Intrahemispheric, interhemispheric, and distal EEG coherence in Alzheimer’s disease

Changes of brain functional network in Alzheimer's disease and frontotemporal dementia: a graph-theoretic analysis

BACKGROUND

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment.

METHODS

We analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters.

RESULTS

Both AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD.

CONCLUSIONS

Our findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.

EEG connectivity patterns in response to gaming and learning-based cognitive stimulations in Rett syndrome

BACKGROUND

Functional connectivity is scarcely studied in Rett syndrome (RTT). Explorations revealed associations between RTT's clinical, genetic profiles, and coherence measures, highlighting an unexplored frontier in understanding RTT's neural mechanisms and cognitive processes.

AIMS

To evaluate the effects of diverse cognitive stimulations-learning-focused versus gaming-oriented-on electroencephalography brain connectivity in RTT. The comparison with resting states aimed to uncover potential biomarkers and insights into the neural processes associated with RTT.

METHODS AND PROCEDURES

The study included 15 girls diagnosed with RTT. Throughout sessions lasting about 25 min, participants alternated between active and passive tasks, using an eyetracker device while their brain activity was recorded with a 20-channel EEG. Results revealed significant alterations during cognitive tasks, notably in delta, alpha and beta bands. Both tasks induced spectral pattern changes and connectivity shifts, hinting at enhanced neural processing. Hemispheric asymmetry decreased during tasks, suggesting more balanced neural processing. Linear and nonlinear connectivity alterations were observed in active tasks compared to resting state, while passive tasks showed no significant changes.

CONCLUSIONS AND IMPLICATIONS

Results underscores the potential of cognitive stimulation for heightened cognitive abilities, promoting enhanced brain connectivity and information flow in Rett syndrome. These findings offer valuable markers for evaluating cognitive interventions and suggest gaming-related activities as effective tools for improving learning outcomes.

Investigating the effects of chiropractic care on resting-state EEG of MCI patients

Introduction

Mild cognitive impairment (MCI) is a stage between health and dementia, with various symptoms including memory, language, and visuospatial impairment. Chiropractic, a manual therapy that seeks to improve the function of the body and spine, has been shown to affect sensorimotor processing, multimodal sensory processing, and mental processing tasks.

Methods

In this paper, the effect of chiropractic intervention on Electroencephalogram (EEG) signals in patients with mild cognitive impairment was investigated. EEG signals from two groups of patients with mild cognitive impairment (n = 13 people in each group) were recorded pre- and post-control and chiropractic intervention. A comparison of relative power was done with the support vector machine (SVM) method and non-parametric cluster-based permutation test showing the two groups could be separately identified with high accuracy.

Results

The highest accuracy was obtained in beta2 (25-35 Hz) and theta (4-8 Hz) bands. A comparison of different brain areas with the SVM method showed that the intervention had a greater effect on frontal areas. Also, interhemispheric coherence in all regions increased significantly after the intervention. The results of the Wilcoxon test showed that intrahemispheric coherence changes in frontal-occipital, frontal-temporal and right temporal-occipital regions were significantly different in two groups.

Discussion

Comparison of the results obtained from chiropractic intervention and previous studies shows that chiropractic intervention can have a positive effect on MCI disease and using this method may slow down the progression of mild cognitive impairment to Alzheimer's disease.

Role of GABAB receptors in cognition and EEG activity in aged APP and PS1 transgenic mice

Alzheimer's disease (AD) is the most common cause of dementia in the elderly. Recent evidence suggests that gamma-aminobutyric acid B (GABAB) receptor-mediated inhibition is a major contributor to AD pathobiology, and GABAB receptors have been hypothesized to be a potential target for AD treatment. The aim of this study is to determine how GABAB regulation alters cognitive function and brain activity in an AD mouse model. Early, middle and late stage (8-23 months) amyloid precursor protein (APP) and presenilin 1 (PS1) transgenic mice were used for the study. The GABAB agonist baclofen (1 and 2.5 mg/kg, i. p.) and the antagonist phaclofen (0.5 mg/kg, i. p.) were used. Primarily, we found that GABAB activation was able to improve spatial and/or working memory performance in early and late stage AD animals. In addition, GABAB activation and inhibition could regulate global and local EEG oscillations in AD animals, with activation mainly regulating low-frequency activity (delta-theta bands) and inhibition mainly regulating mid- and high-frequency activity (alpha-gamma bands), although the regulated magnitude at some frequencies was reduced in AD. The cognitive improvements in AD animals may be explained by the reduced EEG activity in the theta frequency band (2-4 Hz). This study provides evidence for a potential therapeutic effect of baclofen in the elderly AD brain and for GABAB receptor-mediated inhibition as a potential therapeutic target for AD.

