Exact Low-resolution Electromagnetic Tomography Research Articles

Electroencephalographic (EEG) alterations in young women with high subclinical eating pathology levels: a quantitative EEG study.

To explore electroencephalographic (EEG) alterations in young women with different eating disorder (ED) psychopathology levels. Thirty-seven young women completed general and ED psychopathology (i.e., the ED Examination Questionnaire; EDE-Q) measures. EEG power spectra data were investigated in two conditions: (a) 5 min of resting state (RS) and (b) 5 min of RS after a single taste of a milkshake (ML-RS). EEG analyses were performed using exact Low-Resolution Electromagnetic Tomography software (eLORETA). Cluster analysis performed on the EDE-Q responses revealed a group of 17 women with high levels of ED pathology falling into the subclinical (i.e., sub-threshold) EDs category and a group of 20 women with low levels of ED pathology (controls). In the RS conditions, no significant modifications were observed between groups. Compared to controls, women with subclinical EDs showed an increase in theta activity in the parieto-occipital areas in the ML-RS condition. After controlling for body mass index and general psychopathology, theta activity in these brain structures was positively associated with EDE-Q global and subscale (restraint, shape and weight concern) scores. Our results may reflect the neurophysiological substrate of ED psychopathology core features like shape/weight concerns. Previous brain imaging and qEEG studies with full-syndrome ED patients also underscored the involvement of parieto-occipital areas in ED pathophysiology. These studies also found brain alterations in the RS condition, not observed here. This is notable given that full-syndrome and subclinical EDs are considered as different manifestations of the same disease along a severity spectrum. Level V, cross-sectional, electroencephalographic, descriptive study.

Objective and Subjective Characteristics of Vigilance under Different Narrow-Bandwidth Light Conditions: Do Shorter Wavelengths Have an Alertness-Enhancing Effect?

The aim of this study was to explore the effects of 20 min of narrow-bandwidth light exposure of different wavelengths (455, 508, and 629 nm, with irradiance of 14 µW/cm²) on various neuropsychological and neurophysiological parameters of vigilance in healthy volunteers and to provide further evidence of the behavioral (subjective sleepiness, reaction time) and electrophysiological (P300 and spectral characteristics) responses to light. The results show that the short-wavelength light condition (455 nm) was found to be most effective in terms of its alerting effect for the following variables: subjective sleepiness, latency of P300 response, and absolute EEG power in higher beta (24–34 Hz) and gamma (35–50 Hz) range at each of the 19 recording electrodes. However, no differences in current power density were observed at the level of cortical EEG sources estimated by exact low-resolution electromagnetic tomography. Our results are in line with other research that shows significant alerting effects of blue (short-wavelength) light in comparison to lights of longer wavelengths. Our results confirm earlier findings that exposure to short-wavelength light during the day may enhance cognitive performance in task-specific scenarios.

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity.

Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71-channel EEG recorded from 35 healthy adults at two sessions (1-week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal-to-noise ratio, participant-level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait-multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one low-variance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component-based identification of spectral activity (CSD/eLORETA-fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.

The individuality index: a measure to quantify the degree of inter-individual, spatial variability in intra-cerebral brain electric and metabolic activity.

Contemporary neuroscience research primarily focuses on the identification of brain activation patterns commonly deviant across participant groups or experimental conditions. This approach inherently underestimates potentially meaningful intra- and inter-individual variability present in brain physiological measures. We propose a parameter referred to as 'individuality index (II)' that takes individual variability into account. It quantifies the degree of individual variance of brain activation patterns for different brain regions and participants. IIs can be computed based on intra-cerebral source strength values such as the ones derived from the exact low resolution electromagnetic tomography source localization software. We exemplary estimated IIs for simulated datasets. Our results illustrate how IIs are affected by different spatial activation patterns across participants and quantify their distributional properties. They suggest that the proposed indices can meaningfully quantify inter- and intra-individuality of brain activation patterns. Their application to realistic datasets will allow the identification of (1) those brain regions that show particularly heterogeneous activation patterns, the contribution of which is particularly likely to be underestimated by conventional group statistics, (2) those brain regions that can alternatively be recruited by different participants for the same tasks, and (3) their associations with potentially decisive behavioral variables such as individually applied mental strategy.

Electroconvulsive Therapy Modulates Resting-State EEG Oscillatory Pattern and Phase Synchronization in Nodes of the Default Mode Network in Patients With Depressive Disorder.

