Electroencephalographic rhythms in Alzheimer's disease

Association of a DASH diet and magnetoencephalography in dementia-free adults with different risk levels of Alzheimer's disease

This study explored how adherence to the DASH diet relates to electrophysiological measures in individuals at varying Alzheimer's disease (AD) risk due to family history (FH). There were 179 dementia-free subjects. DASH index was calculated, and participants were classified into different DASH adherence groups. Tertiles of relative alpha power in default mode network (DMN) regions were calculated. Multivariate logistic regression models were used to examine the association. Lower DASH adherence was associated with decreased odds of higher relative alpha power in the DMN, observed across the entire sample and specifically among those without a FH of AD. Logistic regression models indicated that participants with poorer DASH adherence had a reduced likelihood of elevated DMN alpha power, potentially influenced by vascular and amyloid-beta mechanisms. These findings underscore the dietary pattern's potential role in neural activity modulation, particularly in individuals not genetically predisposed to AD.

Alzheimer's disease-like features in resting state EEG/fMRI of cognitively intact and healthy middle-aged APOE/PICALM risk carriers

BackgroundGenetic susceptibility is a primary factor contributing to etiology of late-onset Alzheimer's disease (LOAD). The exact mechanisms and timeline through which APOE/PICALM influence brain functions and contribute to LOAD remain unidentified. This includes their effects on individuals prior to the development of the disease.ObjectiveTo investigate the effects of APOE and PICALM risk genes on brain health and function in non-demented individuals. This study aims to differentiate the combined risk effects of both genes from the risk associated solely with APOE, and to examine how PICALM alleles influence the risk linked to APOE.MethodsAPOE/PICALM alleles were assessed to determine the genetic risk of LOAD in 79 healthy, middle-aged participants who underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The resting-state signal was analyzed to estimate relative spectral power, complexity (Higuchi's algorithm), and connectivity (coherence in EEG and independent component analysis-based connectivity in fMRI).ResultsThe main findings indicated that individuals at risk for LOAD exhibited reduced signal complexity and the so-called "slowing of EEG" which are well-known EEG markers of Alzheimer's disease. Additionally, these individuals showed altered functional connectivity in fMRI (within attention-related areas).ConclusionsRisk alleles of APOE/PICALM may affect brain integrity and function prior to the clinical onset of the disease.

Early Diagnosis of Alzheimer's Disease in Human Participants Using EEGConformer and Attention-Based LSTM During the Short Question Task

Background/Objectives: Alzheimer's disease (AD) is a progressive neurodegenerative disorder advancing through subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia, making early diagnosis crucial. Electroencephalography (EEG) is a non-invasive, cost-effective alternative to advanced neuroimaging for detecting early neural changes. While most studies focus on resting-state EEG or handcrafted features with traditional machine learning, deep learning (DL) offers a promising tool for automated EEG analysis. This study classified the AD spectrum (SCD, MCI, AD) using EEG recorded during resting-state and task-based conditions. Specifically, EEG was recorded during a simple yes/no question-answering task, mimicking everyday cognitive activities, and was explored. We hypothesized that brain activity during tasks involving listening, comprehension, and response execution provides diagnostic insights. Methods: We collected 1 min of resting-state EEG and approximately 3 min of task-based EEG from 20, 28, and 10 participants with SCD, MCI, and AD, respectively. Task data included response accuracy and reaction time. After minimal preprocessing, two DL models, attention long short-term memory and EEGConformer, were used for binary (e.g., SCD vs. MCI) and three-class (SCD, MCI, AD) classification. Results: Task-based EEG outperformed resting-state EEG, with a 5-15% improvement in accuracy. The area under the curve (AUC) results consistently demonstrated superior classification performance for task-based EEG compared to resting-state EEG across all group distinctions. No significant performance difference was observed between the two DL models. Conclusions: We proposed a cognitive task-based approach for early AD spectrum diagnosis via EEG, offering greater accuracy by leveraging advanced DL models.

Age-related changes in EEG signal using triple correlation values

The alpha rhythm in human electroencephalography (EEG) is known to decrease in frequency with age. Previous study has shown that elderly individuals with dementia exhibit higher S values (spatial variability) and SD values (temporal variability) in the triple correlation of the occipital region (P3, P4, Oz) compared to healthy elderly individuals. The objective of this research is to examine changes in S and SD values of the alpha band with aging in healthy individuals using triple correlation values from the frontal region. The subjects were 50 healthy elderly subjects (mean age 73.0 ± 5.1 years), 34 healthy younger subjects (mean age 28.1 ± 4.6 years), and 21 dementia patients (mean age 70.1 ± 9.1 years). The methodology involved recording EEG for 5 min during rest with closed eyes, and then calculating S and SD values of the alpha band (8-13 Hz) using three electrodes in the frontal region (F3, F4, Fpz). The findings indicated that the S values of young individuals were significantly higher than those of elderly individuals (p < 0.01), whereas the SD values of young individuals tended to be lower than those of elderly individuals. The elevated S values in young individuals imply greater spatial variability akin to individuals with dementia, whereas the reduced SD values in young individuals suggest lower temporal variability unlike individuals with dementia. The discrepancy between the S value and SD value in healthy young individuals suggests that the normal cortical dipole in the frontal regions might be more abundant in them compared to healthy elderly individuals.

Oscillatory characteristics of resting-state magnetoencephalography reflect pathological and symptomatic conditions of cognitive impairment

Background

Dementia and mild cognitive impairment are characterised by symptoms of cognitive decline, which are typically assessed using neuropsychological assessments (NPAs), such as the Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). Magnetoencephalography (MEG) is a novel clinical assessment technique that measures brain activities (summarised as oscillatory parameters), which are associated with symptoms of cognitive impairment. However, the relevance of MEG and regional cerebral blood flow (rCBF) data obtained using single-photon emission computed tomography (SPECT) has not been examined using clinical datasets. Therefore, this study aimed to investigate the relationships among MEG oscillatory parameters, clinically validated biomarkers computed from rCBF, and NPAs using outpatient data retrieved from hospital records.

Methods

Clinical data from 64 individuals with mixed pathological backgrounds were retrieved and analysed. MEG oscillatory parameters, including relative power (RP) from delta to high gamma bands, mean frequency, individual alpha frequency, and Shannon's spectral entropy, were computed for each cortical region. For SPECT data, three pathological parameters-'severity', 'extent', and 'ratio'-were computed using an easy z-score imaging system (eZIS). As for NPAs, the MMSE and FAB scores were retrieved.

Results

MEG oscillatory parameters were correlated with eZIS parameters. The eZIS parameters associated with Alzheimer's disease pathology were reflected in theta power augmentation and slower shift of the alpha peak. Moreover, MEG oscillatory parameters were found to reflect NPAs. Global slowing and loss of diversity in neural oscillatory components correlated with MMSE and FAB scores, whereas the associations between eZIS parameters and NPAs were sparse.

