EEG spectral profile to stage Alzheimer's disease

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.

Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study

Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and β) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically-validated frequency bands, and feature selection. Following, we assess the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.Clinical relevance- We present an end-to-end pipeline to extract clinically relevant features from rs-EEG recordings that can facilitate the analysis and detection of PD. Further, we provide an ML system that shows a good performance in detecting PD, even in the presence of centers with different acquisition protocols. Our results show the relevance of harmonizing features and provide a good starting point for future studies focusing on rs-EEG analysis and multi-center data.

Decomposing age effects in EEG alpha power

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. Thus, the present report analyzed a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm was utilized that allows the decomposition of the measured signal into periodic and aperiodic signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets was accumulated. It was hypothesized that previously reported age-related alpha power differences will largely diminish when total power is adjusted for the aperiodic signal component. First, the age-related decrease in total alpha power was replicated. Concurrently, decreases of the intercept and slope (i.e. exponent) of the aperiodic signal component were observed. Findings on aperiodic-adjusted alpha power indicated that this general shift of the power spectrum leads to an overestimation of the true age effects in conventional analyses of total alpha power. Thus, the importance of separating neural power spectra into periodic and aperiodic signal components is highlighted. However, also after accounting for these confounding factors, the sequential Bayesian updating analysis provided robust evidence that aging is associated with decreased aperiodic-adjusted alpha power. While the relation of the aperiodic component and aperiodic-adjusted alpha power to cognitive decline demands further investigation, the consistent findings on age effects across independent datasets and high test-retest reliabilities support that these newly emerging measures are reliable markers of the aging brain. Hence, previous interpretations of age-related decreases in alpha power are reevaluated, incorporating changes in the aperiodic signal.

Brainmarker-I Differentially Predicts Remission to Various Attention-Deficit/Hyperactivity Disorder Treatments: A Discovery, Transfer, and Blinded Validation Study

BACKGROUND

Attention-deficit/hyperactivity disorder is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates.

METHODS

The biomarker in this study was developed in a heterogeneous clinical sample (N = 4249) and first applied to two large transfer datasets, a priori stratifying young males (<18 years) with a higher individual alpha peak frequency (iAPF) to methylphenidate (N = 336) and those with a lower iAPF to multimodal neurofeedback complemented with sleep coaching (N = 136). Blinded, out-of-sample validations were conducted in two independent samples. In addition, the association between iAPF and response to guanfacine and atomoxetine was explored.

RESULTS

Retrospective stratification in the transfer datasets resulted in a predicted gain in normalized remission of 17% to 30%. Blinded out-of-sample validations for methylphenidate (n = 41) and multimodal neurofeedback (n = 71) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively.

CONCLUSIONS

This study introduces a clinically interpretable and actionable biomarker based on the iAPF assessed during resting-state electroencephalography. Our findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates.

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.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

TREM2 Deficiency Disrupts Network Oscillations Leading to Epileptic Activity and Aggravates Amyloid-β-Related Hippocampal Pathophysiology in Mice

BACKGROUND

Genetic mutations in triggering receptor expressed on myeloid cells-2 (TREM2) have been strongly associated with increased risk of developing Alzheimer's disease (AD) and other progressive dementias. In the brain, TREM2 protein is specifically expressed on microglia suggesting their active involvement in driving disease pathology. Using various transgenic AD models to interfere with microglial function through TREM2, several recent studies provided important data indicating a causal link between TREM2 and underlying amyloid-β (Aβ) and tau pathology. However, mechanisms by which TREM2 contributes to increased predisposition to clinical AD and influences its progression still remain largely unknown.

OBJECTIVE

Our aim was to elucidate the potential contribution of TREM2 on specific oscillatory dynamic changes associated with AD pathophysiology.

METHODS

Spontaneous and brainstem nucleus pontis oralis stimulation-induced hippocampal oscillation paradigm was used to investigate the impact of TREM2 haploinsufficiency TREM2(Het) or total deficiency TREM2(Hom) on hippocampal network function in wild-type and Aβ overproducing Tg2576 mice under urethane anesthesia.

RESULTS

Partial (TREM2(Het)) or total (TREM2(Hom)) deletion of TREM2 led to increased incidence of spontaneous epileptiform seizures in both wild-type and Tg2576 mice. Importantly, deficiency of TREM2 in Tg2576 mice significantly diminished power of theta oscillation in the hippocampus elicited by brainstem-stimulation compared to wild-type mice. However, it did not affect hippocampal theta-phase gamma-amplitude coupling significantly, since over a 60%reduction was found in coupling in Tg2576 mice regardless of TREM2 function.