Introduction: Electroconvulsive therapy (ECT) has antidepressant effects, but it also has possible cognitive side effects. The effects of ECT on neuronal oscillatory pattern and phase synchronization, and the relationship between clinical response or cognitive change and electroencephalogram (EEG) measurements remain elusive.Methods: Individuals with unipolar depressive disorder receiving bilateral ECT were recruited. Five minutes of resting, eyes-closed, 19-lead EEG recordings were obtained before and after a course of ECT. Non-overlapping 60 artifact-free epocs of 2-s duration were used for the analyses. We used exact low resolution electromagnetic tomography (eLORETA) to compute the whole-brain three-dimensional intracortical distribution of current source density (CSD) and phase synchronization among 28 regions-of-interest (ROIs). Paired t-tests were used to identify cortical voxels and connectivities showing changes after ECT. Montgomery Asberg Depression Rating Scale (MADRS) and Mini-Mental State Examination (MMSE) were used to evaluate the severity of depression and the global cognitive function. Correlation analyses were conducted to identify the relationship between changes in the EEG measurements and changes in MADRS or MMSE.Results: Thirteen depressed patients (five females, mean age: 58.4 years old) were included. ECT increased theta CSD in the anterior cingulate cortex (ACC), and decreased beta CSD in the frontal pole (FP), and gamma CSD in the inferior parietal lobule (IPL). ECT increased theta phase synchronization between the posterior cingulate cortex (PCC) and the anterior frontal cortex, and decreased beta phase synchronization between the PCC and temporal regions. A decline in beta synchronization in the left hemisphere was associated with cognitive changes after ECT.Conclusion: ECT modulated resting-state EEG oscillatory patterns and phase synchronization in central nodes of the default mode network (DMN). Changes in beta synchronization in the left hemisphere might explain the ECT-related cognitive side effects.

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data.

Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge.Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features.Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha1/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha2 being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 “concentration difficulty” scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha2 and frontopolar alpha1.Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, “biomarker”-based treatment approaches.

Short-term meditation modulates EEG activity in subjects with post-traumatic residual disabilities

ObjectiveNeurophysiological changes related to meditation have recently attracted scientific attention. We aimed to detect changes in electroencephalography (EEG) parameters induced by a meditative intervention in subjects with post-traumatic residual disability (PTRD), which has been confirmed for effectiveness and safety in a previous study. This will allow us to estimate the objective effect of this intervention at the neurophysiological level. MethodsTen subjects with PTRD were recruited and underwent psychological assessment and EEG recordings before and after the meditative intervention. Furthermore, 10 additional subjects were recruited as normal controls. Source current density as an EEG parameter was estimated by exact Low Resolution Electromagnetic Tomography (eLORETA). Comparisons of source current density in PTRD subjects after the meditative intervention with normal controls were investigated. Additionally, we compared source current density in PTRD subjects between before and after meditative intervention. Correlations between psychological assessments and source current density were also explored. ResultsAfter meditative intervention, PTRD subjects exhibited increased gamma activity in the left inferior parietal lobule relative to normal controls. In addition, changes of delta activity in the right precuneus correlated with changes in the psychological score on role physical item, one of the quality of life scales reflecting the work or daily difficulty due to physical problems. ConclusionsThese results show that the meditative intervention used in this study produces neurophysiological changes, in particular the modulation of oscillatory activity of the brain. SignificanceOur meditative interventions might induce the neurophysiological changes associated with the improvement of psychological symptoms in the PTRD subjects.

Default mode network alterations in individuals with high-trait-anxiety: An EEG functional connectivity study.

Although several researches investigated Default Mode Network (DMN) alterations in individuals with anxiety disorders, up to now no studies have investigated DMN functional connectivity in non-clinical individuals with high-trait-anxiety using quantitative electroencephalography (EEG). Here, the main aim was to extend previous findings investigating the association between trait anxiety and DMN EEG functional connectivity. Twenty-three individuals with high-trait-anxiety and twenty-four controls were enrolled. EEG was recorded during 5 min of resting state (RS). EEG analyses were conducted by means of the exact Low-Resolution Electromagnetic Tomography software (eLORETA). Compared to controls, individuals with high-trait-anxiety showed a decrease of theta connectivity between right medial prefrontal cortex (mPFC) and right posterior cingulate/retrosplenial cortex. A decrease of beta connectivity was also observed between right mPFC and right anterior cingulate cortex. Furthermore, DMN functional connectivity strength was negatively related with STAI-T total score (i.e., lower connectivity was associated with higher trait anxiety), even when controlling for potential confounding variables (i.e., sex, age, and general psychopathology). Small sample size makes it difficult to draw definitive conclusions. Furthermore, we did not assess state variation of anxiety, which make our interpretation specific to trait anxiety. Taken together, our results suggest that high-trait-anxiety individuals fail to synchronize DMN during RS, reflecting a possible top-down cognitive control deficit. These results may help in the understanding of the individual differences in functional brain networks associated with trait anxiety, a crucial aim in the prevention and in the early etiology understanding of clinical anxiety and related sequelae.