Conclusion

MEG oscillatory parameters correlated with both SPECT (i.e. eZIS) parameters and NPAs, supporting the clinical validity of MEG oscillatory parameters as pathological and symptomatic indicators. The findings indicate that various components of MEG oscillatory characteristics can provide valuable pathological and symptomatic information, making MEG data a rich resource for clinical examinations of patients with cognitive impairments. SPECT (i.e. eZIS) parameters showed no correlations with NPAs. The results contributed to a better understanding of the characteristics of electrophysiological and pathological examinations for patients with cognitive impairments, which will help to facilitate their co-use in clinical application, thereby improving patient care.

rTMS reduces delta and increases theta oscillations in Alzheimer's disease: A visual-evoked and event-related potentials study

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising alternative therapy for Alzheimer's disease (AD) due to its ability to modulate neural networks and enhance cognitive function. This treatment offers the unique advantage of enabling real-time monitoring of immediate cognitive effects and dynamic brain changes through electroencephalography (EEG).

OBJECTIVE

This study focused on exploring the effects of left parietal rTMS stimulation on visual-evoked potentials (VEP) and visual event-related potentials (VERP) in AD patients.

METHODS

Sixteen AD patients were recruited for this longitudinal study. EEG data were collected within a Faraday cage both pre- and post-rTMS to evaluate its impact on potentials.

RESULTS

Significant alterations were found in both VEP and VERP oscillations. Specifically, delta power in VEP decreased, while theta power in VERP increased post-rTMS, indicating a modulation of brain activities.

DISCUSSION

These findings confirm the positive modulatory impact of rTMS on brain activities in AD, evidenced by improved cognitive scores. They align with previous studies highlighting the potential of rTMS in managing hyperexcitability and oscillatory disturbances in the AD cortex.

CONCLUSION

Cognitive improvements post-rTMS endorse its potential as a promising neuromodulatory treatment for cognitive enhancement in AD, thereby providing critical insights into the neurophysiological anomalies in AD and possible therapeutic avenues.

qEEG as Biomarker for Alzheimer's Disease: Investigating Relative PSD Difference and Coherence Analysis

Purpose

Electroencephalography (EEG) is a non-intrusive technique that provides comprehensive insights into the electrical activities of the brain's cerebral cortex. The brain signals obtained from EEGs can be used as a neuropsychological biomarker to detect different stages of Alzheimer's disease (AD) through quantitative EEG (qEEG) analysis. This paper investigates the difference in the abnormalities of resting state EEG (rEEG) signals between eyes-open (EOR) and eyes-closed (ECR) in AD by analyzing 19-scalp electrode EEG signals and making a comparison with healthy controls (HC).

Participants and Methods

The rEEG data from 534 subjects (ages 40-90) consisting of 269 HC and 265 AD subjects in South Korea were used in this study. The qEEG for EOR and ECR states were performed separately for HC and AD subjects to measure the relative power spectrum density (PSD) and coherence with functional connectivity to evaluate abnormalities. The rEEG data were preprocessed and analyzed using EEGlab and Brainstorm toolboxes in MATLAB R2021a software, and statistical analyses were carried out using ANOVA.

Results

Based on the Welch method, the relative PSD of the EEG EOR and ECR states difference in the AD group showed a significant increase in the delta frequency band of 19 EEG channels, particularly in the frontal, parietal, and temporal, than the HC groups. The delta power band on the source level was increased for the AD group and decreased for the HC group. In contrast, the source activities of alpha, beta, and gamma frequency bands were significantly reduced in the AD group, with a high decrease in the beta frequency band in all brain areas. Furthermore, the coherence of rEEG among different EEG electrodes was analyzed in the beta frequency band. It showed that pair-wise coherence between different brain areas in the AD group is remarkably increased in the ECR state and decreased after subtracting out the EOR state.

Conclusion

The findings suggest that examining PSD and functional connectivity through coherence analysis could serve as a promising and comprehensive approach to differentiate individuals with AD from normal, which may benefit our understanding of the disease.

Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer's Disease

The development of early non-invasive diagnosis methods and identification of novel biomarkers are necessary for managing Alzheimer's disease (AD) and facilitating effective prognosis and treatment. AD has multi-factorial nature and involves complex molecular mechanism, which causes neuronal degeneration. The primary challenges in early AD detection include patient heterogeneity and lack of precise diagnosis at the preclinical stage. Several cerebrospinal fluid (CSF) and blood biomarkers have been proposed to show excellent diagnosis ability by identifying tau pathology and cerebral amyloid beta (Aβ) for AD. Intense research endeavors are being made to develop ultrasensitive detection techniques and find potent biomarkers for early AD diagnosis. To mitigate AD worldwide, understanding various CSF biomarkers, blood biomarkers, and techniques that can be used for early diagnosis is imperative. This review attempts to provide information regarding AD pathophysiology, genetic and non-genetic factors associated with AD, several potential blood and CSF biomarkers, like neurofilament light, neurogranin, Aβ, and tau, along with biomarkers under development for AD detection. Besides, numerous techniques, such as neuroimaging, spectroscopic techniques, biosensors, and neuroproteomics, which are being explored to aid early AD detection, have been discussed. The insights thus gained would help in finding potential biomarkers and suitable techniques for the accurate diagnosis of early AD before cognitive dysfunction.

Alterations of resting-state Gamma frequency characteristics in aging and Alzheimer's disease

Alzheimer's disease (AD) is an important brain disease associated with aging. It involves various functional and structural changes which alter the EEG characteristics. Although numerous studies have found changes in delta, theta, alpha, and beta power, fewer studies have looked at the changes in the resting state EEG gamma activity characteristics in AD. This study aimed to investigate the alterations in the frequency and power values of AD patients' resting-state EEG gamma oscillations compared with healthy elderly and young subjects. We performed Fast Fourier Transform (FFT) on the resting state EEG data from 179 participants, including 59 early stage AD patients, 60 healthy elderly, and 60 healthy young subjects. We averaged FFT performed epochs to investigate the power values in the gamma frequency range (28-48 Hz). We then sorted the peaks of power values in the gamma frequency range, and the average of the identified highest three values was named as the gamma dominant peak frequency. The gamma dominant peak frequency of AD patients (Meyes-opened = 33.4 Hz, Meyes-closed = 32.7 Hz) was lower than healthy elderly (Meyes-opened = 35.5 Hz, Meyes-closed = 35.0 Hz) and healthy young subjects (Meyes-opened = 37.2 Hz, Meyes-closed = 37.0 Hz). These results could be related to AD progression and therefore critical for the recent findings regarding the 40 Hz gamma entrainment because it seems they entrain the gamma frequency of AD towards that of healthy young.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09873-4.

