Cognitive decline unlike normal aging is associated with alterations of EEG temporo-spatial characteristics

Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings

Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.

Utilization of EEG microstates as a prospective biomarker for assessing the impact of ketogenic diet in GLUT1-DS

OBJECTIVE

The aim of the study is to analyze microstate patterns in GLUT1-DS, both before and after the ketogenic diet (KD).

METHODS

We conducted microstate analysis of a patient with GLUT-1 DS and 27 healthy controls. A systematic literature review and meta-analysis was done. We compared the parameters of the patients with those of healthy controls and the incorporating findings in literature.

RESULTS

The durations of the patient were notably shorter, and the occurrence rates were longer than those of healthy controls and incorporating findings from the review. After 10 months of KD, the patient's microstate durations exhibited an increase from 53.05 ms, 57.17 ms, 61.80 ms, and 49.49 ms to 60.53 ms, 63.27 ms, 71.11 ms, and 66.55 ms. The occurrence rates changed from 4.0774 Hz, 4.9462 Hz, 4.8006 Hz, and 4.0579 Hz to 3.3354 Hz, 3.7893 Hz, 3.5956 Hz, and 4.1672 Hz. In healthy controls, the durations of microstate class A, B, C, and D were 61.86 ms, 63.58 ms, 70.57 ms, and 72.00 ms, respectively.

CONCLUSIONS

Our findings suggest EEG microstates may be a promising biomarker for monitoring the effect of KD. Administration of KD may normalize the dysfunctional patterns of temporal parameters.

Fractal Analysis in Neurodegenerative Diseases

Neurodegenerative diseases are defined by progressive nervous system dysfunction and death of neurons. The abnormal conformation and assembly of proteins is suggested to be the most probable cause for many of these neurodegenerative disorders, leading to the accumulation of abnormally aggregated proteins, for example, amyloid β (Aβ) (Alzheimer's disease and vascular dementia), tau protein (Alzheimer's disease and frontotemporal lobar degeneration), α-synuclein (Parkinson's disease and Lewy body dementia), polyglutamine expansion diseases (Huntington disease), or prion proteins (Creutzfeldt-Jakob disease). An aberrant gain-of-function mechanism toward excessive intraparenchymal accumulation thus represents a common pathogenic denominator in all these proteinopathies. Moreover, depending upon the predominant brain area involvement, these different neurodegenerative diseases lead to either movement disorders or dementia syndromes, although the underlying mechanism(s) can sometimes be very similar, and on other occasions, clinically similar syndromes can have quite distinct pathologies. Non-Euclidean image analysis approaches such as fractal dimension (FD) analysis have been applied extensively in quantifying highly variable morphopathological patterns, as well as many other connected biological processes; however, their application to understand and link abnormal proteinaceous depositions to other clinical and pathological features composing these syndromes is yet to be clarified. Thus, this short review aims to present the most important applications of FD in investigating the clinical-pathological spectrum of neurodegenerative diseases.

EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training

Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (22 participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Behavioral tests, EEG spectral analysis (covering delta, theta, alpha, beta, gamma frequency bands) and microstate analysis (involving four microstate classes, A to D) were employed to evaluate RI effect of TMNMT. The results of the study showed that TMNMT had a stronger inhibition ability and longer inhibition time according to the behavioral tests compared to Placebo. Spectral analysis showed that RI effect of TMNMT increased significantly the power spectral density (PSD) of delta, theta bands and decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of TMNMT had shorter duration (microstate B, microstate C), higher Occurrence (microstate A, microstate C, microstate D), Coverage (microstate A) and transition probabilities (microstate A to microstate B, microstate A to microstate D and microstate D to microstate A). Meanwhile, RI effect of Placebo decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of Placebo had shorter duration (microstate C, microstate D), higher occurrence (microstate B, microstate C), lower coverage (microstate C, microstate D), higher transition probabilities (microstate A to microstate B, microstate B to microstate A). It was also found that the intensity of tinnitus symptoms was significant positively correlated with the duration of microstate B in five subgroups (ECnm, NMnm, RInm, ECpb, PBpb). Our study provided valuable experimental evidence and practical applications for the effectiveness of TMNMT as a novel music therapy for tinnitus. The observed stronger residual inhibition (RI) ability of TMNMT supported its potential applications in tinnitus treatment. Furthermore, the temporal dynamics of EEG microstates serve as novel functional and trait markers of synchronous brain activity that contribute to a deep understanding of the neural mechanism underlying TMNMT treatment for tinnitus.

Towards a dynamical understanding of microstate analysis of M/EEG data

One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used for microstate computation, and multiple studies suggest that the microstate time series may provide insight into the neural activity of the brain in the resting state. This study addresses two gaps in the literature. Firstly, by applying several state-of-the-art microstate algorithms to a large dataset of EEG recordings, we aim to characterise and describe various microstate algorithms. We demonstrate and discuss why the three "classically" used algorithms ((T)AAHC and modified K-Means) yield virtually the same results, while HMM algorithm generates the most dissimilar results. Secondly, we aim to test the hypothesis that dynamical microstate properties might be, to a large extent, determined by the linear characteristics of the underlying EEG signal, in particular, by the cross-covariance and autocorrelation structure of the EEG data. To this end, we generated a Fourier transform surrogate of the EEG signal to compare microstate properties. Here, we found that these are largely similar, thus hinting that microstate properties depend to a very high degree on the linear covariance and autocorrelation structure of the underlying EEG data. Finally, we treated the EEG data as a vector autoregression process, estimated its parameters, and generated surrogate stationary and linear data from fitted VAR. We observed that such a linear model generates microstates highly comparable to those estimated from real EEG data, supporting the conclusion that a linear EEG model can help with the methodological and clinical interpretation of both static and dynamic human brain microstate properties.

