SEEG Functional Connectivity Measures to Identify Epileptogenic Zones: Stability, Medication Influence, and Recording Condition

Quantitative EEG signatures in patients with and without epilepsy development after a first seizure

OBJECTIVE

Diagnosing epilepsy after a first unprovoked seizure in the absence of visible epileptogenic lesions and interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is challenging. Quantitative EEG analysis and functional connectivity (FC) have shown promise in identifying patterns across epilepsy syndromes. Hence, we retrospectively investigated whether there were differences in FC (imaginary part of coherency) and spectral band power in non-lesional, IED-free, unmedicated patients after a first unprovoked seizure in contrast to controls. Further, we investigated if there were differences between the patients who developed epilepsy and those who remained with a single seizure for at least 6 months after the first seizure.

METHODS

We used 240 s of resting-state EEG (19 channels) recordings of patients (n = 41) after a first unprovoked seizure and age and sex-matched healthy controls (n = 46). Twenty-one patients developed epilepsy (epilepsy group), while 20 had no further seizures during follow-up (single-seizure group). We computed source-reconstructed power and FC in five frequency bands (1 ± 29 Hz). Group differences were assessed using permutation analysis of linear models.

RESULTS

Patients who developed epilepsy showed increased theta power and FC, increased delta power, and decreased delta FC compared to healthy controls. The single-seizure group exhibited reduced beta-1 FC relative to the control group. In comparison with the single-seizure group, patients with epilepsy demonstrated elevated delta and theta power and decreased delta FC.

SIGNIFICANCE

Source-reconstructed data from routine EEGs identified distinct network patterns between non-lesional, IED-free, unmedicated patients who developed epilepsy and those who remained with a single seizure. Increased delta and theta power, along with decreased delta FC, could be a potential epilepsy biomarker. Further, decreases in beta-1 FC after a single seizure may point toward a protective mechanism for patients without further seizures.

PLAIN LANGUAGE SUMMARY

After a first seizure, some people develop epilepsy, while others do not. We looked at brain activity in people who had a seizure but showed no clear signs of epilepsy. By comparing those who later developed epilepsy to those who did not, we found that certain slow brain wave patterns (delta and theta) might indicate a higher risk of developing epilepsy. This could help doctors identify high-risk patients sooner.

EEG signature orchestrating expression of ictal behavior in mesial temporal lobe epilepsy

OBJECTIVES

We investigated EEG features differentiating clinical seizures (CSs) from subclinical seizures (SCSs) to explore the mechanisms underlying the generation of ictal behavior in mesial temporal lobe epilepsy (mTLE).

METHODS

Peri-ictal state of power spectral density (PSD) within seizure onset zone (SOZ) and propagation zone (PZ) were compared between SCSs and CSs. Functional connectivity was analyzed using the nonlinear correlation coefficient h2, outgoing links (OUT) and ingoing links (IN). The EEG epochs of CSs-early part and SCSs were equally divided into four segments to reveal dynamic EEG changes.

RESULTS

During pre-ictal state, PSD at 30-80 Hz in SOZ was higher in CSs than in SCSs. The preictal OUT and IN values in SOZ at 30-80 Hz were greater in CSs than in SCSs. During CSs-early part, PSD displayed an initial increase in SOZ but a late increase in PZ, with enhanced high-frequency activity in temporal regions and increased low-frequency activity in insula.

CONCLUSION

The enhanced pre-ictal gamma activity within the epileptic network was able to distinguish CSs from SCSs. The unique temporospatial alterations within the epileptic network drive the expression of ictal behavior in mTLE.

SIGNIFICANCE

The distinct EEG features between SCSs and CSs offer transformative insights into the mechanisms driving ictal behavior.

Dynamics of magnetic cortico-cortical responses evoked by single-pulse electrical stimulation

OBJECTIVE

Intracranial single-pulse electrical stimulation (SPES) can elicit cortico-cortical evoked potentials. Their investigation with intracranial EEG is biased by the limited number and selected location of electrodes, which could be circumvented by simultaneous non-invasive whole-scalp recording. This study aimed at investigating the ability of magnetoencephalography (MEG) to characterize cortico-cortical evoked fields (CCEFs) and effective connectivity between the epileptogenic zone (EZ) and non-epileptogenic zone (i.e., non-involved [NIZ]).

METHODS

A total of 301 SPES trains (at 0.9 Hz during 120 s) were performed in 10 patients with refractory focal epilepsy. MEG signals were denoised, epoched, averaged, and decomposed using independent component analysis. Significant response deflections and significant source generators were detected. Peak latency/amplitude were compared between each different cortical/subcortical structure of the NIZ containing more than five SPES, and then between the EZ and corresponding brain structures in the NIZ.