Role of Entorhinal and Parahippocampal in Diagnosis of Alzheimer's Disease from Resting State EEG

Alzheimer's disease (AD) poses a significant public health problem. An early diagnosis during the initial development stage of this neurodegenerative brain disorder is a requisite. Initial symptoms of AD involve dysfunctioning in brain memory which later progresses toward language, comprehension, reasoning, and social behavior. The degree of dysfunctionality in AD is accompanied by neuronal damage thereby leading to alteration in functional connectivity of brain regions with AD progression. In literature, substantial studies have focused on topological brain function characteristics, scalp EEG temporal features, and functional MRI or PET scan to diagnose AD. However, source domain based study using EEG is limited. This work establishes the significance of EEG based source domain connectivity in AD diagnosis. In particular, the cortical sources, Parahippocampal and Entorhinal, are particularly studied for cognitive processes and memory in AD and healthy control (HC). A publicly available AD and HC resting state EGG dataset is utilized for this purpose. The dipole imaging method converts surface EEG information into source space. Functional connectivity (FC), supervised classification, frequency analysis, and clustering are then utilized to establish the importance of the entorhinal and parahippocampal in AD diagnosis. The entorhinal source involved in memory is found to be a potential biomarker for AD diagnosis. This memory-associated source dynamics-based approach can further lead to early diagnosis of AD.

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals.Approach. EEG recordings of 17 healthy controls (HCs), 56 subjective cognitive decline (SCD) and 45 mild cognitive impairment (MCI) subjects were acquired at resting state. After preprocessing, we selected sections corresponding to eyes-closed condition. Five different datasets were created by extracting delta, theta, alpha, beta and delta-to-theta frequency bands using bandpass filters. To classify SCDvsMCI and HCvsSCDvsMCI, we propose a framework based on the transformer architecture, which uses multi-head attention to focus on the most relevant parts of the input signals. We trained and validated the model on each dataset with a leave-one-subject-out cross-validation approach, splitting the signals into 10 s epochs. Subjects were assigned to the same class as the majority of their epochs. Classification performances of the transformer were assessed for both epochs and subjects and compared with other DL models.Main results. Results showed that the delta dataset allowed our model to achieve the best performances for the discrimination of SCD and MCI, reaching an Area Under the ROC Curve (AUC) of 0.807, while the highest results for the HCvsSCDvsMCI classification were obtained on alpha and theta with a micro-AUC higher than 0.74.Significance. We demonstrated that DL approaches can support the adoption of non-invasive and economic techniques as EEG to stratify patients in the clinical population at risk for AD. This result was achieved since the attention mechanism was able to learn temporal dependencies of the signal, focusing on the most discriminative patterns, achieving state-of-the-art results by using a deep model of reduced complexity. Our results were consistent with clinical evidence that changes in brain activity are progressive when considering early stages of AD.

Genetic association of apolipoprotein E genotype with EEG alpha rhythm slowing and functional brain network alterations during normal aging

The ε4 allele of the apolipoprotein E (APOE4+) genotype is a major genetic risk factor for Alzheimer’s disease (AD), but the mechanisms underlying its influence remain incompletely understood. The study aimed to investigate the possible effect of the APOE genotype on spontaneous electroencephalogram (EEG) alpha characteristics, resting-state functional MRI (fMRI) connectivity (rsFC) in large brain networks and the interrelation of alpha rhythm and rsFC characteristics in non-demented adults during aging. We examined the EEG alpha subband’s relative power, individual alpha peak frequency (IAPF), and fMRI rsFC in non-demented volunteers (age range 26–79 years) stratified by the APOE genotype. The presence of the APOE4+ genotype was associated with lower IAPF and lower relative power of the 11–13 Hz alpha subbands. The age related decrease in EEG IAPF was more pronounced in the APOE4+ carriers than in the APOE4+ non-carriers (APOE4-). The APOE4+ carriers had a stronger fMRI positive rsFC of the interhemispheric regions of the frontoparietal, lateral visual and salience networks than the APOE4– individuals. In contrast, the negative rsFC in the network between the left hippocampus and the right posterior parietal cortex was reduced in the APOE4+ carriers compared to the non-carriers. Alpha rhythm slowing was associated with the dysfunction of hippocampal networks. Our results show that in adults without dementia APOE4+ genotype is associated with alpha rhythm slowing and that this slowing is age-dependent. Our data suggest predominant alterations of inhibitory processes in large-scale brain network of non-demented APOE4+ carriers. Moreover, dysfunction of large-scale hippocampal network can influence APOE-related alpha rhythm vulnerability.

Integrating music-based interventions with Gamma-frequency stimulation: Implications for healthy ageing

In recent years, music-based interventions (MBIs) have risen in popularity as a non-invasive, sustainable form of care for treating dementia-related disorders, such as Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD). Despite their clinical potential, evidence regarding the efficacy of MBIs on patient outcomes is mixed. Recently, a line of related research has begun to investigate the clinical impact of non-invasive Gamma-frequency (e.g., 40 Hz) sensory stimulation on dementia. Current work, using non-human-animal models of AD, suggests that non-invasive Gamma-frequency stimulation can remediate multiple pathophysiologies of dementia at the molecular, cellular and neural-systems scales, and, importantly, improve cognitive functioning. These findings suggest that the efficacy of MBIs could, in theory, be enhanced by incorporating Gamma-frequency stimulation into current MBI protocols. In the current review, we propose a novel clinical framework for non-invasively treating dementia-related disorders that combines previous MBIs with current approaches employing Gamma-frequency sensory stimulation. We theorize that combining MBIs with Gamma-frequency stimulation could increase the therapeutic power of MBIs by simultaneously targeting multiple biomarkers of dementia, restoring neural activity that underlies learning and memory (e.g., Gamma-frequency neural activity, Theta-Gamma coupling), and actively engaging auditory and reward networks in the brain to promote behavioural change.

Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules?

Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.

Altered theta rhythm and hippocampal-cortical interactions underlie working memory deficits in a hyperglycemia risk factor model of Alzheimer's disease

Diabetes mellitus is a metabolic disease associated with dysregulated glucose and insulin levels and an increased risk of developing Alzheimer's disease (AD) later in life. It is thought that chronic hyperglycemia leads to neuroinflammation and tau hyperphosphorylation in the hippocampus leading to cognitive decline, but effects on hippocampal network activity are unknown. A sustained hyperglycemic state was induced in otherwise healthy animals and subjects were then tested on a spatial delayed alternation task while recording from the hippocampus and anterior cingulate cortex (ACC). Hyperglycemic animals performed worse on long delay trials and had multiple electrophysiological differences throughout the task. We found increased delta power and decreased theta power in the hippocampus, which led to altered theta/delta ratios at the end of the delay period. Cross frequency coupling was significantly higher in multiple bands and delay period hippocampus-ACC theta coherence was elevated, revealing hypersynchrony. The highest coherence values appeared long delays on error trials for STZ animals, the opposite of what was observed in controls, where lower delay period coherence was associated with errors. Consistent with previous investigations, we found increases in phosphorylated tau in STZ animals' hippocampus and cortex, which might account for the observed oscillatory and cognitive changes.

Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.

Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential

Objective: To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia.

Methods: We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by auditory stimulation [sensory event-related potential (ERP)], and selective attention tasks (selective-attention ERP) from 122 elderly participants (dementia, 35; control, 87). We investigated that the association between MMSE and each EEG/ERP variable by using Pearson’s correlation coefficient and performing univariate linear regression analysis. Kernel density estimation was used to examine the distribution of each EEG/ERP variable in the dementia and non-dementia groups. Both Univariate and multiple logistic regression analyses with the estimated odds ratios were conducted to assess the associations between the EEG/ERP variables and dementia prevalence. To develop the predictive models, five-fold cross-validation was applied to multiple classification algorithms.