Pretreatment Rostral Anterior Cingulate Cortex Connectivity With Salience Network Predicts Depression Recovery: Findings From the EMBARC Randomized Clinical Trial

BackgroundBaseline rostral anterior cingulate cortex (rACC) activity is a well-replicated nonspecific predictor of depression improvement. The rACC is a key hub of the default mode network, which prior studies indicate is hyperactive in major depressive disorder. Because default mode network downregulation is reliant on input from the salience network and frontoparietal network, an important question is whether rACC connectivity with these systems contributes to depression improvement. MethodsOur study evaluated this hypothesis in outpatients (N = 238; 151 female) enrolled in the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) 8-week randomized clinical trial of sertraline versus placebo for major depressive disorder. Depression severity was measured using the Hamilton Rating Scale for Depression, and electroencephalography was recorded at baseline and week 1. Exact low-resolution electromagnetic tomography was used to compute activity from the rACC, and key regions within the default mode network (posterior cingulate cortex), frontoparietal network (left dorsolateral prefrontal cortex), and salience network (right anterior insula [rAI]). Connectivity in the theta band (4.5–7 Hz) and beta band (12.5–21 Hz) was computed using lagged phase synchronization. ResultsStronger baseline theta-band rACC–rAI (salience network hub) connectivity predicted greater depression improvement across 8 weeks of treatment for both treatment arms (B = −0.57, 95% confidence interval = −1.07, −0.08, p = .03). Early increases in theta-band rACC–rAI connectivity predicted greater likelihood of achieving remission at week 8 (odds ratio = 2.90, p = .03). ConclusionsAmong patients undergoing treatment, theta-band rACC–rAI connectivity is a prognostic, albeit treatment-nonspecific, indicator of depression improvement, and early connectivity changes may predict clinically meaningful outcomes.

Evaluation of Electroencephalogram Using Exact Low-Resolution Electromagnetic Tomography During Photic Driving Response in Patients with Migraine

Background: Photophobia is a common feature of migraine, which may involve abnormal cortical information processing. In electroencephalograms (EEG), photic driving is known as a reaction to visual stimulation. Both photophobia and photic driving response are present during light stimulation. We hypothesized that cortical response to photic stimulation would differ between migraine patients with and without aura. Methods: We recruited 50 migraine patients (migraine with aura [MWA] = 21; migraine without aura [MWOA] = 29). Spontaneous eyes-closed resting EEG from 20 electrodes on the scalp during the interictal phase was recorded. After recording, each photic stimulation was separately selected. We analyzed EEG by fast Fourier transform and observed the spectrum frequency peaks and topographies in response to photic stimulation. Exact low-resolution electromagnetic tomography (eLORETA) was used to compute the 3-dimensional intracerebral distribution of EEG activity. Results: Photic stimulation at frequencies 5, 8, 15, and 20 Hz showed significant differences between migraine patients with and without aura. MWOA patients consistently had a stronger response to photic stimulation than MWA patients. In all patients, the differential response was located in the visual cortex, except for the stimulation at 20 Hz, where the difference at subharmonic 10 Hz was located in the parietal cortex (Brodmann Area 7). Conclusion: We confirmed high incidences of photic hypersensitivity and photic driving responses in migraine patients. We suggest that repeated occurrences of cortical spreading depression in MWA may suppress cortical function, thus contributing to a weaker visual cortical response to photic stimulation in MWA patients compared with MWOA patients.

Cortical hypoactivation during resting EEG suggests central nervous system pathology in patients with chronic fatigue syndrome

We investigated central fatigue in 50 patients with chronic fatigue syndrome (CFS) and 50 matched healthy controls (HC). Resting state EEG was collected from 19 scalp locations during a 3 min, eyes-closed condition. Current densities were localized using exact low-resolution electromagnetic tomography (eLORETA). The Multidimensional Fatigue Inventory (MFI-20) and the Fatigue Severity Scale (FSS) were administered to all participants. Independent t-tests and linear regression analyses were used to evaluate group differences in current densities, followed by statistical non-parametric mapping (SnPM) correction procedures. Significant differences were found in the delta (1–3 Hz) and beta-2 (19–21 Hz) frequency bands. Delta sources were found predominately in the frontal lobe, while beta-2 sources were found in the medial and superior parietal lobe. Left-lateralized, frontal delta sources were associated with a clinical reduction in motivation. The implications of abnormal cortical sources in patients with CFS are discussed.