Role of de novo lipogenesis in inflammation and insulin resistance in alzheimer's disease

Patients with Alzheimer's disease (AD) display both peripheral tissue and brain insulin resistance, the later could be a potential risk factor for cognitive dysfunction. While certain degree of inflammation is required for inducing insulin resistance, underlying mechanism(s) remains unclear. Evidence from diverse research domains suggest that elevated intracellular fatty acids of de novo pathway can induce insulin resistance even without triggering inflammation; however, the effect of saturated fatty acids (SFAs) could be detrimental due the development of proinflammatory cues. In this context, evidence suggest that while lipid/fatty acid accumulation is a characteristic feature of brain pathology in AD, dysregulated de novo lipogenesis could be a potential source for lipid/fatty acid accumulation. Therefore, therapies aimed at regulating de novo lipogenesis could be effective in improving insulin sensitivity and cognitive function in patients with AD.

Telling functional networks apart using ranked network features stability

Over the past few years, it has become standard to describe brain anatomical and functional organisation in terms of complex networks, wherein single brain regions or modules and their connections are respectively identified with network nodes and the links connecting them. Often, the goal of a given study is not that of modelling brain activity but, more basically, to discriminate between experimental conditions or populations, thus to find a way to compute differences between them. This in turn involves two important aspects: defining discriminative features and quantifying differences between them. Here we show that the ranked dynamical stability of network features, from links or nodes to higher-level network properties, discriminates well between healthy brain activity and various pathological conditions. These easily computable properties, which constitute local but topographically aspecific aspects of brain activity, greatly simplify inter-network comparisons and spare the need for network pruning. Our results are discussed in terms of microstate stability. Some implications for functional brain activity are discussed.

Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal

Objective.Electroencephalography (EEG) microstates (MSs), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. MS topographies are quasi-stable lasting between 60-120 ms. Some evidence suggests that MS are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of MS and their association with functional magnetic resonance imaging (fMRI) remains unclear.Approach. In a cohort of healthy subjects (n= 52), we conducted several statistical and machine learning (ML) approaches analyses on the association among MS spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and ML approaches.Main results.Our results using a generalized linear model showed that MS transitions were largely and negatively associated with BOLD signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between MS transitioning and fMRI signal while revealing that MS dynamics can model BOLD signals and vice versa.Significance.Results suggest that MS transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities measure common aspects of undergoing brain neuronal activities. These results may help to better understand the electrophysiological interpretation of MS.

Resting state electroencephalography microstates in autism spectrum disorder: A mini-review

Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topographical maps represent EEG data. We reviewed the current literature for resting state microstate analysis in ASD and identified eight publications. This current review indicates there is significant alterations in microstate parameters in ASD populations as compared to typically developing (TD) populations. Microstate parameters were also found to change in relation to specific cognitive processes. However, as microstate parameters are found to be changed by cognitive states, the differently acquired data (e.g., eyes closed or open) resting state EEG are likely to produce disparate results. We also review the current understanding of EEG sources of microstates and the underlying brain networks.

Cortical Frontoparietal Network Dysfunction in CHMP2B-Frontotemporal Dementia

A rare cause of inherited frontotemporal dementia (FTD) is a mutation in the CHMP2B gene on chromosome 3 leading to the autosomal dominantly inherited FTD (CHMP2B-FTD). Since CHMP2B-FTD is clinically well-characterized, and patients show a distinct pattern of executive dysfunction, the condition offers possible insight in the early electroencephalographic (EEG) changes in the cortical networks. Specifically, EEG microstate analysis parses the EEG signals into topographies believed to represent discrete network activations. We investigated the EEG dynamics in patients with symptomatic CHMP2B-FTD (n = 5) as well as pre-symptomatic mutation carriers (n = 5) compared to non-carrier family members (n = 6). The data was parsed into four archetypal microstates and global power was calculated. A trend was found for lower occurrence in microstate D in CHMP2B-FTD (p-value = 0.177, F-value = 2.036). Patients with recent symptom onset (<1 year) showed an increased duration of microstate D, whereas patients who had been symptomatic for longer periods (>2 years) showed decreased duration. Patients with CHMP2B-FTD present with executive dysfunction, and microstate D has previously been shown to be associated with the fronto-parietal network. The biphasic pattern may represent the pathophysiological changes in brain dynamics during neurodegeneration, which may apply to other neurodegenerative diseases.

Novelty detection-based approach for Alzheimer's disease and mild cognitive impairment diagnosis from EEG

Alzheimer’s disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer’s disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer’s disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer’s disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer’s disease from EEG records.

Differences in Multimodal Electroencephalogram and Clinical Correlations Between Early-Onset Alzheimer's Disease and Frontotemporal Dementia

Background

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are the two main types of dementia. We investigated the electroencephalogram (EEG) difference and clinical correlation in early-onset Alzheimer’s disease (EOAD), and FTD using multimodal EEG analyses. EOAD had more severe EEG abnormalities than late-onset AD (LOAD). Group comparisons between EOAD and LOAD were also performed.