CONCLUSION

Our findings indicate a role for TREM2-dependent microglial function in the hippocampal neuronal excitability in both wild type and Aβ overproducing mice, whereas deficiency in TREM2 function exacerbates disruptive effects of Aβ on hippocampal network oscillations.

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.

Sleep deprivation and Modafinil affect cortical sources of resting state electroencephalographic rhythms in healthy young adults

OBJECTIVE

It has been reported that sleep deprivation affects the neurophysiological mechanisms underpinning the vigilance. Here, we tested the following hypotheses in the PharmaCog project (www.pharmacog.org): (i) sleep deprivation may alter posterior cortical delta and alpha sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms in healthy young adults; (ii) after the sleep deprivation, a vigilance enhancer may recover those rsEEG source markers.

METHODS

rsEEG data were recorded in 36 healthy young adults before (Pre-sleep deprivation) and after (Post-sleep deprivation) one night of sleep deprivation. In the Post-sleep deprivation, these data were collected after a single dose of PLACEBO or MODAFINIL. rsEEG cortical sources were estimated by eLORETA freeware.

RESULTS

In the PLACEBO condition, the sleep deprivation induced an increase and a decrease in posterior delta (2-4 Hz) and alpha (8-13 Hz) source activities, respectively. In the MODAFINIL condition, the vigilance enhancer partially recovered those source activities.

CONCLUSIONS

The present results suggest that posterior delta and alpha source activities may be both related to the regulation of human brain arousal and vigilance in quiet wakefulness.

SIGNIFICANCE

Future research in healthy young adults may use this methodology to preselect new symptomatic drug candidates designed to normalize brain arousal and vigilance in seniors with dementia.

Electroencephalography and delirium in the postoperative period

Delirium commonly manifests in the postoperative period as a clinical syndrome resulting from acute brain dysfunction or encephalopathy. Delirium is characterized by acute and often fluctuating changes in attention and cognition. Emergence delirium typically presents and resolves within minutes to hours after termination of general anaesthesia. Postoperative delirium hours to days after an invasive procedure can herald poor outcomes. Easily recognized when patients are hyperactive or agitated, delirium often evades diagnosis as it most frequently presents with hypoactivity and somnolence. EEG offers objective measurements to complement clinical assessment of this complex fluctuating disorder. Although EEG features of delirium in the postoperative period remain incompletely characterized, a shift of EEG power into low frequencies is a typical finding shared among encephalopathies that manifest with delirium. In aggregate, existing data suggest that serial or continuous EEG in the postoperative period facilitates monitoring of delirium development and severity and assists in detecting epileptic aetiologies. Future studies are needed to clarify the precise EEG features that can reliably predict or diagnose delirium in the postoperative period, and to provide mechanistic insights into this pathologically diverse neurological disorder.

Neurophysiological signals as predictive translational biomarkers for Alzheimer's disease treatment: effects of donepezil on neuronal network oscillations in TgF344-AD rats

BACKGROUND

Translational research in Alzheimer's disease (AD) pathology provides evidence that accumulation of amyloid-β and hyperphosphorylated tau, neuropathological hallmarks of AD, is associated with complex disturbances in synaptic and neuronal function leading to oscillatory abnormalities in the neuronal networks that support memory and cognition. Accordingly, our recent study on transgenic TgF344-AD rats modeling AD showed an age-dependent reduction of stimulation-induced oscillations in the hippocampus, and disrupted long-range connectivity together with enhanced neuronal excitability in the cortex, reflected in greatly increased expression of high-voltage spindles, an epileptic absence seizure-like activity. To better understand the translational value of observed oscillatory abnormalities in these rats, we examine here the effects of donepezil, an acetylcholine esterase inhibitor clinically approved for AD treatment.

METHODS

Brainstem nucleus pontis oralis stimulation-induced hippocampal oscillations were recorded under urethane anesthesia in adult (6-month-old) and aged (12-month-old) TgF344-AD and wild-type rats. Spontaneous cortical activity was monitored in a cohort of freely behaving aged rats implanted with frontal and occipital cortical electroencephalography (EEG) electrodes.

RESULTS

Subcutaneous administration of donepezil significantly augmented stimulation-induced hippocampal theta oscillation in aged wild-type rats and both adult and aged TgF344-AD rats, which have been previously shown to have diminished response to nucleus pontis oralis stimulation. Moreover, in adult TgF344-AD rats, donepezil also significantly increased theta phase-gamma amplitude coupling in the hippocampus during stimulation. However, neither of these effects were significantly changed in adult wild-type rats. Under freely behaving conditions, donepezil treatment had the opposite effect on cortical oscillatory connectivity in TgF344-AD and wild-type rats, and it reduced the occurrence of high-voltage spindle activity in TgF344-AD rats.