Small-World Network Analysis of Cortical Connectivity in Chronic Fatigue Syndrome Using Quantitative EEG

The aim of this study was to explore the relationship between complex brain networks in people with Chronic Fatigue Syndrome (CFS) and neurocognitive impairment. Quantitative EEG (qEEG) recordings were taken from 14 people with CFS and 15 healthy controls (HCs) during an eye-closed resting condition. Exact low resolution electromagnetic tomography (eLORETA) was used to estimate cortical sources and perform a functional connectivity analysis. The graph theory approach was used to characterize network representations for each participant and derive the “small-worldness” index, a measure of the overall homeostatic balance between local and long-distance connectedness. Results showed that small-worldness for the delta band was significantly lower for patients with CFS compared to HCs. In addition, delta small-worldness was negatively associated with neurocognitive impairment scores on the DePaul Symptom Questionnaire (DSQ). Finally, delta small-worldness indicated a greater risk of complex brain network inefficiency for the CFS group. These results suggest that CFS pathology may be functionally disruptive to small-world networks. In turn, small-world characteristics might serve as a neurophysiological indicator for confirming a biological basis of cognitive symptoms, treatment outcome, and neurophysiological status of people with CFS.

Altered brain functional connectivity induced by physical exercise may improve neuropsychological functions in patients with benign epilepsy

ObjectiveThe objective of this study was to elucidate alteration in functional connectivity (FC) in patients with benign epilepsy with centrotemporal spikes (BECTS) as induced by physical exercise therapy and their correlation to the neuropsychological (NP) functions. MethodsWe analyzed 115 artifact- and spike-free 2-second epochs extracted from resting state EEG recordings before and after 5weeks of physical exercise in eight patients with BECTS. The exact Low Resolution Electromagnetic Tomography (eLORETA) was used for source reconstruction. We evaluated the cortical current source density (CSD) power across five different frequency bands (delta, theta, alpha, beta, and gamma). Altered FC between 34 regions of interests (ROIs) was then examined using lagged phase synchronization (LPS) method. We further investigated the correlation between the altered FC measures and the changes in NP test scores. ResultsWe observed changes in CSD power following the exercise for all frequency bands and statistically significant increases in the right temporal region for the alpha band. There were a number of altered FC between the cortical ROIs in all frequency bands of interest. Furthermore, significant correlations were observed between FC measures and NP test scores at theta and alpha bands. ConclusionThe increased localization power at alpha band may be an indication of the positive impact of exercise in patients with BECTS. Frequency band-specific alterations in FC among cortical regions were associated with the modulation of cognitive and NP functions. The significant correlation between FC and NP tests suggests that physical exercise may mitigate the severity of BECTS, thereby enhancing NP function.

The Brain of Binge Drinkers at Rest: Alterations in Theta and Beta Oscillations in First-Year College Students with a Binge Drinking Pattern.

Background: Previous studies have reported anomalous resting brain activity in the electroencephalogram (EEG) of alcoholics, often reflected as increased power in the beta and theta frequency bands. The effects of binge drinking, the most common pattern of excessive alcohol consumption during adolescence and youth, on brain activity at rest is still poorly known. In this study, we sought to assess the pattern of resting-state EEG oscillations in college-aged binge drinkers (BDs).Methods: Resting-state brain activity during eyes-open and eyes-closed conditions was recorded from 60 channels in 80 first-year undergraduate students (40 controls and 40 BDs). Cortical sources activity of EEG rhythms was estimated using exact Low-Resolution Electromagnetic Tomography (eLORETA) analysis.Results: EEG-source localization analysis revealed that BDs showed, in comparison with controls, significantly higher intracranial current density in the beta frequency band over the right temporal lobe (parahippocampal and fusiform gyri) during eyes-open resting state as well as higher intracranial current density in the theta band over the bilateral occipital cortex (cuneus and lingual gyrus) during eyes-closed resting condition.Conclusions: These findings are in line with previous results observing increased beta and/or theta power following chronic or heavy alcohol drinking in alcohol-dependent subjects and BDs. Increased tonic beta and theta oscillations are suggestive of an augmented cortical excitability and of potential difficulties in the information processing capacity in young BDs. Furthermore, enhanced EEG power in these frequency bands may respond to a neuromaturational delay as a result of excessive alcohol consumption during this critical brain developmental period.