Methods

Thirty patients diagnosed with EOAD, nine patients with LOAD, and 14 patients with FTD (≤65 y) were recruited (2008.1–2020.2), along with 24 healthy controls (≤65 y, n = 18; >65 y, n = 6). Clinical data were reviewed. Visual EEG, EEG microstate, and spectral analyses were performed.

Results

Compared to controls, markedly increased mean microstate duration, reduced mean occurrence, and reduced global field power (GFP) peaks per second were observed in EOAD and FTD. We found increased durations of class B in EOAD and class A in FTD. EOAD had reduced occurrences in classes A, B, and C, while only class C occurrence was reduced in FTD. The visual EEG results did not differ between AD and FTD. Microstate B showed correlations with activities of daily living score (r = 0.780, p = 0.008) and cerebrospinal fluid (CSF) Aβ42 (r = −0.833, p = 0.010) in EOAD. Microstate D occurrence was correlated with the CSF Aβ42 level in FTD (r = 0.786, p = 0.021). Spectral analysis revealed a general slowing EEG, which may contribute to microstate dynamic loss. Power in delta was significantly higher in EOAD than in FTD all over the head. In addition, EOAD had a marked increased duration and decreased occurrence than late-onset AD (LOAD), with no group differences in visual EEG results.

Conclusion

The current study found that EOAD and FTD had different EEG changes, and microstate had an association with clinical severity and CSF biomarkers. EEG microstate is more sensitive than visual EEG and may be useful for the differentiation between AD and FTD. The observations support that EEG can be a potential biomarker for the diagnosis and assessment of early-onset dementias.

The influence of age on EEG-based anaesthesia indices

STUDY OBJECTIVE

In the upcoming years there will be a growing number of elderly patients requiring general anaesthesia. As age is an independent risk factor for postoperative delirium (POD) the incidence of POD will increase concordantly. One approach to reduce the risk of POD would be to avoid excessively high doses of anaesthetics by using neuromonitoring to guide anaesthesia titration. Therefore, we evaluated the influence of patient's age on various electroencephalogram (EEG)-based anaesthesia indices.

DESIGN AND PATIENTS

We conducted an analysis of previously published data by replaying single electrode EEG episodes of maintenance of general anaesthesia from 180 patients (18-90 years; ASA I-IV) into the five different commercially available monitoring systems and evaluated their indices. We included the State/Response Entropy, Narcotrend, qCON/qNOX, bispectral index (BIS), and Treaton MGA-06. For a non-commercial comparison, we extracted the spectral edge frequency (SEF) from the BIS. To evaluate the influence of the age we generated linear regression models. We also assessed the correlation between the various indices.

MAIN RESULTS

During anaesthetic maintenance the values of the SEF, State/Response Entropy, qCON/qNOX and BIS all significantly increased (0.05 Hz/0.19-0.26 index points per year) with the patient's age (p < 0.001); whereas the Narcotrend did not change significantly with age (0.06 index points per year; p = 0.28). The index values of the Treaton device significantly decreased with age (-0.09 index points per year; p < 0.001). These findings were independent of the administered dose of anaesthetics.

CONCLUSIONS

Almost all current neuromonitoring devices are influenced by age, with the potential to result in inappropriately high dosage of anaesthetics. Therefore, anaesthesiologists should be aware of this phenomenon, and the next generation of monitors should correct for these changes.

The Relationships Between Trait Creativity and Resting-State EEG Microstates Were Modulated by Self-Esteem

Numerous studies find that creativity is not only associated with low effort and flexible processes but also associated with high effort and persistent processes especially when defensive behavior is induced by negative emotions. The important role of self-esteem is to buffer negative emotions, and individuals with low self-esteem are prone to instigating various forms of defensive behaviors. Thus, we thought that the relationships between trait creativity and executive control brain networks might be modulated by self-esteem. The resting-state electroencephalogram (RS-EEG) microstates can be divided into four classical types (MS1, MS2, MS3, and MS4), which can reflect the brain networks as well as their dynamic characteristic. Thus, the Williams Creative Tendency Scale (WCTS) and Rosenberg Self-esteem Scale (RSES) were used to investigate the modulating role of self-esteem on the relationships between trait creativity and the RS-EEG microstates. As our results showed, self-esteem consistently modulated the relationships between creativity and the duration and contribution of MS2 related to visual or imagery processing, the occurrence of MS3 related to cingulo-opercular networks, and transitions between MS2 and MS4, which were related to frontoparietal control networks. Based on these results, we thought that trait creativity was related to the executive control of bottom-up processing for individuals with low self-esteem, while the bottom-up information from vision or visual imagery might be related to trait creativity for individuals with high self-esteem.

EEG microstate complexity for aiding early diagnosis of Alzheimer's disease

The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer's disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD.

Study of EEG microstates in Parkinson's disease: a potential biomarker?