RESULTS

MEG detected and localized polymorphic/polyphasic CCEFs, including one to eight significant consecutive deflections. The latency and amplitude of CCEFs within the NIZ differed significantly depending on the stimulated brain structure. Compared with the corresponding NIZ, SPES within the extratemporal EZ demonstrated delayed CCEF latency, whereas SPES within the temporal EZ showed decreased CCEF amplitude. SPES within the EZ elicited a significantly higher rate of CCEFs within the stimulated lobe compared with those within the NIZ.

SIGNIFICANCE

This study reveals polymorphic CCEFs with complex spatiotemporal dynamics both within the NIZ and EZ. It highlights significant differences in effective connectivity of the epileptogenic network. These cortico-cortical evoked responses could thus contribute to increasing the yield of intracranial recordings.

Variability of cortico-cortical evoked potentials in the epileptogenic zone is related to seizure occurrence

INTRODUCTION

Cortico-cortical evoked potentials (CCEPs) were described as reproducible during trains of single-pulse electrical stimulations (SPES). Still, few studies described a variability of CCEPs that was higher within the epileptogenic zone (EZ). This study aimed at characterizing the relationship of CCEP variability with the occurrence of interictal/ictal epileptiform discharges at the temporal vicinity of the stimulation, but not during the stimulation, by effective connectivity modifications.

METHODS

We retrospectively included 20 patients who underwent SPES during their stereo-electroencephalography (SEEG). We analyzed the variability of CCEPs by using the post-stimulation time course of intertrial standard deviation (amplitude) and the timing of peak amplitude signal of CCEP epochs (latency). Values were corrected for the Euclidian distance between stimulating/recording electrodes. Receiver operating characteristics curves were used to assess the relationship with the EZ. The link between CCEP variability and interictal discharges occurrence, seizure frequency prior to the SEEG recording, and number of seizures during SEEG recording was assessed with Spearman's correlations.

RESULTS

A relationship was demonstrated between the EZ and both the distance-corrected latency variation (area under the curve (AUC): 0.73-0.74) and the distance-corrected amplitude variation (AUC: 0.71-0.72) and both were related with the occurrence of seizures.

CONCLUSION

Seizures before/during SEEG impact the dynamics of effective connectivity within the epileptogenic network by reducing the variability of CCEP latency/amplitude when the seizure frequency increases. It suggests a strengthening of the epileptogenic network with the occurrence of many seizures. These findings stress the importance of early epilepsy surgery at a time when the network organization has not yet been complete.

Impact of transcranial electrical stimulation on simultaneous stereoelectroencephalography recordings: A randomized sham-controlled study

OBJECTIVE

Clinical exploitation of transcranial electrical stimulation for focal epilepsy treatment lacks quantification of the underlying neurophysiological changes. This study explores the immediate effects of transcranial alternating (tACS) and direct (tDCS) current stimulation on local and network brain activity using simultaneous stereoelectroencephalography (SEEG) recordings.

METHODS

Patients were randomized for personalized tACS (n = 5) or tDCS (n = 6). Active stimulation (20 min) was preceded by sham stimulation (20 min). Changes in interictal epileptiform discharges (IED), functional connectivity (FC) and power spectral density (PSD) were quantified against baseline.

RESULTS

Results demonstrated variable responses. Spike rate decreased in 2/6 subjects following sham and tDCS, while 2/6 showed an increase. Alpha power and aperiodic PSD components generally increased during and after tDCS but decreased following tACS. FC changes varied among subjects and were detectable even during sham sessions.

CONCLUSIONS

Strong variability suggests that tES does not have a univocal effect on immediate changes in IED or FC, possibly due to the single session format and challenges in affecting subcortical areas.

SIGNIFICANCE

This is the first study to examine intracranial FC changes during tACS and tDCS, highlighting the importance of sham comparisons and individual variability in tES response, offering valuable insights into its application for epilepsy treatment.

The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy

Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8-13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment.

SEEG4D: a tool for 4D visualization of stereoelectroencephalography data

Epilepsy is a prevalent and serious neurological condition which impacts millions of people worldwide. Stereoelectroencephalography (sEEG) is used in cases of drug resistant epilepsy to aid in surgical resection planning due to its high spatial resolution and ability to visualize seizure onset zones. For accurate localization of the seizure focus, sEEG studies combine pre-implantation magnetic resonance imaging, post-implant computed tomography to visualize electrodes, and temporally recorded sEEG electrophysiological data. Many tools exist to assist in merging multimodal spatial information; however, few allow for an integrated spatiotemporal view of the electrical activity. In the current work, we present SEEG4D, an automated tool to merge spatial and temporal data into a complete, four-dimensional virtual reality (VR) object with temporal electrophysiology that enables the simultaneous viewing of anatomy and seizure activity for seizure localization and presurgical planning. We developed an automated, containerized pipeline to segment tissues and electrode contacts. Contacts are aligned with electrical activity and then animated based on relative power. SEEG4D generates models which can be loaded into VR platforms for viewing and planning with the surgical team. Automated contact segmentation locations are within 1 mm of trained raters and models generated show signal propagation along electrodes. Critically, spatial-temporal information communicated through our models in a VR space have potential to enhance sEEG pre-surgical planning.