CONCLUSIONS

Together, these results imply that pharmacologically enhancing cholinergic tone with donepezil could partially reverse oscillatory abnormalities in TgF344-AD rats, which is in line with its clinical effectiveness in AD patients. Therefore, our study suggests good translational opportunities for these neurophysiological signals recorded in TgF344-AD rats, and their application could be considered in drug discovery efforts for developing therapies with disease-modifying potential.

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.

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

•

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.

Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer's Disease and Mild Cognitive Impairment

The discrimination of early Alzheimer’s disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel–Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.

Abnormalities of resting-state functional cortical connectivity in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study

Previous evidence showed abnormal posterior sources of resting-state delta (<4 Hz) and alpha (8-12 Hz) rhythms in patients with Alzheimer's disease with dementia (ADD), Parkinson's disease with dementia (PDD), and Lewy body dementia (DLB), as cortical neural synchronization markers in quiet wakefulness. Here, we tested the hypothesis of additional abnormalities in functional cortical connectivity computed in those sources, in ADD, considered as a "disconnection cortical syndrome", in comparison with PDD and DLB. Resting-state eyes-closed electroencephalographic (rsEEG) rhythms had been collected in 42 ADD, 42 PDD, 34 DLB, and 40 normal healthy older (Nold) participants. Exact low-resolution brain electromagnetic tomography (eLORETA) freeware estimated the functional lagged linear connectivity (LLC) from rsEEG cortical sources in delta, theta, alpha, beta, and gamma bands. The area under receiver operating characteristic (AUROC) curve indexed the classification accuracy between Nold and diseased individuals (only values >0.7 were considered). Interhemispheric and intrahemispheric LLCs in widespread delta sources were abnormally higher in the ADD group and, unexpectedly, normal in DLB and PDD groups. Intrahemispheric LLC was reduced in widespread alpha sources dramatically in ADD, markedly in DLB, and moderately in PDD group. Furthermore, the interhemispheric LLC in widespread alpha sources showed lower values in ADD and DLB than PDD groups. At the individual level, AUROC curves of LLC in alpha sources exhibited better classification accuracies for the discrimination of ADD versus Nold individuals (0.84) than for DLB versus Nold participants (0.78) and PDD versus Nold participants (0.75). Functional cortical connectivity markers in delta and alpha sources suggest a more compromised neurophysiological reserve in ADD than DLB, at both group and individual levels.

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

The association between high levels of cumulative life stress and aberrant resting state EEG dynamics in old age

Cumulative experienced stress produces shortcomings in old adults' cognitive performance. These are reflected in electrophysiological changes tied to task execution. This study explored whether stress-related aberrations in older adults' electroencephalographic (EEG) activity were also apparent in the system at rest. To this effect, the amount of stressful life events experienced by 60 young and 60 elderly participants were assessed in conjunction with resting state power changes in the delta, theta, alpha, and beta frequencies during a resting EEG recording. Findings revealed elevated levels of delta power among elderly individuals reporting high levels of cumulative life stress. These differed significantly from young high and low stress individuals and old adults with low levels of stress. Increases of delta activity have been linked to the emergence of conditions such as Alzheimer's Disease and Mild Cognitive Impairment. Thus, a potential interpretation of our findings associates large amounts of cumulative stress with an increased risk of developing age-related cognitive pathologies in later life.

Abnormalities of cortical neural synchronization mechanisms in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study

The aim of this retrospective exploratory study was that resting state eyes-closed electroencephalographic (rsEEG) rhythms might reflect brain arousal in patients with dementia due to Alzheimer's disease dementia (ADD), Parkinson's disease dementia (PDD), and dementia with Lewy body (DLB). Clinical and rsEEG data of 42 ADD, 42 PDD, 34 DLB, and 40 healthy elderly (Nold) subjects were available in an international archive. Demography, education, and Mini-Mental State Evaluation score were not different between the patient groups. Individual alpha frequency peak (IAF) determined the delta, theta, alpha 1, alpha 2, and alpha 3 frequency bands. Fixed beta 1, beta 2, and gamma bands were also considered. rsEEG cortical sources were estimated by means of the exact low-resolution brain electromagnetic source tomography and were then classified across individuals, on the basis of the receiver operating characteristic curves. Compared to Nold, IAF showed marked slowing in PDD and DLB and moderate slowing in ADD. Furthermore, all patient groups showed lower posterior alpha 2 source activities. This effect was dramatic in ADD, marked in DLB, and moderate in PDD. These groups also showed higher occipital delta source activities, but this effect was dramatic in PDD, marked in DLB, and moderate in ADD. The posterior delta and alpha sources allowed good classification accuracy (approximately 0.85-0.90) between the Nold subjects and patients, and between ADD and PDD patients. In quiet wakefulness, delta and alpha sources unveiled different spatial and frequency features of the cortical neural synchronization underpinning brain arousal in ADD, PDD, and DLB patients. Future prospective cross-validation studies should test these rsEEG markers for clinical applications and drug discovery.

