Resting-state EEG Functional Connectivity Research Articles

Alterations in resting-state EEG functional connectivity in patients with major depressive disorder receiving electroconvulsive therapy: A systematic review.

Electroconvulsive therapy (ECT) is highly efficacious for the treatment of major depressive disorder (MDD), but its mechanisms still require clarification. Even though depression is associated with alterations in functional connectivity (FC), EEG studies investigating effects of ECT on FC have not been systematically reviewed. Understanding these effects may help to identify the role of functional brain circuits in depression and its remission. This systematic review aimed to synthesize EEG studies investigating FC changes in ECT-treated patients with depression. A systematic literature search was conducted following PRISMA guidelines. Peer-reviewed studies on pre-to post-ECT resting-state EEG FC changes in adult patients with MDD were included. Three of 143 studies were included, of which two reported reduced FC in the alpha and beta frequency bands and increased theta band FC in patients with ECT-treated MDD. Changes in alpha band FC were associated with treatment outcomes. Patients with MDD exhibit increased electrophysiological resting-state alpha band FC, particularly frontally, compared with healthy subjects. Thus, ECT-induced decrease might indicate a trend toward normalization of oscillatory brain rhythms. As brain oscillations have been proposed to be involved in neuronal synchronization, which is important for communication between networks, the potential restoration in patients with depression and the association of FC changes with clinical improvement may indicate a potential mechanism of action of ECT. Understanding ECT's underlying mechanisms might ultimately enable treatment optimization, thus enhancing patient care. However, the number of studies is limited, with low-to-moderate EEG study quality, small sample sizes, and different electrophysiological FC measures.

Comparative analysis of resting-state EEG functional connectivity in depression and obsessive-compulsive disorder

Major depressive disorder (MDD) and obsessive-compulsive disorder (OCD) are psychiatric disorders that often co-occur. We aimed to investigate whether their high comorbidity could be traced not only by clinical manifestations, but also at the level of functional brain activity. In this paper, we examined the differences in functional connectivity (FC) at the whole-brain level and within the default mode network (DMN). Resting-state EEG was obtained from 43 controls, 26 OCD patients, and 34 MDD patients. FC was analyzed between 68 cortical sources, and between-group differences in the 4–30 Hz range were assessed via the Network Based Statistic method. The strength of DMN intra-connectivity was compared between groups in the theta, alpha and beta frequency bands. A cluster of 67 connections distinguished the OCD, MDD and control groups. The majority of the connections, 8 of which correlated with depressive symptom severity, were found to be weaker in the clinical groups. Only 3 connections differed between the clinical groups, and one of them correlated with OCD severity. The DMN strength was reduced in the clinical groups in the alpha and beta bands. It can be concluded that the high comorbidity of OCD and MDD can be traced at the level of FC.

A prospective study on EEG default mode network associated with subsequent posttraumatic stress disorder following sexual assault

This study aimed to explore the predictors of posttraumatic stress disorder (PTSD) in women who have recently experienced sexual assault, by examining psychological and neurophysiological factors using a prospective design with resting-state electroencephalogram (EEG) functional connectivity. The study enrolled 33 women who had been recently traumatized by sexual assault and conducted assessments within a month of the trauma. These survivors were evaluated for PTSD three months later and were classified into two groups: PTSD positive (n = 12) and PTSD negative (n = 21). They were compared to two control groups comprising women who had not experienced any extremely traumatic events: 25 with depression and 25 healthy controls. The evaluation focused on resting-state EEG functional connectivity within default mode network (DMN) using small-worldness (SW), based on graph theory. We also assessed self-reported levels of depression, anxiety, anger, and executive functions. The findings indicated that survivors who developed PTSD three months post-trauma exhibited higher anxiety levels and reduced DMN SW in the beta 3 frequency, compared to those who did not develop PTSD. Contrary to expectations, survivors without PTSD showed decreased executive functioning and lower prefrontal centrality compared to those with PTSD. This study underscores the importance of early assessment and intervention for sexual assault survivors at risk of developing PTSD.

