Altered Functional Connectivity after Epileptic Seizure Revealed by Scalp EEG

Lasting and extensive consequences of left mesial temporal lobe seizures on electrical cortical activity

BACKGROUND AND OBJECTIVES

Focal epilepsies disrupt long-range networks with seizure recurrence driving both regional and global alterations in connectivity networks. While prior studies have focused on the interictal consequences, limited data exist on the direct aftermath of focal seizures. We hypothesize that mesial temporal lobe seizures lead to enduring cortical disorganization. The aim was to assess the effects of a mesial temporal lobe seizure on cortical activity and understand how the side of seizure onset influences these consequences.

METHODS

In this retrospective study, high-resolution EEG of patients with mesial temporal lobe epilepsy (mTLE) were analyzed. Groups of patients were identified based on the side of seizure onset. We compared relative powers in different frequency bands between interictal (prior to the seizure) and late postictal (one hour following the seizure) periods. Network-based statistics were employed to compare functional connectivity at source level between periods.

RESULTS

Twenty-three patients were included (13 left and 10 right mesial temporal lobe seizures). In patients with left mTLE, we observed a post-seizure increase in the relative spectral power in the delta band (p = 0.001) and a decrease in the relative spectral power in the alpha band (p = 0.013) over the left temporofrontal regions. We isolated a subnetwork that presented a decrease in connectivity strength in alpha band, primarily involving long-range left hemisphere connections (p = 0.042). We also identified a subnetwork that presented a decrease in connectivity strength in theta band, primarily involving interhemispheric connections (p = 0.039). No significant post-seizure changes were found in patients with right mTLE.

DISCUSSION

Left mesial temporal lobe seizures appear to be associated with lasting and widespread disorganization of cortical activity. We propose that the postictal state is associated with a prolonged functional deafferentation of the affected region in patients with left mTLE. This leads to a widespread disorganization of the functional networks, which may be associated with cognitive impairments and promote the progression of epilepsy. Further studies are required to fully understand the functional repercussions.

Electroencephalogram synchronization measure as a predictive biomarker of Vagus nerve stimulation response in refractory epilepsy: A retrospective study

There are currently no established biomarkers for predicting the therapeutic effectiveness of Vagus Nerve Stimulation (VNS). Given that neural desynchronization is a pivotal mechanism underlying VNS action, EEG synchronization measures could potentially serve as predictive biomarkers of VNS response. Notably, an increased brain synchronization in delta band has been observed during sleep-potentially due to an activation of thalamocortical circuitry, and interictal epileptiform discharges are more frequently observed during sleep. Therefore, investigation of EEG synchronization metrics during sleep could provide a valuable insight into the excitatory-inhibitory balance in a pro-epileptogenic state, that could be pathological in patients exhibiting a poor response to VNS. A 19-channel-standard EEG system was used to collect data from 38 individuals with Drug-Resistant Epilepsy (DRE) who were candidates for VNS implantation. An EEG synchronization metric-the Weighted Phase Lag Index (wPLI)-was extracted before VNS implantation and compared between sleep and wakefulness, and between responders (R) and non-responders (NR). In the delta band, a higher wPLI was found during wakefulness compared to sleep in NR only. However, in this band, no synchronization difference in any state was found between R and NR. During sleep and within the alpha band, a negative correlation was found between wPLI and the percentage of seizure reduction after VNS implantation. Overall, our results suggest that patients exhibiting a poor VNS efficacy may present a more pathological thalamocortical circuitry before VNS implantation. EEG synchronization measures could provide interesting insights into the prerequisites for responding to VNS, in order to avoid unnecessary implantations in patients showing a poor therapeutic efficacy.

EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit

Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

Hemodynamic correlates of emotion regulation in frontal lobe epilepsy patients and healthy participants

The ability to regulate emotions is indispensable for maintaining psychological health. It heavily relies on frontal lobe functions which are disrupted in frontal lobe epilepsy. Accordingly, emotional dysregulation and use of maladaptive emotion regulation strategies have been reported in frontal lobe epilepsy patients. Therefore, it is of clinical and scientific interest to investigate emotion regulation in frontal lobe epilepsy. We studied neural correlates of upregulating and downregulating emotions toward aversive pictures through reappraisal in 18 frontal lobe epilepsy patients and 17 healthy controls using functional magnetic resonance imaging. Patients tended to report more difficulties with impulse control than controls. On the neural level, patients had diminished activity during upregulation in distributed left-sided regions, including ventrolateral and dorsomedial prefrontal cortex, angular gyrus and anterior temporal gyrus. Patients also showed less activity than controls in the left precuneus for upregulation compared to downregulation. Unlike controls, they displayed no task-related activity changes in the left amygdala, whereas the right amygdala showed task-related modulations in both groups. Upregulation-related activity changes in the left inferior frontal gyrus, insula, orbitofrontal cortex, anterior and posterior cingulate cortex, and precuneus were correlated with questionnaire data on habitual emotion regulation. Our results show that structural or functional impairments in the frontal lobes disrupt neural mechanisms underlying emotion regulation through reappraisal throughout the brain, including posterior regions involved in semantic control. Findings on the amygdala as a major target of emotion regulation are in line with the view that specifically the left amygdala is connected with semantic processing networks.

Evaluation of EEG dynamic connectivity around seizure onset with principal component analysis

Seizures represent a brain activity state charac-terised by extended synchronised firing in multiple regions that prevent normal brain functioning. It is important to develop methods to distinguish between normal and abnormal synchro-nisation in epilepsy, as well as to localise the networks involved in seizures. To this end, we perform a preliminary investigation in the use of principal components analysis (PCA) to assess the change in dynamic electroencephalogram (EEG) connectivity before and after seizure onset. Source estimation was performed for an openly available EEG dataset from 14 patients with epilepsy. By applying PCA onto the EEG data processed into dynamic connectivity (dFC) matrices, we identified a set of connectivity topologies (eigenconnectivities) that explain high levels of variance in the dynamic connectivity. We compare the dimensionality reduction results obtained on source-level vs. scalp-level connectivity. We identified eigenconnectivities with differences in preictal vs. ictal activity and the brain networks associated with these activations. The work illustrates a data-driven approach for identification of topologies of brain networks that change with seizure onset. Clinical relevance We identified networks that are signifi-cantly varying with preictal vs. ictal brain activity some of which verify preexistent epilepsy markers in a data-driven way.

Effects of seizure burden on structural global brain networks in patients with unilateral hippocampal sclerosis

BACKGROUND AND PURPOSE

Temporal lobe epilepsy secondary to hippocampal sclerosis is related to epileptogenic networks rather than a focal epileptogenic source. Graph-theoretical gray and white matter networks may help to identify alterations within these epileptogenic networks.

METHODS

Twenty-seven patients with hippocampal sclerosis and 14 controls underwent magnetic resonance imaging, including 3D-T1, fluid-attenuated inversion recovery, and diffusion tensor imaging. Subject-specific structural gray and white matter network properties (normalized path length, clustering, and small-worldness) were reconstructed. Group differences and differences between those with higher and lower seizure burden (<4 vs. ≥4 average monthly seizures in the last year) in network parameters were evaluated. Additionally, correlations between network properties and disease-related variables were calculated.

RESULTS

All patients with hippocampal sclerosis as one group did not have altered gray or white matter network properties (all p > .05). Patients with lower seizure burden had significantly lower gray matter small-worldness and normalized clustering compared to controls and those with higher seizure burden (all p < .04). A higher number of monthly seizures was significantly associated with increased gray and white matter small-worldness, indicating a more rigid network.

CONCLUSION

Overall, there were no differences in network properties in this group of patients with hippocampal sclerosis. However, patients with lower seizure burden had significantly lower gray matter network indices, indicating a more random organization. The correlation between higher monthly seizures and a more rigid network is driven by those with higher seizure burden, who presented a more rigid network compared to those with a lower seizure burden.