AnyWave: a cross-platform and modular software for visualizing and processing electrophysiological signals

Sentence superiority in the reading brain

When a sequence of written words is briefly presented and participants are asked to identify just one word at a post-cued location, then word identification accuracy is higher when the word is presented in a grammatically correct sequence compared with an ungrammatical sequence. This sentence superiority effect has been reported in several behavioral studies and two EEG investigations. Taken together, the results of these studies support the hypothesis that the sentence superiority effect is primarily driven by rapid access to a sentence-level representation via partial word identification processes that operate in parallel over several words. Here we used MEG to examine the neural structures involved in this early stage of written sentence processing, and to further specify the timing of the different processes involved. Source activities over time showed grammatical vs. ungrammatical differences first in the left inferior frontal gyrus (IFG: 321-406 ms), then the left anterior temporal lobe (ATL: 466-531 ms), and finally in both left IFG (549-602 ms) and left posterior superior temporal gyrus (pSTG: 553-622 ms). We interpret the early IFG activity as reflecting the rapid bottom-up activation of sentence-level representations, including syntax, enabled by partly parallel word processing. Subsequent activity in ATL and pSTG is thought to reflect the constraints imposed by such sentence-level representations on on-going word-based semantic activation (ATL), and the subsequent development of a more detailed sentence-level representation (pSTG). These results provide further support for a cascaded interactive-activation account of sentence reading.

Modification of brain conductivity in human focal epilepsy: A model-based estimation from stereoelectroencephalography

OBJECTIVE

We have developed a novel method for estimating brain tissue electrical conductivity using low-intensity pulse stereoelectroencephalography (SEEG) stimulation coupled with biophysical modeling. We evaluated the hypothesis that brain conductivity is correlated with the degree of epileptogenicity in patients with drug-resistant focal epilepsy.

METHODS

We used bipolar low-intensity biphasic pulse stimulation (.2 mA) followed by a postprocessing pipeline for estimating brain conductivity. This processing is based on biophysical modeling of the electrical potential induced in brain tissue between the stimulated contacts in response to pulse stimulation. We estimated the degree of epileptogenicity using a semi-automatic method quantifying the dynamic of fast discharge at seizure onset: the epileptogenicity index (EI). We also investigated how the location of stimulation within specific anatomical brain regions or within lesional tissue impacts brain conductivity.

RESULTS

We performed 1034 stimulations of 511 bipolar channels in 16 patients. We found that brain conductivity was lower in the epileptogenic zone (EZ; unpaired median difference = .064, p < .001) and inversely correlated with the epileptogenic index value (p < .001, Spearman rho = -.32). Conductivity values were also influenced by anatomical site, location within lesion, and delay between SEEG electrode implantation and stimulation, and had significant interpatient variability. Mixed model multivariate analysis showed that conductivity is significantly associated with EI (F = 13.45, p < .001), anatomical regions (F = 5.586, p < .001), delay since implantation (F = 14.71, p = .003), and age at SEEG (F = 6.591, p = .027), but not with the type of lesion (F = .372, p = .773) or the delay since last seizure (F = 1.592, p = .235).

SIGNIFICANCE

We provide a novel model-based method for estimating brain conductivity from SEEG low-intensity pulse stimulations. The brain tissue conductivity is lower in EZ as compared to non-EZ. Conductivity also varies significantly across anatomical brain regions. Involved pathophysiological processes may include changes in the extracellular space (especially volume or tortuosity) in epileptic tissue.

How fast does the brain recover after an epileptic seizure?

The postictal state, an abnormal cerebral condition following a seizure until the return to the interictal baseline, is frequently overlooked, despite often exceeding ictal duration and significantly impacting patients' lives. This study analyzes stereo-EEG (SEEG) signal dynamics using permutation entropy to quantify recovery time (postictal alteration time - PAT) in focal epilepsy and its clinical correlations. The average PAT was 4.5 min, extending up to an hour and was highest in temporal epilepsy and hippocampal sclerosis. Correlating with age at seizure onset and at SEEG, PAT provides a solution for operationally defining the postictal state and guiding interventions.