Disruption of the Posterior Medial Network during the Acute Stage of Transient Global Amnesia: A Preliminary Study

Acute perturbation of the corticohippocampal circuitry is a primary pathophysiological mechanism underlying transient global amnesia (TGA). With regard to memory, 2 distinct corticohippocampal circuitries potentially exist: the anterior temporal network and the posterior medial network. We used electroencephalography (EEG) spectral analysis to determine which network is disrupted during the acute stage of TGA. Patients with TGA who visited Seoul National University Bundang Hospital within 24 hours after symptom onset were retrospectively identified. Twenty patients underwent EEG twice, once in the acute stage (<24 hours after symptom onset) and once in the resolved stage (>2 months after symptom onset). A fast Fourier transform was applied to compute the spectral power of the 6 frequency bands: delta, theta, alpha, beta 1, beta 2, and gamma. We assumed that the frontocentral and temporal regions belonged to the anterior temporal network, whereas the parieto-occipital regions belonged to the posterior medial network. A paired Student's t test was used to evaluate the difference in the regional spectral powers in each frequency band between the acute and resolved TGA stages. Compared with the resolved stage, relative theta power in the left parieto-occipital region was increased and relative alpha power in the right parieto-occipital region was reduced during the acute stage of TGA, with a statistical significance of P<.05 (uncorrected). The cortical regions that belonged to the posterior medial network showed alterations of neuronal activity, which reflects disruption of the posterior medial network during the acute stage of TGA.

Neuronal Network Oscillations in Neurodegenerative Diseases

Cognitive and behavioral acts go along with highly coordinated spatiotemporal activity patterns in neuronal networks. Most of these patterns are synchronized by coherent membrane potential oscillations within and between local networks. By entraining multiple neurons into a common time regime, such network oscillations form a critical interface between cellular activity and large-scale systemic functions. Synaptic integrity is altered in neurodegenerative diseases, and it is likely that this goes along with characteristic changes of coordinated network activity. This notion is supported by EEG recordings from human patients and from different animal models of such disorders. However, our knowledge about the pathophysiology of network oscillations in neurodegenerative diseases is surprisingly incomplete, and increased research efforts are urgently needed. One complicating factor is the pronounced diversity of network oscillations between different brain regions and functional states. Pathological changes must, therefore, be analyzed separately in each condition and affected area. However, cumulative evidence from different diseases may result, in the future, in more unifying "oscillopathy" concepts of neurodegenerative diseases. In this review, we report present evidence for pathological changes of network oscillations in Alzheimer's disease (AD), one of the most prominent and challenging neurodegenerative disorders. The heterogeneous findings from AD are contrasted to Parkinson's disease, where motor-related changes in specific frequency bands do already fulfill criteria of a valid biomarker.

Effects of physical exercise on individual resting state EEG alpha peak frequency

Previous research has shown that both acute and chronic physical exercises can induce positive effects on brain function and this is associated with improvements in cognitive performance. However, the neurophysiological mechanisms underlying the beneficial effects of exercise on cognitive processing are not well understood. This study examined the effects of an acute bout of physical exercise as well as four weeks of exercise training on the individual resting state electroencephalographic (EEG) alpha peak frequency (iAPF), a neurophysiological marker of the individual's state of arousal and attention, in healthy young adults. The subjects completed a steady state exercise (SSE) protocol or an exhaustive exercise (EE) protocol, respectively, on two separate days. EEG activity was recorded for 2 min before exercise, immediately after exercise, and after 10 min of rest. All assessments were repeated following four weeks of exercise training to investigate whether an improvement in physical fitness modulates the resting state iAPF and/or the iAPF response to an acute bout of SSE and EE. The iAPF was significantly increased following EE (P = 0.012) but not following SSE. It is concluded that the iAPF is increased following intense exercise, indicating a higher level of arousal and preparedness for external input.

