High-frequency brain networks undergo modular breakdown during epileptic seizures

Failure to breathe persists without air hunger or alarm following amygdala seizures

Postictal apnea is thought to be a major cause of sudden unexpected death in epilepsy (SUDEP). However, the mechanisms underlying postictal apnea are unknown. To understand causes of postictal apnea, we used a multimodal approach to study brain mechanisms of breathing control in 20 patients (ranging from pediatric to adult) undergoing intracranial electroencephalography for intractable epilepsy. Our results indicate that amygdala seizures can cause postictal apnea. Moreover, we identified a distinct region within the amygdala where electrical stimulation was sufficient to reproduce prolonged breathing loss persisting well beyond the end of stimulation. The persistent apnea was resistant to rising CO2 levels, and air hunger failed to occur, suggesting impaired CO2 chemosensitivity. Using es-fMRI, a potentially novel approach combining electrical stimulation with functional MRI, we found that amygdala stimulation altered blood oxygen level-dependent (BOLD) activity in the pons/medulla and ventral insula. Together, these findings suggest that seizure activity in a focal subregion of the amygdala is sufficient to suppress breathing and air hunger for prolonged periods of time in the postictal period, likely via brainstem and insula sites involved in chemosensation and interoception. They further provide insights into SUDEP, may help identify those at greatest risk, and may lead to treatments to prevent SUDEP.

Frequency-Dependent Dynamics of Functional Connectivity Networks During Seizure Termination in Childhood Absence Epilepsy: A Magnetoencephalography Study

Objective: Our aim was to investigate the dynamics of functional connectivity (FC) networks during seizure termination in patients with childhood absence epilepsy (CAE) using magnetoencephalography (MEG) and graph theory (GT) analysis.

Methods: MEG data were recorded from 22 drug-naïve patients diagnosed with CAE. FC analysis was performed to evaluate the FC networks in seven frequency bands of the MEG data. GT analysis was used to assess the topological properties of FC networks in different frequency bands.

Results: The patterns of FC networks involving the frontal cortex were altered significantly during seizure termination compared with those during the ictal period. Changes in the topological parameters of FC networks were observed in specific frequency bands during seizure termination compared with those in the ictal period. In addition, the connectivity strength at 250–500 Hz during the ictal period was negatively correlated with seizure frequency.

Conclusions: FC networks associated with the frontal cortex were involved in the termination of absence seizures. The topological properties of FC networks in different frequency bands could be used as new biomarkers to characterize the dynamics of FC networks related to seizure termination.

High-Frequency Hubs of the Ictal Cross-Frequency Coupling Network Predict Surgical Outcome in Epilepsy Patients

Seizure generation is thought to be a process driven by epileptogenic networks; thus, network analysis tools can help determine the efficacy of epilepsy treatment. Studies have suggested that low-frequency (LF) to high-frequency (HF) cross-frequency coupling (CFC) is a useful biomarker for localizing epileptogenic tissues. However, it remains unclear whether the LF or HF coordinated CFC network hubs are more critical in determining the treatment outcome. We hypothesize that HF hubs are primarily responsible for seizure dynamics. Stereo-electroencephalography (SEEG) recordings of 36 seizures from 16 intractable epilepsy patients were analyzed. We propose a new approach to model the temporal-spatial-spectral dynamics of CFC networks. Graph measures are then used to characterize the HF and LF hubs. In the patient group with Engel Class (EC) I outcome, the strength of HF hubs was significantly higher inside the resected regions during the early and middle stages of seizure, while such a significant difference was not observed in the EC III group and only for the early stage in the EC II group. For the LF hubs, a significant difference was identified at the late stage and only in the EC I group. Our findings suggest that HF hubs increase at early and middle stages of the ictal interval while LF hubs increase activity at the late stages. In addition, HF hubs can predict treatment outcomes more precisely, compared to the LF hubs of the CFC network. The proposed method promises to identify more accurate targets for surgical interventions or neuromodulation therapies.

Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity

Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source-space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.