Stereo-EEG-based ictal functional connectivity in patients with periventricular nodular heterotopia-related epilepsy

Nodular heterotopia (NH)-related drug-resistant epilepsy is challenging due to the deep location of the NH and the complexity of the underlying epileptogenic network. Using ictal stereo-electroencephalography (SEEG) and functional connectivity (FC) analyses in 14 patients with NH-related drug-resistant epilepsy, we aimed to determine the leading structure during seizures. For this purpose, we compared node IN and OUT strength between bipolar channels inside the heterotopia and inside gray matter, at the group level and at the individual level. At seizure onset, the channels within NH belonging to the epileptogenic and/or propagation network showed higher node OUT-strength than the channels within the gray matter (p = .03), with higher node OUT-strength than node IN-strength (p = .03). These results are in favor of a "leading" role of NH during seizure onset when involved in the epileptogenic- or propagation-zone network (50% of patients). However, when looking at the individual level, no significant difference between NH and gray matter was found, except for one patient (in two of three seizures). This result confirms the heterogeneity and the complexity of the epileptogenic network organization in NH and the need for SEEG exploration to characterize more precisely patient-specific epileptogenic network organization.

How Can a Focal Seizure Lead to a Dacrystic Behavior? A Case Analyzed with Functional Connectivity in Stereoelectroencephalography

We present a case of a patient with focal non-motor emotional seizures with dacrystic expression in the context of drug-resistant magnetic resonance imaging negative epilepsy. The pre-surgical evaluation suggested a hypothesis of a right fronto-temporal epileptogenic zone. Stereoelectroencephalography recorded dacrystic seizures arising from the right anterior operculo-insular (pars orbitalis) area with secondary propagation to temporal and parietal cortices during the dacrystic behavior. We analyzed functional connectivity during the ictal dacrystic behavior and found an increase of the functional connectivity within a large right fronto-temporo-insular network, broadly similar to the "emotional excitatory" network. It suggests that focal seizure, potentially, from various origins but leading to disorganization of these physiological networks may generate dacrystic behavior.

Permutation entropy-derived parameters to estimate the epileptogenic zone network

OBJECTIVE

Quantification of the epileptogenic zone network (EZN) most frequently implies analysis of seizure onset. However, important information can also be obtained from the postictal period, characterized by prominent changes in the EZN. We used permutation entropy (PE), a measure of signal complexity, to analyze the peri-ictal stereoelectroencephalography (SEEG) signal changes with emphasis on the postictal state. We sought to determine the best PE-derived parameter (PEDP) for identifying the EZN.

METHODS

Several PEDPs were computed retrospectively on SEEG-recorded seizures of 86 patients operated on for drug-resistant epilepsy: mean baseline preictal entropy, minimum ictal entropy, maximum postictal entropy, the ratio between the maximum postictal and the minimum ictal entropy, and the ratio between the maximum postictal and the baseline preictal entropy. The performance of each biomarker was assessed by comparing the identified epileptogenic contacts or brain regions against the EZN defined by clinical analysis incorporating the Epileptogenicity Index and the connectivity epileptogenicity index methods (EZNc), using the receiver-operating characteristic and precision-recall.

RESULTS

The ratio between the maximum postictal and the minimum ictal entropy (defined as the Permutation Entropy Index [PEI]) proved to be the best-performing PEDP to identify the EZNC . It demonstrated the highest area under the curve (AUC) and F1 score at the contact level (AUC 0.72; F1 0.39) and at the region level (AUC 0.78; F1 0.47). PEI values gradually decreased between the EZN, the propagation network, and the non-involved regions. PEI showed higher performance in patients with slow seizure-onset patterns than in those with fast seizure-onset patterns. The percentage of resected epileptogenic regions defined by PEI was significantly correlated with surgical outcome.

SIGNIFICANCE

PEI is a promising tool to improve the delineation of the EZN. PEI combines ease and robustness in a routine clinical setting with high sensitivity for seizures without fast activity at seizure onset.

Acute Effect of Vagus Nerve Stimulation in Patients with Drug-Resistant Epilepsy: A Preliminary Exploration via Stereoelectroencephalogram

As the pathophysiological mechanisms of vagus nerve stimulation (VNS) causing individual differences in the vagal ascending network remains unclear, stereoelectroencephalography (SEEG) provides a unique platform to explore the brain networks affected by VNS and helps to understand the anti-seizure mechanism of VNS more comprehensively. This study presents a preliminary exploration of the acute effect of VNS. SEEG signals were collected to assess the acute effect of VNS on neural synchronization in patients with drug-resistant epilepsy, especially in epileptogenic networks. The results show that the better the efficacy of VNS, the wider the spread of desynchronization assessed by weighted phase lag index at a high frequency band caused by VNS. Future studies should focus on the association between the change in synchronization and the efficacy of VNS, exploring the possibility of synchronization as a biomarker for patient screening and parameter programming.