Frontal delta event-related oscillations relate to frontal volume in mild cognitive impairment and healthy controls

Amnesic mild cognitive impairment (MCI) represents a risk of developing Alzheimer's disease (AD), but not all MCI subjects progress to dementia of AD type. Magnetic resonance imaging (MRI) of cortical and hippocampal atrophy supports early diagnosis of AD in MCI subjects, while frontal event-related oscillations (EROs) at delta frequencies (<4Hz) are appealing markers for this purpose, as they are both cost-effective and largely available. The present study tested the hypothesis that these EROs reflect cortical frontal neurodegeneration in the continuum between normal and amnesic MCI subjects. EROs and volumetric MRI data were recorded in 28 amnesic MCI and in 28 healthy elderly controls (HCs). EROs were collected during a standard visual oddball paradigm including frequent (66.6%) and rare (33.3%; targets to be mentally counted) stimuli. Peak-to-peak amplitude of delta target EROs (<4Hz) was measured. Volume of frontal cortex was estimated from MRIs. Frontal volume was lower in MCI compared to the HC group. Furthermore, widespread delta target EROs were lower in amplitude in the former than in the latter group. Finally, there was a positive correlation between frontal volume and frontal delta target EROs in MCI and HC subjects as a whole group. These results suggest that frontal delta EROs reflect frontal neurodegeneration in the continuum between normal and amnesic MCI subjects.

Occipital sources of resting-state alpha rhythms are related to local gray matter density in subjects with amnesic mild cognitive impairment and Alzheimer's disease

Occipital sources of resting-state electroencephalographic (EEG) alpha rhythms are abnormal, at the group level, in patients with amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD). Here, we evaluated the hypothesis that amplitude of these occipital sources is related to neurodegeneration in occipital lobe as measured by magnetic resonance imaging. Resting-state eyes-closed EEG rhythms were recorded in 45 healthy elderly (Nold), 100 MCI, and 90 AD subjects. Neurodegeneration of occipital lobe was indexed by weighted averages of gray matter density, estimated from structural MRIs. EEG rhythms of interest were alpha 1 (8-10.5 Hz) and alpha 2 (10.5-13 Hz). EEG cortical sources were estimated by low-resolution brain electromagnetic tomography. Results showed a positive correlation between occipital gray matter density and amplitude of occipital alpha 1 sources in Nold, MCI, and AD subjects as a whole group (r = 0.3, p = 0.000004, N = 235). Furthermore, there was a positive correlation between the amplitude of occipital alpha 1 sources and cognitive status as revealed by Mini Mental State Examination score across all subjects (r = 0.38, p = 0.000001, N = 235). Finally, amplitude of occipital alpha 1 sources allowed a moderate classification of individual Nold and AD subjects (sensitivity: 87.8%; specificity: 66.7%; area under the receiver operating characteristic curve: 0.81). These results suggest that the amplitude of occipital sources of resting-state alpha rhythms is related to AD neurodegeneration in occipital lobe along pathologic aging.

Working memory training with tDCS improves behavioral and neurophysiological symptoms in pilot group with post-traumatic stress disorder (PTSD) and with poor working memory

This pilot study investigated the feasibility of treating people suffering from both post-traumatic stress disorder (PTSD) and poor working memory by employing a combination of computerized working memory training and transcranial direct current stimulation (tDCS). After treatment, all four participants showed clinically significant improvements on a range of cognitive and emotional performance measures. Moreover, these improvements were accompanied by theoretically significant neurophysiological changes between pre- and post-treatment electroencephalographic (EEG) recordings. Specifically, the P3a component of participants' event related potentials (ERP) in response to novelty stimuli, characteristically abnormal in this clinical population, shifted significantly toward database norms. So, participants' initially slow alpha peak frequency (APF), theorized to underlie impaired cognitive processing abilities, also increased in both frequency and amplitude as a result of treatment. On the basis of these promising results, more extensive controlled studies are warranted.

Quantitative electroencephalogram utility in predicting conversion of mild cognitive impairment to dementia with Lewy bodies

Mild cognitive impairment (MCI) as a precursor of dementia with Lewy bodies (DLB) is the focus of recent research, trying to explore the early mechanisms and possible biomarkers of DLB. Quantitative electroencephalogram (QEEG) methods are able to differentiate early DLB from Alzheimer's disease (AD). The aim of the present study was to assess whether QEEG abnormalities, characterized by dominant frequency <8 Hz and dominant frequency variability >1.5 Hz, typical of early DLB, are already present at the stage of MCI and to evaluate whether EEG abnormalities can predict the development of DLB. Forty-seven MCI subjects were followed for 3 years. EEG recordings were obtained at admission and at the end of the study. At the end of follow-up, 20 subjects had developed probable DLB (MCI-DLB), 14 had probable AD (MCI-AD), 8 did not convert to dementia, 5 developed a non-AD/DLB dementia. One hundred percent of MCI-DLB showed EEG abnormalities at admission. Ninety three percent of MCI-AD maintained a normal EEG throughout the study. QEEG may represent a powerful tool to predict the progression from MCI to DLB with a sensitivity and specificity close to 100%.

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We studied in mild cognitive impairment (MCI) subjects the predictive value of electroencephalogram (EEG) alterations for the development of dementia with Lewy bodies (DLB).