The spontaneous activity of the brain is dynamic even at rest and the deviation from this normal pattern of dynamics can lead to different pathological states. EEG microstate analysis of resting-state neuronal activity in Parkinson's disease (PD) could provide insight into altered brain dynamics of patients exhibiting dementia. Resting-state EEG microstate maps were derived from 128 channel EEG data in 20 PD without dementia (PDND), 18 PD with dementia (PDD) and 20 Healthy controls (CON) using Cartool and sLORETA softwares. Microstate map parameters including global explained variance, mean duration, frequency of occurrence (TF) and time coverage were compared statistically among the groups. Eight maps that explained 72% of the topographic variance were identified and only three maps differed significantly across the groups. TF of Map1 was lower in both PDND and PDD (p < 0.001) and that of Map3 (p = 0.02) in PDND compared to control. Cortical sources showed higher activation in precuneus, cuneus and superior parietal lobe (Threshold: Log-F = 1.74, p < 0.05) with maximum activity in the precuneus region (MNI co-ordinates: - 25, - 75, - 40; Log-F = 1.9) in PDND compared to control only for Map1. Lower TF of Map1 (prototypical microstate D) may potentially serve as a biomarker for PD with or without dementia whereas higher activation of precuneus, cuneus and superior parietal lobe at resting-state could favour signal processing, lack of which could be associated with dementia in Parkinson's disorder.

Ongoing Electroencephalographic Rhythms Related to Exploratory Movements in Transgenic TASTPM Mice

BACKGROUND

The European PharmaCog study (http://www.pharmacog.org) has reported a reduction in delta (1-6 Hz) electroencephalographic (EEG) power (density) during cage exploration (active condition) compared with quiet wakefulness (passive condition) in PDAPP mice (hAPP Indiana V717F mutation) modeling Alzheimer's disease (AD) amyloidosis and cognitive deficits.

OBJECTIVE

Here, we tested the reproducibility of that evidence in TASTPM mice (double mutation in APP KM670/671NL and PSEN1 M146V), which develop brain amyloidosis and cognitive deficits over aging. The reliability of that evidence was examined in four research centers of the PharmaCog study.

METHODS

Ongoing EEG rhythms were recorded from a frontoparietal bipolar channel in 29 TASTPM and 58 matched "wild type" C57 mice (range of age: 12-24 months). Normalized EEG power was calculated. Frequency and amplitude of individual delta and theta frequency (IDF and ITF) peaks were considered during the passive and active conditions.

RESULTS

Compared with the "wild type" group, the TASTPM group showed a significantly lower reduction in IDF power during the active over the passive condition (p < 0.05). This effect was observed in 3 out of 4 EEG recording units.

CONCLUSION

TASTPM mice were characterized by "poor reactivity" of delta EEG rhythms during the cage exploration in line with previous evidence in PDAPP mice. The reliability of that result across the centers was moderate, thus unveiling pros and cons of multicenter preclinical EEG trials in TASTPM mice useful for planning future studies.

Changes in the left temporal microstate are a sign of cognitive decline in patients with Alzheimer's disease

INTRODUCTION

Large-scale brain networks are disrupted in the early stages of Alzheimer's disease (AD). Electroencephalography microstate analysis, a promising method for studying brain networks, parses EEG signals into topographies representing discrete, sequential network activations. Prior studies indicate that patients with AD show a pattern of global microstate disorganization. We investigated whether any specific microstate changes could be found in patients with AD and mild cognitive impairment (MCI) compared to healthy controls (HC).

MATERIALS AND METHODS

Standard EEGs were obtained from 135 HC, 117 patients with MCI, and 117 patients with AD from six Nordic memory clinics. We parsed the data into four archetypal microstates.

RESULTS

There was significantly increased duration, occurrence, and coverage of microstate A in patients with AD and MCI compared to HC. When looking at microstates in specific frequency bands, we found that microstate A was affected in delta (1-4 Hz), theta (4-8 Hz), and beta (13-30 Hz), while microstate D was affected only in the delta and theta bands. Microstate features were able to separate HC from AD with an accuracy of 69.8% and HC from MCI with an accuracy of 58.7%.

CONCLUSIONS

Further studies are needed to evaluate whether microstates represent a valuable disease classifier. Overall, patients with AD and MCI, as compared to HC, show specific microstate alterations, which are limited to specific frequency bands. These alterations suggest disruption of large-scale cortical networks in AD and MCI, which may be limited to specific frequency bands.

Neurophysiological Markers of Alzheimer's Disease: Quantitative EEG Approach

Currently established and employed biomarkers of Alzheimer's disease (AD) predominantly mirror AD-associated molecular and structural brain changes. While they are necessary for identifying disease-specific neuropathology, they lack a clear and robust relationship with the clinical presentation of dementia; they can be altered in healthy individuals, while they often inadequately mirror the degree of cognitive and functional deficits in affected subjects. There is growing evidence that synaptic loss and dysfunction are early events during the trajectory of AD pathogenesis that best correlate with the clinical symptoms, suggesting measures of brain functional deficits as candidate early markers of AD. Resting-state electroencephalography (EEG) is a widely available and noninvasive diagnostic method that provides direct insight into brain synaptic activity in real time. Quantitative EEG (qEEG) analysis additionally provides information on physiologically meaningful frequency components, dynamic alterations and topography of EEG signal generators, i.e. neuronal signaling. Numerous studies have shown that qEEG measures can detect disruptions in activity, topographical distribution and synchronization of neuronal (synaptic) activity such as generalized EEG slowing, reduced global synchronization and anteriorization of neuronal generators of fast-frequency resting-state EEG activity in patients along the AD continuum. Moreover, qEEG measures appear to correlate well with surrogate markers of AD neuropathology and discriminate between different types of dementia, making them promising low-cost and noninvasive markers of AD. Future large-scale longitudinal clinical studies are needed to elucidate the diagnostic and prognostic potential of qEEG measures as early functional markers of AD on an individual subject level.

