Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer's disease patients

Alpha rhythm and Alzheimer's disease: Has Hans Berger's dream come true?

In this "centenary" paper, an expert panel revisited Hans Berger's groundbreaking discovery of human restingstate electroencephalographic (rsEEG) alpha rhythms (8-12 Hz) in 1924, his foresight of substantial clinical applications in patients with "senile dementia," and new developments in the field, focusing on Alzheimer's disease (AD), the most prevalent cause of dementia in pathological aging. Clinical guidelines issued in 2024 by the US National Institute on Aging-Alzheimer's Association (NIA-AA) and the European Neuroscience Societies did not endorse routine use of rsEEG biomarkers in the clinical workup of older adults with cognitive impairment. Nevertheless, the expert panel highlighted decades of research from independent workgroups and different techniques showing consistent evidence that abnormalities in rsEEG delta, theta, and alpha rhythms (< 30 Hz) observed in AD patients correlate with wellestablished AD biomarkers of neuropathology, neurodegeneration, and cognitive decline. We posit that these abnormalities may reflect alterations in oscillatory synchronization within subcortical and cortical circuits, inducing cortical inhibitory-excitatory imbalance (in some cases leading to epileptiform activity) and vigilance dysfunctions (e.g., mental fatigue and drowsiness), which may impact AD patients' quality of life. Berger's vision of using EEG to understand and manage dementia in pathological aging is still actual.

Comparison of Quantitative-Electroencephalogram (q-EEG) Measurements Between Patients of Dementia with Lewy Bodies (DLB) and Parkinson Disease Dementia (PDD)

Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) are synucleinopathy syndromes with similar symptom profiles that are distinguished clinically based on the arbitrary rule of the time of symptom onset. Identifying reliable electroencephalographic (EEG) biomarkers would provide a precise method for better diagnosis, treatment, and monitoring of treatment response in these two types of dementia. From April 2015 to March 2021, the records of new referrals to a neurology clinic were retrospectively reviewed and 28 DLB(70.3% male) and 20 PDD (80.8% male) patients with appropriate EEG were selected for this study. Artifact-free 60-s EEG signals (21 channels) at rest with eyes closed were analyzed using EEGLAB, and regional spectral power ratios were extracted. Marked diffuse slowing was found in DLB patients compared to PDD patients in all regions in terms of decrease in alpha and increase in theta band. Although, these findings demean between groups after adjusting for MMSE scores, the significant difference still remained in terms of the mean relative alpha powers, particularly in the anterior and central regions. QEEG measures may have the potential to discriminate between these two syndromes. However, further prospective and longitudinal studies are required to improve the early differentiation of these dementia syndromes and to elucidate the underlying causes and pathogenesis and specific treatment.

Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review

Background

Alzheimer's disease (AD) might be best conceptualized as a disconnection syndrome, such that symptoms may be largely attributable to disrupted communication between brain regions, rather than to deterioration within discrete systems. EEG is uniquely capable of directly and non-invasively measuring neural activity with precise temporal resolution; connectivity quantifies the relationships between such signals in different brain regions. EEG research on connectivity in AD and mild cognitive impairment (MCI), often considered a prodromal phase of AD, has produced mixed results and has yet to be synthesized for comprehensive review. Thus, we performed a systematic review of EEG connectivity in MCI and AD participants compared with cognitively healthy older adult controls.

Methods

We searched PsycINFO, PubMed, and Web of Science for peer-reviewed studies in English on EEG, connectivity, and MCI/AD relative to controls. Of 1,344 initial matches, 124 articles were ultimately included in the systematic review.

Results

The included studies primarily analyzed coherence, phase-locked, and graph theory metrics. The influence of factors such as demographics, design, and approach was integrated and discussed. An overarching pattern emerged of lower connectivity in both MCI and AD compared to healthy controls, which was most prominent in the alpha band, and most consistent in AD. In the minority of studies reporting greater connectivity, theta band was most commonly implicated in both AD and MCI, followed by alpha. The overall prevalence of alpha effects may indicate its potential to provide insight into nuanced changes associated with AD-related networks, with the caveat that most studies were during the resting state where alpha is the dominant frequency. When greater connectivity was reported in MCI, it was primarily during task engagement, suggesting compensatory resources may be employed. In AD, greater connectivity was most common during rest, suggesting compensatory resources during task engagement may already be exhausted.

Conclusion

The review highlighted EEG connectivity as a powerful tool to advance understanding of AD-related changes in brain communication. We address the need for including demographic and methodological details, using source space connectivity, and extending this work to cognitively healthy older adults with AD risk toward advancing early AD detection and intervention.

EEG reveals brain network alterations in chronic aphasia during natural speech listening

Aphasia is a common consequence of a stroke which affects language processing. In search of an objective biomarker for aphasia, we used EEG to investigate how functional network patterns in the cortex are affected in persons with post-stroke chronic aphasia (PWA) compared to healthy controls (HC) while they are listening to a story. EEG was recorded from 22 HC and 27 PWA while they listened to a 25-min-long story. Functional connectivity between scalp regions was measured with the weighted phase lag index. The Network-Based Statistics toolbox was used to detect altered network patterns and to investigate correlations with behavioural tests within the aphasia group. Differences in network geometry were assessed by means of graph theory and a targeted node-attack approach. Group-classification accuracy was obtained with a support vector machine classifier. PWA showed stronger inter-hemispheric connectivity compared to HC in the theta-band (4.5-7 Hz), whilst a weaker subnetwork emerged in the low-gamma band (30.5-49 Hz). Two subnetworks correlated with semantic fluency in PWA respectively in delta- (1-4 Hz) and low-gamma-bands. In the theta-band network, graph alterations in PWA emerged at both local and global level, whilst only local changes were found in the low-gamma-band network. Network metrics discriminated PWA and HC with AUC = 83%. Overall, we demonstrate the potential of EEG-network metrics for the development of informative biomarkers to assess natural speech processing in chronic aphasia. We hypothesize that the detected alterations reflect compensatory mechanisms associated with recovery.

Resting-state EEG spectral and fractal features in dementia with Lewy bodies with and without visual hallucinations

OBJECTIVE

Complex visual hallucinations (VH) are a core feature of dementia with Lewy bodies (DLB), though they may not occur in all patients. Power spectral density (PSD) analysis of resting-state EEG (rs-EEG) shows associations between some frequency bands (e.g., theta), individual alpha frequency (IAF) and VH. However, new tools that improve early differential diagnosis and symptom-based stratification with higher sensitivity and specificity, even within the DLB population, are desirable. We aimed to assess differences in rs-EEG data between DLB patients with VH (DLB-VH+) and without VH (DLB-VH-), comparing innovative non-linear approaches with more traditional linear ones.

METHODS

We retrospectively analyzed rs-EEG recordings of DLB-VH+, DLB-VH-, Alzheimer's disease patients and age-matched healthy controls. EEG was analyzed using the nonlinear Higuchi's Fractal Dimension (FD) measure, and the results were compared with those of entropy and standard linear methods based on PSD and IAF.

RESULTS

Only the FD measure could discriminate between DLB-VH+ and DLB-VH-.

CONCLUSIONS

In conclusion, rs-EEG differences between DLB-VH+ and DLB-VH- are better characterized by FD analysis than by a more traditional power spectrum approach.

SIGNIFICANCE

This suggests that the presence of complex VH is associated with less complex brain dynamics at rest, as reflected by the FD measure.