Predicting radiofrequency thermocoagulation surgical outcomes in refractory focal epilepsy patients using functional coupled neural mass model

Objective

The success rate of achieving seizure freedom after radiofrequency thermocoagulation surgery for patients with refractory focal epilepsy is about 20-40%. This study aims to enhance the prediction of surgical outcomes based on preoperative decisions through network model simulation, providing a reference for clinicians to validate and optimize surgical plans.

Methods

Twelve patients with epilepsy who underwent radiofrequency thermocoagulation were retrospectively reviewed in this study. A coupled model based on model subsets of the neural mass model was constructed by calculating partial directed coherence as the coupling matrix from stereoelectroencephalography (SEEG) signals. Multi-channel time-varying model parameters of excitation and inhibitions were identified by fitting the real SEEG signals with the coupled model. Further incorporating these model parameters, the coupled model virtually removed contacts destroyed in radiofrequency thermocoagulation or selected randomly. Subsequently, the coupled model after virtual surgery was simulated.

Results

The identified excitatory and inhibitory parameters showed significant difference before and after seizure onset (p < 0.05), and the trends of parameter changes aligned with the seizure process. Additionally, excitatory parameters of epileptogenic contacts were higher than that of non-epileptogenic contacts, and opposite findings were noticed for inhibitory parameters. The simulated signals of postoperative models to predict surgical outcomes yielded an area under the curve (AUC) of 83.33% and an accuracy of 91.67%.

Conclusion

The multi-channel coupled model proposed in this study with physiological characteristics showed a desirable performance for preoperatively predicting patients' prognoses.

Using scalp EEG to predict seizure recurrence and electrical status epilepticus in children with idiopathic focal epilepsy

PURPOSE

Some individuals with idiopathic focal epilepsy (IFE) experience recurring seizures accompanied by the evolution of electrical status epilepticus during sleep (ESES). Here, we aimed to develop a predictor for the early detection of seizure recurrence with ESES in children with IFE using resting state electroencephalogram (EEG) data.

METHODS

The study group included 15 IFE patients who developed seizure recurrence with ESES. There were 17 children in the control group who did not experience seizure recurrence with ESES during at least 2-year follow-up. We used the degree value of the partial directed coherence (PDC) from the EEG data to predict seizure recurrence with ESES via 6 machine learning (ML) algorithms.

RESULTS

Among the models, the Xgboost Classifier (XGBC) model achieved the highest specificity of 0.90, and a remarkable sensitivity and accuracy of 0.80 and 0.85, respectively. The CATC showed balanced performance with a specificity of 0.85, sensitivity of 0.73, and an accuracy of 0.80, with an AUC equal to 0.78. For both of these models, F4, Fz and T4 were the overlaps of the top 4 features.

CONCLUSIONS

Considering its high classification accuracy, the XGBC model is an effective and quantitative tool for predicting seizure recurrence with ESES evolution in IFE patients. We developed an ML-based tool for predicting the development of IFE using resting state EEG data. This could facilitate the diagnosis and treatment of patients with IFE.

Imaging and Stereotactic Electroencephalography Functional Networks to Guide Epilepsy Surgery

Epilepsy surgery is a potentially curative treatment of drug-resistant epilepsy that has remained underutilized both due to inadequate referrals and incomplete localization hypotheses. The complexity of patients evaluated for epilepsy surgery has increased, thus new approaches are necessary to treat these patients. The paradigm of epilepsy surgery has evolved to match this challenge, now considering the entire seizure network with the goal of disrupting it through resection, ablation, neuromodulation, or a combination. The network paradigm has the potential to aid in identification of the seizure network as well as treatment selection.

Complementary structural and functional abnormalities to localise epileptogenic tissue

BACKGROUND

When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy.

METHODS

We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study.

FINDINGS

Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p = 0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients.

INTERPRETATION

Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations.

FUNDING

This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.

Complementary structural and functional abnormalities to localise epileptogenic tissue

Background

When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy.

Methods

We retrospectively investigated data from 43 patients with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study.

Findings

Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p=0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients.

Interpretation

Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations.

Funding

This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes' nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II-IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47-100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.

Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG

Introduction: Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures, and also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely functional connectivity (FC). Results: FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. Significance: This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. Aim: In this article, we review the available data concerning interictal FC estimated from intracranial electroencephalograhy (EEG) in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG imaging) and modeling studies.