Disruption of EEG resting state functional connectivity in patients with focal epilepsy

BackgroundEpilepsy is a network disease and EEG could be used to evaluate dynamic inter-regional connectivity. The aim of the current study is to explore disruption of resting state EEG functional connectivity in focal epilepsy using coherence and phase lag degree. This cross-sectional study included 30 patients with focal epilepsy and 30 matched healthy controls. One to two minutes of EEG segmented into 2-s epochs during awake eye-closed state were analyzed using fast Fourier transform to yield four frequency bands: delta, theta, alpha and beta. Coherence and phase lag degree were computed between each pair of 19 EEG electrodes and were assessed at the intra-hemispheric (frontal–parietal and frontal–temporal) and inter-hemispheric (frontal, temporal and parietal) levels. The frequency of interictal epileptiform discharges (IEDs) was calculated from a 60-min EEG recording session.ResultsCompared to healthy controls, patients had lower theta coherence at left frontal–parietal (P = 0.017), lower delta coherence at inter-frontal (P = 0.045), higher theta phase lag at right frontal–parietal (P = 0.01) and lower delta phase lag at inter-temporal (P = 0.046) levels. Patients with left-sided epilepsy had lower theta coherence at left frontal–parietal (P = 0.026), higher theta phase lag at right frontal–parietal (P < 0.001), higher delta phase lag at right frontal–temporal (P = 0.036) and higher theta phase lag at inter-parietal (P = 0.028) levels. The frequency of IEDs correlated with phase lag of delta (P = 0.036, r = 0.406) and theta (P = 0.005, r = 0.513).ConclusionsPatients with focal epilepsy had significant interictal functional connectivity disruption detected by coherence and phase lag degree of delta and theta waves and correlated with frequency of IEDs.

Resting-State EEG Connectivity at High-Frequency Bands and Attentional Performance Dysfunction in Stabilized Schizophrenia Patients.

Background and Objectives: Attentional dysfunction has long been viewed as one of the fundamental underlying cognitive deficits in schizophrenia. There is an urgent need to understand its neural underpinning and develop effective treatments. In the process of attention, neural oscillation has a central role in filtering information and allocating resources to either stimulus-driven or goal-relevant objects. Here, we asked if resting-state EEG connectivity correlated with attentional performance in schizophrenia patients. Materials and Methods: Resting-state EEG recordings were obtained from 72 stabilized patients with schizophrenia. Lagged phase synchronization (LPS) was used to measure whole-brain source-based functional connectivity between 84 intra-cortical current sources determined by eLORETA (exact low-resolution brain electromagnetic tomography) for five frequencies. The Conners' Continuous Performance Test-II (CPT-II) was administered for evaluating attentional performance. Linear regression with a non-parametric permutation randomization procedure was used to examine the correlations between the whole-brain functional connectivity and the CPT-II measures. Results: Greater beta-band right hemispheric fusiform gyrus (FG)-lingual gyrus (LG) functional connectivity predicted higher CPT-II variability scores (r = 0.44, p < 0.05, corrected), accounting for 19.5% of variance in the CPT-II VAR score. Greater gamma-band right hemispheric functional connectivity between the cuneus (Cu) and transverse temporal gyrus (TTG) and between Cu and the superior temporal gyrus (STG) predicted higher CPT-II hit reaction time (HRT) scores (both r = 0.50, p < 0.05, corrected), accounting for 24.6% and 25.1% of variance in the CPT-II HRT score, respectively. Greater gamma-band right hemispheric Cu-TTG functional connectivity predicted higher CPT-II HRT standard error (HRTSE) scores (r = 0.54, p < 0.05, corrected), accounting for 28.7% of variance in the CPT-II HRTSE score. Conclusions: Our study indicated that increased right hemispheric resting-state EEG functional connectivity at high frequencies was correlated with poorer focused attention in schizophrenia patients. If replicated, novel approaches to modulate these networks may yield selective, potent interventions for improving attention deficits in schizophrenia.