Exploring easily accessible neurophysiological biomarkers for predicting Alzheimer's disease progression: a systematic review

Alzheimer disease (AD) remains a significant global health concern. The progression from preclinical stages to overt dementia has become a crucial point of interest for researchers. This paper reviews the potential of neurophysiological biomarkers in predicting AD progression, based on a systematic literature search following PRISMA guidelines, including 55 studies. EEG-based techniques have been predominantly employed, whereas TMS studies are less common. Among the investigated neurophysiological measures, spectral power measurements and event-related potentials-based measures, including P300 and N200 latencies, have emerged as the most consistent and reliable biomarkers for predicting the likelihood of conversion to AD. In addition, TMS-based indices of cortical excitability and synaptic plasticity have also shown potential in assessing the risk of conversion to AD. However, concerns persist regarding the methodological discrepancies among studies, the accuracy of these neurophysiological measures in comparison to established AD biomarkers, and their immediate clinical applicability. Further research is needed to validate the predictive capabilities of EEG and TMS measures. Advancements in this area could lead to cost-effective, reliable biomarkers, enhancing diagnostic processes and deepening our understanding of AD pathophysiology.

Alpha tACS Improves Cognition and Modulates Neurotransmission in Dementia with Lewy Bodies

BACKGROUND

Dementia with Lewy bodies (DLB) is characterized by a marked shift of electroencephalographic (EEG) power and dominant rhythm, from the α toward the θ frequency range. Transcranial alternate current stimulation (tACS) is a non-invasive brain stimulation technique that allows entrainment of cerebral oscillations at desired frequencies.

OBJECTIVES

Our goal is to evaluate the effects of occipital α-tACS on cognitive functions and neurophysiological measures in patients with DLB.

METHODS

We conducted a double-blind, randomized, sham-controlled, cross-over clinical trial in 14 participants with DLB. Participants were randomized to receive either α-tACS (60 minutes of 3 mA peak-to-peak stimulation at 12 Hz) or sham stimulation applied over the occipital cortex. Clinical evaluations were performed to assess visuospatial and executive functions, as well as verbal episodic memory. Neurophysiological assessments and EEG recordings were conducted at baseline and following both α-tACS and sham stimulations.

RESULTS

Occipital α-tACS was safe and well-tolerated. We observed a significant enhancement in visuospatial abilities and executive functions, but no improvement in verbal episodic memory. We observed an increase in short latency afferent inhibition, a neurophysiological marker indirectly and partially dependent on cholinergic transmission, coinciding with an increase in α power and a decrease in Δ power following α-tACS stimulation, effects not seen with sham stimulation.

CONCLUSIONS

This study demonstrates that occipital α-tACS is safe and enhances visuospatial and executive functions in patients with DLB. Improvements in indirect markers of cholinergic transmission and EEG changes indicate significant neurophysiological engagement. These findings justify further exploration of α-tACS as a therapeutic option for DLB patients. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Resting-state EEG predicts cognitive decline in a neuropathologically diagnosed longitudinal community autopsied cohort

OBJECTIVE

To assess correlative strengths of quantitative electroencephalography (qEEG) and visual rating scale EEG features on cognitive outcomes in only autopsied cases from the Arizona Study of Neurodegenerative Disorders (AZSAND). We hypothesized that autopsy proven Parkinson Disease will show distinct EEG features from Alzheimer's Disease prior to dementia (mild cognitive impairment).

BACKGROUND

Cognitive decline is debilitating across neurodegenerative diseases. Resting-state EEG analysis, including spectral power across frequency bins (qEEG), has shown significant associations with neurodegenerative disease classification and cognitive status, with autopsy confirmed diagnosis relatively lacking.

METHODS

Biannual EEG was analyzed from autopsied cases in AZSAND who had at least one rsEEG (>1 min eyes closed±eyes open). Analysis included global relative spectral power and a previously described visual rating scale (VRS). Linear mixed regression was performed for neuropsychological assessment and testing within 2 years of death (n = 236, 594 EEG exams) in a mixed linear regression model.

RESULTS

The cohort included cases with final clinicopathologic diagnoses of Parkinson's disease (n = 73), Alzheimer disease (n = 65), and tauopathy not otherwise specified (n = 56). A VRS score of 3 diffuse or frequent generalized slowing) over the study duration was associated with an increase in consensus diagnosis cognitive worsening at 4.9 (3.1) years (HR 2.02, CI 1.05-3.87). Increases in global theta power% and VRS were the most consistently associated with large regression coefficients inversely with cognitive performance measures.

CONCLUSION

Resting-state EEG analysis was meaningfully related to cognitive performance measures in a community-based autopsy cohort. EEG deserves further study and use as a cognitive biomarker.

EEG Functional Connectivity Differences Predict Future Conversion to Dementia in Mild Cognitive Impairment With Lewy Body or Alzheimer Disease

BACKGROUND

Predicting which individuals may convert to dementia from mild cognitive impairment (MCI) remains difficult in clinical practice. Electroencephalography (EEG) is a widely available investigation but there is limited research exploring EEG connectivity differences in patients with MCI who convert to dementia.

METHODS

Participants with a diagnosis of MCI due to Alzheimer's disease (MCI-AD) or Lewy body disease (MCI-LB) underwent resting state EEG recording. They were followed up annually with a review of the clinical diagnosis (n = 66). Participants with a diagnosis of dementia at year 1 or year 2 follow up were classed as converters (n = 23) and those with a diagnosis of MCI at year 2 were classed as stable (n = 43). We used phase lag index (PLI) to estimate functional connectivity as well as analysing dominant frequency (DF) and relative band power. The Network-based statistic (NBS) toolbox was used to assess differences in network topology.

RESULTS

The converting group had reduced DF (U = 285.5, p = 0.005) and increased relative pre-alpha power (U = 702, p = 0.005) consistent with previous findings. PLI showed reduced average beta band synchrony in the converting group (U = 311, p = 0.014) as well as significant differences in alpha and beta network topology. Logistic regression models using regional beta PLI values revealed that right central to right lateral (Sens = 56.5%, Spec = 86.0%, -2LL = 72.48, p = 0.017) and left central to right lateral (Sens = 47.8%, Spec = 81.4%, -2LL = 71.37, p = 0.012) had the best classification accuracy and fit when adjusted for age and MMSE score.

CONCLUSION

Patients with MCI who convert to dementia have significant differences in EEG frequency, average connectivity and network topology prior to the onset of dementia. The MCI group is clinically heterogeneous and have underlying physiological differences that may be driving the progression of cognitive symptoms. EEG connectivity could be useful to predict which patients with MCI-AD and MCI-LB convert to dementia, regardless of the neurodegenerative aetiology.

Quantitative EEG in the Differential Diagnosis of Dementia Subtypes

OBJECTIVE

Most neurodegenerative dementias present with substantial overlap in clinical features. Therefore, differential diagnosis is often a challenging task necessitating costly and sometimes invasive diagnostic procedures. A promising, non-invasive and cost-effective method is the widely available electroencephalography (EEG).

METHODS

Twenty-three subjects with Alzheimer's disease (AD), 28 subjects with dementia with Lewy bodies (DLB), 15 subjects with frontotemporal dementias (FTDs), and 22 healthy controls (HC) were enrolled. Nineteen channel computerized EEG recordings were acquired. Mean relative powers were calculated using the standard frequency bands. Theta/alpha ratio (TAR), theta/beta ratio (TBR), a spectral index of (alpha + beta)/(theta + delta) and an alpha reactivity index (alpha in eyes-open condition/alpha in eyes-closed condition) were also calculated. Receiver operating characteristic (ROC) analyses were performed to assess diagnostic accuracy.