The EEG microstate topography is predominantly determined by intracortical sources in the alpha band

Human brain electric activity can be measured at high temporal and fairly good spatial resolution via electroencephalography (EEG). The EEG microstate analysis is an increasingly popular method used to investigate this activity at a millisecond resolution by segmenting it into quasi-stable states of approximately 100 ms duration. These so-called EEG microstates were postulated to represent atoms of thoughts and emotions and can be classified into four classes of topographies A through D, which explain up to 90% of the variance of continuous EEG. The present study investigated whether these topographies are primarily driven by alpha activity originating from the posterior cingulate cortex (all topographies), left and right posterior cortices, and the anterior cingulate cortex (topographies A, B, and C, respectively).We analyzed two 64-channel resting state EEG datasets (N = 61 and N = 78) of healthy participants. Sources of head-surface signals were determined via exact low resolution electromagnetic tomography (eLORETA). The Hilbert transformation was applied to identify instantaneous source strength of four EEG frequency bands (delta through beta). These source strength values were averaged for each participant across time periods belonging to a particular microstate. For each dataset, these averages of the different microstate classes were compared for each voxel. Consistent differences across datasets were identified via a conjunction analysis.The intracortical strength and spatial distribution of alpha band activity mainly determined whether a head-surface topography of EEG microstate class A, B, C, or D was induced. EEG microstate class C was characterized by stronger alpha activity compared to all other classes in large portions of the cortex. Class A was associated with stronger left posterior alpha activity than classes B and D, and class B was associated with stronger right posterior alpha activity than A and D.Previous results indicated that EEG microstate dynamics reflect a fundamental mechanism of the human brain that is altered in different mental states in health and disease. They are characterized by systematic transitions between four head-surface topographies, the EEG microstate classes. Our results show that intra-cortical alpha oscillations, which likely reflect decreased cortical excitability, primarily account for the emergence of these classes. We suggest that microstate class dynamics reflect transitions between four global attractor states that are characterized by selective inhibition of specific intra-cortical regions.

Electroencephalography Source Functional Connectivity Reveals Abnormal High-Frequency Communication Among Large-Scale Functional Networks in Depression

BackgroundFunctional magnetic resonance imaging studies of resting-state functional connectivity have shown that major depressive disorder (MDD) is characterized by increased connectivity within the default mode network (DMN) and between the DMN and the frontoparietal network (FPN). However, much remains unknown about abnormalities in higher frequency (>1 Hz) synchronization. Findings of abnormal synchronization in specific frequencies would contribute to a better understanding of the potential neurophysiological origins of disrupted functional connectivity in MDD. MethodsWe used the high temporal resolution of electroencephalography to compare the spectral properties of resting-state functional connectivity in individuals with MDD (n = 65) with healthy control subjects (n = 79) and examined the extent to which connectivity disturbances were evident in a third sample of individuals in remission from depression (n = 30). Exact low resolution electromagnetic tomography was used to compute intracortical activity from regions within the DMN and FPN, and functional connectivity was computed using lagged phase synchronization. ResultsCompared to control subjects, the MDD group showed greater within-DMN beta 2 band (18.5–21 Hz) connectivity and greater beta 1 band (12.5–18 Hz) connectivity between the DMN and FPN. This hyperconnectivity was not observed in the remitted MDD group. However, greater beta 1 band DMN–FPN connectivity was associated with more frequent depressive episodes since first depression onset, even after controlling for current symptom severity. ConclusionsThese findings extend our understanding of the neurophysiological basis of abnormal resting-state functional connectivity in MDD and indicate that elevations in high-frequency DMN–FPN connectivity may be a neural marker linked to a more recurrent illness course.

Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis.

Alzheimer's disease (AD) is a dementia that affects a large contingent of the elderly population characterized by the presence of neurofibrillary tangles and senile plaques. Traumatic brain injury (TBI) is a non-degenerative injury caused by an external mechanical force. One of the main causes of TBI is diffuse axonal injury (DAI), promoted by acceleration-deceleration mechanisms.ObjectiveTo understand the electroencephalographic differences in functional mechanisms between AD and DAI groups.MethodsThe study included 20 subjects with AD, 19 with DAI and 17 healthy adults submitted to high resolution EEG with 128 channels. Cortical sources of EEG rhythms were estimated by exact low-resolution electromagnetic tomography (eLORETA) analysis.ResultsThe eLORETA analysis showed that, in comparison to the control (CTL) group, the AD group had increased theta activity in the parietal and frontal lobes and decreased alpha 2 activity in the parietal, frontal, limbic and occipital lobes. In comparison to the CTL group, the DAI group had increased theta activity in the limbic, occipital sublobar and temporal areas.ConclusionThe results suggest that individuals with AD and DAI have impairment of electrical activity in areas important for memory and learning.