Results: Most prefrontal EEG/ERP variables, previously known to be associated with cognitive decline, show correlations with the MMSE score (strongest correlation has |r| = 0.68). Although variables such as the frontal asymmetry of the resting-state EEG are not well correlated with the MMSE score, they indicate risk factors for dementia. The selective-attention ERP and resting-state EEG variables outperform the MMSE scores in dementia prediction (areas under the receiver operating characteristic curve of 0.891, 0.824, and 0.803, respectively). In addition, combining EEG/ERP variables and MMSE scores improves the model predictive performance, whereas adding demographic risk factors do not improve the prediction accuracy.

Conclusion: Prefrontal EEG markers outperform MMSE scores in predicting dementia, and additional prediction accuracy is expected when combining them with MMSE scores.

Significance: Prefrontal EEG is effective for screening dementia when used independently or in combination with MMSE.

A Comparison of Traditional and Serious Game-Based Digital Markers of Cognition in Older Adults with Mild Cognitive Impairment and Healthy Controls

Background:

Electroencephalography (EEG) has been used to assess brain activity while users are playing an immersive serious game.

Objective:

To assess differences in brain activation as measured with a non-intrusive wearable EEG device, differences in game performance and correlations between EEG power, game performance and global cognition, between cognitively impaired and non-impaired older adults, during the administration of a novel self-administered serious game-based test, the Virtual Supermarket Test (VST).

Methods:

43 older adults with subjective cognitive decline (SCD) and 33 older adults with mild cognitive impairment (MCI) were recruited from day centers for cognitive disorders. Global cognition was assessed with the Montreal Cognitive Assessment (MoCA). Brain activity was measured with a non-intrusive wearable EEG device in a resting state condition and while they were administered the VST.

Results:

During resting state condition, the MCI group showed increased alpha, beta, delta, and theta band power compared to the SCD group. During the administration of the VST, the MCI group showed increased beta and theta band power compared to the SCD group. Regarding game performance, alpha, beta, delta, and theta rhythms were positively correlated with average duration, while delta rhythm was positively correlated with mean errors. MoCA correlated with alpha, beta, delta, and theta rhythms and with average game duration and mean game errors indicating that elevated EEG rhythms in MCI may be associated with an overall cognitive decline.

Conclusion:

VST performance can be used as a digital biomarker. Cheap commercially available wearable EEG devices can be used for obtaining brain activity biomarkers.

Dual-Task Training Affect Cognitive and Physical Performances and Brain Oscillation Ratio of Patients With Alzheimer's Disease: A Randomized Controlled Trial

This study aimed to investigate the effect of 12 weeks of dual-task training on cognitive status, physical performance, and brain oscillation of patients with Alzheimer's disease (AD). Twenty-six AD patients were randomly assigned to two groups, the training group (TG) and control group (CG). TG executed progressive combined exercises with visual stimulation twice a week for 12 weeks. Training included muscle endurance, balance, flexibility, and aerobic exercises with eyes closed and opened. Brain oscillation on electroencephalography (EEG) and a series of physical, cognitive, and mental tests were taken before and post-intervention. There was a significant improvement after training protocol in cognitive function, particularly in short-term and working memory, attention, and executive function (p < 0.01). Besides, there were substantial improvements in depression status (GDS scale), aerobic fitness (6 min walking), flexibility (chair sit and reach) functional ability (chair stand, timed up and go test), strength (knee extensions, preacher biceps curl, handgrip) in TG compared to CG. These signs of progress were associated with a significant increase (p < 0.05) in the frequency of brain oscillation and a decrease in the theta/alpha ratio. In addition to physical performance, the regular combined training with visual stimulation improves brain health as indicated by improving cognitive function and reducing the theta/alpha ratio. Clinical Trial Registration: Iranian Registry of Clinical Trials (IRCT) https://www.irct.ir/, identifier IRCT20190504043468N1-August 5, 2020.

Sleep/Wake Behavior and EEG Signatures of the TgF344-AD Rat Model at the Prodromal Stage

Transgenic modification of the two most common genes (APPsw, PS1ΔE9) related to familial Alzheimer's disease (AD) in rats has produced a rodent model that develops pathognomonic signs of AD without genetic tau-protein modification. We used 17-month-old AD rats (n = 8) and age-matched controls (AC, n = 7) to evaluate differences in sleep behavior and EEG features during wakefulness (WAKE), non-rapid eye movement sleep (NREM), and rapid eye movement sleep (REM) over 24-h EEG recording (12:12h dark-light cycle). We discovered that AD rats had more sleep-wake transitions and an increased probability of shorter REM and NREM bouts. AD rats also expressed a more uniform distribution of the relative spectral power. Through analysis of information content in the EEG using entropy of difference, AD animals demonstrated less EEG information during WAKE, but more information during NREM. This seems to indicate a limited range of changes in EEG activity that could be caused by an AD-induced change in inhibitory network function as reflected by increased GABAAR-β2 expression but no increase in GAD-67 in AD animals. In conclusion, this transgenic rat model of Alzheimer's disease demonstrates less obvious EEG features of WAKE during wakefulness and less canonical features of sleep during sleep.

A nonparametric Bayesian model for estimating spectral densities of resting-state EEG twin data

Electroencephalography (EEG) is a noninvasive neuroimaging modality that captures electrical brain activity many times per second. We seek to estimate power spectra from EEG data that ware gathered for 557 adolescent twin pairs through the Minnesota Twin Family Study (MTFS). Typically, spectral analysis methods treat time series from each subject separately, and independent spectral densities are fit to each time series. Since the EEG data were collected on twins, it is reasonable to assume that the time series have similar underlying characteristics, so borrowing information across subjects can significantly improve estimation. We propose a Nested Bernstein Dirichlet prior model to estimate the power spectrum of the EEG signal for each subject by smoothing periodograms within and across subjects while requiring minimal user input to tuning parameters. Furthermore, we leverage the MTFS twin study design to estimate the heritability of EEG power spectra with the hopes of establishing new endophenotypes. Through simulation studies designed to mimic the MTFS, we show our method out-performs a set of other popular methods.