RESULTS

For the comparison of EEG measures across groups, we performed a multivariate ANOVA followed by univariate ANOVAs controlling for the effects of age, with post hoc tests. Theta power and TBR were increased in DLB compared to other groups. Alpha power was decreased in DLB compared to HC and FTD; and in AD compared to FTD. Beta power was decreased in DLB compared to AD and HC. Furthermore, regional analyses demonstrated a unique pattern of theta power increase in DLB; affecting frontal, central, parietal, occipital, and temporal regions. In AD, theta power increased compared to HC in parietal, occipital, and right temporal regions. TAR was increased in DLB compared to other groups; and in AD compared to HC. Finally, alpha reactivity index was higher in DLB compared to HC and FTD. In AD, EEG slowing was associated with cognitive impairment, while in DLB, this was associated with higher DLB characteristics. In the ROC analyses to distinguish DLB from FTD and AD, measures of EEG slowing yielded high area under curve values, with good specificities. Also, decreased alpha reactivity could distinguish DLB from FTD with good specificity. EEG slowing in DLB showed a diffuse pattern compared to AD, where a posterior and temporal slowing predominated.

CONCLUSION

We showed that EEG slowing was satisfactory in distinguishing DLB patients from AD and FTD patients. Notably, this slowing was a characteristic finding in DLB patients, even at early stages, while it paralleled disease progression in AD. Furthermore, EEG slowing in DLB showed a diffuse pattern compared to AD, where a posterior and temporal slowing predominated. These findings align with the previous evidence of the diencephalic dysfunction in DLB.

Advanced qEEG analyses discriminate between dementia subtypes

BACKGROUND

Dementia is caused by neurodegenerative conditions and characterized by cognitive decline. Diagnostic accuracy for dementia subtypes, such as Alzheimer's Disease (AD), Dementia with Lewy Bodies (DLB) and Parkinson's Disease with dementia (PDD), remains challenging.

METHODS

Here, different methods of quantitative electroencephalography (qEEG) analyses were employed to assess their effectiveness in distinguishing dementia subtypes from healthy controls under eyes closed (EC) and eyes open (EO) conditions.

RESULTS

Classic Fast-Fourier Transform (FFT) and autoregressive (AR) power analyses differentiated between all conditions for the 4-8 Hz theta range. Only individuals with dementia with Lewy Bodies (DLB) differed from healthy subjects for the wider 4-15 Hz frequency range, encompassing the actual dominant frequency of all individuals. FFT results for this range yielded wider significant discriminators vs AR, also detecting differences between AD and DLB. Analyses of the inclusive dominant / peak frequency range (4-15 Hz) indicated slowing and reduced variability, also discriminating between synucleinopathies vs controls and AD. Dissociation of periodic oscillations and aperiodic components of AR spectra using Fitting-Oscillations-&-One-Over-F (FOOOF) modelling delivered a reliable and subtype-specific slowing of brain oscillatory peaks during EC and EO for all groups. Distinct and robust differences were particularly strong for aperiodic parameters, suggesting their potential diagnostic power in detecting specific changes resulting from age and cognitive status.

CONCLUSION

Our findings indicate that qEEG methods can reliably detect dementia subtypes. Spectral analyses comprising an integrated, multi-parameter EEG approach discriminating between periodic and aperiodic components under EC and EO conditions may enhance diagnostic accuracy in the future.

Minimum spanning tree analysis of unimpaired individuals at risk of Alzheimer's disease

Identifying early and non-invasive biomarkers to detect individuals in the earliest stages of the Alzheimer's disease continuum is crucial. As a result, electrophysiology and plasma biomarkers are emerging as great candidates in this pursuit due to their low invasiveness. This is the first magnetoencephalography study to assess the relationship between minimum spanning tree parameters, an alternative to overcome the comparability and thresholding problem issues characteristic of conventional brain network analyses, and plasma phosphorylated tau231 levels in unimpaired individuals, with different risk levels of Alzheimer's disease. Seventy-six individuals with available magnetoencephalography recordings and phosphorylated tau231 plasma determination were included. The minimum spanning tree for the theta, alpha and beta bands for each subject was obtained, and the leaf fraction, tree hierarchy and diameter were calculated. To study the relationship between these topological parameters and phosphorylated tau231, we performed correlation analyses, for the whole sample and considering the two risk sub-groups separately. Increasing concentrations of phosphorylated tau231 were associated with greater leaf fraction and tree hierarchy values, along with lower diameter values, for the alpha and theta frequency bands. These results emerged for the whole sample and the higher risk group, but not for the lower risk group. Our results indicate that the network topology of cognitively unimpaired individuals with elevated plasma phosphorylated tau231 levels, a marker of Alzheimer's disease pathology and amyloid-β accumulation, is already altered, shifting towards a more integrated network increasing its vulnerability and hub-dependency, mostly in the alpha band. This is indicated by increases in leaf fraction and tree hierarchy, along with reductions in diameter. These results match the initial trajectory proposed by theoretical models of disease progression and network disruption and suggest that changes in brain function and organization begin early on.

Dynamic functional connectivity is modulated by the amount of p-Tau231 in blood in cognitively intact participants

INTRODUCTION

Electrophysiology and plasma biomarkers are early and non-invasive candidates for Alzheimer's disease detection. The purpose of this paper is to evaluate changes in dynamic functional connectivity measured with magnetoencephalography, associated with the plasma pathology marker p-tau231 in unimpaired adults.

METHODS

73 individuals were included. Static and dynamic functional connectivity were calculated using leakage corrected amplitude envelope correlation. Each source's strength entropy across trials was calculated. A data-driven statistical analysis was performed to find the association between functional connectivity and plasma p-tau231 levels. Regression models were used to assess the influence of other variables over the clusters' connectivity.

RESULTS

Frontotemporal dynamic connectivity positively associated with p-tau231 levels. Linear regression models identified pathological, functional and structural factors that influence dynamic functional connectivity.

DISCUSSION

These results expand previous literature on dynamic functional connectivity in healthy individuals at risk of AD, highlighting its usefulness as an early, non-invasive and more sensitive biomarker.

A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations

Previous studies suggest that there may be important links between functional connectivity, disease mechanisms underpinning the Dementia with Lewy Body (DLB) and the key clinical symptoms, but the exact relationship remains unclear. We performed a systematic literature review to address this gap by summarising the research findings while critically considering the impact of methodological differences on findings. The main methodological choices of fMRI articles included data-driven, seed-based or regions of interest approaches, or their combinations. Most studies focused on examining large-scale resting-state networks, which revealed a consistent decrease in connectivity and some associations with non-cognitive symptoms. Although the inter-network connectivity showed mixed results, the main finding is consistent with theories positing disconnection between visual and attentional areas of the brain implicated in the aetiology of psychotic symptoms in the DLB. The primary methodological choice of EEG studies was implementing the phase lag index and using graph theory. The EEG studies revealed a consistent decrease in connectivity on alpha and beta frequency bands. While the overall trend of findings showed decreased connectivity, more subtle changes in the directionality of connectivity were observed when using a hypothesis-driven approach. Problems with cognition were also linked with greater functional connectivity disturbances. In summary, connectivity measures can capture brain disturbances in the DLB and remain crucial in uncovering the causal relationship between the networks' disorganisation and underlying mechanisms resulting in psychotic, motor, and cognitive symptoms of the DLB.