Dynamic evolution of the anterior cingulate-insula network during seizures

OBJECTIVES

In physiological situations, the anterior cingulate cortex (ACC) and anterior insular cortex (AIC) are prone to coactivation. The functional connectivity and interaction between ACC and AIC in the context of epilepsy remain unclear. This study aimed to investigate the dynamic coupling between these two brain regions during seizures.

METHODS

Patients who underwent stereoelectroencephalography (SEEG) recording were included in this study. The SEEG data were visually inspected and quantitatively analyzed. The narrowband oscillations and aperiodic components at seizure onset were parameterized. The frequency-specific non-linear correlation analysis was applied to the functional connectivity. The excitation/inhibition ratio (E:I ratio) reflected by the aperiodic slope was performed to evaluate the excitability.

RESULTS

Twenty patients were included in the study, with 10 diagnosed with anterior cingulate epilepsy and 10 with anterior insular epilepsy. In both types of epilepsy, the correlation coefficient (h2 ) between the ACC and AIC at seizure onset exhibited a significantly higher value than that during interictal and preictal periods (p < 0.05). The direction index (D) showed a significant increase at seizure onset, serving as an indicator for the direction of information flow between these two brain regions with up to 90% accuracy. The E:I ratio increased significantly at seizure onset, with the seizure-onset zone (SOZ) demonstrating a more pronounced increase compared to non-SOZ (p < 0.05). For seizures originating from AIC, the E:I ratio was significantly higher in the AIC than in the ACC (p = 0.0364).

CONCLUSIONS

In the context of epilepsy, the ACC and AIC are dynamically coupled during seizures. The functional connectivity and excitability exhibit a significant increase at seizure onset. By analyzing connectivity and excitability, the SOZ in ACC and AIC can be identified. The direction index (D) serves as an indicator for the direction of information flow from SOZ to non-SOZ. Notably, the excitability of SOZ changes more significantly than that of non-SOZ.

The role of quantitative markers in surgical prognostication after stereoelectroencephalography

OBJECTIVE

Stereoelectroencephalography (SEEG) is the reference method in the presurgical exploration of drug-resistant focal epilepsy. However, prognosticating surgery on an individual level is difficult. A quantified estimation of the most epileptogenic regions by searching for relevant biomarkers can be proposed for this purpose. We investigated the performances of ictal (Epileptogenicity Index, EI; Connectivity EI, cEI), interictal (spikes, high-frequency oscillations, HFO [80-300 Hz]; Spikes × HFO), and combined (Spikes × EI; Spikes × cEI) biomarkers in predicting surgical outcome and searched for prognostic factors based on SEEG-signal quantification.

METHODS

Fifty-three patients operated on following SEEG were included. We compared, using precision-recall, the epileptogenic zone quantified using different biomarkers (EZq ) against the visual analysis (EZC ). Correlations between the EZ resection rates or the EZ extent and surgical prognosis were analyzed.

RESULTS

EI and Spikes × EI showed the best precision against EZc (0.74; 0.70), followed by Spikes × cEI and cEI, whereas interictal markers showed lower precision. The EZ resection rates were greater in seizure-free than in non-seizure-free patients for the EZ defined by ictal biomarkers and were correlated with the outcome for EI and Spikes × EI. No such correlation was found for interictal markers. The extent of the quantified EZ did not correlate with the prognosis.

INTERPRETATION

Ictal or combined ictal-interictal markers overperformed the interictal markers both for detecting the EZ and predicting seizure freedom. Combining ictal and interictal epileptogenicity markers improves detection accuracy. Resection rates of the quantified EZ using ictal markers were the only statistically significant determinants for surgical prognosis.

Localizing epileptogenic zones with high-frequency oscillations and directed connectivity

PURPOSE

Precise localization of the epileptogenic zone (EZ) is essential for epilepsy surgery. Existing methods often fail to detect slow onset patterns or similar neural activities presented in the recorded signals. To address this issue, we propose a new measure to quantify epileptogenicity, i.e., the connectivity high-frequency epileptogenicity index (cHFEI).

METHODS

The cHFEI method combines directed connectivity and high-frequency oscillations (HFOs) to measure the epileptogenicity of regions involved in a brain network. By applying this method to stereoelectroencephalography (SEEG) recordings of 49 seizures in 20 patients, we calculated the accuracy, sensitivity, and precision with a visually identified epileptogenic zone as a reference. The performance was evaluated by the confusion matrix and the area under the receiver operating characteristic (ROC) curve.