Microstate Changes Associated With Alzheimer's Disease in Persons With Down Syndrome

Down syndrome (DS) is associated with development of dementia due to Alzheimer’s disease (AD). However, due to considerable heterogeneity in intellectual function among persons with DS, it is difficult to assess whether a person with DS has developed dementia due to AD (DS-AD). EEG spectral power has previously shown very promising results with increased slowing in DS-AD compared to DS. However, another technique called microstates may be used to assess whole-brain dynamics and has to our knowledge not previously been investigated in either DS or DS-AD. The aim of the current study was to assess whether microstates could be used to differentiate between adults with DS, and DS-AD. We included EEGs from 10 persons with DS and 15 persons with DS-AD in the analysis. For the microstate analyses, we calculated four global maps, which were then back-fitted to all the EEGs. Lastly, we extracted the duration, occurrence, and coverage for each of the microstates. Here, we found the four archetypical maps as has previously been reported in the literature. We did not find any significant difference between DS and DS-AD but the largest difference in microstate duration between DS and DS-AD was found in microstate A and D. These findings are in line with structural MR studies showing that both the frontal and temporal lobes are affected in persons with DS-AD. Microstates may potentially serve as a diagnostic marker, but larger studies are needed to confirm these findings.

EEG time signature in Alzheimer´s disease: Functional brain networks falling apart

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EEG microstate topographies differ between controls and memory clinic patients.

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Microstate parameters differ in a gradient-like manner in SCD, MCI and AD patients.

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Changes in topography of microstate class C correlate with CSF Aβ42 levels.

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Changes in topography of microstate class B correlate with CSF p-tau levels.

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EEG microstates detect early disruption of neurocognitive networks in AD.

Spontaneous mental activity is characterized by dynamic alterations of discrete and stabile brain states called functional microstates that are thought to represent distinct steps of human information processing. Electroencephalography (EEG) directly reflects functioning of brain synapses with a uniquely high temporal resolution, necessary for investigation of brain network dynamics. Since synaptic dysfunction is an early event and best correlate of cognitive status and decline in patients along Alzheimer's disease (AD) continuum, EEG microstates might serve as valuable early markers of AD. The present study investigated differences in EEG microstate topographies and parameters (duration, occurrence and contribution) between a large cohort of healthy elderly (n = 308) and memory clinic patients: subjective cognitive decline (SCD, n = 210); mild cognitive impairment (MCI, n = 230) and AD (n = 197) and how they correlate to conventional cerebrospinal fluid (CSF) markers of AD. Four most representative microstate maps assigned as classes A, B (asymmetrical), C and D (symmetrical) were computed from the resting state EEGs since it has been shown previously that this is sufficient to explain most of the resting state EEG data. Statistically different topography of microstate maps were found between the controls and the patient groups for microstate classes A, C and D. Changes in the topography of microstate class C were associated with the CSF Aβ42 levels, whereas changes in the topography of class B were linked with the CSF p-tau levels. Gradient-like increase in the contribution of asymmetrical (A and B) and gradient-like decrease in the contribution of symmetrical (C and D) maps were observed with the more severe stage of cognitive impairment. Our study demonstrated extensive relationship of resting state EEG microstates topographies and parameters with the stage of cognitive impairment and AD biomarkers. Resting state EEG microstates might therefore serve as functional markers of early disruption of neurocognitive networks in patients along AD continuum.

Microstates as Disease and Progression Markers in Patients With Mild Cognitive Impairment

Network dysfunction is well established in patients with Alzheimer's disease (AD) and has been shown to be present early in the disease. This is especially interesting in patients with mild cognitive impairment (MCI) since they are more likely to develop AD. In EEG, one type of network analysis is microstates where the EEG is divided into quasi-stable states and these microstates have been linked to networks found with resting state functional MRI. In the current exploratory study, we therefore wanted to explore the changes in microstates in MCI, and AD compared to healthy controls (HC) and whether microstates were able to separate patients with MCI who progressed (pMCI) and those who remained stable (sMCI). EEGs were recorded at baseline for 17 patients with AD, 27 patients with MCI, and 38 older HC and the patients were followed for 3 years. To investigate whole-brain dynamics we extracted different microstate parameters. We found that patients with MCI, and AD had significantly higher occurrence (p-value = 0.028), and coverage (p-value = 0.010) for microstate A compared to HC. However, we did not find any significant systematic deviation of the transition probabilities from randomness for any of the groups. No significant differences were found between pMCI and sMCI but the largest difference in duration was found for microstate D. Microstate A has been linked to the temporal lobes in studies combining EEG and fMRI and the temporal lobes are the most affected by AD pathology in the early stages of the disease. This supports our idea that microstate A may be the first affected microstate in early AD. Even though not significant between pMCI and sMCI, Microstate D has previously been shown to be associated with both frontal and parietal areas as measured with fMRI and may correspond to underlying pathological changes in the progression of MCI to AD. However, larger studies are needed to confirm these findings.