Widespread Reductions of Spontaneous Neurophysiological Activity in Leber’s Disease—An Application of EEG Source Current Density Reconstruction

Leber’s hereditary optic neuropathy (LHON) is a rare, maternally inherited genetic disease caused by a mutation of mitochondrial DNA. Classical descriptions have highlighted structural abnormalities in various parts of patients’ optic tracts; however, current studies have proved that changes also affect many cortical and subcortical structures, not only these belonging to the visual system. This study aimed at improving our understanding of neurophysiological impairments in LHON. First of all, we wanted to know if there were any differences between the health control and LHON subjects in the whole-brain source electroencephalography (EEG) analysis. Second, we wanted to investigate the associations between the observed results and some selected aspects of Leber’s disease’s clinical picture. To meet these goals, 20 LHON patients and 20 age-matched healthy control (HC) subjects were examined. To investigate the electrophysiological differences between the HC and LHON groups, a quantitative analysis of the whole-brain current source density was performed. The signal analysis method was based on scalp EEG data and an inverse solution method called low-resolution brain electromagnetic tomography (eLORETA). In comparison with the healthy subjects, LHON participants showed significantly decreased neuronal activity in the alpha and gamma bands; more specifically, in the alpha band, the decrease was mainly found in the occipital lobes and secondary visual cortex, whereas, in the gamma band, the reduced activity occurred in multiple cortical areas. Additionally, a correlation was found between the alpha band activity of the right secondary visual cortex and the averaged thickness of the right retinal nerve fiber layer in the LHON participants. Our study suggests that LHON is associated with widespread cortical de-activation, rather than simply abnormalities of structures constituting the visual system.

Genetic Association Between Alzheimer's Disease Risk Variant of the PICALM Gene and EEG Functional Connectivity in Non-demented Adults

Genome wide association studies (GWAS) have identified and validated the association of the PICALM genotype with Alzheimer's disease (AD). The PICALM rs3851179 A allele is thought to have a protective effect, whereas the G allele appears to confer risk for AD. The influence of the PICALM genotype on brain functional connectivity in non-demented subjects remains largely unknown. We examined the association of the PICALM rs3851179 genotype with the characteristics of lagged linear connectivity (LLC) of resting EEG sources in 104 non-demented adults younger than 60 years of age. The EEG analysis was performed using exact low-resolution brain electromagnetic tomography (eLORETA) freeware (Pascual-Marqui et al., 2011). We found that the carriers of the A PICALM allele (PICALM AA and AG genotypes) had higher widespread interhemispheric LLC of alpha sources compared to the carriers of the GG PICALM allele. An exploratory correlation analysis showed a moderate positive association between the alpha LLC interhemispheric characteristics and the corpus callosum size and between the alpha interhemispheric LLC characteristics and the Luria word memory scores. These results suggest that the PICALM rs3851179 A allele provides protection against cognitive decline by facilitating neurophysiological reserve capacities in non-demented adults. In contrast, lower functional connectivity in carriers of the AD risk variant, PICALM GG, suggests early functional alterations in alpha rhythm networks.

Non-pharmacological treatment changes brain activity in patients with dementia

Non-pharmacological treatment (NPT) improves cognitive functions and behavioural disturbances in patients with dementia, but the underlying neural mechanisms are unclear. In this observational study, 21 patients with dementia received NPTs for several months. Patients were scanned using magnetoencephalography twice during the NPT period to evaluate NPT effects on resting-state brain activity. Additionally, cognitive functions and behavioural disturbances were measured using the Mini-Mental State Examination (MMSE-J) and a short version of the Dementia Behaviour Disturbance Scale (DBD-13) at the beginning and the end of the NPT period. In contrast to the average DBD-13 score, the average MMSE-J score improved after the NPT period. Magnetoencephalography data revealed a reduced alpha activity in the right temporal lobe and fusiform gyrus, as well as an increased low-gamma activity in the right angular gyrus. DBD-13 score changes were correlated with beta activity in the sensorimotor area. These findings corroborate previous studies confirming NPT effects on brain activity in healthy participants and people at risk of dementia. Our results provide additional evidence that brains of patients with dementia have the capacity for plasticity, which may be responsible for the observed NPT effects. In dementia, NPT might lead to improvements in the quality of life.

Neurophysiological signatures in Alzheimer's disease are distinctly associated with TAU, amyloid-β accumulation, and cognitive decline

Neural synchrony is intricately balanced in the normal resting brain but becomes altered in Alzheimer's disease (AD). To determine the neurophysiological manifestations associated with molecular biomarkers of AD neuropathology, in patients with AD, we used magnetoencephalographic imaging (MEGI) and positron emission tomography with amyloid-beta (Aβ) and TAU tracers. We found that alpha oscillations (8 to 12 Hz) were hyposynchronous in occipital and posterior temporoparietal cortices, whereas delta-theta oscillations (2 to 8 Hz) were hypersynchronous in frontal and anterior temporoparietal cortices, in patients with AD compared to age-matched controls. Regional patterns of alpha hyposynchrony were unique in each neurobehavioral phenotype of AD, whereas the regional patterns of delta-theta hypersynchrony were similar across the phenotypes. Alpha hyposynchrony strongly colocalized with TAU deposition and was modulated by the degree of TAU tracer uptake. In contrast, delta-theta hypersynchrony colocalized with both TAU and Aβ depositions and was modulated by both TAU and Aβ tracer uptake. Furthermore, alpha hyposynchrony but not delta-theta hypersynchrony was correlated with the degree of global cognitive dysfunction in patients with AD. The current study demonstrates frequency-specific neurophysiological signatures of AD pathophysiology and suggests that neurophysiological measures from MEGI are sensitive indices of network disruptions mediated by TAU and Aβ and associated cognitive decline. These findings facilitate the pursuit of novel therapeutic approaches toward normalizing network synchrony in AD.

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson's correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.

Alzheimer's Disease Diagnosis and Severity Level Detection Based on Electroencephalography Modulation Spectral "Patch" Features

Over the last two decades, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). Typically, resting-state EEG (rsEEG) signals have been used, and traditional frequency bands (delta, theta, alpha, beta and gamma) have been explored. Recent studies, however, have suggested that non-conventional bands may lead to improved diagnostic performance. In this work, we propose a new type of features derived from the 2-dimensional modulation spectral domain representation of the rsEEG signal in order to characterize the neuromodulatory deficit emergent with AD. The proposed features are computed as the power in specific "patches" or regions of interest in the power modulation spectrogram, which are shown to be highly discriminant of AD severity levels. The proposed features were compared with traditional features used in the rsEEG AD monitoring literature. Results showed the proposed features not only achieving improved performance at discriminating between healthy normal elderly controls (Nold) and AD patients with varying severity levels, but also at monitoring severity levels (i.e., mild AD versus moderate AD). Moreover, the proposed features were shown to outperform traditional rsEEG features. Finally, we validated the biological origin of the proposed features by using source localization and comparing the obtained results with ones reported in the AD literature.