RESULTS

Epileptic network estimation based on cHFEI successfully distinguished brain regions involved in seizure onset from the propagation network. Moreover, cHFEI outperformed other existing detection methods in the estimation of EZs in all patients, with an average area under the ROC curve of 0.88 and an accuracy of 0.85.

CONCLUSIONS

cHFEI can characterize EZ in a robust manner despite various seizure onset patterns and has potential application in epilepsy therapy.

The Differing Effects of Sleep on Ictal and Interictal Network Dynamics in Drug-Resistant Epilepsy

OBJECTIVE

Sleep has important influences on focal interictal epileptiform discharges (IEDs), and the rates and spatial extent of IEDs are increased in non-rapid eye movement (NREM) sleep. In contrast, the influence of sleep on seizures is less clear, and its effects on seizure topography are poorly documented. We evaluated the influences of NREM sleep on ictal spatiotemporal dynamics and contrasted these with interictal network dynamics.

METHODS

We included patients with drug-resistant focal epilepsy who underwent continuous intracranial electroencephalography (iEEG) with depth electrodes. Patients were selected if they had 1 to 3 seizures from each vigilance state, wakefulness and NREM sleep, within a 48-hour window, and under the same antiseizure medication. A 10-minute epoch of the interictal iEEG was selected per state, and IEDs were detected automatically. A total of 25 patients (13 women; aged 32.5 ± 7.1 years) were included.

RESULTS

The seizure onset pattern, duration, spatiotemporal propagation, and latency of ictal high-frequency activity did not differ significantly between wakefulness and NREM sleep (all p > 0.05). In contrast, IED rates and spatial distribution were increased in NREM compared with wakefulness (p < 0.001, Cliff's d = 0.48 and 0.49). The spatial overlap between vigilance states was higher for seizures (57.1 ± 40.1%) than IEDs (41.7 ± 46.2%; p = 0.001, Cliff's d = 0.51).

INTERPRETATION

In contrast to its effects on IEDs, NREM sleep does not affect ictal spatiotemporal dynamics. This suggests that once the brain surpasses the seizure threshold, it will follow the underlying epileptic network irrespective of the vigilance state. These findings offer valuable insights into neural network dynamics in epilepsy and have important clinical implications for localizing seizure foci. ANN NEUROL 2023.

Pre-ictal fluctuation of EEG functional connectivity discriminates seizure phenotypes in mesial temporal lobe epilepsy

OBJECTIVE

We explored whether quantifiable differences between clinical seizures (CSs) and subclinical seizures (SCSs) occur in the pre-ictal state.

METHODS

We analyzed pre-ictal stereo-electroencephalography (SEEG) retrospectively across mesial temporal lobe epilepsy patients with recorded CSs and SCSs. Power spectral density and functional connectivity (FC) were quantified within and between the seizure onset zone (SOZ) and the early propagation zone (PZ), respectively. To evaluate the fluctuation of neural connectivity, FC variability was computed. Measures were further verified by a logistic regression model to evaluate their classification potentiality through the area under the receiver-operating-characteristics curve (AUC).

RESULTS

Fifty-four pre-ictal SEEG epochs (27 CSs and 27 SCSs) were selected among 14 patients. Within the SOZ, pre-ictal FC variability of CSs was larger than SCSs in 1-45 Hz during 30 seconds before seizure onset. Pre-ictal FC variability between the SOZ and PZ was larger in SCSs than CSs in 55-80 Hz within 1 minute before onset. Using these two variables, the logistic regression model achieved an AUC of 0.79 when classifying CSs and SCSs.

CONCLUSIONS

Pre-ictal FC variability within/between epileptic zones, not signal power or FC value, distinguished SCSs from CSs.

SIGNIFICANCE

Pre-ictal epileptic network stability possibly marks seizure phenotypes, contributing insights into ictogenesis and potentially helping seizure prediction.

The anterior and pulvinar thalamic nuclei interactions in mesial temporal lobe seizure networks

OBJECTIVE

To evaluate the respective roles of the anterior thalamic nucleus (ANT) and the medial pulvinar (PuM) during mesial temporal lobe seizures recorded by stereoelectroencephalography (SEEG).