Pre vs post‐task modulation of resting state EEG functional connectivity and network topology

AbstractBackgroundRecently, we have shown a significant decrease in resting‐state EEG (rsEEG) spectral power at post‐task as compared to pre‐task cognitive engagement in older adults. Further, older adults with mild cognitive impairment (MCI) showed greater decreases in rsEEG in temporal and central cortical regions at post‐task than healthy controls (HC). Here, we present functional connectivity (FC) and network topology derived from the same pre‐ to post‐rsEEG changes.MethodWe studied a sample of community dwelling African American older participants over age 60 years, consisting of consensus‐based diagnosis for 58 HC and 41 MCI. rsEEGs were recorded before and after approximately 20 minutes of a visual motion direction discrimination task. FC between channels was assessed by the phase lag index, while minimum spanning tree (MST) was used to characterize network topology.ResultFor both HC and MCI groups, when compared to pre‐task measurements, the post‐task FC was significantly increased within the middle frontal, middle central, and left temporal regions in the lower alpha frequency band. An opposite pattern with decreased FC was found within the right parietal region in the delta, theta, lower alpha, upper alpha, and beta frequency bands. In contrast, compared to HC, the MCI group did show significantly decreased FC within the middle frontal region in the delta band, following the cognitive task. All results from network topology failed to demonstrate differences between HC and MCI. On average, both groups' post‐task functional network topology showed significant decreased eccentricity and diameter in the delta and beta bands, indicating a higher global network integration.ConclusionOur findings suggest that visual‐based cognitive engagement affects both post‐task FC and network topology similarly in both HC and MCI. For the most part, increased FC and functional topology in both groups are likely associated with after‐task effects, resulting from additional time to return to baseline, though decreased FC in MCI as compared to HC was seen within the middle frontal region in the delta. Our results demonstrate that both FC and topoloty are responsive to cognitive challenge, though frontal/executive‐driven differences in FC may be an area of future promise in distinguishing HC and MCI.

Resting-state EEG functional connectivity predicts post-traumatic stress disorder subtypes in veterans

Objective. Post-traumatic stress disorder (PTSD) is highly heterogeneous, and identification of quantifiable biomarkers that could pave the way for targeted treatment remains a challenge. Most previous electroencephalography (EEG) studies on PTSD have been limited to specific handpicked features, and their findings have been highly variable and inconsistent. Therefore, to disentangle the role of promising EEG biomarkers, we developed a machine learning framework to investigate a wide range of commonly used EEG biomarkers in order to identify which features or combinations of features are capable of characterizing PTSD and potential subtypes. Approach. We recorded 5 min of eyes-closed and 5 min of eyes-open resting-state EEG from 202 combat-exposed veterans (53% with probable PTSD and 47% combat-exposed controls). Multiple spectral, temporal, and connectivity features were computed and logistic regression, random forest, and support vector machines with feature selection methods were employed to classify PTSD. To obtain robust results, we performed repeated two-layer cross-validation to test on an entirely unseen test set. Main results. Our classifiers obtained a balanced test accuracy of up to 62.9% for predicting PTSD patients. In addition, we identified two subtypes within PTSD: one where EEG patterns were similar to those of the combat-exposed controls, and another that were characterized by increased global functional connectivity. Our classifier obtained a balanced test accuracy of 79.4% when classifying this PTSD subtype from controls, a clear improvement compared to predicting the whole PTSD group. Interestingly, alpha connectivity in the dorsal and ventral attention network was particularly important for the prediction, and these connections were positively correlated with arousal symptom scores, a central symptom cluster of PTSD. Significance. Taken together, the novel framework presented here demonstrates how unsupervised subtyping can delineate heterogeneity and improve machine learning prediction of PTSD, and may pave the way for better identification of quantifiable biomarkers.

Functional Connectivity Increases in Response to High-Definition Transcranial Direct Current Stimulation in Patients with Chronic Disorder of Consciousness.