Multi-approach comparative study of EEG patterns associated with the most common forms of dementia

Electroencephalography's (EEG) sensitivity in discriminating dementia syndromes remains unclear. This study aimed to investigate EEG markers in patients with major cognitive disorders. The studied population included 4 groups of patients: Alzheimer's disease with associated vascular lesions, Alzheimer's disease without vascular lesions (AD-V), Lewy body disease and vascular dementia (VaD); and completed by a control group composed by cognitively unimpaired patients. EEGs were analysed quantitatively using spectral analysis, functional connectivity and micro-states. By comparison to the controls, expected slowing and alterations of functional connectivity were detected in patients with dementia. Among these patients, an overall increase in power in the alpha band was observed in the VaD group, mainly when compared to the 2 AD groups, while the Alzheimer's disease without vascular lesions group exhibited increased power in the beta-2 band and higher functional connectivity in the same frequency band. Micro-state analyses revealed differences in temporal dynamics for the VaD group. A number of EEG modifications reported as markers of some syndromes were found, but others were not reproduced.

Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis

OBJECTIVE

This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach.

METHODS

We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature.

RESULTS

For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs.

CONCLUSIONS

Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD.

SIGNIFICANCE

Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Dementia is a term that represents a set of symptoms that affect the ability of the brain's cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer's is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient's quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients.

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.

One Minute of EEG Data Provides Sufficient and Reliable Data for Identifying Lewy Body Dementia

OBJECTIVE

To determine the minimum duration of electroencephalography (EEG) data necessary to differentiate EEG features of Lewy body dementia (LBD), that is, dementia with Lewy bodies and Parkinson disease dementia, from non-LBD patients, that is, Alzheimer disease and Parkinson disease.

METHODS

We performed quantitative EEG analysis for 16 LBD and 14 non-LBD patients. After artifact removal, a fast Fourier transform was performed on 90, 60, and thirty 2-second epochs to derive dominant frequency; dominant frequency variability; and dominant frequency prevalence.

RESULTS

In LBD patients, there were no significant differences in EEG features derived from 90, 60, and thirty 2-second epochs (all P >0.05). There were no significant differences in EEG features derived from 3 different groups of thirty 2-second epochs (all P >0.05). When analyzing EEG features derived from ninety 2-second epochs, we found that LBD had significantly reduced dominant frequency, reduced dominant frequency variability, and reduced dominant frequency prevalence alpha compared with the non-LBD group (all P <0.05). These same differences were observed between the LBD and non-LBD groups when analyzing thirty 2-second epochs.

CONCLUSIONS

There were no differences in EEG features derived from 1 minute versus 3 minutes of EEG data, and both durations of EEG data equally differentiated LBD from non-LBD.

(Dis-)Connected Dots in Dementia with Lewy Bodies-A Systematic Review of Connectivity Studies

Studies on dementia with Lewy bodies (DLB) have mainly focused on the degeneration of distinct cortical and subcortical regions related to the deposition of Lewy bodies. In view of the proposed trans-synaptic spread of the α-synuclein pathology, investigating the disease only in this segregated fashion would be detrimental to our understanding of its progression. In this systematic review, we summarize findings on structural and functional brain connectivity in DLB, as connectivity measures may offer better insights on how the brain is affected by the spread of the pathology. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Web of Science, PubMed, and SCOPUS for relevant articles published up to November 1, 2021. Of 1215 identified records, we selected and systematically reviewed 53 articles that compared connectivity features between patients with DLB and healthy controls. Structural and functional magnetic resonance imaging, positron emission tomography, single-positron emission computer tomography, and electroencephalography assessments of patients revealed widespread abnormalities within and across brain networks in DLB. Frontoparietal, default mode, and visual networks and their connections to other brain regions featured the most consistent disruptions, which were also associated with core clinical features and cognitive impairments. Furthermore, graph theoretical measures revealed disease-related decreases in local and global network efficiency. This systematic review shows that structural and functional connectivity characteristics in DLB may be particularly valuable at early stages, before overt brain atrophy can be observed. This knowledge may help improve the diagnosis and prognosis in DLB as well as pinpoint targets for future disease-modifying treatments. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Implication of EEG theta/alpha and theta/beta ratio in Alzheimer's and Lewy body disease

We evaluated the patterns of quantitative electroencephalography (EEG) in patients with Alzheimer's disease (AD), Lewy body disease (LBD), and mixed disease. Sixteen patients with AD, 38 with LBD, 20 with mixed disease, and 17 control participants were recruited and underwent EEG. The theta/alpha ratio and theta/beta ratio were measured. The relationship of the log-transformed theta/alpha ratio (TAR) and theta/beta ratio (TBR) with the disease group, the presence of AD and LBD, and clinical symptoms were evaluated. Participants in the LBD and mixed disease groups had higher TBR in all lobes except for occipital lobe than those in the control group. The presence of LBD was independently associated with higher TBR in all lobes and higher central and parietal TAR, while the presence of AD was not. Among cognitively impaired patients, higher TAR was associated with the language, memory, and visuospatial dysfunction, while higher TBR was associated with the memory and frontal/executive dysfunction. Increased TBR in all lobar regions and temporal TAR were associated with the hallucinations, while cognitive fluctuations and the severity of Parkinsonism were not. Increased TBR could be a biomarker for LBD, independent of AD, while the presence of mixed disease could be reflected as increased TAR.

Generalized Rhythmic Delta Activity Frontally Predominant Differentiates Dementia with Lewy Bodies From Alzheimer's Disease and Parkinson's Disease Dementia: A Conventional Electroencephalography Visual Analysis

Introduction. An easily accessible and inexpensive neurophysiological technique such as conventional electroencephalography may provide an accurate and generally applicable biomarker capable of differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) and Parkinson's disease-associated dementia (PDD). Method. We carried out a retrospective visual analysis of resting-state electroencephalography (EEG) recording of 22 patients with a clinical diagnosis of 19 probable and 3 possible DLB, 22 patients with probable AD and 21 with PDD, matched for age, duration, and severity of cognitive impairment. Results. By using the grand total EEG scoring method, the total score and generalized rhythmic delta activity frontally predominant (GRDAfp) alone or, even better, coupled with a slowing of frequency of background activity (FBA) and its reduced reactivity differentiated DLB from AD at an individual level with an high accuracy similar to that obtained with quantitative EEG (qEEG). GRDAfp alone could also differentiate DLB from PDD with a similar level of diagnostic accuracy. AD differed from PDD only for a slowing of FBA. The duration and severity of cognitive impairment did not differ between DLB patients with and without GRDAfp, indicating that this abnormal EEG pattern should not be regarded as a disease progression marker. Conclusions. The findings of this investigation revalorize the role of conventional EEG in the diagnostic workup of degenerative dementias suggesting the potential inclusion of GRDAfp alone or better coupled with the slowing of FBA and its reduced reactivity, in the list of supportive diagnostic biomarkers of DLB.

Theta Band-Power Shapes Amyloid-Driven Longitudinal EEG Changes in Elderly Subjective Memory Complainers At-Risk for Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) includes progressive symptoms spread along a continuum of preclinical and clinical stages. Although numerous studies uncovered the neuro-cognitive changes of AD, very little is known on the natural history of brain lesions and modifications of brain networks in elderly cognitively-healthy memory complainers at risk of AD for carrying pathophysiological biomarkers (amyloidopathy and tauopathy).