Alzheimer's Disease Evaluation Through Visual Explainability by Means of Convolutional Neural Networks

Background and Objective: Alzheimer's disease is nowadays the most common cause of dementia. It is a degenerative neurological pathology affecting the brain, progressively leading the patient to a state of total dependence, thus creating a very complex and difficult situation for the family that has to assist him/her. Early diagnosis is a primary objective and constitutes the hope of being able to intervene in the development phase of the disease. Methods: In this paper, a method to automatically detect the presence of Alzheimer's disease, by exploiting deep learning, is proposed. Five different convolutional neural networks are considered: ALEX_NET, VGG16, FAB_CONVNET, STANDARD_CNN and FCNN. The first two networks are state-of-the-art models, while the last three are designed by authors. We classify brain images into one of the following classes: non-demented, very mild demented and mild demented. Moreover, we highlight on the image the areas symptomatic of Alzheimer presence, thus providing a visual explanation behind the model diagnosis. Results: The experimental analysis, conducted on more than 6000 magnetic resonance images, demonstrated the effectiveness of the proposed neural networks in the comparison with the state-of-the-art models in Alzheimer's disease diagnosis and localization. The best results in terms of metrics are the best with STANDARD_CNN and FCNN with accuracy, precision and recall between 98% and 95%. Excellent results also from a qualitative point of view are obtained with the Grad-CAM for localization and visual explainability. Conclusions: The analysis of the heatmaps produced by the Grad-CAM algorithm shows that in almost all cases the heatmaps highlight regions such as ventricles and cerebral cortex. Future work will focus on the realization of a network capable of analyzing the three anatomical views simultaneously.

Quantitative EEG Spectral and Connectivity Analysis for Cognitive Decline in Amnestic Mild Cognitive Impairment

BACKGROUND

Mild cognitive impairment (MCI) is considered to be the borderline of cognitive changes associated with aging and very early dementia. Cognitive functions in MCI can improve, remain stable or progress to clinically probable AD. Quantitative electroencephalography (qEEG) can become a useful tool for using the analytical techniques to quantify EEG patterns indicating cognitive impairment.

OBJECTIVE

The aim of our study was to assess spectral and connectivity analysis of the EEG resting state activity in amnestic MCI (aMCI) patients in comparison with healthy control group (CogN).

METHODS

30 aMCI patients and 23 CogN group, matched by age and education, underwent equal neuropsychological assessment and EEG recording, according to the same protocol.

RESULTS

qEEG spectral analysis revealed decrease of global relative beta band power and increase of global relative theta and delta power in aMCI patients. Whereas, decreased coherence in centroparietal right area considered to be an early qEEG biomarker of functional disconnection of the brain network in aMCI patients. In conclusion, the demonstrated changes in qEEG, especially, the coherence patterns are specific biomarkers of cognitive impairment in aMCI.

CONCLUSIONS

Therefore, qEEG measurements appears to be a useful tool that complements neuropsychological diagnostics, assessing the risk of progression and provides a basis for possible interventions designed to improve cognitive functions or even inhibit the progression of the disease.

Age-Related Modifications of Electroencephalogram Coherence in Mice Models of Alzheimer's Disease and Amyotrophic Lateral Sclerosis

Evident similarities in pathological features in aging and Alzheimer's disease (AD) raise the question of a role for natural age-related adaptive mechanisms in the prevention/elimination of disturbances in interrelations between different brain areas. In our previous electroencephalogram (EEG) studies on 5xFAD- and FUS-transgenic mice, as models of AD and amyotrophic lateral sclerosis (ALS), this suggestion was indirectly confirmed. In the current study, age-related changes in direct EEG synchrony/coherence between the brain structures were evaluated.

METHODS

In 5xFAD mice of 6-, 9-, 12-, and 18-month ages and their wild-type (WT_5xFAD) littermates, we analyzed baseline EEG coherence between the cortex, hippocampus/putamen, ventral tegmental area, and substantia nigra. Additionally, EEG coherence between the cortex and putamen was analyzed in 2- and 5-month-old FUS mice.

RESULTS

In the 5xFAD mice, suppressed levels of inter-structural coherence vs. those in WT_5xFAD littermates were observed at ages of 6, 9, and 12 months. In 18-month-old 5xFAD mice, only the hippocampus ventral tegmental area coherence was significantly reduced. In 2-month-old FUS vs. WT_FUS mice, the cortex-putamen coherence suppression, dominated in the right hemisphere, was observed. In 5-month-old mice, EEG coherence was maximal in both groups.

CONCLUSION

Neurodegenerative pathologies are accompanied by the significant attenuation of intracerebral EEG coherence. Our data are supportive for the involvement of age-related adaptive mechanisms in intracerebral disturbances produced by neurodegeneration.

Functional changes in brain oscillations in dementia: a review

A growing body of evidence indicates that several characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) play a functional role in cognition and could be linked to the progression of cognitive decline in some neurological diseases such as dementia. The present paper reviews previous studies investigating changes in brain oscillations associated to the most common types of dementia, namely Alzheimer's disease (AD), frontotemporal degeneration (FTD), and vascular dementia (VaD), with the aim of identifying pathology-specific patterns of alterations and supporting differential diagnosis in clinical practice. The included studies analysed changes in frequency power, functional connectivity, and event-related potentials, as well as the relationship between electrophysiological changes and cognitive deficits. Current evidence suggests that an increase in slow wave activity (i.e., theta and delta) as well as a general reduction in the power of faster frequency bands (i.e., alpha and beta) characterizes AD, VaD, and FTD. Additionally, compared to healthy controls, AD exhibits alteration in latencies and amplitudes of the most common event related potentials. In the reviewed studies, these changes generally correlate with performances in many cognitive tests. In conclusion, particularly in AD, neurophysiological changes can be reliable early markers of dementia.

Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms

Introduction

Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections).

Methods

Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used.

Results

Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants.

Discussion

In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.

Sleep and cognition in aging dogs. A polysomnographic study

Introduction

Sleep is fundamental for cognitive homeostasis, especially in senior populations since clearance of amyloid beta (key in the pathophysiology of Alzheimer's disease) occurs during sleep. Some electroencephalographic characteristics of sleep and wakefulness have been considered a hallmark of dementia. Owners of dogs with canine cognitive dysfunction syndrome (a canine analog to Alzheimer's disease) report that their dogs suffer from difficulty sleeping. The aim of this study was to quantify age-related changes in the sleep-wakefulness cycle macrostructure and electroencephalographic features in senior dogs and to correlate them with their cognitive performance.