Electromagnetic signatures of the preclinical and prodromal stages of Alzheimer's disease

Biomarkers useful for the predementia stages of Alzheimer’s disease are needed. Electroencephalography and magnetoencephalography (MEG) are expected to provide potential biomarker candidates for evaluating the predementia stages of Alzheimer’s disease. However, the physiological relevance of EEG/MEG signal changes and their role in pathophysiological processes such as amyloid-β deposition and neurodegeneration need to be elucidated. We evaluated 28 individuals with mild cognitive impairment and 38 cognitively normal individuals, all of whom were further classified into amyloid-β-positive mild cognitive impairment (n = 17, mean age 74.7 ± 5.4 years, nine males), amyloid-β-negative mild cognitive impairment (n = 11, mean age 73.8 ± 8.8 years, eight males), amyloid-β-positive cognitively normal (n = 13, mean age 71.8 ± 4.4 years, seven males), and amyloid-β-negative cognitively normal (n = 25, mean age 72.5 ± 3.4 years, 11 males) individuals using Pittsburgh compound B-PET. We measured resting state MEG for 5 min with the eyes closed, and investigated regional spectral patterns of MEG signals using atlas-based region of interest analysis. Then, the relevance of the regional spectral patterns and their associations with pathophysiological backgrounds were analysed by integrating information from Pittsburgh compound B-PET, fluorodeoxyglucose-PET, structural MRI, and cognitive tests. The results demonstrated that regional spectral patterns of resting state activity could be separated into several types of MEG signatures as follows: (i) the effects of amyloid-β deposition were expressed as the alpha band power augmentation in medial frontal areas; (ii) the delta band power increase in the same region was associated with disease progression within the Alzheimer’s disease continuum and was correlated with entorhinal atrophy and an Alzheimer’s disease-like regional decrease in glucose metabolism; and (iii) the global theta power augmentation, which was previously considered to be an Alzheimer’s disease-related EEG/MEG signature, was associated with general cognitive decline and hippocampal atrophy, but was not specific to Alzheimer’s disease because these changes could be observed in the absence of amyloid-β deposition. The results suggest that these MEG signatures may be useful as unique biomarkers for the predementia stages of Alzheimer’s disease.

Improving the performance of empirical mode decomposition via Tsallis entropy: Application to Alzheimer EEG analysis

Alzheimer is a degenerative disorder that attacks neurons, resulting in loss of memory, thinking, language skills, and behavioral changes. Computer-aided detection methods can uncover crucial information recorded by electroencephalograms. A systematic literature search presents the wavelet transform as a frequently used technique in Alzheimer's detection. However, it requires a defined basis function considered a significant problem. In this work, the concept of empirical mode decomposition is introduced as an alternative to process Alzheimer signals. The performance of empirical mode decomposition heavily relies on a parameter called threshold. In our previous works, we found that the existing thresholding techniques were not able to highlight relevant information. The use of Tsallis entropy as a thresholder is evaluated through the combination of empirical mode decomposition and neural networks. Thanks to the extraction of better features that boost the classification accuracy, the proposed approach outperforms the state-of-the-art in terms of peak signal to noise ratio and root mean square error. Hence, our methodology is more likely to succeed than methods based on other landmarks such as Bayes, Normal and Visu shrink. We finally report an accuracy rate of 80%, while the aforementioned techniques only yield performances of 65%, 60% and 40%, respectively.

Chronic pulmonary exposure to traffic-related fine particulate matter causes brain impairment in adult rats

BACKGROUND

Effects of air pollution on neurotoxicity and behavioral alterations have been reported. The objective of this study was to investigate the pathophysiology caused by particulate matter (PM) in the brain. We examined the effects of traffic-related particulate matter with an aerodynamic diameter of < 1 μm (PM₁), high-efficiency particulate air (HEPA)-filtered air, and clean air on the brain structure, behavioral changes, brainwaves, and bioreactivity of the brain (cortex, cerebellum, and hippocampus), olfactory bulb, and serum after 3 and 6 months of whole-body exposure in 6-month-old Sprague Dawley rats.

RESULTS

The rats were exposed to 16.3 ± 8.2 (4.7~ 68.8) μg/m³ of PM₁ during the study period. An MRI analysis showed that whole-brain and hippocampal volumes increased with 3 and 6 months of PM₁ exposure. A short-term memory deficiency occurred with 3 months of exposure to PM₁ as determined by a novel object recognition (NOR) task, but there were no significant changes in motor functions. There were no changes in frequency bands or multiscale entropy of brainwaves. Exposure to 3 months of PM₁ increased 8-isoporstance in the cortex, cerebellum, and hippocampus as well as hippocampal inflammation (interleukin (IL)-6), but not in the olfactory bulb. Systemic CCL11 (at 3 and 6 months) and IL-4 (at 6 months) increased after PM₁ exposure. Light chain 3 (LC3) expression increased in the hippocampus after 6 months of exposure. Spongiosis and neuronal shrinkage were observed in the cortex, cerebellum, and hippocampus (neuronal shrinkage) after exposure to air pollution. Additionally, microabscesses were observed in the cortex after 6 months of PM₁ exposure.

CONCLUSIONS

Our study first observed cerebral edema and brain impairment in adult rats after chronic exposure to traffic-related air pollution.

Chronic pulmonary exposure to traffic-related fine particulate matter causes brain impairment in adult rats

Background

Effects of air pollution on neurotoxicity and behavioral alterations have been reported. The objective of this study was to investigate the pathophysiology caused by particulate matter (PM) in the brain. We examined the effects of traffic-related particulate matter with an aerodynamic diameter of < 1 μm (PM₁), high-efficiency particulate air (HEPA)-filtered air, and clean air on the brain structure, behavioral changes, brainwaves, and bioreactivity of the brain (cortex, cerebellum, and hippocampus), olfactory bulb, and serum after 3 and 6 months of whole-body exposure in 6-month-old Sprague Dawley rats.

Results

The rats were exposed to 16.3 ± 8.2 (4.7~ 68.8) μg/m³ of PM₁ during the study period. An MRI analysis showed that whole-brain and hippocampal volumes increased with 3 and 6 months of PM₁ exposure. A short-term memory deficiency occurred with 3 months of exposure to PM₁ as determined by a novel object recognition (NOR) task, but there were no significant changes in motor functions. There were no changes in frequency bands or multiscale entropy of brainwaves. Exposure to 3 months of PM₁ increased 8-isoporstance in the cortex, cerebellum, and hippocampus as well as hippocampal inflammation (interleukin (IL)-6), but not in the olfactory bulb. Systemic CCL11 (at 3 and 6 months) and IL-4 (at 6 months) increased after PM₁ exposure. Light chain 3 (LC3) expression increased in the hippocampus after 6 months of exposure. Spongiosis and neuronal shrinkage were observed in the cortex, cerebellum, and hippocampus (neuronal shrinkage) after exposure to air pollution. Additionally, microabscesses were observed in the cortex after 6 months of PM₁ exposure.

Conclusions

Our study first observed cerebral edema and brain impairment in adult rats after chronic exposure to traffic-related air pollution.

Electronic supplementary material

The online version of this article (10.1186/s12989-018-0281-1) contains supplementary material, which is available to authorized users.

Successful Object Encoding Induces Increased Directed Connectivity in Presymptomatic Early-Onset Alzheimer's Disease

BACKGROUND

Recent studies report increases in neural activity in brain regions critical to episodic memory at preclinical stages of Alzheimer's disease (AD). Although electroencephalography (EEG) is widely used in AD studies, given its non-invasiveness and low cost, there is a need to translate the findings in other neuroimaging methods to EEG.

OBJECTIVE

To examine how the previous findings using functional magnetic resonance imaging (fMRI) at preclinical stage in presenilin-1 E280A mutation carriers could be assessed and extended, using EEG and a connectivity approach.