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One hundred percent of MCI subjects converted to DLB already had EEG alterations typical of DLB (dominant frequency <8 Hz and dominant frequency variability ≥1.5 Hz) at admission to the study.

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Quantitative EEG may represent a powerful tool to predict the progression from MCI to DLB.

Patients with mild cognitive impairment have an abnormal upper-alpha event-related desynchronization/synchronization (ERD/ERS) during a task of temporal attention

There are several evidences indicating that an impairment in attention-executive functions is present in prodromal Alzheimer's disease and predict future global cognitive decline. In particular, the issue of temporal orienting of attention in patients with mild cognitive impairment (MCI) due to Alzheimer's disease has been overlooked. The present research aimed to explore whether subtle deficits of cortical activation are present in these patients early in the course of the disease. We studied the upper-alpha event-related synchronization/desynchronization phenomenon during a paradigm of temporal orientation of attention. MCI patients (n = 27) and healthy elderly controls (n = 15) performed a task in which periodically omitted tones had to be predicted and their virtual onset time had to be marked by pressing a button. Single-trial responses were measured, respectively, before and after the motor response. Then, upper-alpha responses were compared to upper-alpha power during eyes-closed resting state. The time course of the task was characterized by two different behavioral conditions: (1) a pre-event epoch, in which the subject awaited the virtual onset of the omitted tone, (2) a post-event epoch (after button pressing), in which the subject was in a post-motor response condition. The principal findings are: (1) during the waiting epoch, only healthy elderly had an upper-alpha ERD at the level of both temporal and posterior brain regions; (2) during the post-motor epoch, the aMCI patients had a weaker upper-alpha ERS on prefrontal regions; (3) only healthy elderly showed a laterality effect: (a) during the waiting epoch, the upper-alpha ERD was greater at the level of the right posterior-temporal lead; during the post-motor epoch, the upper alpha ERS was greater on the left prefrontal lead. The relevance of these findings is that the weaker upper-alpha response observed in aMCI patients is evident even if the accuracy of the behavioral performance (i.e., button pressing) is still spared. This abnormal upper-alpha response might represent an early biomarker of the attention-executive network impairment in MCI due to Alzheimer's disease.

MEG spectral analysis in subtypes of mild cognitive impairment

Mild cognitive impairment (MCI) has been described as an intermediate stage between normal aging and dementia. Previous studies characterized the alterations of brain oscillatory activity at this stage, but little is known about the differences between single and multidomain amnestic MCI patients. In order to study the patterns of oscillatory magnetic activity in amnestic MCI subtypes, a total of 105 subjects underwent an eyes-closed resting-state magnetoencephalographic recording: 36 healthy controls, 33 amnestic single domain MCIs (a-sd-MCI), and 36 amnestic multidomain MCIs (a-md-MCI). Relative power values were calculated and compared among groups. Subsequently, relative power values were correlated with neuropsychological tests scores and hippocampal volumes. Both MCI groups showed an increase in relative power in lower frequency bands (delta and theta frequency ranges) and a decrease in power values in higher frequency bands (alpha and beta frequency ranges), as compared with the control group. More importantly, clear differences emerged from the comparison between the two amnestic MCI subtypes. The a-md-MCI group showed a significant power increase within delta and theta ranges and reduced relative power within alpha and beta ranges. Such pattern correlated with the neuropsychological performance, indicating that the a-md-MCI subtype is associated not only with a "slowing" of the spectrum but also with a poorer cognitive status. These results suggest that a-md-MCI patients are characterized by a brain activity profile that is closer to that observed in Alzheimer disease. Therefore, it might be hypothesized that the likelihood of conversion to dementia would be higher within this subtype.

Spectral Analysis of EEG in Familial Alzheimer's Disease with E280A Presenilin-1 Mutation Gene

To evaluate the hypothesis that quantitative EEG (qEEG) analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD) preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects), an asymptomatic carrier (ACr) group (21 subjects), with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D (2)) was calculated between groups. To evaluate the diagnostic efficiency of this statistic D (2), the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D (2) using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89) and between AD probable and ACr groups (area ROC = 0.91). This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function.

Spectral changes in spontaneous MEG activity across the lifespan

OBJECTIVE

The aim of this study is to explore the spectral patterns of spontaneous magnetoencephalography (MEG) activity across the lifespan.

APPROACH

Relative power (RP) in six frequency bands (delta, theta, alpha, beta-1, beta-2 and gamma) was calculated in a sample of 220 healthy subjects with ages ranging from 7 to 84 years.

