EEG Connectivity Research Articles

The Association of Nocturnal Seizures and Interictal Cardiac/Central Autonomic Function in Frontal Lobe Epilepsy: Heart Rate Variability and Central Autonomic Network Analysis.

Patients with epilepsy frequently experience autonomic dysfunction, closely related to sudden unexplained death in epilepsy (SUDEP). SUDEP occurs most often at night or during sleep, and frequent nocturnal seizures are an established risk factor. This study investigated the influence of nocturnal seizures on autonomic dysfunction in epilepsy. This retrospective study enrolled frontal lobe epilepsy (FLE) patients who performed 24-hour EEG monitoring. All participants were divided into nocturnal FLE (NFLE, > 90% of seizures occurring during sleep) or diurnal FLE (DFLE) groups. EEG and ECG signals were simultaneously obtained during waking and sleep stages. EEG current density source and connectivity analysis of the autonomic network were performed. ECG was analyzed across time and frequency domains heart rate variability (HRV) analysis method was used. The obtained parameters were compared between the NFLE and DFLE groups. Fifteen NFLE and 16 DFLE patients were enrolled with no significant difference in age, sex, disease duration, seizure frequency, or the number of anti-seizure medications between the two groups. During sleep, a decrease in HRV parameters and an increase of the beta-1 (13-22 Hz) current source density power in the bilateral paracentral lobule (BA4,5,6), precuneus (BA7), and cingulate (BA31) were observed in the NFLE group compared to DFLE group. The NFLE group also showed hyperconnectivity in the central autonomic (12 edges distributed over 10 nodes), sympathetic (2 edges distributed over 3 nodes), and parasympathetic (4 edges distributed over 6 nodes) beta-1 frequency band networks during sleep. During wakefulness, central and cardiac autonomic variables were not significantly different between the NFLE and DFLE groups. Interictal cardiac and central autonomic dysfunction occurred simultaneously and can be attributed to the brain-heart autonomic axis. Our findings suggest that nocturnal seizures may contribute to interictal autonomic dysfunction during sleep in people with epilepsy.

EEG Connectivity Diversity Differences between Children with Autism and Typically Developing Children: A Comparative Study.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication, and repetitive or stereotyped behaviors. Previous studies have reported altered brain connectivity in ASD children compared to typically developing children. In this study, we investigated the diversity of connectivity patterns between children with ASD and typically developing children using phase lag entropy (PLE), a measure of the variability of phase differences between two time series. We also developed a novel wavelet-based PLE method for the calculation of PLE at specific scales. Our findings indicated that the diversity of connectivity in ASD children was higher than that in typically developing children at Delta and Alpha frequency bands, both within brain regions and across hemispheric brain regions. These findings provide insight into the underlying neural mechanisms of ASD and suggest that PLE may be a useful tool for investigating brain connectivity in ASD.

Classification of Mental Stress Levels using EEG Connectivity and Convolutional Neural Networks.

Classifying mental stress is important as it helps in identifying the type and severity of stress, which can inform the most appropriate treatment or intervention. In this study, we propose utilizing electroencephalography (EEG) signals with convolutional neural networks (CNNs) to classify four mental states: rest, control-alert, stress and stress mitigation. The mental stress state was induced using Stroop color word test (SCWT) with time constrains and was then mitigated using 16 Hz Binaural beat stimulation (BBs). We quantified the four mental states using the reaction time (RT) to stimuli, accuracy of target detection, subjective score, and functional connectivity images of EEG estimated by Phase Locking Value (PLV). Our results show that, the SCWT reduced the accuracy of target detection by 70% with (F= 24.56, p = .00001), and the BBs improved the accuracy by 28% (F= 4.54, p = .00470). The functional connectivity network showed different patterns between the frontal/occipital and parietal regions, under the four mental states. The proposed CNNs with PLV images differentiated between the four mental states with highest classification performance at beta frequency band with 80.95% accuracy, 80.36% sensitivity, 94.75% specificity, 83.63% precision and 81.96% F-score. The overall results suggest that 16 Hz BBs can be used as an effective method to mitigate stress and the proposed CNNs with EEG-PLV images as a promising method for classifying different mental states.