Highlights Functional connectivity induced by HD-tDCS in DLPFC has different trends in CRS-R score improvers and non-improvers.An increase in theta PLV in the left frontal–parietooccipital region was significantly associated with CRS-R changes.DOC patients with increased PLV of the alpha band in the intra-bifrontal region have a better prognosis than those without. High-definition transcranial direct current stimulation (HD-tDCS) has been shown to play an important role in improving consciousness in patients with disorders of consciousness (DOCs), but its neuroelectrophysiological evidence is still lacking. To better explain the electrophysiological mechanisms of the effects of HD-tDCS on patients with DOCs, 22 DOC patients underwent 10 anodal HD-tDCS sessions of the left dorsolateral prefrontal cortex (DLPFC). This study used the Coma Recovery Scale-Revised (CRS-R) to assess the level of consciousness in DOC patients. According to whether the CRS-R score increased before and after stimulation, DOC patients were divided into a responsive group and a non-responsive group. By comparing the differences in resting-state EEG functional connectivity between different frequency bands and brain regions, as well as the relationship between functional connectivity values and clinical scores, the electrophysiological mechanism of the clinical effects of HD-tDCS was further explored. The change of the phase locking value (PLV) on the theta frequency band in the left frontal–parietooccipital region was positively correlated with the change in the CRS-R scores. As the number of interventions increased, we observed that in the responsive group, the change in PLV showed an upward trend, and the increase in the PLV appeared in the left frontal–parietooccipital region at 4–8 Hz and in the intra-bifrontal region at 8–13 Hz. In the non-responsive group, although the CRS-R scores did not change after stimulation, the PLV showed a downward trend, and the decrease in the PLV appeared in the intra-bifrontal region at 8–13 Hz. In addition, at the three-month follow-up, patients with increased PLV in the intra-bifrontal region at 8–13 Hz after repeated HD-tDCS stimulation had better outcomes than those without. Repeated anodal stimulation of the left DLPFC with HD-tDCS resulted in improved consciousness in some patients with DOCs. The increase in functional connectivity in the brain regions may be associated with the improvement of related awareness after HD-tDCS and may be a predictor of better long-term outcomes.

Resting-State EEG Functional Connectivity in Children with Rolandic Spikes with or without Clinical Seizures.

Alterations in dynamic brain network function are increasingly recognized in epilepsy. Benign childhood epilepsy with centrotemporal spikes (BECTS), or benign rolandic seizures, is the most common idiopathic focal epilepsy in children. In this study, we analyzed EEG functional connectivity (FC) among children with rolandic spikes with or without clinical seizures as compared to controls, to investigate the relationship between FC and clinical parameters in children with rolandic spikes. The FC analysis based on graph theory and network-based statistics in different frequency bands evaluated global efficiency, clustering coefficient, betweenness centrality, and nodal strength in four frequency bands. Similar to BECTS patients with seizures, children with rolandic spikes without seizures had significantly increased global efficiency, mean clustering coefficient, mean nodal strength, and connectivity strength, specifically in the theta frequency band at almost all proportional thresholds, compared with age-matched controls. Decreased mean betweenness centrality was only present in BECTS patients with seizures. Age at seizure onset was significantly positively associated with the strength of EEG-FC. The decreased function of betweenness centrality was only presented in BECTS patients with clinical seizures, suggesting weaker local connectivity may lower the seizure threshold. These findings may affect treatment policy in children with rolandic spikes.

Schizophrenia Classification Using Resting State EEG Functional Connectivity: Source Level Outperforms Sensor Level.

Disrupted functional and structural connectivity measures have been used to distinguish schizophrenia patients from healthy controls. Classification methods based on functional connectivity derived from EEG signals are limited by the volume conduction problem. Recorded time series at scalp electrodes capture a mixture of common sources signals, resulting in spurious connections. We have transformed sensor level resting state EEG times series to source level EEG signals utilizing a source reconstruction method. Functional connectivity networks were calculated by computing phase lag values between brain regions at both the sensor and source level. Brain complex network analysis was used to extract features and the best features were selected by a feature selection method. A logistic regression classifier was used to distinguish schizophrenia patients from healthy controls at five different frequency bands. The best classifier performance was based on connectivity measures derived from the source space and the theta band.The transformation of scalp EEG signals to source signals combined with functional connectivity analysis may provide superior features for machine learning applications.

