results showed that Cross-SampEn outperformed Cross-ApEn, revealing higher number of significant connections among the three groups (Kruskal-Wallis test, FDR-corrected p-values < 0.05). AD patients exhibited statistically significant lower similarity values at θ and β 1 frequency bands compared to HC. MCI is also characterized by a global decrease of similarity in all bands, being only significant at β 1