OBJECTIVE

We analyzed resting-state electroencephalography (EEG) of 318 cognitively-healthy subjective memory complainers from the INSIGHT-preAD cohort at the time of their first visit (M0) and two-years later (M24).

METHODS

Using 18F-florbetapir PET-scanner, subjects were stratified between amyloid negative (A-; n = 230) and positive (A+; n = 88) groups. Differences between A+ and A-were estimated at source-level in each band-power of the EEG spectrum.

RESULTS

At M0, we found an increase of theta power in the mid-frontal cortex in A+ compared to A-. No significant association was found between mid-frontal theta and the individuals' cognitive performance. At M24, theta power increased in A+ relative to A-individuals in the posterior cingulate cortex and the pre-cuneus. Alpha band revealed a peculiar decremental trend in posterior brain regions in the A+ relative to the A-group only at M24. Theta power increase over the mid-frontal and mid-posterior cortices suggests an hypoactivation of the default-mode network in the A+ individuals and a non-linear longitudinal progression at M24.

CONCLUSION

We provide the first source-level longitudinal evidence on the impact of brain amyloidosis on the EEG dynamics of a large-scale, monocentric cohort of elderly individuals at-risk for AD.

Investigating the power of eyes open resting state EEG for assisting in dementia diagnosis

INTRODUCTION

The differentiation of Lewy body dementia from other common dementia types clinically is difficult, with a considerable number of cases only being found post-mortem. Consequently, there is a clear need for inexpensive and accurate diagnostic approaches for clinical use. Electroencephalography (EEG) is one potential candidate due to its relatively low cost and non-invasive nature. Previous studies examining the use of EEG as a dementia diagnostic have focussed on the eyes closed (EC) resting state; however, eyes open (EO) EEG may also be a useful adjunct to quantitative analysis due to clinical availability.

METHODS

We extracted spectral properties from EEG signals recorded under research study protocols (1024 Hz sampling rate, 10:5 EEG layout). The data stems from a total of 40 dementia patients with an average age of 74.42, 75.81 and 73.88 years for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), respectively, and 15 healthy controls (HC) with an average age of 76.93 years. We utilised k-nearest neighbour, support vector machine and logistic regression machine learning to differentiate between groups utilising spectral data from the delta, theta, high theta, alpha and beta EEG bands.

RESULTS

We found that the combination of EC and EO resting state EEG data significantly increased inter-group classification accuracy compared to methods not using EO data. Secondly, we observed a distinct increase in the dominant frequency variance for HC between the EO and EC state, which was not observed within any dementia subgroup. For inter-group classification, we achieved a specificity of 0.87 and sensitivity of 0.92 for HC vs dementia classification and 0.75 specificity and 0.91 sensitivity for AD vs DLB classification, with a k-nearest neighbour machine learning model which outperformed other machine learning methods.

CONCLUSIONS

The findings of our study indicate that the combination of both EC and EO quantitative EEG features improves overall classification accuracy when classifying dementia types in older age adults. In addition, we demonstrate that healthy controls display a definite change in dominant frequency variance between the EC and EO state. In future, a validation cohort should be utilised to further solidify these findings.

Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology and disorder specificity

Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments.

The potential of brain network characteristics to serve as biomarker of neurological and psychiatric pathology has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. MST leaf fraction but not diameter decreased with increasing network size. Contradicting findings remain in the literature on MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders; (2) in epilepsy there are frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology is found across disorders associated with attention impairments.

Functional and structural brain network correlates of visual hallucinations in Lewy body dementia

Visual hallucinations are a common feature of Lewy body dementia. Previous studies have shown that visual hallucinations are highly specific in differentiating Lewy body dementia from Alzheimer’s disease dementia and Alzheimer–Lewy body mixed pathology cases. Computational models propose that impairment of visual and attentional networks is aetiologically key to the manifestation of visual hallucinations symptomatology. However, there is still a lack of experimental evidence on functional and structural brain network abnormalities associated with visual hallucinations in Lewy body dementia.

We used EEG source localization and network based statistics to assess differential topographical patterns in Lewy body dementia between 25 participants with visual hallucinations and 17 participants without hallucinations. Diffusion tensor imaging was used to assess structural connectivity between thalamus, basal forebrain and cortical regions belonging to the functionally affected network component in the hallucinating group, as assessed with network based statistics. The number of white matter streamlines within the cortex and between subcortical and cortical regions was compared between hallucinating and not hallucinating groups and correlated with average EEG source connectivity of the affected subnetwork. Moreover, modular organization of the EEG source network was obtained, compared between groups and tested for correlation with structural connectivity.

Network analysis showed that compared to non-hallucinating patients, those with hallucinations feature consistent weakened connectivity within the visual ventral network, and between this network and default mode and ventral attentional networks, but not between or within attentional networks. The occipital lobe was the most functionally disconnected region. Structural analysis yielded significantly affected white matter streamlines connecting the cortical regions to the nucleus basalis of Meynert and the thalamus in hallucinating compared to not hallucinating patients. The number of streamlines in the tract between the basal forebrain and the cortex correlated with cortical functional connectivity in non-hallucinating patients, while a correlation emerged for the white matter streamlines connecting the functionally affected cortical regions in the hallucinating group.

This study proposes, for the first time, differential functional networks between hallucinating and not hallucinating Lewy body dementia patients, and provides empirical evidence for existing models of visual hallucinations. Specifically, the outcome of the present study shows that the hallucinating condition is associated with functional network segregation in Lewy body dementia and supports the involvement of the cholinergic system as proposed in the current literature.

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals

This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.

EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks

The interaction of acute exercise and the central nervous system evokes increasing interest in interdisciplinary research fields of neuroscience. Novel approaches allow to monitor large-scale brain networks from mobile electroencephalography (EEG) applying graph theory, but it is yet uncertain whether brain graphs extracted after exercise are reliable. We therefore aimed to investigate brain graph reliability extracted from resting state EEG data before and after submaximal exercise twice within one week in male participants. To obtain graph measures, we extracted global small-world-index (SWI), clustering coefficient (CC) and characteristic path length (PL) based on weighted phase leg index (wPLI) and spectral coherence (Coh) calculation. For reliability analysis, Intraclass-Correlation-Coefficient (ICC) and Coefficient of Variation (CoV) were computed for graph measures before (REST) and after POST) exercise. Overall results revealed poor to excellent measures at PRE and good to excellent ICCs at POST in the theta, alpha-1 and alpha-2, beta-1 and beta-2 frequency band. Based on bootstrap-analysis, a positive effect of exercise on reliability of wPLI based measures was observed, while exercise induced a negative effect on reliability of Coh-based graph measures. Findings indicate that brain graphs are a reliable tool to analyze brain networks in exercise contexts, which might be related to the neuroregulating effect of exercise inducing functional connections within the connectome. Relative and absolute reliability demonstrated good to excellent reliability after exercise. Chosen graph measures may not only allow analysis of acute, but also longitudinal studies in exercise-scientific contexts.

EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance

Background: While decreased alpha and beta-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer's disease, it is not yet entirely known whether these differences can mark cognitive decline in the early stages of the disease. Our study aimed to analyze electroencephalography (EEG) FC and network differences in the alpha and beta frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints. Methods: Functional connectivity and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task with varying memory load. FC between EEG channels was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree (MST) approach, which reconstructs the critical backbone of the original network. Results: Memory load (increasing number of to-be-learned items) enhanced the mean AEC-c in the control group in both frequency bands. In contrast to that, after an initial increase, the MCI group showed significantly (p < 0.05) diminished FC in the alpha band in the highest memory load condition, while in the beta band this modulation was absent. Moreover, mean alpha and beta AEC-c correlated significantly with the size of medial temporal lobe structures in the entire sample. The network analysis revealed increased maximum degree, betweenness centrality, and degree divergence, and decreased diameter and eccentricity in the MCI group compared to the control group in both frequency bands independently of the memory load. This suggests a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution. Conclusion: Alpha- and beta-band FC measured by AEC-c correlates with cognitive load-related modulation, with subtle medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the "hub overload and failure" framework and might be part of a compensatory mechanism or a consequence of neural disinhibition.

Exploring intensity-dependent modulations in EEG resting-state network efficiency induced by exercise

PURPOSE

Exhaustive cardiovascular load can affect neural processing and is associated with decreases in sensorimotor performance. The purpose of this study was to explore intensity-dependent modulations in brain network efficiency in response to treadmill running assessed from resting-state electroencephalography (EEG) measures.

METHODS

Sixteen trained participants were tested for individual peak oxygen uptake (VO2 peak) and performed an incremental treadmill exercise at 50% (10 min), 70% (10 min) and 90% speed VO2 peak (all-out) followed by cool-down running and active recovery. Before the experiment and after each stage, borg scale (BS), blood lactate concentration (BLa), resting heartrate (HRrest) and 64-channel EEG resting state were assessed. To analyze network efficiency, graph theory was applied to derive small world index (SWI) from EEG data in theta, alpha-1 and alpha-2 frequency bands.

RESULTS

Analysis of variance for repeated measures revealed significant main effects for intensity on BS, BLa, HRrest and SWI. While BS, BLa and HRrest indicated maxima after all-out, SWI showed a reduction in the theta network after all-out.

CONCLUSION

Our explorative approach suggests intensity-dependent modulations of resting-state brain networks, since exhaustive exercise temporarily reduces brain network efficiency. Resting-state network assessment may prospectively play a role in training monitoring by displaying the readiness and efficiency of the central nervous system in different training situations.

Delineating between-subject heterogeneity in alpha networks with Spatio-Spectral Eigenmodes

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A data-driven modal decomposition describes oscillations by their resonant frequency, damping time and network structure.

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We show that the full multivariate transfer function can be rewritten as a linear superposition of these modes.

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These modal coordinates factorise oscillatory systems without pre-specification of frequency bands or regions of interest.

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Using these modes, we find a spatial gradient in alpha peak frequency between Occipital and Parietal cortex .

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This gradient is highly variable between participants, showing shifts in spatial structure and peak frequency.

Between subject variability in the spatial and spectral structure of oscillatory networks can be highly informative but poses a considerable analytic challenge. Here, we describe a data-driven modal decomposition of a multivariate autoregressive model that simultaneously identifies oscillations by their peak frequency, damping time and network structure. We use this decomposition to define a set of Spatio-Spectral Eigenmodes (SSEs) providing a parsimonious description of oscillatory networks. We show that the multivariate system transfer function can be rewritten in these modal coordinates, and that the full transfer function is a linear superposition of all modes in the decomposition. The modal transfer function is a linear summation and therefore allows for single oscillatory signals to be isolated and analysed in terms of their spectral content, spatial distribution and network structure. We validate the method on simulated data and explore the structure of whole brain oscillatory networks in eyes-open resting state MEG data from the Human Connectome Project. We are able to show a wide between participant variability in peak frequency and network structure of alpha oscillations and show a distinction between occipital ’high-frequency alpha’ and parietal ’low-frequency alpha’. The frequency difference between occipital and parietal alpha components is present within individual participants but is partially masked by larger between subject variability; a 10Hz oscillation may represent the high-frequency occipital component in one participant and the low-frequency parietal component in another. This rich characterisation of individual neural phenotypes has the potential to enhance analyses into the relationship between neural dynamics and a person’s behavioural, cognitive or clinical state.

Electroencephalogram in dementia with Lewy bodies: a systematic review

Dementia with Lewy bodies (DLB) belongs to the spectrum of Lewy body dementia (LBD) that also encompasses Parkinson's disease dementia (PDD). It is a common neurodegenerative disorder characterized by memory decline, cognitive fluctuations, visual hallucinations, autonomic nervous system disturbance, REM sleep behavior disorder, and parkinsonism. Definite diagnosis can be established only through neuropathological confirmation of Lewy bodies' presence in brain tissue. Probable or possible diagnosis relies upon clinical features, imaging, polysomnography, and electroencephalogram (EEG) findings. Potential neurophysiological biomarkers for the diagnosis, management, and evaluation of treatment-response in DLB should be affordable and widely available outside academic centers. Increasing evidence supports the use of quantitative EEG (qEEG) as a potential DLB biomarker, with promising results in discriminating DLB from other dementias and in identifying subjects who are on the trajectory to develop DLB. Several studies evaluated the diagnostic value of EEG in DLB. Visual analysis and qEEG techniques have been implemented, showing a superiority of the last in terms of sensitivity and objectivity. In this systematic review, we attempt to provide a general synthesis of the current knowledge on EEG application in DLB. We review the findings from original studies and address the issues remaining to be further clarified.

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.

[Fluctuation of cognitive functions in dementia with Lewy bodies]

Fluctuations of cognitive function (FCF) is one of the core diagnostic features of dementia with Lewy bodies (DLB). However, identification, pathophysiology, management of this unusual phenomena remain poor understood. The review presents modern ideas about phenomenology, causes, systematization, clinical significance and current methods of diagnosis and treatment of FCF in patients with DLB.

The Role of EEG in the Diagnosis, Prognosis and Clinical Correlations of Dementia with Lewy Bodies-A Systematic Review

Despite improvements in diagnostic criteria for dementia with Lewy bodies (DLB), the ability to discriminate DLB from Alzheimer’s disease (AD) and other dementias remains suboptimal. Electroencephalography (EEG) is currently a supportive biomarker in the diagnosis of DLB. We performed a systematic review to better clarify the diagnostic and prognostic role of EEG in DLB and define the clinical correlates of various EEG features described in DLB. MEDLINE, EMBASE, and PsycINFO were searched using search strategies for relevant articles up to 6 August 2020. We included 43 studies comparing EEG in DLB with other diagnoses, 42 of them included a comparison of DLB with AD, 10 studies compared DLB with Parkinson’s disease dementia, and 6 studies compared DLB with other dementias. The studies were visual EEG assessment (6), quantitative EEG (35) and event-related potential studies (2). The most consistent observation was the slowing of the dominant EEG rhythm (<8 Hz) assessed visually or through quantitative EEG, which was observed in ~90% of patients with DLB and only ~10% of patients with AD. Other findings based on qualitative rating, spectral power analyses, connectivity, microstate and machine learning algorithms were largely heterogenous due to differences in study design, EEG acquisition, preprocessing and analysis. EEG protocols should be standardized to allow replication and validation of promising EEG features as potential biomarkers in DLB.

Quantitative EEG as a biomarker in mild cognitive impairment with Lewy bodies

Objectives

To investigate using quantitative EEG the (1) differences between patients with mild cognitive impairment with Lewy bodies (MCI-LB) and MCI with Alzheimer’s disease (MCI-AD) and (2) its utility as a potential biomarker for early differential diagnosis.