METHODS

We assessed functional connectivity (FC) in 15 SEEG recorded seizures from 6 patients using a non-linear correlation method. Functional interactions were explored between the mesial temporal region, the temporal neocortex, ANT and PuM. The node total-strength (the summed connectivity of the node with all other nodes) as well as the directionality of the links (IN and OUT strengths) were calculated to estimate drivers and receivers during the cortico-thalamic interactions.

RESULTS

Significant increased thalamo-cortical FC during seizures was observed, with the node total-strength reaching a maximum at seizure end. There was no significant difference in global connectivity values between ANT and PuM. Regarding directionality, significantly higher thalamic IN strength values were observed. However, compared to ANT, PuM appeared to be the driver at the end of seizures with synchronous termination.

CONCLUSIONS

This work demonstrates that during temporal seizures, both thalamic nuclei are highly connected with the mesial temporal region and that PuM could play a role in seizure termination.

SIGNIFICANCE

Understanding functional connectivity between the mesial temporal and thalamic nuclei could contribute to the development of target-specific deep brain stimulation strategies for drug-resistant epilepsy.

Studying memory processes at different levels with simultaneous depth and surface EEG recordings

Investigating cognitive brain functions using non-invasive electrophysiology can be challenging due to the particularities of the task-related EEG activity, the depth of the activated brain areas, and the extent of the networks involved. Stereoelectroencephalographic (SEEG) investigations in patients with drug-resistant epilepsy offer an extraordinary opportunity to validate information derived from non-invasive recordings at macro-scales. The SEEG approach can provide brain activity with high spatial specificity during tasks that target specific cognitive processes (e.g., memory). Full validation is possible only when performing simultaneous scalp SEEG recordings, which allows recording signals in the exact same brain state. This is the approach we have taken in 12 subjects performing a visual memory task that requires the recognition of previously viewed objects. The intracranial signals on 965 contact pairs have been compared to 391 simultaneously recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. By analyzing a joint scalp and SEEG dataset, we investigated whether the two types of signals carry complementary information that might improve the machine-learning classifier performance. This joint analysis revealed that the results are driven by the modality exhibiting best individual performance, namely SEEG.

Reconstruction and localization of auditory sources from intracerebral SEEG using independent component analysis

Stereo-electroencephalography (SEEG) is the surgical implantation of electrodes in the brain to better localize the epileptic network in pharmaco-resistant epileptic patients. This technique has exquisite spatial and temporal resolution. Still, the number and the position of the electrodes in the brain is limited and determined by the semiology and/or preliminary non-invasive examinations, leading to a large number of unexplored brain structures in each patient. Here, we propose a new approach to reconstruct the activity of non-sampled structures in SEEG, based on independent component analysis (ICA) and dipole source localization. We have tested this approach with an auditory stimulation dataset in ten patients. The activity directly recorded from the auditory cortex served as ground truth and was compared to the ICA applied on all non-auditory electrodes. Our results show that the activity from the auditory cortex can be reconstructed at the single trial level from contacts as far as ∼40 mm from the source. Importantly, this reconstructed activity is localized via dipole fitting in the proximity of the original source. In addition, we show that the size of the confidence interval of the dipole fitting is a good indicator of the reliability of the result, which depends on the geometry of the SEEG implantation. Overall, our approach allows reconstructing the activity of structures far from the electrode locations, partially overcoming the spatial sampling limitation of intracerebral recordings.

Comparison of beamformer and ICA for dynamic connectivity analysis: A simultaneous MEG-SEEG study

Magnetoencephalography (MEG) is a powerful tool for estimating brain connectivity with both good spatial and temporal resolution. It is particularly helpful in epilepsy to characterize non-invasively the epileptic networks. However, using MEG to map brain networks requires solving a difficult inverse problem that introduces uncertainty in the activity localization and connectivity measures. Our goal here was to compare independent component analysis (ICA) followed by dipole source localization and the linearly constrained minimum-variance beamformer (LCMV-BF) for characterizing regions with interictal epileptic activity and their dynamic connectivity. After a simulation study, we compared ICA and LCMV-BF results with intracerebral EEG (stereotaxic EEG, SEEG) recorded simultaneously in 8 epileptic patients, which provide a unique 'ground truth' to which non-invasive results can be confronted. We compared the signal time courses extracted applying ICA and LCMV-BF on MEG data to that of SEEG, both for the actual signals and the dynamic connectivity computed using cross-correlation (evolution of links in time). With our simulations, we illustrated the different effect of the temporal and spatial correlation among sources on the two methods. While ICA was more affected by the temporal correlation but robust against spatial configurations, LCMV-BF showed opposite behavior. Moreover, ICA seems more suited to retrieve the simulated networks. In case of real patient data, good MEG/SEEG correlation and good localization were obtained in 6 out of 8 patients. In 4 of them ICA had the best performance (higher correlation, lower localization distance). In terms of dynamic connectivity, the evolution in time of the cross-correlation links could be retrieved in 5 patients out of 6, however, with more variable results in terms of correlation and distance. In two patients LCMV-BF had better results than ICA. In one patient the two methods showed equally good outcomes, and in the remaining two patients ICA performed best. In conclusion, our results obtained by exploiting simultaneous MEG/SEEG recordings suggest that ICA and LCMV-BF have complementary qualities for retrieving the dynamics of interictal sources and their network interactions.