Methods

We performed polysomnographic recordings in 28 senior dogs during a 2 h afternoon nap. Percentage of time spent in wakefulness, drowsiness, NREM, and REM sleep, as well as latency to the three sleep states were calculated. Spectral power, coherence, and Lempel Ziv Complexity of the brain oscillations were estimated. Finally, cognitive performance was evaluated by means of the Canine Dementia Scale Questionnaire and a battery of cognitive tests. Correlations between age, cognitive performance and sleep-wakefulness cycle macrostructure and electroencephalographic features were calculated.

Results

Dogs with higher dementia scores and with worse performance in a problem-solving task spent less time in NREM and REM sleep. Additionally, quantitative electroencephalographic analyses showed differences in dogs associated with age or cognitive performance, some of them reflecting shallower sleep in more affected dogs.

Discussion

Polysomnographic recordings in dogs can detect sleep-wakefulness cycle changes associated with dementia. Further studies should evaluate polysomnography's potential clinical use to monitor the progression of canine cognitive dysfunction syndrome.

Who's driving? The default mode network in healthy elderly individuals at risk of cognitive decline

Introduction

Age is the main risk factor for the development of neurocognitive disorders, with Alzheimer's disease being the most common. Its physiopathological features may develop decades before the onset of clinical symptoms. Quantitative electroencephalography (qEEG) is a promising and cost-effective tool for the prediction of cognitive decline in healthy older individuals that exhibit an excess of theta activity. The aim of the present study was to evaluate the feasibility of brain connectivity variable resolution electromagnetic tomography (BC-VARETA), a novel source localization algorithm, as a potential tool to assess brain connectivity with 19-channel recordings, which are common in clinical practice.

Methods

We explored differences in terms of functional connectivity among the nodes of the default mode network between two groups of healthy older participants, one of which exhibited an EEG marker of risk for cognitive decline.

Results

The risk group exhibited increased levels of delta, theta, and beta functional connectivity among nodes of the default mode network, as well as reversed directionality patterns of connectivity among nodes in every frequency band when compared to the control group.

Discussion

We propose that an ongoing pathological process may be underway in healthy elderly individuals with excess theta activity in their EEGs, which is further evidenced by changes in their connectivity patterns. BC-VARETA implemented on 19-channels EEG recordings appears to be a promising tool to detect dysfunctions at the connectivity level in clinical settings.

Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease

OBJECTIVE

This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram (EEG), a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer's disease (AD).

APPROACH

The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a Revised Hilbert-Huang Transformation cross-spectrum as a biomarker, the Support Vector Machine classifier is used to distinguish AD from healthy controls (HC).

MAIN RESULTS

With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD.

SIGNIFICANCE

Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.

qEEG Analysis in the Diagnosis of Alzheimer’s Disease: A Comparison of Functional Connectivity and Spectral Analysis

Alzheimer’s disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain and decline of cognitive abilities. This study compared an FFT-based spectral analysis against a functional connectivity analysis for the diagnosis of AD. Both quantitative methods were applied on an EEG dataset including 20 diagnosed AD patients and 20 age-matched healthy controls (HC). The obtained results showed an advantage of the functional connectivity analysis when compared to the spectral analysis; while the latter could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting a ‘phase-locked’ state in AD-affected brains. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized to the centro-parietal and centro-temporal areas in the theta frequency band (4–8 Hz). This study applies a neural metric for Alzheimer’s detection from a data science perspective rather than from a neuroscience one and shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.

Odour Retrieval Processing in Mice: Cholinergic Modulation of Oscillatory Coupling in Olfactory Bulb-Piriform Networks

BACKGROUND/AIMS

Olfactory dysfunction can provide valuable insight into early pathophysiological processes of brain disorders. Olfactory processing of chemosensory and odour sensitivity relies on segregating salient odours from background odours cues. Odour-evoked fast oscillations in the olfactory bulb (OB) are hypothesized to be an important index of odour quality coding. The present preclinical work aimed at better understanding connectivity associated with odour coding and behavioural odour discrimination.

METHODS

Network oscillations and functional connectivity (FC) were measured in C57BL/6 mice performing the olfactory associative odour learning (OL) test, using multichannel local field potential recordings in key olfactory networks. Cholinergic modulation of odour processing was investigated using the muscarinic antagonist scopolamine.

RESULTS

At the behavioural level, olfactory memory, which refers to the acquisition and recollection of a reference odour by reduced exploration time, was observed in animals that correctly learned the task. Significant decrease in mean investigation and retrieval time of the associated odour-food reward was observed between trials. At the network level, the associated odour during sniffing behaviour was associated with enhanced coherence in the β and γ frequency oscillations across the olfactory pathway, with marked changes observed between the OB and anterior piriform cortex (PC). The enhanced phase-amplitude cross-frequency coupling in the OB and the weak coupling index in the hippocampal CA1 suggests a role of the OB network in olfaction encoding and processing. Scopolamine impaired behavioural and FC underlying recall and retrieval of the associated odour.

CONCLUSION

The results suggest that the acquisition and formation of odour reference memory rely primarily on FC at the OB-PC network and confirm the role of muscarinic receptors in olfactory retrieval processing.