Methods

We analyzed eyes-closed, resting-state, high-density EEG data from highly phenotyped participants (39 MCI-LB, 36 MCI-AD, and 31 healthy controls). EEG measures included spectral power in different frequency bands (delta, theta, pre-alpha, alpha, and beta), theta/alpha ratio, dominant frequency, and dominant frequency variability. Receiver operating characteristic (ROC) analyses were performed to assess diagnostic accuracy.

Results

There was a shift in power from beta and alpha frequency bands towards slower frequencies in the pre-alpha and theta range in MCI-LB compared to healthy controls. Additionally, the dominant frequency was slower in MCI-LB compared to controls. We found significantly increased pre-alpha power, decreased beta power, and slower dominant frequency in MCI-LB compared to MCI-AD. EEG abnormalities were more apparent in MCI-LB cases with more diagnostic features. There were no significant differences between MCI-AD and controls. In the ROC analysis to distinguish MCI-LB from MCI-AD, beta power and dominant frequency showed the highest area under the curve values of 0.71 and 0.70, respectively. While specificity was high for some measures (up to 0.97 for alpha power and 0.94 for theta/alpha ratio), sensitivity was generally much lower.

Conclusions

Early EEG slowing is a specific feature of MCI-LB compared to MCI-AD. However, there is an overlap between the two MCI groups which makes it difficult to distinguish between them based on EEG alone.

Characterization of EEG-based functional brain networks in myotonic dystrophy type 1

OBJECTIVE

In the autosomal dominant, multisystem, chronic progressive disease myotonic dystrophy type 1 (DM1), cognitive deficits may originate from disrupted functional brain networks. We aimed to use network analysis of resting-state electro-encephalography (EEG) recordings of patients with DM1 and matched unaffected controls to investigate changes in network organization in large-scale functional brain networks and correlations with cognitive deficits.

METHODS

In this cross-sectional study, 28 adult patients with genetically confirmed DM1 and 26 age-, sex- and education-matched unaffected controls underwent resting-state EEG and neuropsychological assessment. We calculated the Phase Lag Index (PLI) to determine EEG frequency-dependent functional connectivity between brain regions. Functional brain networks were characterized by applying concepts from graph theory and compared between-groups. Network topology was evaluated using the minimum spanning tree (MST). We evaluated correlations between network metrics and neuropsychological tests that showed statistically significant between-group differences.

RESULTS

Functional connectivity estimated as whole-brain median PLI for DM1 patients versus healthy controls was higher in theta band (0.141 [0.050] versus 0.125 [0.018], p = 0.029), and lower in the upper alpha band (0.154 [0.048] versus 0.182 [0.073], p = 0.038), respectively. Functional MST-constructed networks in DM1 patients were significantly dissimilar from healthy controls in the delta, (p = 0.009); theta, (p = 0.009); lower alpha, (p = 0.036); and upper alpha, (p = 0.008) bands. In evaluation of local MST network measures, trends toward networks with higher global integration in the theta band and lower global integration in the upper alpha band were observed. Compared to unaffected controls, DM1 patients performed worse on tests of attention, motor function, executive function and visuospatial memory. Visuospatial memory correlated with the global median PLI in the upper alpha band; the Stroop interference test correlated with betweenness centrality in this band.

CONCLUSION

This study supports the hypothesis that brain changes in DM1 give rise to disrupted functional network organization, as modelled with EEG-based networks. Further study may help unravel the relations with clinical brain-related DM1 symptoms.

SIGNIFICANCE

EEG network analysis has potential to help understand brain related DM1 phenotypes.

FUNDING

This work was supported by the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 305697 (OPTIMISTIC) and the Marigold Foundation.

Fluctuating cognition in the Lewy body dementias

Fluctuating cognition is a core diagnostic feature of dementia with Lewy bodies and is also a key clinical feature of Parkinson's disease dementia. These dementias share common pathological features and are referred to as Lewy body dementias. Whilst highly prevalent in Lewy body dementia, with up to 90% of patients experiencing the symptom at some point in the disease trajectory, clinical identification of fluctuating cognition is often challenging. Furthermore, its underlying pathophysiological processes remain unclear. However, neuroimaging and neurophysiological techniques have recently provided insight into potential drivers of the phenomenon. In this update, we review data pertaining to clinical features and underlying mechanisms of fluctuating cognition in Lewy body dementia. We collate evidence for different proposed aetiologies: fluctuating cognition as an attentional disorder, as a consequence of loss of cholinergic drive, as a manifestation of failure in neuronal efficiency and synchrony, and as a disorder of sleep/arousal. We also review data relating to putative mechanisms that have received less attention to date. Increased understanding of fluctuating cognition may help to illuminate pathophysiological mechanisms in cognitive processing in Lewy body dementia, guide future research, and facilitate the design of targeted therapeutic approaches.

EEG alpha reactivity and cholinergic system integrity in Lewy body dementia and Alzheimer's disease

BACKGROUND

Lewy body dementia (LBD), which includes dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), is characterised by marked deficits within the cholinergic system which are more severe than in Alzheimer's disease (AD) and are mainly caused by degeneration of the nucleus basalis of Meynert (NBM) whose widespread cholinergic projections provide the main source of cortical cholinergic innervation. EEG alpha reactivity, which refers to the reduction in alpha power over occipital electrodes upon opening the eyes, has been suggested as a potential marker of cholinergic system integrity.

METHODS

Eyes-open and eyes-closed resting state EEG data were recorded from 41 LBD patients (including 24 patients with DLB and 17 with PDD), 21 patients with AD, and 40 age-matched healthy controls. Alpha reactivity was calculated as the relative reduction in alpha power over occipital electrodes when opening the eyes. Structural MRI data were used to assess volumetric changes within the NBM using a probabilistic anatomical map.

RESULTS

Alpha reactivity was reduced in AD and LBD patients compared to controls with a significantly greater reduction in LBD compared to AD. Reduced alpha reactivity was associated with smaller volumes of the NBM across all groups (ρ = 0.42, pFDR = 0.0001) and in the PDD group specifically (ρ = 0.66, pFDR = 0.01).

CONCLUSIONS

We demonstrate that LBD patients show an impairment in alpha reactivity upon opening the eyes which distinguishes this form of dementia from AD. Furthermore, our results suggest that reduced alpha reactivity might be related to a loss of cholinergic drive from the NBM, specifically in PDD.

Weighted network measures reveal differences between dementia types: An EEG study

The diagnosis of dementia with Lewy bodies (DLB) versus Alzheimer's disease (AD) can be difficult especially early in the disease process. However, one inexpensive and non‐invasive biomarker which could help is electroencephalography (EEG). Previous studies have shown that the brain network architecture assessed by EEG is altered in AD patients compared with age‐matched healthy control people (HC). However, similar studies in Lewy body diseases, that is, DLB and Parkinson's disease dementia (PDD) are still lacking. In this work, we (a) compared brain network connectivity patterns across conditions, AD, DLB and PDD, in order to infer EEG network biomarkers that differentiate between these conditions, and (b) tested whether opting for weighted matrices led to more reliable results by better preserving the topology of the network. Our results indicate that dementia groups present with reduced connectivity in the EEG α band, whereas DLB shows weaker posterior–anterior patterns within the β‐band and greater network segregation within the θ‐band compared with AD. Weighted network measures were more consistent across global thresholding levels, and the network properties reflected reduction in connectivity strength in the dementia groups. In conclusion, β‐ and θ‐band network measures may be suitable as biomarkers for discriminating DLB from AD, whereas the α‐band network is similarly affected in DLB and PDD compared with HC. These variations may reflect the impairment of attentional networks in Parkinsonian diseases such as DLB and PDD.