Ictal EEG desynchronization during low-voltage fast activity for prediction of surgical outcomes in focal epilepsy

OBJECTIVE

The authors investigated alterations in functional connectivity (FC) and EEG power during ictal onset patterns of low-voltage fast activity (LVFA) in drug-resistant focal epilepsy. They hypothesized that such changes would be useful to classify epilepsy surgical outcomes.

METHODS

In a cohort of 79 patients with drug-resistant focal epilepsy who underwent stereoelectroencephalography (SEEG) evaluation as well as resective surgery, FC changes during the peri-LVFA period were measured using nonlinear regression (h2) and power spectral properties within/between three regions: the seizure onset zone (SOZ), early propagation zone (PZ), and noninvolved zone (NIZ). Desynchronization and power desynchronization h2 indices were calculated to assess the degree of EEG desynchronization during LVFA. Multivariate logistic regression was employed to control for confounding factors. Finally, receiver operating characteristic curves were generated to evaluate the performance of desynchronization indices in predicting surgical outcome.

RESULTS

Fifty-three patients showed ictal LVFA and distinct zones of the SOZ, PZ, and NIZ. Among them, 39 patients (73.6%) achieved seizure freedom by the final follow-up. EEG desynchronization, measured by h2 analysis, was found in the seizure-free group during LVFA: FC decreased within the SOZ and between regions compared with the pre-LVFA and post-LVFA periods. In contrast, the non-seizure-free group showed no prominent EEG desynchronization. The h2 desynchronization index, but not the power desynchronization index, enabled classification of seizure-free versus non-seizure-free patients after resective surgery.

CONCLUSIONS

EEG desynchronization during the peri-LVFA period, measured by within-zone and between-zone h2 analysis, may be helpful for identifying patients with favorable postsurgical outcomes and also may potentially improve epileptogenic zone identification in the future.

Virtual MEG sensors based on beamformer and independent component analysis can reconstruct epileptic activity as measured on simultaneous intracerebral recordings

The prevailing gold standard for presurgical determination of epileptogenic brain networks is intracerebral EEG, a potent yet invasive approach. Magnetoencephalography (MEG) is a state-of-the art non-invasive method for investigating epileptiform discharges. However, it is not clear at what level the precision offered by MEG can reach that of SEEG. Here, we present a strategy for non-invasively retrieving the constituents of the interictal network, with high spatial and temporal precision. Our method is based on MEG and a combination of spatial filtering and independent component analysis (ICA). We validated this approach in twelve patients with drug-resistant focal epilepsy, thanks to the unprecedented ground truth provided by simultaneous recordings of MEG and SEEG. A minimum variance adaptive beamformer estimated the source time series and ICA was used to further decompose these time series into network constituents (MEG-ICs), each having a time series (virtual electrode) and a topography (spatial distribution of amplitudes in the brain). We show that MEG has a considerable sensitivity of 0.80 and 0.84 and a specificity of 0.93 and 0.91 for reconstructing deep and superficial sources, respectively, when compared to the ground truth (SEEG). For each epileptic MEG-IC (n = 131), we found at least one significantly correlating SEEG contact close to zero lag after correcting for multiple comparisons. All the patients except one had at least one epileptic component that was highly correlated (Spearman rho>0.3) with that of SEEG traces. MEG-ICs correlated well with SEEG traces. The strength of correlation coefficients did not depend on the depth of the SEEG contacts or the clinical outcome of the patient. A significant proportion of the MEG-ICs (n = 83/131) were localized in proximity with their maximally correlating SEEG, within a mean distance of 20±12.18mm. Our research is the first to validate the MEG-retrieved beamformer IC sources against SEEG-derived ground truth in a simultaneous MEG-SEEG framework. Observations from the present study suggest that non-invasive MEG source components may potentially provide additional information, comparable to SEEG in a number of instances.