EEG Microstates Temporal Dynamics Differentiate Individuals with Mood and Anxiety Disorders From Healthy Subjects

Electroencephalography (EEG) measures the brain’s electrophysiological spatio-temporal activities with high temporal resolution. Multichannel and broadband analysis of EEG signals is referred to as EEG microstates (EEG-ms) and can characterize such dynamic neuronal activity. EEG-ms have gained much attention due to the increasing evidence of their association with mental activities and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). Spatially independent EEG-ms are quasi-stationary topographies (e.g., stable, lasting a few dozen milliseconds) typically classified into four canonical classes (microstates A through D). They can be identified by clustering EEG signals around EEG global field power (GFP) maxima points. We examined the EEG-ms properties and the dynamics of cohorts of mood and anxiety (MA) disorders subjects (n = 61) and healthy controls (HCs; n = 52). In both groups, we found four distinct classes of EEG-ms (A through D), which did not differ among cohorts. This suggests a lack of significant structural cortical abnormalities among cohorts, which would otherwise affect the EEG-ms topographies. However, both cohorts’ brain network dynamics significantly varied, as reflected in EEG-ms properties. Compared to HC, the MA cohort features a lower transition probability between EEG-ms B and D and higher transition probability from A to D and from B to C, with a trend towards significance in the average duration of microstate C. Furthermore, we harnessed a recently introduced theoretical approach to analyze the temporal dependencies in EEG-ms. The results revealed that the transition matrices of MA group exhibit higher symmetrical and stationarity properties as compared to HC ones. In addition, we found an elevation in the temporal dependencies among microstates, especially in microstate B for the MA group. The determined alteration in EEG-ms temporal dependencies among the cohorts suggests that brain abnormalities in mood and anxiety disorders reflect aberrant neural dynamics and a temporal dwelling among ceratin brain states (i.e., mood and anxiety disorders subjects have a less dynamicity in switching between different brain states).

EEG Microstates Change in Response to Increase in Dopaminergic Stimulation in Typical Parkinson's Disease Patients

Objectives: Characterizing pharmacological response in Parkinson's Disease (PD) patients may be a challenge in early stages but gives valuable clues for diagnosis. Neurotropic drugs may modulate Electroencephalography (EEG) microstates (MS). We investigated EEG-MS default-mode network changes in response to dopaminergic stimulation in PD. Methods: Fourteen PD subjects in HY stage III or less were included, and twenty-one healthy controls. All patients were receiving dopaminergic stimulation with levodopa or dopaminergic agonists. Resting EEG activity was recorded before the first daily PD medication dose and 1 h after drug intake resting EEG activity was again recorded. Time and frequency variables for each MS were calculated. Results: Parkinson's disease subjects MS A duration decreases after levodopa intake, MS B appears more often than before levodopa intake. MS E was not present, but MS G was. There were no significant differences between control subjects and patients after medication intake. Conclusion: Clinical response to dopaminergic drugs in PD is characterized by clear changes in MS profile. Significance: This work demonstrates that there are clear EEG MS markers of PD dopaminergic stimulation state. The characterization of the disease and its response to dopaminergic medication may be of help for early therapeutic diagnosis.

Calculation and Analysis of Microstate Related to Variation in Executed and Imagined Movement of Force of Hand Clenching

Objective

In order to investigate electroencephalogram (EEG) instantaneous activity states related to executed and imagined movement of force of hand clenching (grip force: 4 kg, 10 kg, and 16 kg), we utilized a microstate analysis in which the spatial topographic map of EEG behaves in a certain number of discrete and stable global brain states.

Approach

Twenty subjects participated in EEG collection; the global field power of EEG and its local maximum were calculated and then clustered using cross validation and statistics; the 4 parameters of each microstate (duration, occurrence, time coverage, and amplitude) were calculated from the clustering results and statistically analyzed by analysis of variance (ANOVA); finally, the relationship between the microstate and frequency band was analyzed.

Main Results

The experimental results showed that all microstates related to executed and imagined grip force tasks were clustered into 3 microstate classes (A, B, and C); these microstates generally transitioned from A to B and then from B to C. With the increase of the target value of executed and imagined grip force, the duration and time coverage of microstate B gradually decreased, while these parameters of microstate C gradually increased. The occurrence times of microstate B and C related to executed grip force were significantly more than those related to imagined grip force; furthermore, the amplitudes of these 3 microstates related to executed grip force were significantly greater than those related to imagined grip force. The correlation coefficients between the microstates and the frequency bands indicated that the microstates were correlated to mu rhythm and beta frequency bands, which are consistent with event-related desynchronization/synchronization (ERD/ERS) phenomena of sensorimotor rhythm.

Significance

It is expected that this microstate analysis may be used as a new method for observing EEG instantaneous activity patterns related to variation in executed and imagined grip force and also for extracting EEG features related to these tasks. This study may lay a foundation for the application of executed and imagined grip force training for rehabilitation of hand movement disorders in patients with stroke in the future.