Altered Functional Connectivity after Epileptic Seizure Revealed by Scalp EEG

Epileptic seizures are considered to be a brain network dysfunction, and chronic recurrent seizures can cause severe brain damage. However, the functional brain network underlying recurrent epileptic seizures is still left unveiled. This study is aimed at exploring the differences in a related brain activity before and after chronic repetitive seizures by investigating the power spectral density (PSD), fuzzy entropy, and functional connectivity in epileptic patients. The PSD analysis revealed differences between the two states at local area, showing postseizure energy accumulation. Besides, the fuzzy entropies of preseizure in the frontal, central, and temporal regions are higher than that of postseizure. Additionally, attenuated long-range connectivity and enhanced local connectivity were also found. Moreover, significant correlations were found between network metrics (i.e., characteristic path length and clustering coefficient) and individual seizure number. The PSD, fuzzy entropy, and network analysis may indicate that the brain is gradually impaired along with the occurrence of epilepsy, and the accumulated effect of brain impairment is observed in individuals with consecutive epileptic bursts. The findings of this study may provide helpful insights into understanding the network mechanism underlying chronic recurrent epilepsy.

Recurrence quantification analysis of dynamic brain networks

Evidence suggests that brain network dynamics are a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting-state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than in healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in healthy brain function and in other neurological disorders besides epilepsy.

Multifrequency Dynamics of Cortical Neuromagnetic Activity Underlying Seizure Termination in Absence Epilepsy

Purpose

This study aimed to investigate the spectral and spatial signatures of neuromagnetic activity underlying the termination of absence seizures.

Methods

Magnetoencephalography (MEG) data were recorded from 18 drug-naive patients with childhood absence epilepsy (CAE). Accumulated source imaging (ASI) was used to analyze MEG data at the source level in seven frequency ranges: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), gamma (30–80 Hz), ripple (80–250 Hz), and fast ripple (250–500 Hz).

Result

In the 1–4, 4–8, and 8–12 Hz ranges, the magnetic source during seizure termination appeared to be consistent over the ictal period and was mainly localized in the frontal cortex (FC) and parieto-occipito-temporal junction (POT). In the 12–30 and 30–80 Hz ranges, a significant reduction in source activity was observed in the frontal lobe during seizure termination as well as a decrease in peak source strength. The ictal peak source strength in the 1–4 Hz range was negatively correlated with the ictal duration of the seizure, whereas in the 30–80 Hz range, it was positively correlated with the course of epilepsy.

Conclusion

The termination of absence seizures is associated with a dynamic neuromagnetic process. Frequency-dependent changes in the FC were observed during seizure termination, which may be involved in the process of neural network interaction. Neuromagnetic activity in different frequency bands may play different roles in the pathophysiological mechanism during absence seizures.

Identifying the Epileptogenic Zone With the Relative Strength of High-Frequency Oscillation: A Stereoelectroencephalography Study

Background

High-frequency oscillation (HFO) represents a promising biomarker of epileptogenicity. However, the significant interindividual differences among patients limit its application in clinical practice. Here, we applied and evaluated an individualized, frequency-based approach of HFO analysis in stereoelectroencephalography (SEEG) data for localizing the epileptogenic zones (EZs).

Methods

Clinical and SEEG data of 19 patients with drug-resistant focal epilepsy were retrospectively analyzed. The individualized spectral power of all signals recorded by electrode array, i.e., the relative strength of HFO, was computed with a wavelet method for each patient. Subsequently, the clinical value of the relative strength of HFO for identifying the EZ was evaluated.

Results

Focal increase in the relative strength of HFO in SEEG recordings were identified in all 19 patients. HFOs identified inside the clinically identified seizure onset zone had more spectral power than those identified outside (p < 0.001), and HFOs in 250–500 Hz band (fast ripples) seemed to be more specific identifying the EZ than in those in 80–250 Hz band (ripples) (p < 0.01). The resection of brain regions generating HFOs resulted in a favorable seizure outcome in 17 patients (17/19; 89.5%), while in the cases of other patients with poor outcomes, the brain regions generating HFOs were not removed completely.

Conclusion

The relative strength of HFO, especially fast ripples, is a promising effective biomarker for identifying the EZ and can lead to a favorable seizure outcome if used to guide epilepsy surgery.