Reorganization of Resting-State EEG Functional Connectivity Patterns in Children with Cerebral Palsy Following a Motor Imagery Virtual-Reality Intervention

Motor imagery (MI) has been suggested to provide additional benefits when included in traditional approaches of physical therapy for children with cerebral palsy (CP). Regardless, little is understood about the underlying neurological substrates that might justify its supposed benefits. In this work, we studied resting-state (RS) electroencephalography (EEG) recordings of five children with CP that underwent a MI virtual-reality (VR) intervention. Our aim was to explore functional connectivity (FC) patterns alterations following this intervention through the formalism of graph theory, performing both group and subject-specific analyses. We found that FC patterns were more consistent across subjects prior to the MI-VR intervention, shifting along the anterior-posterior axis, post-intervention, for the β and γ bands. Additionally, group FC patterns were not found for the α range. Furthermore, intra-subject analyses reinforced the existence of large inter-subject variability and the need for a careful exploration of individual pattern alterations. Such patterns also hinted at a dependency between short-term functional plasticity mechanisms and the EEG frequency bands. Although our sample size is small, we provide a longitudinal analysis framework that can be replicated in future studies, especially at the group level, and whose foundation can be easily extended to verify the validity of our hypotheses.

Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity.

The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven’s total scores and E series), and the ability to operate with verbal information (the “Conclusions” verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven’s total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.

Time and frequency dependent changes in resting state EEG functional connectivity following lipopolysaccharide challenge in rats.

Research has shown that inflammatory processes affect brain function and behavior through several neuroimmune pathways. However, high order brain functions affected by inflammation largely remain to be defined. Resting state functional connectivity of synchronized oscillatory activity is a valid approach to understand network processing and high order brain function under different experimental conditions. In the present study multi-electrode EEG recording in awake, freely moving rats was used to study resting state connectivity after administration of lipopolysaccharides (LPS). Male Wistar rats were implanted with 10 cortical surface electrodes and administered with LPS (2 mg/kg) and monitored for symptoms of sickness at 3, 6 and 24 h. Resting state connectivity and power were computed at baseline, 6 and 24 h. Three prominent connectivity bands were identified using a method resistant to spurious correlation: alpha (5–15 Hz), beta-gamma (20–80 Hz), and high frequency oscillation (150–200 Hz). The most prominent connectivity band, alpha, was strongly reduced 6 h after LPS administration, and returned to baseline at 24 h. Beta-gamma connectivity was also reduced at 6 h and remained reduced at 24 h. Interestingly, high frequency oscillation connectivity remained unchanged at 6 h and was impaired 24 h after LPS challenge. Expected elevations in delta and theta power were observed at 6 h after LPS administration, when behavioral symptoms of sickness were maximal. Notably, gamma and high frequency power were reduced 6 h after LPS and returned to baseline by 24 h, when the effects on connectivity were more evident. Finally, increases in cross-frequency coupling elicited by LPS were detected at 6 h for theta-gamma and at 24 h for theta-high frequency oscillations. These studies show that LPS challenge profoundly affects EEG connectivity across all identified bands in a time-dependent manner indicating that inflammatory processes disrupt both bottom-up and top-down communication across the cortex during the peak and resolution of inflammation.

T07. Resting-state EEG functional connectivity and outcome following mesial temporal lobe epilepsy surgery