Short timescale abnormalities in the states of spontaneous synchrony in the functional neural networks in Alzheimer's disease

Alzheimer's disease (AD) is a prevalent neurodegenerative condition that can lead to severe cognitive and functional deterioration. Functional magnetic resonance imaging (fMRI) revealed abnormalities in AD in intrinsic synchronization between spatially separate regions in the so-called default mode network (DMN) of the brain. To understand the relationship between this disruption in large-scale synchrony and the cognitive impairment in AD, it is critical to determine whether and how the deficit in the low frequency hemodynamic fluctuations recorded by fMRI translates to much faster timescales of memory and other cognitive processes. The present study employed magnetoencephalography (MEG) and a Hidden Markov Model (HMM) approach to estimate spontaneous synchrony variations in the functional neural networks with high temporal resolution. In a group of cognitively healthy (CH) older adults, we found transient (mean duration of 150-250 ms) network activity states, which were visited in a rapid succession, and were characterized by spatially coordinated changes in the amplitude of source-localized electrophysiological oscillations. The inferred states were similar to those previously observed in younger participants using MEG, and the estimated cortical source distributions of the state-specific activity resembled the classic functional neural networks, such as the DMN. In patients with AD, inferred network states were different from those of the CH group in short-scale timing and oscillatory features. The state of increased oscillatory amplitudes in the regions overlapping the DMN was visited less often in AD and for shorter periods of time, suggesting that spontaneous synchronization in this network was less likely and less stable in the patients. During the visits to this state, in some DMN nodes, the amplitude change in the higher-frequency (8-30 Hz) oscillations was less robust in the AD than CH group. These findings highlight relevance of studying short-scale temporal evolution of spontaneous activity in functional neural networks to understanding the AD pathophysiology. Capacity of flexible intrinsic synchronization in the DMN may be crucial for memory and other higher cognitive functions. Our analysis yielded metrics that quantify distinct features of the neural synchrony disorder in AD and may offer sensitive indicators of the neural network health for future investigations.

Altered Brain Microstate Dynamics in Adolescents with Narcolepsy

Narcolepsy is a chronic sleep disorder caused by a loss of hypocretin-1 producing neurons in the hypothalamus. Previous neuroimaging studies have investigated brain function in narcolepsy during rest using positron emission tomography (PET) and single photon emission computed tomography (SPECT). In addition to hypothalamic and thalamic dysfunction they showed aberrant prefrontal perfusion and glucose metabolism in narcolepsy. Given these findings in brain structure and metabolism in narcolepsy, we anticipated that changes in functional magnetic resonance imaging (fMRI) resting state network (RSN) dynamics might also be apparent in patients with narcolepsy. The objective of this study was to investigate and describe brain microstate activity in adolescents with narcolepsy and correlate these to RSNs using simultaneous fMRI and electroencephalography (EEG). Sixteen adolescents (ages 13-20) with a confirmed diagnosis of narcolepsy were recruited and compared to age-matched healthy controls. Simultaneous EEG and fMRI data were collected during 10 min of wakeful rest. EEG data were analyzed for microstates, which are discrete epochs of stable global brain states obtained from topographical EEG analysis. Functional MRI data were analyzed for RSNs. Data showed that narcolepsy patients were less likely than controls to spend time in a microstate which we found to be related to the default mode network and may suggest a disruption of this network that is disease specific. We concluded that adolescents with narcolepsy have altered resting state brain dynamics.

Microstates in resting-state EEG: current status and future directions

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable "microstates" that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease.

Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG microstates

INTRODUCTION

Resting-state EEG microstates are thought to reflect the momentary local states and interactions of distributed neural networks in the brain. Several changes in resting-state EEG microstates have been described in acutely ill patients with schizophrenia, but it is not known whether these represent trait or state abnormalities. The present study aimed to investigate this issue by assessing EEG microstate characteristics in high-risk individuals (HR) and clinically stable first-episode patients with schizophrenia (SZ) with low symptom levels, compared to each other and healthy controls (HC).

METHOD

Participants were 18 HR, 18 SZ, and 22 HC subjects. 64-channel resting-state EEG recordings were used for microstate analyses. Microstates were clustered into four classes (A-D) according to their topography. Temporal parameters and topographies of microstates were compared among groups.

RESULTS

Microstate class A displayed higher coverage and occurrence in HR than SZ and HC, while microstate class B covered significantly more time in SZ compared to both HR and HC. Microstate class B displayed an aberrant spatial configuration in SZ, and to a lesser extent also in HR, compared to HC, with patients exhibiting significantly higher activity in the vicinity of the left posterior cingulate.

DISCUSSION

Microstate abnormalities observed in HR were similar to those previously reported in acutely ill patients with schizophrenia. Moreover, there was evidence that HR and SZ might share specific disturbances in brain functional connectivity. These findings raise the possibility that certain abnormalities in resting-state EEG microstates might be associated with an increased risk for psychosis.

Brain functional network in Alzheimer's disease: diagnostic markers for diagnosis and monitoring

Alzheimer's disease (AD) is the most common type of dementia that is clinically characterized by the presence of memory impairment and later by impairment in other cognitive domains. The clinical diagnosis is based on interviews with the patient and his/her relatives and on neuropsychological assessment, which are also used to monitor cognitive decline over time. Several biomarkers have been proposed for detecting AD in its earliest stages, that is, in the predementia stage. In an attempt to find noninvasive biomarkers, researchers have investigated the feasibility of neuroimaging tools, such as MR, SPECT, and FDG-PET imaging, as well as neurophysiological measurements using EEG. In this paper, we investigate the brain functional networks in AD, focusing on main neurophysiological techniques, integrating with most relevant functional brain imaging findings.