Neuronal oscillations and synchronicity associated with gamma-hydroxybutyrate during resting-state in healthy male volunteers.

Gamma-hydroxybutyrate (GHB) is a putative neurotransmitter, a drug of abuse, an anesthetic agent, and a treatment for neuropsychiatric disorders. In previous electroencephalography (EEG) studies, GHB was shown to induce an electrophysiological pattern of "paradoxical EEG-behavioral dissociation" characterized by increased delta and theta oscillations usually associated with sleep during awake states. However, no detailed source localization of these alterations and no connectivity analyses have been performed yet. We tested the effects of GHB (20 and 35mg/kg, p.o.) on current source density (CSD), lagged phase synchronization (LPS), and global omega complexity (GOC) of neuronal oscillations in a randomized, double-blind, placebo-controlled, balanced cross-over study in 19 healthy, male participants using exact low-resolution electromagnetic tomography (eLORETA) of resting-state high-density EEG recordings. Compared to placebo, GHB increased CSD of theta oscillations (5-7Hz) in the posterior cingulate cortex (PCC) and alpha1 (8-10Hz) oscillations in the anterior cingulate cortex. Higher blood plasma values were associated with higher LPS values of delta (2-4Hz) oscillations between the PCC and the right inferior parietal lobulus. Additionally, GHB decreased GOC of alpha1 oscillations. These findings indicate that alterations in neuronal oscillations in the PCC mediate the psychotropic effects of GHB. Theta oscillations emerging from the PCC in combination with stability of functional connectivity within the default mode network might explain the GHB-related "paradoxical EEG-behavioral dissociation." Our findings related to GOC suggest a reduced number of relatively independent neuronal processes, an effect that has also been demonstrated for other anesthetic agents.

Cerebrospinal Fluid Biomarkers of Alzheimer's Disease Correlate With Electroencephalography Parameters Assessed by Exact Low-Resolution Electromagnetic Tomography (eLORETA).

Recently, cerebrospinal fluid (CSF) biomarkers related to Alzheimer's disease (AD) have garnered a lot of clinical attention. To explore neurophysiological traits of AD and parameters for its clinical diagnosis, we examined the association between CSF biomarkers and electroencephalography (EEG) parameters in 14 probable AD patients. Using exact low-resolution electromagnetic tomography (eLORETA), artifact-free 40-sesond EEG data were estimated with current source density (CSD) and lagged phase synchronization (LPS) as the EEG parameters. Correlations between CSF biomarkers and the EEG parameters were assessed. Patients with AD showed significant negative correlation between CSF beta-amyloid (Aβ)-42 concentration and the logarithms of CSD over the right temporal area in the theta band. Total tau concentration was negatively correlated with the LPS between the left frontal eye field and the right auditory area in the alpha-2 band in patients with AD. Our study results suggest that AD biomarkers, in particular CSF Aβ42 and total tau concentrations are associated with the EEG parameters CSD and LPS, respectively. Our results could yield more insights into the complicated pathology of AD.

EEG Resting State Functional Connectivity Analysis in Children with Benign Epilepsy with Centrotemporal Spikes.

In this study, we investigated changes in functional connectivity (FC) of the brain networks in patients with benign epilepsy with centrotemporal spikes (BECTS) compared to healthy controls using high-density EEG data collected under eyes-closed resting state condition. EEG source reconstruction was performed with exact Low Resolution Electromagnetic Tomography (eLORETA). We investigated FC between 84 Brodmann areas using lagged phase synchronization (LPS) in four frequency bands (δ, θ, α, and β). We further computed the network degree, clustering coefficient and efficiency. Compared to controls, patients displayed higher θ and α and lower β LPS values. In these frequency bands, patients were also characterized by less well ordered brain networks exhibiting higher global degrees and efficiencies and lower clustering coefficients. In the β band, patients exhibited reduced functional segregation and integration due to loss of both local and long-distance functional connections. These findings suggest that benign epileptic brain networks might be functionally disrupted due to their altered functional organization especially in the α and β frequency bands.