MAIN RESULTS

A significant RP decrease in low-frequency bands (i.e. delta and theta) and a significant increase in high bands (mainly beta-1 and beta-2) were found from childhood to adolescence. This trend was observed until the sixth decade of life, though only slight changes were found. Additionally, healthy aging was characterized by a power increase in low-frequency bands. Our results show that spectral changes across the lifespan may follow a quadratic relationship in delta, theta, alpha, beta-2 and gamma bands with peak ages being reached around the fifth or sixth decade of life.

SIGNIFICANCE

Our findings provide original insights into the definition of the 'normal' behavior of age-related MEG spectral patterns. Furthermore, our study can be useful for the forthcoming MEG research focused on the description of the abnormalities of different brain diseases in comparison to cognitive decline in normal aging.

Cortical sources of resting state electroencephalographic alpha rhythms deteriorate across time in subjects with amnesic mild cognitive impairment

Cortical sources of resting state electroencephalographic (EEG) rhythms are abnormal in subjects with mild cognitive impairment (MCI). Here, we tested the hypothesis that these sources in amnesic MCI subjects further deteriorate over 1 year. To this aim, the resting state eyes-closed EEG data were recorded in 54 MCI subjects at baseline (Mini Mental State Examination I = 26.9; standard error [SE], 0.2) and at approximately 1-year follow-up (13.8 months; SE, 0.5; Mini Mental State Examination II = 25.8; SE, 0.2). As a control, EEG recordings were also performed in 45 normal elderly and in 50 mild Alzheimer's disease subjects. EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), and beta2 (20-30 Hz). Cortical EEG sources were estimated using low-resolution brain electromagnetic tomography. Compared with the normal elderly and mild Alzheimer's disease subjects, the MCI subjects were characterized by an intermediate power of posterior alpha1 sources. In the MCI subjects, the follow-up EEG recordings showed a decreased power of posterior alpha1 and alpha2 sources. These results suggest that the resting state EEG alpha sources were sensitive-at least at the group level-to the cognitive decline occurring in the amnesic MCI group over 1 year, and might represent cost-effective, noninvasive and widely available markers to follow amnesic MCI populations in large clinical trials.

Spectral analysis of intracranial pressure signals recorded during infusion studies in patients with hydrocephalus

Hydrocephalus includes a number of disorders characterised by clinical symptoms, enlarged ventricles (observable using neuroimaging techniques) and altered cerebrospinal fluid (CSF) dynamics. Infusion tests are one of the available procedures to study CSF circulation in patients with clinical and radiological features of hydrocephalus. In them, intracranial pressure (ICP) is deliberately raised and CSF circulation disorders evaluated through measurements of the resulting ICP. In this study, we analysed seventy-seven ICP signals recorded during infusion tests using four spectral-based parameters: median frequency (MF) and relative power (RP) in three frequency bands. These measures provide a novel perspective for the analysis of ICP signals in the frequency domain. Each signal was divided into four artefact-free epochs (corresponding to the basal, early infusion, plateau and recovery phases of the infusion study). The four spectral parameters were calculated for each epoch. We analysed differences between epochs of the infusion test and correlations between these epochs and patient data. Statistically significant differences (p < 1.7 × 10(-3), Bonferroni-corrected Wilcoxon signed-rank tests) were found between epochs of the infusion test using MF and RP. Furthermore, some spectral parameters (MF in the basal phase, RP for the first frequency band and in the early infusion phase, RP for the second frequency band and in all phases of the infusion study and RP in the third frequency band and in the basal phase) revealed significant correlations (p < 0.01) between epochs of the infusion test and signal amplitude in the basal and plateau phases. Our results suggest that spectral analysis of ICP signals could be useful for understanding CSF dynamics in hydrocephalus.

Assisted closed-loop optimization of SSVEP-BCI efficiency

We designed a novel assisted closed-loop optimization protocol to improve the efficiency of brain-computer interfaces (BCI) based on steady state visually evoked potentials (SSVEP). In traditional paradigms, the control over the BCI-performance completely depends on the subjects' ability to learn from the given feedback cues. By contrast, in the proposed protocol both the subject and the machine share information and control over the BCI goal. Generally, the innovative assistance consists in the delivery of online information together with the online adaptation of BCI stimuli properties. In our case, this adaptive optimization process is realized by (1) a closed-loop search for the best set of SSVEP flicker frequencies and (2) feedback of actual SSVEP magnitudes to both the subject and the machine. These closed-loop interactions between subject and machine are evaluated in real-time by continuous measurement of their efficiencies, which are used as online criteria to adapt the BCI control parameters. The proposed protocol aims to compensate for variability in possibly unknown subjects' state and trait dimensions. In a study with N = 18 subjects, we found significant evidence that our protocol outperformed classic SSVEP-BCI control paradigms. Evidence is presented that it takes indeed into account interindividual variabilities: e.g., under the new protocol, baseline resting state EEG measures predict subjects' BCI performances. This paper illustrates the promising potential of assisted closed-loop protocols in BCI systems. Probably their applicability might be expanded to innovative uses, e.g., as possible new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research.