Cross-bispectrum connectivity of intracranial EEG: A novel approach to seizure onset zone localization.

Connectivity analyses of intracranial electroencephalography (iEEG) could guide surgical planning for epilepsy surgery by improving the delineation of the seizure onset zone. Traditional approaches fail to quantify important interactions between frequency components. To assess if effective connectivity based on cross-bispectrum -a measure of nonlinear multivariate cross-frequency coupling- can quantitatively identify generators of seizure activity, cross-bispectrum connectivity between channels was computed from iEEG recordings of 5 patients (34 seizures) with good postsurgical outcome. Personalized thresholds of 50% and 80% of the maximum coupling values were used to identify generating electrode channels. In all patients, outflow coupling between α (8-15 Hz) and β (16-31 Hz) frequencies identified at least one electrode inside the resected seizure onset zone. With the 50% and 80% thresholds respectively, an average of 5 (44.7%; specificity = 82.6%) and 2 (22.5%; specificity = 99.0%) resected electrodes were correctly identified. Results show promise for the automatic identification of the seizure onset zone based on cross-bispectrum connectivity analysis.

ID: 215151 Widespread Network Connectivity in Intracranial EEG: Electrographic Biomarkers for Responsive Neuromodulation in Temporal Lobe Epilepsy

Is there a way to pre-operatively identify epilepsy patients who best respond to neuromodulation? One-third of patients with epilepsy experience seizures that are resistant to medication. Furthermore, resective and/or ablative surgeries may be contraindicated. Closed-loop neuromodulation — for instance, responsive neurostimulation (RNS) — is a promising treatment option. However, clinical outcomes vary widely across patients.1 Researchers have used pre-operative global connectivity from magnetoencephalography (MEG) signals, as well as global measures of synchronizability from intracranial EEG (iEEG), as pre-operative electrographic biomarkers associated with successful RNS outcomes.2,3 An analysis specific to the RNS-implanted regions may allow for a better understanding of the patient-specific response to RNS therapy. Thus, we seek to use pre-operative iEEG recordings of eventual RNS implanted regions to investigate possible electrographic biomarkers of successful RNS therapy.

Compensatory mechanisms of reduced interhemispheric EEG connectivity during sleep in patients with apnea

We performed a mathematical analysis of functional connectivity in electroencephalography (EEG) of patients with obstructive sleep apnea (OSA) (N = 10; age: 52.8 ± 13 years; median age: 49 years; male/female ratio: 7/3), compared with a group of apparently healthy participants (N = 15; age: 51.5 ± 29.5 years; median age: 42 years; male/female ratio: 8/7), based on the calculation of wavelet bicoherence from nighttime polysomnograms. Having observed the previously known phenomenon of interhemispheric synchronization deterioration, we demonstrated a compensatory increase in intrahemispheric connectivity, as well as a slight increase in the connectivity of the central and occipital areas for high-frequency EEG activity. Significant changes in functional connectivity were extremely stable in groups of apparently healthy participants and OSA patients, maintaining the overall pattern when comparing different recording nights and various sleep stages. The maximum variability of the connectivity was observed at fast oscillatory processes during REM sleep. The possibility of observing some changes in functional connectivity of brain activity in OSA patients in a state of passive wakefulness opens up prospects for further research. Developing the methods of hypnogram evaluation that are independent of functional connectivity may be useful for implementing a medical decision support system.

A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment.

Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta burst stimulation (iTBS) have been proven effective non-invasive treatments for patients with drug-resistant major depressive disorder (MDD). However, some depressed patients do not respond to these treatments. Therefore, the investigation of reliable and valid brain oscillations as potential indices for facilitating the precision of diagnosis and treatment protocols has become a critical issue. The current review focuses on brain oscillations that, mostly based on EEG power analysis and connectivity, distinguish between MDD and controls, responders and non-responders, and potential depression severity indices, prognostic indicators, and potential biomarkers for rTMS or iTBS treatment. The possible roles of each biomarker and the potential reasons for heterogeneous results are discussed, and the directions of future studies are proposed.

N°94 – Reduction of EEG connectivity induced by cenobamate correlates with clinical and cognitive outcomes

N°94 – Reduction of EEG connectivity induced by cenobamate correlates with clinical and cognitive outcomes

N°167 – EEG connectivity measures have a superior prognostic value to predict the outcome in patients with disorders of consciousness – Meta-analysis and systematic review

N°167 – EEG connectivity measures have a superior prognostic value to predict the outcome in patients with disorders of consciousness – Meta-analysis and systematic review

Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex Increases Posterior Theta Rhythm and Reduces Latency of Motor Imagery

Experiments show activation of the left dorsolateral prefrontal cortex (DLPFC) in motor imagery (MI) tasks, but its functional role requires further investigation. Here, we address this issue by applying repetitive transcranial magnetic stimulation (rTMS) to the left DLPFC and evaluating its effect on brain activity and the latency of MI response. This is a randomized, sham-controlled EEG study. Participants were randomly assigned to receive sham (15 subjects) or real high-frequency rTMS (15 subjects). We performed EEG sensor-level, source-level, and connectivity analyses to evaluate the rTMS effects. We revealed that excitatory stimulation of the left DLPFC increases theta-band power in the right precuneus (PrecuneusR) via the functional connectivity between them. The precuneus theta-band power negatively correlates with the latency of the MI response, so the rTMS speeds up the responses in 50% of participants. We suppose that posterior theta-band power reflects attention modulation of sensory processing; therefore, high power may indicate attentive processing and cause faster responses.

Sitting and Standing Intention Detection Based on Dynamical Region Connectivity and Entropy of EEG

Based on the brain signals, decoding and analyzing the gait features to make a reliable prediction of action intention are the core issues in the brain computer interface (BCI)-based hybrid rehabilitation and intelligent walking aid robot system. In order to realize the classification and recognition of the most basic gait processes such as standing, sitting, and quiet, this paper proposes a feature representation method based on the signal complexity and entropy of each brain region. Through the statistical analysis of these parameters between different conditions, these characteristics which sensitive to different actions are determined as a feature vector, and the classification and recognition of these actions are completed by combing support vector machine, linear discriminant analysis, and logistic regression. Experimental results show the proposed method can better realize the recognition of the aforementioned action intention. The recognition accuracy of standing, sitting, and quiet of 13 subjects is higher than 80.9%, and the highest one can reach 86.8%. Directed dynamic brain network analysis of the 8 brain regions shows that the occurrence of lower limb movement will weaken the dependence between brain regions, resulting in the weakening of network topological connection. The result has significant value for understanding human’s brain cognitive characteristics in the process of lower limb movement and carrying out the study of BCI based strategy and system for lower limb rehabilitation.

EEG spectral and connectivity parameters as cognitive biomarkers in Parkinson disease

BackgroundCognitive decline is a common presentation of Parkinson’s disease (PD) and a continued search exists for a reliable biomarker for early identification and management of this clinical problem. The objective of this study is to select the most useful biomarker in assessment of PD-related cognitive decline. This cross-sectional study included 47 patients with PD and 47 matched healthy controls. All participants were assessed by quantitative electroencephalography (QEEG) spectral (relative power and background peak frequency) and connectivity measures (coherence and phase lag degree), in addition to clinical evaluation using Unified Parkinson’s Disease Rating Scale (UPDRS)and Modified Hoehn and Yahr staging scale and neuropsychological assessment of the patients using Montreal Cognitive Assessment (MoCA).ResultsPD patients showed significantly higher relative power in all frequency bands over the right temporal region with no significant changes in peak frequency, coherence and phase lag degree compared to healthy controls. PD patients with impaired cognition (MoCA < 26) had significantly lower global relative power, more marked in alpha and beta frequency bands compared to PD patients with normal cognition. Alpha and beta relative power in frontal and temporal regions showed significant correlation with different cognitive domains of MoCA score.ConclusionsQEEG measures especially spectral relative power could be used as adjunct to neuropsychological assessment in evaluation of PD-related cognitive decline.