A Magnetoencephalography Study of Pediatric Interictal Neuromagnetic Activity Changes and Brain Network Alterations Caused by Epilepsy in the High Frequency (80-1000 Hz)

More and more studies propose that high frequency brain signals are promising biomarkers of epileptogenic zone. In this paper, our aim is to investigate the neuromagnetic changes and brain network topological alterations during an interictal period at high frequency ranges (80-1000 Hz) between healthy controls and epileptic patients with Magnetoencephalography. We analyzed neuromagnetic activities with accumulated source imaging, and constructed brain network based on graph theory. Neuromagnetic activity changes and brain network alterations between two groups were analyzed in three frequency bands: ripple (80-250 Hz), fast ripples (FRs, 250-500 Hz), and very high frequency oscillations (VHFO, 500-1000 Hz). We found that epileptic patients showed significantly altered patterns of neuromagnetic source localization and altered brain network patterns. And, we also found that mean functional connectivity and the number of modules from epileptic patients significantly increased in the ripple and FRs bands, and mean clustering coefficient from epileptic patients significantly decreased in the ripple and FRs bands. We also found that the mean functional connectivity was positively correlated with duration of epilepsy in the ripple and VHFO bands, and the number of modules was positively correlated with the duration of epilepsy in the ripple, FRs, and VHFO bands. Our results indicate that epilepsy can alter patients' neuromagnetic activities and brain networks in the high-frequency ranges, and these alterations become more pathological as the duration of epilepsy grows longer.

Resting state connectivity in neocortical epilepsy: The epilepsy network as a patient-specific biomarker

OBJECTIVE

Localization related epilepsy (LRE) is increasingly accepted as a network disorder. To better understand the network specific characteristics of LRE, we defined individual epilepsy networks and compared them across patients.

METHODS

The epilepsy network was defined in the slow cortical potential frequency band in 10 patients using intracranial EEG data obtained during interictal periods. Cortical regions were included in the epilepsy network if their connectivity pattern was similar to the connectivity pattern of the seizure onset electrode contact. Patients were subdivided into frontal, temporal, and posterior quadrant cohorts according to the anatomic location of seizure onset. Jaccard similarity was calculated within each cohort to assess for similarity of the epilepsy network between patients within each cohort.

RESULTS

All patients exhibited an epilepsy network in the slow cortical potential frequency band. The topographic distribution of this correlated network activity was found to be unique at the single subject level.

CONCLUSIONS

The epilepsy network was unique at the single patient level, even between patients with similar seizure onset locations.

SIGNIFICANCE

We demonstrated that the epilepsy network is patient-specific. This is in keeping with our current understanding of brain networks and identifies the patient-specific epilepsy network as a possible biomarker in LRE.

Decision Support System for Seizure Onset Zone Localization Based on Channel Ranking and High-Frequency EEG Activity

Interictal high-frequency oscillations (HFO) are a promising biomarker that can help define the seizure onset zone (SOZ) and predict the surgical outcome after the epilepsy surgery. The utility of HFO in planning the surgery, though, is unclear. Reasons include the variability of the HFO across patients and brain regions and the influence of the sleep-wake cycle, which causes large fluctuations in the ratio between the HFO observed in SOZ and non-SOZ regions. To cope with these limitations, a rank-based solution is proposed to identify the SOZ by using the HFO in multichannel intracranial EEG. A time-varying index of the epileptic susceptibility of the different brain areas is derived from the HFO rate and a support vector machine is applied on this index to identify the SOZ. The solution is trained and tested on separate groups of patients to avoid the use of patient-specific information and provides optimal SOZ prediction using as little as 30 min of recordings per channel (window). Tested on 14 patients with various combinations of seizure type, epilepsy etiology, and SOZ arrangement (172.7 ± 90.1 h/channel per patient and 75.6 ± 23.5 channels/patient, mean ± S.D.), our solution identified the SOZ with 0.92 ± 0.03 accuracy and 0.91 ± 0.03 area under the ROC curve (mean ± S.D.) across patients. For each patient, the window onset time was varied over 72 continuous hours and the prediction of the SOZ remained insensitive to the onset time, thus showing potential for surgery planning.