Quantitative analysis of inter- and intrahemispheric coherence on epileptic electroencephalography signal

When an epileptic seizure occurs, the neuron's activity of the brain is dynamically changed, which affects the connectivity between brain regions. The connectivity of each brain region can be quantified by electroencephalography (EEG) coherence, which measures the statistical correlation between electrodes spatially separated on the scalp. Previous studies conducted a coherence analysis of all EEG electrodes covering all parts of the brain. However, in an epileptic condition, seizures occur in a specific region of the brain then spreading to other areas. Therefore, this study applies an energy-based channel selection process to determine the coherence analysis in the most active brain regions during the seizure. This paper presents a quantitative analysis of inter- and intrahemispheric coherence in epileptic EEG signals and the correlation with the channel activity to glean insights about brain area connectivity changes during epileptic seizures. The EEG signals are obtained from ten patients’ data from the CHB-MIT dataset. Pair-wise electrode spectral coherence is calculated in the full band and five sub-bands of EEG signals. The channel activity level is determined by calculating the energy of each channel in all patients. The EEG coherence observation in the preictal (Coh_pre) and ictal (Coh_ictal) conditions showed a significant decrease of Coh_ictal in the most active channel, especially in the lower EEG sub-bands. This finding indicates that there is a strong correlation between the decrease of mean spectral coherence and channel activity. The decrease of coherence in epileptic conditions (Coh_ictal <Coh_pre) indicates low neuronal connectivity. There are some exceptions in some channel pairs, but a constant pattern is found in the high activity channel. This shows a strong correlation between the decrease of coherence and the channel activity. The finding in this study demonstrates that the neuronal connectivity of epileptic EEG signals is suitable to be analyzed in the more active brain regions.

Convolutional neural network based on recurrence plot for EEG recognition

Electroencephalogram (EEG) is a typical physiological signal. The classification of EEG signals is of great significance to human beings. Combining recurrence plot and convolutional neural network (CNN), we develop a novel method for classifying EEG signals. We select two typical EEG signals, namely, epileptic EEG and fatigue driving EEG, to verify the effectiveness of our method. We construct recurrence plots from EEG signals. Then, we build a CNN framework to classify the EEG signals under different brain states. For the classification of epileptic EEG signals, we design three different experiments to evaluate the performance of our method. The results suggest that the proposed framework can accurately distinguish the normal state and the seizure state of epilepsy. Similarly, for the classification of fatigue driving EEG signals, the method also has a good classification accuracy. In addition, we compare with the existing methods, and the results show that our method can significantly improve the detection results.

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (-3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.

Functional (un-)Coupling: Impairment, Compensation, and Future Progression in Alzheimer's Disease

Background: Functional (un-)coupling (task-related change of functional connectivity) between different sites of the brain is a mechanism of general importance for cognitive processes. In Alzheimer's disease (AD), prior research identified diminished cortical connectivity as a hallmark of the disease. However, little is known about the relation between the amount of functional (un-)coupling and cognitive performance and decline in AD. Method: Cognitive performance (based on CERAD-Plus scores) and electroencephalogram (EEG)-based functional (un-)coupling measures (connectivity changes from rest to a Face-Name-Encoding task) were assessed in 135 AD patients (age: M = 73.8 years; SD = 9.0). Of these, 68 patients (M = 73.9 years; SD = 8.9) participated in a follow-up assessment of their cognitive performance 1.5 years later. Results: The amounts of functional (un-)coupling in left anterior-posterior and homotopic interhemispheric connections in beta1-band were related to cognitive performance at baseline (β = .340; p < .001; β = .274; P = .001, respectively). For both markers, a higher amount of functional coupling was associated with better cognitive performance. Both markers also were significant predictors for cognitive decline. However, while patients with greater functional coupling in left anterior-posterior connections declined less in cognitive performance (β = .329; P = .035) those with greater functional coupling in interhemispheric connections declined more (β = -.402; P = .010). Conclusion: These findings suggest an important role of functional coupling mechanisms in left anterior-posterior and interhemispheric connections in AD. Especially the complex relationship with cognitive decline in AD patients might be an interesting aspect for future studies.

Functional Connectivity Alterations Based on the Weighted Phase Lag Index: An Exploratory Electroencephalography Study on Alzheimer's Disease

OBJECTIVE

Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer's Disease (AD), but there are no consistent results especially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD.

METHODS

We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores.

RESULTS

In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores.

CONCLUSION

Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.

Nonlinear Phase Synchronization Analysis of EEG Signals in Amnesic Mild Cognitive Impairment with Type 2 Diabetes Mellitus

Studying the nonlinear synchronization of electroencephalogram (EEG) in type 2 diabetic mellitus (T2DM) to find the EEG characteristics related to cognitive impairment is beneficial to the early prevention and diagnosis of mild cognitive impairment. Correlation between probabilities of recurrence (CPR) is a nonlinear phase synchronization method based on recurrence and recurrence probability, which had shown its superiority in detecting epilepsy. In this study, CPR method was used for the first time to analyze the synchronization of eye-closed resting EEG signals with T2DM. The 27 participants were divided into amnesic mild cognitive impairment (aMCI) group (17 case) and control group (10 cases with age and education matched). The CPR values in two groups were statistically analyzed by Mann-Whitney U test, and the correlation between EEG synchronization and cognitive function was studied by Spearman's correlation. The results showed that aMCI group had lower CPR values at each electrode pair than control group, and two groups had decreased CPR values with the increase of the spatial distance of the electrode pair in inter hemispheric. The CPR values were significantly different in frontal, parietal and temporal regions in intra hemispheric between two groups. The CPR values of C3-F7, F4-C4 and FP2-T6 were significantly positively correlated with the MOCA values. This study showed that the synchronization values of EEG signals obtained by the CPR method were significantly different between aMCI and control group, and they were the EEG characteristics associated with cognitive impairment in T2DM.