In this study we explore the reorganization of brain functional connectivity in patients with refractory mesial temporal lobe epilepsy that underwent surgery. We carried out a resting-state EEG functional connectivity analysis in a baseline period before and two years after the surgical intervention. Changes in synchronization features and graph theoretical properties were quantified by synchronization likelihood in patients with different surgical outcome, where outcome was defined as seizure-free (good outcome) or not seizure-free (poor outcome) 1 year after surgery. EEG segments were manually selected and included at least 2 min in closed eyes resting state. In all segments, synchronization likelihood (SL) and graph parameters in delta (1–4 Hz), theta (4–7 Hz), alpha1 (8–10 Hz), alpha2 (10–13 Hz), beta1 (13–15 Hz) and beta2 (15–20 Hz) frequency bands were calculated. Results showed that strength of the overall spatial synchronization was different 2 years after surgery in patients that were free of seizures. The change in synchronization was characterized by lower synchronization in alpha 1 but higher synchronization in alpha2. No changes between pre- and 2 years post-surgery periods were present in patients with seizures’ recurrence. Network properties as clustering coefficient and modularity in alpha2, beta1 and beta2 were higher postoperatively in the group free of seizures compared to baseline. Local efficiency was also higher in theta, alpha2 and beta bands. Interestingly the group with seizures’ recurrence showed a higher modularity only in the delta band but the clustering coefficient and the local efficiency were higher in all frequency bands 2 years after surgery. Outcome after surgery is related to a specific reorganization in brain networks as manifested in changes of functional connectivity and network topology. Reorganization toward a small-world network within the alpha2 and beta bands seems to be mediating seizure resolution.

Altered Functional Connectivity in Idiopathic Rapid Eye Movement Sleep Behavior Disorder: A Resting-State EEG Study.

Idiopathic rapid eye movement sleep behavior disorder (iRBD) is considered as a prodromal stage of synucleinopathy. Although loss of functional connectivity is implicated in neurodegenerative diseases, network characteristics of electroencephalography (EEG) in iRBD are unknown. Therefore, we evaluated resting-state EEG functional connectivity to identify the brain network changes in patients with iRBD. We prospectively enrolled 20 patients with polysomnography-confirmed iRBD and 16 controls. Four patients with mild cognitive impairment were excluded from the analysis after cognitive function tests. EEG was recorded during relaxed wakefulness. We computed the weighted phase lag index as a measure of functional connectivity from EEG recordings. All patients with iRBD (mean age 64.3 years; men, 68.8%) had no overt manifestations of neurodegenerative diseases such as Parkinsonism or dementia. The mean duration from symptom onset was 4.8 years. Overall connectivity strength did not differ between the two groups in all frequency bands. However, comparisons of each functional connection with the nonparametric permutation test demonstrated iRBD had decreased delta-band functional connectivity in the frontal regions. There were no significantly increased functional connections in all frequencies. The altered connections had a significant correlation with RBD questionnaire scores. Notably, delta-band weighted phase lag index between left frontal and central regions was correlated with verbal fluency performance (r = 0.486, p = .007). Resting-state brain network of iRBD was characterized by a loss of delta-band functional connectivity. Therefore, functional networks in iRBD are altered at the early phase of disease.

Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease.

Functional connectivity has proven useful to characterise electroencephalogram (EEG) activity in Alzheimer's disease (AD). However, most current functional connectivity analyses have been static, disregarding any potential variability of the connectivity with time. In this pilot study, we compute short-time resting state EEG functional connectivity based on the imaginary part of coherency for 12 AD patients and 11 controls. We derive binary unweighted graphs using the cluster-span threshold, an objective binary threshold. For each short-time binary graph, we calculate its local clustering coefficient (Cloc), degree (K), and efficiency (E). The distribution of these graph metrics for each participant is then characterised with four statistical moments: mean, variance, skewness, and kurtosis. The results show significant differences between groups in the mean of K and E, and the kurtosis of Cloc and K. Although not significant when considered alone, the skewness of Cloc is the most frequently selected feature for the discrimination of subject groups. These results suggest that the variability of EEG functional connectivity may convey useful information about AD.