Combining sodium MRI, proton MR spectroscopic imaging, and intracerebral EEG in epilepsy

Whole brain ionic and metabolic imaging has potential as a powerful tool for the characterization of brain diseases. We combined sodium MRI (²³ Na MRI) and ¹ H-MR Spectroscopic Imaging (¹ H-MRSI), assessing changes within epileptogenic networks in comparison with electrophysiologically normal networks as defined by stereotactic EEG (SEEG) recordings analysis. We applied a multi-echo density adapted 3D projection reconstruction pulse sequence at 7 T (²³ Na-MRI) and a 3D echo-planar spectroscopic imaging sequence at 3 T (¹ H-MRSI) in 19 patients suffering from drug-resistant focal epilepsy who underwent presurgical SEEG. We investigated ²³ Na MRI parameters including total sodium concentration (TSC) and the sodium signal fraction associated with the short component of T₂ * decay (f), alongside the level of metabolites N-acetyl aspartate (NAA), choline compounds (Cho), and total creatine (tCr). All measures were extracted from spherical regions of interest (ROIs) centered between two adjacent SEEG electrode contacts and z-scored against the same ROI in controls. Group comparison showed a significant increase in f only in the epileptogenic zone (EZ) compared to controls and compared to patients' propagation zone (PZ) and non-involved zone (NIZ). TSC was significantly increased in all patients' regions compared to controls. Conversely, NAA levels were significantly lower in patients compared to controls, and lower in the EZ compared to PZ and NIZ. Multiple regression analyzing the relationship between sodium and metabolites levels revealed significant relations in PZ and in NIZ but not in EZ. Our results are in agreement with the energetic failure hypothesis in epileptic regions associated with widespread tissue reorganization.

A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG

Cognitive tasks are commonly used to identify brain networks involved in the underlying cognitive process. However, inferring the brain networks from intracranial EEG data presents several challenges related to the sparse spatial sampling of the brain and the high variability of the EEG trace due to concurrent brain processes. In this manuscript, we use a well-known facial emotion recognition task to compare three different ways of analyzing the contrasts between task conditions: permutation cluster tests, machine learning (ML) classifiers, and a searchlight implementation of multivariate pattern analysis (MVPA) for intracranial sparse data recorded from 13 patients undergoing presurgical evaluation for drug-resistant epilepsy. Using all three methods, we aim at highlighting the brain structures with significant contrast between conditions. In the absence of ground truth, we use the scientific literature to validate our results. The comparison of the three methods’ results shows moderate agreement, measured by the Jaccard coefficient, between the permutation cluster tests and the machine learning [0.33 and 0.52 for the left (LH) and right (RH) hemispheres], and 0.44 and 0.37 for the LH and RH between the permutation cluster tests and MVPA. The agreement between ML and MVPA is higher: 0.65 for the LH and 0.62 for the RH. To put these results in context, we performed a brief review of the literature and we discuss how each brain structure’s involvement in the facial emotion recognition task.

Cortico-cortical and thalamo-cortical connectivity during non-REM and REM sleep: Insights from intracranial recordings in humans

OBJECTIVE

To study changes of thalamo-cortical and cortico-cortical connectivity during wakefulness, non-Rapid Eye Movement (non-REM) sleep, including N2 and N3 stages, and REM sleep, using stereoelectroencephalography (SEEG) recording in humans.

METHODS

We studied SEEG recordings of ten patients during wakefulness, non-REM sleep and REM sleep, in seven brain regions of interest including the thalamus. We calculated directed and undirected functional connectivity using a measure of non-linear correlation coefficient h².

RESULTS

The thalamus was more connected to other brain regions during N2 stage and REM sleep than during N3 stage during which cortex was more connected than the thalamus. We found two significant directed links: the first from the prefrontal region to the lateral parietal region in the delta band during N3 sleep and the second from the thalamus to the insula during REM sleep.

CONCLUSIONS

These results showed that cortico-cortical connectivity is more prominent in N3 stage than in N2 and REM sleep. During REM sleep we found significant thalamo-insular connectivity, with a driving role of the thalamus.

SIGNIFICANCE

We found a pattern of cortical connectivity during N3 sleep concordant with antero-posterior traveling slow waves. The thalamus seemed particularly involved as a hub of connectivity during REM sleep.