METHODS

EEG signals were acquired during resting and encoding in 30 normal cognitive young subjects, from an autosomal dominant early-onset AD kindred from Antioquia, Colombia. Regions of the brain previously reported as hyperactive were used for connectivity analysis.

RESULTS

Mutation carriers exhibited increasing connectivity at analyzed regions. Among them, the right precuneus exhibited the highest changes in connectivity.

CONCLUSION

Increased connectivity in hyperactive cerebral regions is seen in individuals, genetically-determined to develop AD, at preclinical stage. The use of a connectivity approach and a widely available neuroimaging technique opens the possibility to increase the use of EEG in early detection of preclinical AD.

Alzheimer's disease disrupts alpha and beta-band resting-state oscillatory network connectivity

OBJECTIVE

Neuroimaging studies in Alzheimer's disease (AD) yield conflicting results due to selective investigation. We conducted a comprehensive magnetoencephalography study of connectivity changes in AD and healthy ageing in the resting-state.

METHODS

We performed a whole-brain, source-space assessment of oscillatory neural signalling in multiple frequencies comparing AD patients, elderly and young controls. We compared eyes-open and closed group oscillatory envelope activity in networks obtained through temporal independent component analysis, and calculated whole-brain node-based amplitude and phase connectivity.

RESULTS

In bilateral parietotemporal areas, oscillatory envelope amplitude increased with healthy ageing, whereas both local amplitude and node-to-global connectivity decreased with AD. AD-related decreases were spatially specific and restricted to the alpha and beta bands. A significant proportion of the variance in areas of peak group difference was explained by cognitive integrity, in addition to group. None of the groups differed in phase connectivity. Results were highly similar for eyes-open and closed resting-state.

CONCLUSIONS

These results support the disconnection syndrome hypothesis and suggest that AD shows distinct and unique patterns of disrupted neural functioning, rather than accelerated healthy ageing.

SIGNIFICANCE

Whole-brain assessments show that disrupted regional oscillatory envelope amplitude and connectivity in the alpha and beta bands play a key role in AD.

Early functional network alterations in asymptomatic elders at risk for Alzheimer's disease

Amyloid-β (Aβ) deposition is known to starts decades before the onset of clinical symptoms of Alzheimer's disease (AD), however, the detailed pathophysiological processes underlying this preclinical period are not well understood. This study aimed to investigate functional network alterations in cognitively intact elderly individuals at risk for AD, and assessed the association between these network alterations and changes in Aβ deposition, glucose metabolism, and brain structure. Forty-five cognitively normal elderly subjects, who were classified into Aβ-positive (CN+) and Aβ-negative (CN-) groups using ¹¹C-Pittsburgh compound B PET, underwent resting state magnetoencephalography measurements, ¹⁸F-fluorodeoxyglucose PET (FDG-PET) and structural MRI. Results demonstrated that in the CN+ group, functional connectivity (FC) within the precuneus was significantly decreased, whereas it was significantly enhanced between the precuneus and the bilateral inferior parietal lobules in the low-frequency bands (theta and delta). These changes were suggested to be associated with local cerebral Aβ deposition. Most of Aβ+ individuals in this study did not show any metabolic or anatomical changes, and there were no significant correlations between FC values and FDG-PET or MRI volumetry data. These results demonstrate that functional network alterations, which occur in association with Aβ deposition, are detectable using magnetoencephalography before metabolic and anatomical changes are seen.

Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks

Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation XCorr and peak signal to noise ratio (PSNR) (ANOVA, p ˂ 0.05). The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, p ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing.

Slow and Fast Neocortical Oscillations in the Senescence-Accelerated Mouse Model SAMP8

The senescence-accelerated mouse prone 8 (SAMP8) model is characterized by accelerated, progressive cognitive decline as well as Alzheimer’s disease (AD)-like neurodegenerative changes, and resembles the etiology of multicausal, sporadic late-onset/age-related AD in humans. Our aim was to find whether these AD-like pathological features, together with the cognitive deficits present in the SAMP8 strain, are accompanied by disturbances in cortical network activity with respect to control mice (SAM resistance 1, SAMR1) and, if so, how the alterations in cortical activity progress with age. For this purpose, we characterized the extracellular spontaneous oscillatory activity in different regions of the cerebral cortex of SAMP8 and SAMR1 mice under ketamine anesthesia at 5 and 7 months of age. Under these conditions, slow oscillations and fast rhythms generated in the cortical network were recorded and different parameters of these oscillations were quantified and compared between SAMP8 and their control, SAMR1 mice. The average frequency of slow oscillations in SAMP8 mice was decreased with respect to the control mice at both studied ages. An elongation of the silent periods or Down states was behind the decreased slow oscillatory frequency while the duration of active or Up states remained stable. SAMP8 mice also presented increased cycle variability and reduced high frequency components during Down states. During Up states, the power peak in the gamma range was displaced towards lower frequencies in all the cortical areas of SAMP8 with respect to control mice suggesting that the spectral profile of SAMP8 animals is shifted towards lower frequencies. This shift is reminiscent to one of the principal hallmarks of electroencephalography (EEG) abnormalities in patients with Alzheimer’s disease, and adds evidence in support of the suitability of the SAMP8 mouse as a model of this disease. Although some of the differences between SAMP8 and control mice were emphasized with age, the evolution of the studied parameters as SAMR1 mice got older indicates that the SAMR1 phenotype tends to converge with that of SAMP8 animals. To our knowledge, this is the first systematic characterization of the cortical slow and fast rhythms in the SAMP8 strain and it provides useful insights about the cellular and synaptic mechanisms underlying the reported alterations.

Early neurovascular dysfunction in a transgenic rat model of Alzheimer's disease

Alzheimer's disease (AD), pathologically characterized by amyloid-β peptide (Aβ) accumulation, neurofibrillary tangle formation, and neurodegeneration, is thought to involve early-onset neurovascular abnormalities. Hitherto studies on AD-associated neurovascular injury have used animal models that exhibit only a subset of AD-like pathologies and demonstrated some Aβ-dependent vascular dysfunction and destabilization of neuronal network. The present work focuses on the early stage of disease progression and uses TgF344-AD rats that recapitulate a broader repertoire of AD-like pathologies to investigate the cerebrovascular and neuronal network functioning using in situ two-photon fluorescence microscopy and laminar array recordings of local field potentials, followed by pathological analyses of vascular wall morphology, tau hyperphosphorylation, and amyloid plaques. Concomitant to widespread amyloid deposition and tau hyperphosphorylation, cerebrovascular reactivity was strongly attenuated in cortical penetrating arterioles and venules of TgF344-AD rats in comparison to those in non-transgenic littermates. Blood flow elevation to hypercapnia was abolished in TgF344-AD rats. Concomitantly, the phase-amplitude coupling of the neuronal network was impaired, evidenced by decreased modulation of theta band phase on gamma band amplitude. These results demonstrate significant neurovascular network dysfunction at an early stage of AD-like pathology. Our study identifies early markers of pathology progression and call for development of combinatorial treatment plans.