Estudo da coerência do eletrencefalograma na banda de frequência alfa em indivíduos adultos normais: resultados preliminares em 10 casos

A coerência espectral inter-hemisférica do eletrencefalograma da região occipital do escalpo (eletrodos O1 e O2) foi estimada usando a transformada rápida de Fourier. As médias de coerências na banda de freqüência alfa (alfa1 - 8,0 a 10,0 Hz e alfa2 -10,1 a 12,5 Hz) em indivíduos normais com mais de 50 anos foram comparadas com as obtidas em adultos jovens com idade inferior a 50 anos. Nossos resultados mostraram que não há diferença significativa dos níveis de coerência na banda alfa entre indivíduos em faixas etárias mais avançadas comparados aos adultos jovens.

Wave-number spectrum of electrocorticographic signals

A physiologically based continuum model of corticothalamic electrodynamics is generalized and used to derive the theoretical form of the electrocorticographic (ECoG) wave-number spectrum. A one-dimensional projection of the spectrum is derived, as is the azimuthally averaged two-dimensional spectrum for isotropic and anisotropic cortices. The predicted spectra are found to consist of a low-k plateau followed by three regions of power-law decrease, which result from filtering of the electrical activity through physical structures at different scales in the cortex. The magnitude of the maximum theoretical power-law exponent is larger for the two-dimensional (2D) spectrum than for its 1D counterpart. The predicted spectra agree well with experimental data obtained from 1D and 2D recording arrays on the cortical surface, enabling the structures in the brain that are important in determining spatial cortical dynamics to be identified. The cortical dispersion relation predicted by our model is also investigated, providing insight into the relationships between temporal and spatial brain dynamics.

Wave-number spectrum of electroencephalographic signals

A recently developed, physiologically based continuum model of corticothalamic electrodynamics is used to derive the theoretical form of the electroencephalographic wave-number spectrum and its projection onto a one-dimensional recording array. The projected spectrum is found to consist of a plateau followed by regions of power-law decrease with various exponents, which are dependent on both model parameters and temporal frequency. The theoretical spectrum is compared with experimental results obtained in other studies, showing good agreement. The model provides a framework for understanding the nature of the spatial power spectrum by linking the underlying physiology with the large-scale dynamics of the brain.

Millisecond by millisecond, year by year: normative EEG microstates and developmental stages

Most studies of continuous EEG data have used frequency transformation, which allows the quantification of brain states that vary over seconds. For the analysis of shorter, transient EEG events, it is possible to identify and quantify brain electric microstates as subsecond time epochs with stable field topography. These microstates may correspond to basic building blocks of human information processing. Microstate analysis yields a compact and comprehensive repertoire of short lasting classes of brain topographic maps, which may be considered to reflect global functional states. Each microstate class is described by topography, mean duration, frequency of occurrence and percentage analysis time occupied. This paper presents normative microstate data for resting EEG obtained from a database of 496 subjects between the age of 6 and 80 years. The extracted microstate variables showed a lawful, complex evolution with age. The pattern of changes with age is compatible with the existence of developmental stages as claimed by developmental psychologists. The results are discussed in the framework of state dependent information processing and suggest the existence of biologically predetermined top-down processes that bias brain electric activity to functional states appropriate for age-specific learning and behavior.

Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG

OBJECTIVE

We explored the ability of specifically designed and trained recurrent neural networks (RNNs), combined with wavelet preprocessing, to discriminate between the electroencephalograms (EEGs) of patients with mild Alzheimer's disease (AD) and their age-matched control subjects.

METHODS

Twomin recordings of resting eyes-closed continuous EEGs (as well as their wavelet-filtered subbands) obtained from parieto-occipital channels of 10 early AD patients and 10 healthy controls were input into RNNs for training and testing purposes. The RNNs were chosen because they can implement extremely non-linear decision boundaries and possess memory of the state, which is crucial for the considered task.

RESULTS

The best training/testing results were achieved using a 3-layer RNN on left parietal channel level 4 high-pass wavelet subbands. When trained on 3 AD and 3 control recordings, the resulting RNN tested well on all remaining controls and 5 out of 7 AD patients. This represented a significantly better than chance performance of about 80% sensitivity at 100% specificity.

CONCLUSION

The suggested combined wavelet/RNN approach may be useful in analyzing long-term continuous EEGs for early recognition of AD. This approach should be extended on larger patient populations before its clinical diagnostic value can be established. Further lines of investigation might also require that EEGs be recorded from patients engaged in certain mental (cognitive) activities.

[Alpha band coherence analysis of EEG in healthy adult's and Alzheimer's type dementia patients]

We studied the occipital inter-hemispheric coherence (IHCoh) of EEG (electrodes O1-O2) for alpha band (alpha1 - 8,0 to 10,0 Hz and alpha2 - 10,1 to 12,5 Hz) in healthy adults and Alzheimer's type dementia (ATD) subjects, to observe if there is any significant difference between these two groups that could help in the early diagnosis of ATD. We found a decrease of occipital IHCoh in ATD group for both alpha sub-bands. We believe that Coh analysis of EEG is a powerful auxiliary method in ATD diagnosis.