Connectome-Wide Phenotypical and Genotypical Associations in Focal Dystonia

Isolated focal dystonia is a debilitating movement disorder of unknown pathophysiology. Early studies in focal dystonias have pointed to segregated changes in brain activity and connectivity. Only recently has the notion that dystonia pathophysiology may lie in abnormalities of large-scale brain networks appeared in the literature. Here, we outline a novel concept of functional connectome-wide alterations that are linked to dystonia phenotype and genotype. Using a neural community detection strategy and graph theoretical analysis of functional MRI data in human patients with the laryngeal form of dystonia (LD) and healthy controls (both males and females), we identified an abnormally widespread hub formation in LD, which particularly affected the primary sensorimotor and parietal cortices and thalamus. Left thalamic regions formed a delineated functional community that highlighted differences in network topology between LD patients with and without family history of dystonia. Conversely, marked differences in the topological organization of parietal regions were found between phenotypically different forms of LD. The interface between sporadic genotype and adductor phenotype of LD yielded four functional communities that were primarily governed by intramodular hub regions. Conversely, the interface between familial genotype and abductor phenotype was associated with numerous long-range hub nodes and an abnormal integration of left thalamus and basal ganglia. Our findings provide the first comprehensive atlas of functional topology across different phenotypes and genotypes of focal dystonia. As such, this study constitutes an important step toward defining dystonia as a large-scale network disorder, understanding its causative pathophysiology, and identifying disorder-specific markers.SIGNIFICANCE STATEMENT The architecture of the functional connectome in focal dystonia was analyzed in a large population of patients with laryngeal dystonia. Breaking with the empirical concept of dystonia as a basal ganglia disorder, we discovered large-scale alterations of neural communities that are significantly influenced by the disorder's clinical phenotype and genotype.

High frequency oscillations and high frequency functional network characteristics in the intraoperative electrocorticogram in epilepsy

OBJECTIVE

High frequency oscillations (HFOs; > 80 Hz), especially fast ripples (FRs, 250-500 Hz), are novel biomarkers for epileptogenic tissue. The pathophysiology suggests enhanced functional connectivity within FR generating tissue. Our aim was to determine the relation between brain areas showing FRs and 'baseline' functional connectivity within EEG networks, especially in the high frequency bands.

METHODS

We marked FRs, ripples (80-250 Hz) and spikes in the electrocorticogram of 14 patients with refractory temporal lobe epilepsy. We assessed 'baseline' functional connectivity in epochs free of epileptiform events within these recordings, using the phase lag index. We computed the Eigenvector Centrality (EC) per channel in the FR and gamma band network. We compared EC between channels that did or did not show events at other moments in time.

RESULTS

FR-band EC was higher in channels with than without spikes. Gamma-band EC was lower in channels with ripples and FRs.

CONCLUSIONS

We confirmed previous findings of functional isolation in the gamma-band and found a first proof of functional integration in the FR-band network of channels covering presumed epileptogenic tissue.

SIGNIFICANCE

'Baseline' high-frequency network parameters might help intra-operative recognition of epileptogenic tissue without the need for waiting for events. These findings can increase our understanding of the 'architecture' of epileptogenic networks and help unravel the pathophysiology of HFOs.

Stability of Network Communities as a Function of Task Complexity

The analysis of the community architecture in functional brain networks has revealed important relations between specific behavioral patterns and characteristic features of the associated functional organization. Numerous studies have assessed changes in functional communities during different states of awareness, learning, information processing, and various behavioral patterns. The robustness of detected communities within a network has been an often-discussed topic in complex systems research. However, our knowledge regarding the intersubject stability of functional communities in the human brain while performing different tasks is still lacking. In this study, we examined the variability of functional communities in weighted undirected graphs based on fMRI recordings of healthy participants across three conditions: the resting state, syllable production as a simple vocal motor task, and meaningful speech production representing a complex behavioral pattern with cognitive involvement. On the basis of the constructed empirical networks, we simulated a large cohort of artificial graphs and performed a leave-one-out stability analysis to assess the sensitivity of communities in the group-averaged networks with respect to perturbations in the averaging cohort. We found that the stability of partitions derived from group-averaged networks depended on task complexity. The determined community architecture in mean networks reflected within-behavior network stability and between-behavior flexibility of the human functional connectome. The sensitivity of functional communities increased from rest to syllable production to speaking, which suggests that the approximation quality of the community structure in the average network to reflect individual per-participant partitions depends on task complexity.