Non-invasive brain stimulation of the aging brain: State of the art and future perspectives

Favored by increased life expectancy and reduced birth rate, worldwide demography is rapidly shifting to older ages. The golden age of aging is not only an achievement but also a big challenge because of the load of the elderly on social and medical health care systems. Moreover, the impact of age-related decline of attention, memory, reasoning and executive functions on self-sufficiency emphasizes the need of interventions to maintain cognitive abilities at a useful degree in old age. Recently, neuroscientific research explored the chance to apply Non-Invasive Brain Stimulation (NiBS) techniques (as transcranial electrical and magnetic stimulation) to healthy aging population to preserve or enhance physiologically-declining cognitive functions. The present review will update and address the current state of the art on NiBS in healthy aging. Feasibility of NiBS techniques will be discussed in light of recent neuroimaging (either structural or functional) and neurophysiological models proposed to explain neural substrates of the physiologically aging brain. Further, the chance to design multidisciplinary interventions to maximize the efficacy of NiBS techniques will be introduced as a necessary future direction.

Fuzzy-entropy threshold based on a complex wavelet denoising technique to diagnose Alzheimer disease

The presence of irregularities in electroencephalographic (EEG) signals entails complexities during the Alzheimer's disease (AD) diagnosis. In addition, the uncertainty presented on EEG raises major issues in the improvement of the classification rate. The multi-resolution analysis through an optimum threshold will likely achieve better results in distinguishing AD and normal EEG signals. Hence, a fuzzy-entropy concept defined in a complex multi-resolution wavelet has been proposed to obtain the most appropriate threshold. First, the complex coefficients are fuzzified using a Gaussian membership function. Afterwards, the ability of the proposed fuzzy-entropy threshold has been compared with traditional thresholds in complex wavelet domain. Experimental results show that the authors' methodology produces a higher signal-to-noise ratio and a lower root-mean-square error than traditional approaches. Moreover, a neural network scheme is performed along several features to classify AD from normal EEG signals obtaining a specificity of 87.5%.

Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.

Power spectral density and coherence analysis of Alzheimer's EEG

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

Electroencephalogram and Alzheimer's disease: clinical and research approaches

Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled "Cognitive Signal Processing Lab," which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.

Age-dependent effect of Alzheimer's risk variant of CLU on EEG alpha rhythm in non-demented adults

Polymorphism in the genomic region harboring the CLU gene (rs11136000) has been associated with the risk for Alzheimer’s disease (AD). CLU C allele is assumed to confer risk for AD and the allele T may have a protective effect. We investigated the influence of the AD-associated CLU genotype on a common neurophysiological trait of brain activity (resting-state alpha-rhythm activity) in non-demented adults and elucidated whether this influence is modified over the course of aging. We examined quantitative electroencephalography (EEG) in a cohort of non-demented individuals (age range 20–80) divided into young (age range 20–50) and old (age range 51–80) cohorts and stratified by CLU polymorphism. To rule out the effect of the apolipoprotein E (ApoE) genotype on EEG characteristics, only subjects without the ApoE ε4 allele were included in the study. The homozygous presence of the AD risk variant CLU CC in non-demented subjects was associated with an increase of alpha3 absolute power. Moreover, the influence of CLU genotype on alpha3 was found to be higher in the subjects older than 50 years of age. The study also showed age-dependent alterations of alpha topographic distribution that occur independently of the CLU genotype. The increase of upper alpha power has been associated with hippocampal atrophy in patients with mild cognitive impairment (Moretti etal., 2012a). In our study, the CLU CC-dependent increase in upper alpha rhythm, particularly enhanced in elderly non-demented individuals, may imply that the genotype is related to preclinical dysregulation of hippocampal neurophysiology in aging and that this factor may contribute to the pathogenesis of AD.

Index of alpha/theta ratio of the electroencephalogram: a new marker for Alzheimer's disease

Objective: We evaluated quantitative EEG measures to determine a screening index to discriminate Alzheimer’s disease (AD) patients from normal individuals.

Methods: Two groups of individuals older than 50 years, comprising a control group of 57 normal volunteers and a study group of 50 patients with probable AD, were compared. EEG recordings were obtained from subjects in a wake state with eyes closed at rest for 30 min. Logistic regression analysis was conducted.

Results: Spectral potentials of the alpha and theta bands were computed for all electrodes and the alpha/theta ratio calculated. Logistic regression of alpha/theta of the mean potential of the C3 and O1 electrodes was carried out. A formula was calculated to aid the diagnosis of AD yielding 76.4% sensitivity and 84.6% specificity for AD with an area under the ROC curve of 0.92.

Conclusion: Logistic regression of alpha/theta of the spectrum of the mean potential of EEG represents a good marker discriminating AD patients from normal controls.

Brain oscillations as biomarkers in neuropsychiatric disorders: following an interactive panel discussion and synopsis

This survey covers the potential use of neurophysiological changes as a biomarker in four neuropsychiatric diseases (attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), bipolar disorder (BD), and schizophrenia (SZ)). Great developments have been made in the search of biomarkers in these disorders, especially in AD. Nevertheless, there is a tremendous need to develop an efficient, low-cost, potentially portable, non-invasive biomarker in the diagnosis, course, or treatment of the above-mentioned disorders. Electrophysiological methods would provide a tool that would reflect functional brain dynamic changes within milliseconds and also may be used as an ensemble of biomarkers that is greatly needed in the evaluation of cognitive changes seen in these disorders. The strategies for measuring cognitive changes include spontaneous electroencephalography (EEG), sensory evoked oscillation (SEO), and event-related oscillations (ERO). Further selective connectivity deficit in sensory or cognitive networks is reflected by coherence measurements. Possible candidate biomarkers discussed in an interactive panel can be summarized as follows: for ADHD: (a) elevation of delta and theta, (b) diminished alpha and beta responses in spontaneous EEG; for SZ: (a) decrease of ERO gamma responses, (b) decreased ERO in all other frequency ranges, (c) invariant ERO gamma response in relation to working memory demand; for euthymic BD: (a) decreased event-related gamma coherence, (b) decreased alpha in ERO and in spontaneous EEG; for manic BD: (a) lower alpha and higher beta in ERO, (b) decreased event-related gamma coherence, (c) lower alpha and beta in ERO after valproate; and for AD: (a) decreased alpha and beta, and increased theta and delta in spontaneous EEG, (b) hyperexcitability of motor cortices as shown by transcortical magnetic stimulation, (c) hyperexcitability of visual sensory cortex as indicated by increased SEO theta responses, (d) lower delta ERO, (e) lower delta, theta, and alpha event-related coherence, (f) higher theta synchrony and higher alpha event-related coherence in cholinergically treated AD subjects. In further research in the search for biomarkers, multimodal methods should be introduced to electrophysiology for validation purposes. Also, providing the protocols for standardization and harmonization of user-friendly acquisition or analysis methods that would be applied in larger cohort populations should be used to incorporate these electrophysiologic methods into the clinical criteria. In an extension to conventional anatomical, biochemical and brain imaging biomarkers, the use of neurophysiologic markers may lead to new applications for functional interpretrations and also the possibility to monitor treatments tailored for individuals.