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.

Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough?

ObjectiveUsing EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities. MethodsEEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested. ResultsThe correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated. ConclusionsSeveral graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data. SignificanceCharacterisation of functional brain networks derived from low-density EEG warrants careful consideration.

P-82 EEG and fMRI connectivity analysis in a frontotemporal auditory information processing network: Implications in individuals at high risk of developing psychosis

P-82 EEG and fMRI connectivity analysis in a frontotemporal auditory information processing network: Implications in individuals at high risk of developing psychosis

Source space connectomics of neurodegeneration: One-metric approach does not fit all

Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patients<HCs) involving convergent temporo-parieto-occipital regions in AD, and fronto-temporo-parietal areas in bvFTD. Hyperconnectivity (patients>HCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.

Neurofeedback Effects on EEG Connectivity among Children with Reading Disorders: I. Coherence

Electroencephalograms (EEGs) of children with reading disorders (RDs) are characterized by a higher theta and a lower alpha than those of typically developing children. Neurofeedback (NFB) may be helpful for treating learning disorders by reinforcing a reduction in the theta/alpha ratio. Several studies have suggested that NFB may lead to EEG power normalization and cognitive improvements. To further explore brain changes in isolated areas, the aim of this study was to explore the effects of an NFB protocol on functional connectivity (coherence) among children with RDs. Twenty children with an RD and an abnormally high theta/alpha ratio underwent 30 NFB sessions, and five children with the same characteristics received a sham NFB treatment. On average, the children in the NFB group showed an increase in reading accuracy and comprehension scores; their coherence diminished in the delta, theta, and beta bands and increased in the alpha band, primarily the theta intrahemispheric coherences of the left hemisphere, which is closely associated with reading. In contrast, children who received the sham NFB treatment did not show reading changes and had few changes in their coherence patterns. These preliminary results suggest that NFB can positively impact reading-related functions in the brain networks of children with RDs.

Altered electroencephalography resting state network coherence in remitted MDD

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.

EEG cortical activity and connectivity correlates of early sympathetic response during cold pressor test

Previous studies have identified several brain regions involved in the sympathetic response and its integration with pain, cognition, emotions and memory processes. However, little is known about how such regions dynamically interact during a sympathetic activation task. In this study, we analyzed EEG activity and effective connectivity during a cold pressor test (CPT). A source localization analysis identified a network of common active sources including the right precuneus (r-PCu), right and left precentral gyri (r-PCG, l-PCG), left premotor cortex (l-PMC) and left anterior cingulate cortex (l-ACC). We comprehensively analyzed the network dynamics by estimating power variation and causal interactions among the network regions through the direct directed transfer function (dDTF). A connectivity pattern dominated by interactions in alpha (8–12) Hz band was observed in the resting state, with r-PCu acting as the main hub of information flow. After the CPT onset, we observed an abrupt suppression of such alpha-band interactions, followed by a partial recovery towards the end of the task. On the other hand, an increase of delta-band (1–4) Hz interactions characterized the first part of CPT task. These results provide novel information on the brain dynamics induced by sympathetic stimuli. Our findings suggest that the observed suppression of alpha and rise of delta dynamical interactions could reflect non-pain-specific arousal and attention-related response linked to stimulus’ salience.

Neuroplasticity and EEG connectivity, how to measure and to use as potential biomarkers in neuro-rehabilitation treatment

Neuroplasticity and EEG connectivity, how to measure and to use as potential biomarkers in neuro-rehabilitation treatment