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

Electroencephalographic functional connectivity is altered in persons with Down syndrome and Alzheimer's disease

BACKGROUND

Persons with Down syndrome (DS) are at increased risk of developing Alzheimer's dementia (DS-AD). Due to heterogeneity in the functioning in persons with DS, it is difficult to use cognitive testing to assess whether a person with DS has developed dementia due to AD. Electroencephalography (EEG) functional connectivity has shown promising results as a diagnostic tool for AD in persons without DS. In the current exploratory study, we investigated whether EEG functional connectivity could be used as a diagnostic marker of AD in persons with DS and the association with symptoms.

METHODS

Electroencephalography from 12 persons with DS and 16 persons with DS-AD were analysed, and both coherence and weighted phase lag index were calculated. In addition, we calculated the average coherence for fronto-parietal and temporo-parietal connections. Lastly, we investigated the correlation between the informant-based Dementia Screening Questionnaire in Intellectual Disability (DSQIID) and total alpha coherence.

RESULTS

Decreased alpha and increased delta coherence and weighted phase lag index were observed in DS-AD as compared with DS. The decrease in alpha coherence was more marked in the fronto-parietal connections as compared with the temporo-parietal connections. No significant correlation was found between DSQIID and total alpha coherence (P value = 0.095, rho = -0.335).

CONCLUSION

The decreased alpha coherence and weighted phase lag index have previously been found in AD. The increased delta coherence and weighted phase lag index may indicate a different initial neurophysiological presentation as compared with patients with AD or may be a sign of more advanced disease. Larger studies are needed to confirm the current findings.

Gauging Working Memory Capacity From Differential Resting Brain Oscillations in Older Individuals With A Wearable Device

Working memory is a core cognitive function and its deficits is one of the most common cognitive impairments. Reduced working memory capacity manifests as reduced accuracy in memory recall and prolonged speed of memory retrieval in older adults. Currently, the relationship between healthy older individuals’ age-related changes in resting brain oscillations and their working memory capacity is not clear. Eyes-closed resting electroencephalogram (rEEG) is gaining momentum as a potential neuromarker of mild cognitive impairments. Wearable and wireless EEG headset measuring key electrophysiological brain signals during rest and a working memory task was utilized. This research’s central hypothesis is that rEEG (e.g., eyes closed for 90 s) frequency and network features are surrogate markers for working memory capacity in healthy older adults. Forty-three older adults’ memory performance (accuracy and reaction times), brain oscillations during rest, and inter-channel magnitude-squared coherence during rest were analyzed. We report that individuals with a lower memory retrieval accuracy showed significantly increased alpha and beta oscillations over the right parietal site. Yet, faster working memory retrieval was significantly correlated with increased delta and theta band powers over the left parietal sites. In addition, significantly increased coherence between the left parietal site and the right frontal area is correlated with the faster speed in memory retrieval. The frontal and parietal dynamics of resting EEG is associated with the “accuracy and speed trade-off” during working memory in healthy older adults. Our results suggest that rEEG brain oscillations at local and distant neural circuits are surrogates of working memory retrieval’s accuracy and processing speed. Our current findings further indicate that rEEG frequency and coherence features recorded by wearable headsets and a brief resting and task protocol are potential biomarkers for working memory capacity. Additionally, wearable headsets are useful for fast screening of cognitive impairment risk.

Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review

Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

Deep Brain Stimulation for Alzheimer's Disease: Tackling Circuit Dysfunction

OBJECTIVES

Treatments for Alzheimer's disease are urgently needed given its enormous human and economic costs and disappointing results of clinical trials targeting the primary amyloid and tau pathology. On the other hand, deep brain stimulation (DBS) has demonstrated success in other neurological and psychiatric disorders leading to great interest in DBS as a treatment for Alzheimer's disease.

MATERIALS AND METHODS

We review the literature on 1) circuit dysfunction in Alzheimer's disease and 2) DBS for Alzheimer's disease. Human and animal studies are reviewed individually.

RESULTS

There is accumulating evidence of neural circuit dysfunction at the structural, functional, electrophysiological, and neurotransmitter level. Recent evidence from humans and animals indicate that DBS has the potential to restore circuit dysfunction in Alzheimer's disease, similarly to other movement and psychiatric disorders, and may even slow or reverse the underlying disease pathophysiology.

CONCLUSIONS

DBS is an intriguing potential treatment for Alzheimer's disease, targeting circuit dysfunction as a novel therapeutic target. However, further exploration of the basic disease pathology and underlying mechanisms of DBS is necessary to better understand how circuit dysfunction can be restored. Additionally, robust clinical data in the form of ongoing phase III clinical trials are needed to validate the efficacy of DBS as a viable treatment.

Intrinsic Synchronization Analysis of Brain Activity in Obsessive-compulsive Disorders

Obsessive-compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.

Altered Low-Frequency EEG Connectivity in Mild Cognitive Impairment as a Sign of Clinical Progression

BACKGROUND

Mild cognitive impairment (MCI) is associated with clinical progression to Alzheimer's disease (AD) but not all patients with MCI convert to AD. However, it is important to have methods that can differentiate between patients with MCI who progress (pMCI) and those who remain stable (sMCI), i.e., for timely administration of disease-modifying drugs.