Theta-band functional connectivity in the dorsal fronto-parietal network predicts goal-directed attention

Functional imaging studies have identified a dorsal fronto-parietal network whose activity reflects shifts of attention in space and is sensitive to the behavioural relevance of stimuli. In patients with severe deficits of spatial attention this network is often structurally preserved. Here, we show that resting-state EEG functional connectivity in the dorsal fronto-parietal network predicts impaired goal-directed processing in stroke patients with spatial attention deficits. Eleven right-hemisphere damaged patients with different degrees of contralesional spatial deficits and sixteen age-matched healthy controls performed a visuo-spatial task which required them to react to a central target while ignoring task-relevant distracters presented left or right of fixation. Unlike controls, performance of patients was not modulated by the goal-relevance of peripheral distracters. Compared to controls patients showed a significant decrease in theta-band connectivity between the right dorsolateral prefrontal cortex and the right superior parietal region. Moreover, in both groups we observed a significant correlation between fronto-parietal connectivity and the behavioural effect of distracter relevance. These findings indicate that fronto-parietal functional connectivity is impaired in patients with spatial attention deficits and predicts effects of goal-relevant information on target processing.

Increased resting-state EEG functional connectivity in benign childhood epilepsy with centro-temporal spikes

PurposeTo explore intrahemispheric, cortico-cortical EEG functional connectivity (EEGfC) in benign childhood epilepsy with rolandic spikes (BECTS). Methods21-channel EEG was recorded in 17 non-medicated BECTS children and 19 healthy controls. 180s of spike- and artifact-free activity was selected for EEGfC analysis. Correlation of Low Resolution Electromagnetic Tomography- (LORETA-) defined current source density time series were computed between two cortical areas (region of interest, ROI). Analyses were based on broad-band EEGfC results. Groups were compared by statistical parametric network (SPN) method. Statistically significant differences between group EEGfC values were emphasized at p<0.05 corrected for multiple comparison by local false discovery rate (FDR). Results(1) Bilaterally increased beta EEGfC occurred in the BECTS group as compared to the controls. Greatest beta abnormality emerged between frontal and frontal, as well as frontal and temporal ROIs. (2) Locally increased EEGfC emerged in all frequency bands in the right parietal area. ConclusionsAreas of increased EEGfC topographically correspond to cortical areas that, based on relevant literature, are related to speech and attention deficit in BECTS children.

Functional Connectivity and Quantitative EEG in Women with Alcohol Use Disorders: A Resting-State Study.

This study was aimed at exploring the electroencephalographic features associated with alcohol use disorders (AUD) during a resting-state condition, by using quantitative EEG and Functional Connectivity analyses. In addition, we explored whether EEG functional connectivity is associated with trait impulsivity. Absolute and relative powers and Synchronization Likelihood (SL) as a measure of functional connectivity were analyzed in 15 AUD women and fifteen controls matched in age, gender and education. Correlation analysis between self-report impulsivity as measured by the Barratt impulsiveness Scale (BIS-11) and SL values of AUD patients were performed. Our results showed increased absolute and relative beta power in AUD patients compared to matched controls, and reduced functional connectivity in AUD patients predominantly in the beta and alpha bands. Impaired connectivity was distributed at fronto-central and occipito-parietal regions in the alpha band, and over the entire scalp in the beta band. We also found that impaired functional connectivity particularly in alpha band at fronto-central areas was negative correlated with non-planning dimension of impulsivity. These findings suggest that functional brain abnormalities are present in AUD patients and a disruption of resting-state EEG functional connectivity is associated with psychopathological traits of addictive behavior.

Heritability of Resting State EEG Functional Connectivity Patterns

We examined the genetic architecture of functional brain connectivity measures in resting state electroencephalographic (EEG) recordings. Previous studies in Dutch twins have suggested that genetic factors are a main source of variance in functional brain connectivity derived from EEG recordings. In addition, qualitative descriptors of the brain network derived from graph analysis - network clustering and average path length - are also heritable traits. Here we replicated previous findings for connectivity, quantified by the synchronization likelihood, and the graph theoretical parameters cluster coefficient and path length in an Australian sample of 16-year-old twins (879) and their siblings (93). Modeling of monozygotic and dizygotic twins and sibling resemblance indicated heritability estimates of the synchronization likelihood (27-74%) and cluster coefficient and path length in the alpha and theta band (40-44% and 23-40% respectively) and path length in the beta band frequency (41%). This corroborates synchronization likelihood and its graph theoretical derivatives cluster coefficient and path length as potential endophenotypes for behavioral traits and neurological disorders.