Resting state in Alzheimer's disease: a concurrent analysis of Flash-Visual Evoked Potentials and quantitative EEG

BACKGROUND

To investigate to what extent Alzheimer's Disease (AD) affects Resting State activity, the possible impairment of independent electrophysiological parameters was determined in Eye-open and Eye-closed Conditions. Specifically, Flash-Visual Evoked Potential (F-VEP) and quantitative EEG (q-EEG) were examined to establish whether abnormalities of the former were systematically associated with changes of the latter.

METHODS

Concurrently recorded F-VEP and q-EEG were comparatively analysed under Eye-open and Eye-closed Conditions in 11 Controls and 19 AD patients presenting a normal Pattern-Visual Evoked Potential (P-VEP). Between Condition differences in latencies of P2 component were matched to variations in spectral components of q-EEG.

RESULTS

P2 latency increased in 10 AD patients with Abnormal Latency (AD-AL) under Eye-closed Condition. In these patients reduction of alpha activity joined an increased delta power so that their spectral profile equated that recorded under Eye-open Condition. On the opposite, in Controls as well as in AD patients with Normal P2 Latency (AD-NL) spectral profiles recorded under Eye-open and Eye-closed Conditions significantly differed from each other. At the baseline, under Eye-open Condition, the spectra overlapped each other in the three Groups.

CONCLUSION

Under Eye-closed Condition AD patients may present a significant change in both F-VEP latency and EEG rhythm modulation. The presence of concurrent changes of independent parameters suggests that the neurodegenerative process can impair a control system active in Eye-closed Condition which the electrophysiological parameters depend upon. F-VEP can be viewed as a reliable marker of such impairment.

Improvement of EEG slowing in OSAS after CPAP treatment

OBJECTIVES

This study was done to investigate change of electroencephalography (EEG) slowing and its relationship to daytime sleepiness and cognitive functions by continuous positive airway pressure (CPAP) in patients with obstructive sleep apnea syndrome (OSAS).

METHODS

We enrolled thirteen male subjects with severe OSAS, and all the subjects were treated with CPAP for 3 months. Quantitative EEG (QEEG) and neuropsychological tests were performed before and after CPAP treatment.

RESULTS

After CPAP treatment, delta absolute power decreased in the frontal, central, parietal and temporal regions and the slowing ratio was reduced in the frontal region. The Epworth Sleepiness Scale (ESS) score was reduced after CPAP treatment. Reduction in the ESS score was correlated with a decrease in delta absolute power in the frontal region (r=0.559) and a decrease in slowing ratio in frontal, central, parietal, and temporal regions (frontal, r=0.650; other regions, r=0.603). Results of neuropsychological tests assessing memory and attention were improved after CPAP treatment.

CONCLUSIONS

EEG slowing was decreased across all cerebral regions in patients with severe OSAS after CPAP treatment accompanied by improvement of cognitive functions involving several brain areas. These findings suggest that CPAP can induce improvement of cerebral function in OSAS without regional specificity.

Cortical sources of EEG rhythms in congestive heart failure and Alzheimer's disease

INTRODUCTION

The brain needs continuous oxygen supply even in resting-state. Hypoxia enhances resting-state electroencephalographic (EEG) rhythms in the delta range, and reduces those in the alpha range, with a pattern similar to that observed in Alzheimer's disease (AD). Here we tested whether resting-state cortical EEG rhythms in patients with congestive heart failure (CHF), as a model of acute hypoxia, present frequency similarities with AD patients, comparable by cognitive status revealed by the mini mental state examination (MMSE).

METHODS

Eyes-closed EEG data were recorded in 10 CHF patients, 20 AD patients, and 20 healthy elderly subjects (Nold) as controls. LORETA software estimated cortical EEG generators.

RESULTS

Compared to Nold, both AD and CHF groups presented higher delta (2-4Hz) and lower alpha (8-13Hz) temporal sources. The highest delta and lowest alpha sources were observed in CHF subjects. In these subjects, the global amplitude of delta sources correlated with brain natriuretic peptide (BNP) level in the blood, as a marker of disease severity.

CONCLUSIONS

Resting-state delta and alpha rhythms suggest analogies between the effects of acute hypoxia and AD neurodegeneration on the cortical neurons' synchronization.

SIGNIFICANCE

Acute ischemic hypoxia could affect the mechanisms of cortical neural synchronization generating resting state EEG rhythms, inducing the "slowing" of EEG rhythms typically observed in AD patients.