OBJECTIVE

In the current study, we wanted to investigate whether quantitative EEG coherence and imaginary part of coherency (iCoh) could be used to differentiate between pMCI and sMCI.

METHODS

17 patients with AD, 27 patients with MCI, and 38 older healthy controls were recruited and followed for three years and 2nd year was used to determine progression. EEGs were recorded at baseline and coherence and iCoh were calculated after thorough preprocessing.

RESULTS

Between pMCI and sMCI, the largest difference in total coherence was found in the theta and delta bands. Here, the significant differences for coherence and iCoh were found in the lower frequency bands involving the temporal-frontal connections for coherence and parietal-frontal connections for iCoh. Furthermore, we found a significant negative correlation between theta coherence and the Addenbrooke's Cognitive Examination (ACE) (p = 0.0378; rho = -0.2388).

CONCLUSION

These findings suggest that low frequency coherence and iCoh can be used to determine, which patients with MCI will progress to AD and is associated with the ACE score. Low-frequency coherence has previously been associated with increased hippocampal atrophy and degeneration of the cholinergic system and may be an early marker of AD pathology.

Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer's Disease

Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among wide-spread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-and-regional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods to neurophysiological data may provide a novel understanding of the neural network processes in both healthy brains and pathological conditions.

Complex network analysis of MCI-AD EEG signals under cognitive and resting state

OBJECTIVE

The purpose of this study is to characterize functional connectivity changes in mild cognitive impaired Alzheimer's disease (MCI-AD) under resting and cognitive task conditions.

METHOD

EEG signals were recorded under resting states (Eyes closed (EC) and Eyes open (EO)) and cognitive states (Mental Arithmetic Eyes closed (MAEC) and Mental Arithmetic Eyes open (MAEO)) conditions. Functional connectivity metrics were calculated using weighted phase lag index (WPLI). Topological features of the functional connectivity network were analyzed through minimum spanning tree (MST) algorithm. Betweenness centrality was estimated in five different regions of the brain to study hub importance.

RESULTS

An increase in values of eccentricity and diameter were observed in patient group in five frequency bands of delta, theta, alpha1, alpha 2 and beta bands under resting and cognitive states. A reduction in leaf fraction was observed in alpha 1 band of EO condition. The results indicated a reduction in functional integration in the brain network of MCI-AD patients. Analysis of MST parameters revealed a higher disintegrated network for the alpha band under EO protocol. The study of hub status in the network displayed an elevated hub status in the central region for the patient group under cognitive task. The study also observed increased integration in gamma band in MCI - AD subjects than healthy controls under cognitive load.

CONCLUSION

Disintegration of functional network in alpha band under eyes open protocol and elevated hub strength in central region during cognitive task could be distinguishing features that could aid early detection of AD.

Association of Polygenic Liability for Alcohol Dependence and EEG Connectivity in Adolescence and Young Adulthood

Differences in the connectivity of large-scale functional brain networks among individuals with alcohol use disorders (AUD), as well as those at risk for AUD, point to dysfunctional neural communication and related cognitive impairments. In this study, we examined how polygenic risk scores (PRS), derived from a recent GWAS of DSM-IV Alcohol Dependence (AD) conducted by the Psychiatric Genomics Consortium, relate to longitudinal measures of interhemispheric and intrahemispheric EEG connectivity (alpha, theta, and beta frequencies) in adolescent and young adult offspring from the Collaborative Study on the Genetics of Alcoholism (COGA) assessed between ages 12 and 31. Our findings indicate that AD PRS (p-threshold < 0.001) was associated with increased fronto-central, tempo-parietal, centro-parietal, and parietal-occipital interhemispheric theta and alpha connectivity in males only from ages 18-31 (beta coefficients ranged from 0.02-0.06, p-values ranged from 10-6-10-12), but not in females. Individuals with higher AD PRS also demonstrated more performance deficits on neuropsychological tasks (Tower of London task, visual span test) as well as increased risk for lifetime DSM-5 alcohol and opioid use disorders. We conclude that measures of neural connectivity, together with neurocognitive performance and substance use behavior, can be used to further understanding of how genetic risk variants from large GWAS of AUD may influence brain function. In addition, these data indicate the importance of examining sex and developmental effects, which otherwise may be masked. Understanding of neural mechanisms linking genetic variants emerging from GWAS to risk for AUD throughout development may help to identify specific points when neurocognitive prevention and intervention efforts may be most effective.

Reachability Analysis of Neural Masses and Seizure Control Based on Combination Convolutional Neural Network

Epileptic seizures arise from synchronous firing of multiple spatially separated neural masses; therefore, many synchrony measures are used for seizure detection and characterization. However, synchrony measures reflect only the overall interaction strength among populations of neurons but cannot reveal the coupling strengths among individual populations, which is more important for seizure control. The concepts of reachability and reachable cluster were proposed to denote the coupling strengths of a set of neural masses. Here, we describe a seizure control method based on coupling strengths using combination convolutional neural network (CCNN) modeling. The neurophysiologically based neural mass model (NMM), which can bridge signal processing and neurophysiology, was used to simulate the proposed controller. Although the adjacency matrix and reachability matrix could not be identified perfectly, the vast majority of adjacency values were identified, reaching 95.64% using the CCNN with an optimal threshold. For cases of discrete and continuous coupling strengths, the proposed controller maintained the average reachable cluster strengths at about 0.1, indicating effective seizure control.

Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network

Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the massive workload of reviewing continuous EEGs. In this work, a novel approach, combining Stockwell transform (S-transform) with deep Convolutional Neural Networks (CNN), is proposed to detect seizure onsets in long-term intracranial EEG recordings. Primarily, raw EEG data is filtered with wavelet decomposition. Then, S-transform is used to obtain a proper time-frequency representation of each EEG segment. After that, a 15-layer deep CNN using dropout and batch normalization serves as a robust feature extractor and classifier. Finally, smoothing and collar technique are applied to the outputs of CNN to improve the detection accuracy and reduce the false detection rate (FDR). The segment-based and event-based evaluation assessments and receiver operating characteristic (ROC) curves are employed for the performance evaluation on a public EEG database containing 21 patients. A segment-based sensitivity of 97.01% and a specificity of 98.12% are yielded. For the event-based assessment, this method achieves a sensitivity of 95.45% with an FDR of 0.36/h.

High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease

Alzheimer’s Disease (AD) is a neurological disorder characterized by a progressive deterioration of brain functions that affects, above all, older adults. It can be difficult to make an early diagnosis because its first symptoms are often associated with normal aging. Electroencephalography (EEG) can be used for evaluating the loss of brain functional connectivity in AD patients. The purpose of this paper is to study the brain network parameters through the estimation of Lagged Linear Connectivity (LLC), computed by eLORETA software, applied to High-Density EEG (HD-EEG) for 84 regions of interest (ROIs). The analysis involved three groups of subjects: 10 controls (CNT), 21 Mild Cognitive Impairment patients (MCI) and 9 AD patients. In particular, the purpose is to compare the results obtained using a 256-channel EEG, the corresponding 10–10 system 64-channel EEG and the corresponding 10–20 system 18-channel EEG, both of which are extracted from the 256-electrode configuration. The computation of the Characteristic Path Length, the Clustering Coefficient, and the Connection Density from HD-EEG configuration reveals a weakening of small-world properties of MCI and AD patients in comparison to healthy subjects. On the contrary, the variation of the network parameters was not detected correctly when we employed the standard 10–20 configuration. Only the results from HD-EEG are consistent with the expected behavior of the AD brain network.

Leveraging network analysis to support experts in their analyses of subjects with MCI and AD

In this paper, we propose a network analysis-based approach to help experts in their analyses of subjects with mild cognitive impairment (hereafter, MCI) and Alzheimer's disease (hereafter, AD) and to investigate the evolution of these subjects over time. The inputs of our approach are the electroencephalograms (hereafter, EEGs) of the patients to analyze, performed at a certain time and, again, 3 months later. Given an EEG of a subject, our approach constructs a network with nodes that represent the electrodes and edges that denote connections between electrodes. Then, it applies several network-based techniques allowing the investigation of subjects with MCI and AD and the analysis of their evolution over time. (i) A connection coefficient, supporting experts to distinguish patients with MCI from patients with AD; (ii) A conversion coefficient, supporting experts to verify if a subject with MCI is converting to AD; (iii) Some network motifs, i.e., network patterns very frequent in one kind of patient and absent, or very rare, in the other. Patients with AD, just by the very nature of their condition, cannot be forced to stay motionless while undergoing examinations for a long time. EEG is a non-invasive examination that can be easily done on them. Since AD and MCI, if prodromal to AD, are associated with a loss of cortical connections, the adoption of network analysis appears suitable to investigate the effects of the progression of the disease on EEG. This paper confirms the suitability of this idea Graphical Abstract Ability of our proposed model to distinguish a control subject from a patient with MCI and a patient with AD. Blue edges represent strong connections among the corresponding brain areas; red edges denote middle connections, whereas green edges indicate weak connections. In the control subject (at the top), most connections are blue. In the patient with MCI (at the middle), most connections are red and green. In the patient with AD (at the bottom), most connections are either absent or green. .

Using Dictionary Pair Learning for Seizure Detection

Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal linearly separable. In DPL, the synthesis dictionary and analysis dictionary are learned jointly from original training samples with alternating minimization method, and sparse coefficients are obtained by using of linear projection instead of costly [Formula: see text]-norm or [Formula: see text]-norm optimization. At last, the reconstructed residuals associated with seizure and nonseizure sub-dictionary pairs are calculated as the decision values, and the postprocessing is performed for improving the recognition rate and reducing the false detection rate of the system. A total of 530[Formula: see text]h from 20 patients with 81 seizures were used to evaluate the system. Our proposed method has achieved an average segment-based sensitivity of 93.39%, specificity of 98.51%, and event-based sensitivity of 96.36% with false detection rate of 0.236/h.

Abnormalities of functional cortical source connectivity of resting-state electroencephalographic alpha rhythms are similar in patients with mild cognitive impairment due to Alzheimer's and Lewy body diseases

Previous evidence has shown different resting-state eyes-closed electroencephalographic delta (<4 Hz) and alpha (8-10.5 Hz) source connectivity in subjects with dementia due to Alzheimer's (ADD) and Lewy body (DLB) diseases. The present study tested if the same differences may be observed in the prodromal stages of mild cognitive impairment (MCI). Here, clinical and resting-state eyes-closed electroencephalographic data in age-, gender-, and education-matched 30 ADMCI, 23 DLBMCI, and 30 healthy elderly (Nold) subjects were available in our international archive. Mini-Mental State Evaluation (MMSE) score was matched in the ADMCI and DLBMCI groups. The eLORETA freeware estimated delta and alpha source connectivity by the tool called lagged linear connectivity (LLC). Area under receiver operating characteristic curve (AUROCC) indexed the classification accuracy among individuals. Results showed that widespread interhemispheric and intrahemispheric LLC solutions in alpha sources were abnormally lower in both MCI groups compared with the Nold group, but with no differences were found between the 2 MCI groups. AUROCCs of LLC solutions in alpha sources exhibited significant accuracies (0.72-0.75) in the discrimination of Nold versus ADMCI-DLBMCI individuals, but not between the 2 MCI groups. These findings disclose similar abnormalities in ADMCI and DLBMCI patients as revealed by alpha source connectivity. It can be speculated that source connectivity mostly reflects common cholinergic impairment in prodromal state of both AD and DLB, before a substantial dopaminergic derangement in the dementia stage of DLB.

Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment

Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.

Gaussian Discriminant Analysis for Optimal Delineation of Mild Cognitive Impairment in Alzheimer’s Disease

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer’s disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

Denoising Sparse Autoencoder-Based Ictal EEG Classification

Automatic seizure detection technology can automatically mark the EEG by using the epileptic detection algorithm, which is helpful to the diagnosis and treatment of epileptic diseases. This paper presents an EEG classification framework based on the denoising sparse autoencoder. The denoising sparse autoencoder (DSAE) is an improved unsupervised deep neural network over sparse autoencoder and denoising autoencoder, which can learn the closest representation of the data. The sparsity constraint applied in the hidden layer of the network makes the expression of data as sparse as possible so as to obtain a more efficient representation of EEG signals. In addition, corrupting operation used in input data help to enhance the robustness of the system and make it suitable for the analysis of non-stationary epileptic EEG signals. In this paper, we first imported the pre-processed training data to the DSAE network and trained the network. A logistic regression classifier was connected to the top of the DSAE. Then, put the test data into the system for classification. Finally, the output results of the overall network were post-processed to obtain the final epilepsy detection results. In the two-class (nonseizure and seizure EEGs) problem, the system has achieved effective results with the average sensitivity of 100%, specificity of 100%, and recognition of 100%, showing that the proposed framework can be efficient for the classification of epileptic EEGs.

A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer’s Disease and Healthy Controls

Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. Methods: 20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states. Results: In the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17, p < 0.0001). The EO/EC synchronization ratio, a measure of the dynamic changes between the two states, also showed significant differences between these two groups (AD ratio 0.78 versus HC ratio 0.37 p < 0.0001). EO synchronization was also significantly different between AD and FTD (FTD = 0.075; IQR 0.03, p < 0.0001). However, the EO/EC ratio was not informative in the FTD group due to very low levels of synchronization in both states (EO and EC). Conclusion: In this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.

Levetiracetam Alters Oscillatory Connectivity in Alzheimer's Disease

Seizures occur at a higher frequency in people with Alzheimer's disease (AD) but overt, clinically obvious events are infrequent. Evidence from animal models and studies in mild cognitive impairment suggest that subclinical epileptic discharges may play a role in the clinical and pathophysiological manifestations of AD. In this feasibility study, the neurophysiological and cognitive effects of acute administration of levetiracetam (LEV) are measured in patients with mild AD to test whether it could have a therapeutic benefit. AD participants were administered low dose LEV (2.5 mg/kg), higher dose LEV (7.5 mg/kg), or placebo in a double-blind, within-subject repeated measures study with EEG recorded at rest before and after administration. After administration of higher dose of LEV, we found significant decreases in coherence in the delta band (1-3.99 Hz) and increases in the low beta (13-17.99 Hz) and the high beta band (24-29.99 Hz). Furthermore, we found trends toward increased power in the frontal and central regions in the high beta band (24-29.99 Hz). However, there were no significant changes in cognitive performance after this single dose administration. The pattern of decreased coherence in the lower frequency bands and increased coherence in the higher frequency bands suggests a beneficial effect of LEV for patients with AD. Larger longitudinal studies and studies with healthy age-matched controls are needed to determine whether this represents a relative normalization of EEG patterns, whether it is unique to AD as compared to normal aging, and whether longer term administration is associated with a beneficial clinical effect.

Coherence and phase synchrony analyses of EEG signals in Mild Cognitive Impairment (MCI): A study of functional brain connectivity

This paper presents an EEG study for coherence and phase synchrony in mild cognitive impairment (MCI) subjects. MCI is characterized by cognitive decline, which is an early stage of Alzheimer’s disease (AD). AD is a neurodegenerative disorder with symptoms such as memory loss and cognitive impairment. EEG coherence is a statistical measure of correlation between signals from electrodes spatially separated on the scalp. The magnitude of phase synchrony is expressed in the phase locking value (PLV), a statistical measure of neuronal connectivity in the human brain. Brain signals were recorded using an Emotiv Epoc 14-channel wireless EEG at a sampling frequency of 128 Hz. In this study, we used 22 elderly subjects consisted of 10 MCI subjects and 12 healthy subjects as control group. The coherence between each electrode pair was measured for all frequency bands (delta, theta, alpha and beta). In the MCI subjects, the value of coherence and phase synchrony was generally lower than in the healthy subjects especially in the beta frequency. A decline of intrahemisphere coherence in the MCI subjects occurred in the left temporo-parietal-occipital region. The pattern of decline in MCI coherence is associated with decreased cholinergic connectivity along the path that connects the temporal, occipital, and parietal areas of the brain to the frontal area of the brain. EEG coherence and phase synchrony are able to distinguish persons who suffer AD in the early stages from healthy elderly subjects.

Analysis of long range dependence in the EEG signals of Alzheimer patients

Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.