Next-generation neural mass and field modeling

The Wilson-Cowan population model of neural activity has greatly influenced our understanding of the mechanisms for the generation of brain rhythms and the emergence of structured brain activity. As well as the many insights that have been obtained from its mathematical analysis, it is now widely used in the computational neuroscience community for building large-scale in silico brain networks that can incorporate the increasing amount of knowledge from the Human Connectome Project. Here, we consider a neural population model in the spirit of that originally developed by Wilson and Cowan, albeit with the added advantage that it can account for the phenomena of event-related synchronization and desynchronization. This derived mean-field model provides a dynamic description for the evolution of synchrony, as measured by the Kuramoto order parameter, in a large population of quadratic integrate-and-fire model neurons. As in the original Wilson-Cowan framework, the population firing rate is at the heart of our new model; however, in a significant departure from the sigmoidal firing rate function approach, the population firing rate is now obtained as a real-valued function of the complex-valued population synchrony measure. To highlight the usefulness of this next-generation Wilson-Cowan style model, we deploy it in a number of neurobiological contexts, providing understanding of the changes in power spectra observed in electro- and magnetoencephalography neuroimaging studies of motor cortex during movement, insights into patterns of functional connectivity observed during rest and their disruption by transcranial magnetic stimulation, and to describe wave propagation across cortex.

Cognitive fluctuations in Lewy body dementia: towards a pathophysiological framework

Fluctuating cognition is a complex and disabling symptom that is seen most frequently in the context of Lewy body dementias encompassing dementia with Lewy bodies and Parkinson’s disease dementia. In fact, since their description over three decades ago, cognitive fluctuations have remained a core diagnostic feature of dementia with Lewy bodies, the second most common dementia in the elderly. In the absence of reliable biomarkers for Lewy body pathology, the inclusion of such patients in therapeutic trials depends on the accurate identification of such core clinical features. Yet despite their diagnostic relevance, cognitive fluctuations remain poorly understood, in part due to the lack of a cohesive clinical and scientific explanation of the phenomenon itself. Motivated by this challenge, the present review examines the history, clinical phenomenology and assessment of cognitive fluctuations in the Lewy body dementias. Based on these data, the key neuropsychological, neurophysiological and neuroimaging correlates of cognitive fluctuations are described and integrated into a novel testable heuristic framework from which new insights may be gained.

Dysfunctional brain dynamics and their origin in Lewy body dementia

Lewy body dementia includes dementia with Lewy bodies and Parkinson's disease dementia and is characterized by transient clinical symptoms such as fluctuating cognition, which might be driven by dysfunction of the intrinsic dynamic properties of the brain. In this context we investigated whole-brain dynamics on a subsecond timescale in 42 Lewy body dementia compared to 27 Alzheimer's disease patients and 18 healthy controls using an EEG microstate analysis in a cross-sectional design. Microstates are transiently stable brain topographies whose temporal characteristics provide insight into the brain's dynamic repertoire. Our additional aim was to explore what processes in the brain drive microstate dynamics. We therefore studied associations between microstate dynamics and temporal aspects of large-scale cortical-basal ganglia-thalamic interactions using dynamic functional MRI measures given the putative role of these subcortical areas in modulating widespread cortical function and their known vulnerability to Lewy body pathology. Microstate duration was increased in Lewy body dementia for all microstate classes compared to Alzheimer's disease (P < 0.001) and healthy controls (P < 0.001), while microstate dynamics in Alzheimer's disease were largely comparable to healthy control levels, albeit with altered microstate topographies. Correspondingly, the number of distinct microstates per second was reduced in Lewy body dementia compared to healthy controls (P < 0.001) and Alzheimer's disease (P < 0.001). In the dementia with Lewy bodies group, mean microstate duration was related to the severity of cognitive fluctuations (ρ = 0.56, PFDR = 0.038). Additionally, mean microstate duration was negatively correlated with dynamic functional connectivity between the basal ganglia (r = - 0.53, P = 0.003) and thalamic networks (r = - 0.38, P = 0.04) and large-scale cortical networks such as visual and motor networks in Lewy body dementia. The results indicate a slowing of microstate dynamics and disturbances to the precise timing of microstate sequences in Lewy body dementia, which might lead to a breakdown of the intricate dynamic properties of the brain, thereby causing loss of flexibility and adaptability that is crucial for healthy brain functioning. When contrasted with the largely intact microstate dynamics in Alzheimer's disease, the alterations in dynamic properties in Lewy body dementia indicate a brain state that is less responsive to environmental demands and might give rise to the apparent slowing in thinking and intermittent confusion which typify Lewy body dementia. By using Lewy body dementia as a probe pathology we demonstrate a potential link between dynamic functional MRI fluctuations and microstate dynamics, suggesting that dynamic interactions within the cortical-basal ganglia-thalamic loop might play a role in the modulation of EEG dynamics.

Dementia with Lewy bodies: an update and outlook

Dementia with Lewy bodies (DLB) is an age-associated neurodegenerative disorder producing progressive cognitive decline that interferes with normal life and daily activities. Neuropathologically, DLB is characterised by the accumulation of aggregated α-synuclein protein in Lewy bodies and Lewy neurites, similar to Parkinson’s disease (PD). Extrapyramidal motor features characteristic of PD, are common in DLB patients, but are not essential for the clinical diagnosis of DLB. Since many PD patients develop dementia as disease progresses, there has been controversy about the separation of DLB from PD dementia (PDD) and consensus reports have put forward guidelines to assist clinicians in the identification and management of both syndromes. Here, we present basic concepts and definitions, based on our current understanding, that should guide the community to address open questions that will, hopefully, lead us towards improved diagnosis and novel therapeutic strategies for DLB and other synucleinopathies.

Quantitative electroencephalography as a marker of cognitive fluctuations in dementia with Lewy bodies and an aid to differential diagnosis

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EEG slowing was evident in dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) and less in Alzheimer’s disease (AD) patients compared to controls.

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Dominant rhythm variability was larger in AD but only correlated with cognitive fluctuations in DLB.

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QEEG variables classified DLB and AD patients with high sensitivity and specificity.

Objective

We investigated for quantitative EEG (QEEG) differences between Alzheimer’s disease (AD), dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) patients and healthy controls, and for QEEG signatures of cognitive fluctuations (CFs) in DLB.

Methods

We analysed eyes-closed, resting state EEGs from 18 AD, 17 DLB and 17 PDD patients with mild dementia, and 21 age-matched controls. Measures included spectral power, dominant frequency (DF), frequency prevalence (FP), and temporal DF variability (DFV), within defined EEG frequency bands and cortical regions.

Results

DLB and PDD patients showed a leftward shift in the power spectrum and DF. AD patients showed greater DFV compared to the other groups. In DLB patients only, greater DFV and EEG slowing were correlated with CFs, measured by the clinician assessment of fluctuations (CAF) scale. The diagnostic accuracy of the QEEG measures was 94% (90.4–97.9%), with 92.26% (80.4–100%) sensitivity and 83.3% (73.6–93%) specificity.

Conclusion

Although greater DFV was only shown in the AD group, within the DLB group a positive DFV – CF correlation was found. QEEG measures could classify DLB and AD patients with high sensitivity and specificity.

Significance

The findings add to an expanding literature suggesting that EEG is a viable diagnostic and symptom biomarker in dementia, particularly DLB.