Local neuronal excitation and global inhibition during epileptic fast ripples in humans

Understanding the neuronal basis of epileptic activity is a major challenge in neurology. Cellular integration into larger scale networks is all the more challenging. In the local field potential, interictal epileptic discharges can be associated with fast ripples (200-600 Hz), which are a promising marker of the epileptogenic zone. Yet, how neuronal populations in the epileptogenic zone and in healthy tissue are affected by fast ripples remain unclear. Here, we used a novel 'hybrid' macro-micro depth electrode in nine drug-resistant epileptic patients, combining classic depth recording of local field potentials (macro-contacts) and two or three tetrodes (four micro-wires bundled together) enabling up to 15 neurons in local circuits to be simultaneously recorded. We characterized neuronal responses (190 single units) with the timing of fast ripples (2233 fast ripples) on the same hybrid and other electrodes that target other brain regions. Micro-wire recordings reveal signals that are not visible on macro-contacts. While fast ripples detected on the closest macro-contact to the tetrodes were always associated with fast ripples on the tetrodes, 82% of fast ripples detected on tetrodes were associated with detectable fast ripples on the nearest macro-contact. Moreover, neuronal recordings were taken in and outside the epileptogenic zone of implanted epileptic subjects and they revealed an interlay of excitation and inhibition across anatomical scales. While fast ripples were associated with increased neuronal activity in very local circuits only, they were followed by inhibition in large-scale networks (beyond the epileptogenic zone, even in healthy cortex). Neuronal responses to fast ripples were homogeneous in local networks but differed across brain areas. Similarly, post-fast ripple inhibition varied across recording locations and subjects and was shorter than typical inter-fast ripple intervals, suggesting that this inhibition is a fundamental refractory process for the networks. These findings demonstrate that fast ripples engage local and global networks, including healthy tissue, and point to network features that pave the way for new diagnostic and therapeutic strategies. They also reveal how even localized pathological brain dynamics can affect a broad range of cognitive functions.

Connectivity Alterations in Emotional and Cognitive Networks During a Manic State Induced by Direct Electrical Stimulation

Mania is characterized by affective and cognitive alterations, with heightened external and self-awareness that are opposite to the alteration of awareness during epileptic seizures. Electrical stimulations carried out routinely during stereotactic intracerebral EEG (SEEG) recordings for presurgical evaluation of epilepsy may represent a unique opportunity to study the pathophysiology of such complex emotional-behavioral phenomenon, particularly difficult to reproduce in experimental setting. We investigated SEEG signals-based functional connectivity between different brain regions involved in emotions and in consciousness processing during a manic state induced by electrical stimulation in a patient with drug-resistant focal epilepsy. The stimulation inducing manic state and an asymptomatic stimulation of the same site, as well as a seizure with alteration of awareness (AOA) were analyzed. Functional connectivity analysis was performed by measuring interdependencies (nonlinear regression analysis based on the h2 coefficient) between broadband SEEG signals and within typical sub-bands, before and after stimulation, or before and during the seizure with AOA, respectively. Stimulation of the right lateral prefrontal cortex induced a manic state lasting several hours. Its onset was associated with significant increase of broadband-signal functional coupling between the right hemispheric limbic nodes, the temporal pole and the claustrum, whereas significant decorrelation between the right lateral prefrontal and the anterior cingulate cortex was observed in theta-band. In contrast, ictal alteration of awareness was associated with increased broadband and sub-bands synchronization within and between the internal and external awareness networks, including the anterior and middle cingulate, the mesial and lateral prefrontal, the inferior parietal and the temporopolar cortex. Our data suggest the existence of network- and frequency-specific functional connectivity patterns during manic state. A transient desynchronization of theta activity between the external and internal awareness network hubs is likely to increase awareness, with potential therapeutic effect.

Epileptic spasms are associated with increased stereo-electroencephalography derived functional connectivity in tuberous sclerosis complex

OBJECTIVE

Epileptic spasms (ES) are common in tuberous sclerosis complex (TSC). However, the underlying network alterations and relationship with epileptogenic tubers are poorly understood. We examined interictal functional connectivity (FC) using stereo-electroencephalography (SEEG) in patients with TSC to investigate the relationship between tubers, epileptogenicity, and ES.

METHODS

We analyzed 18 patients with TSC who underwent SEEG (mean age = 11.5 years). The dominant tuber (DT) was defined as the most epileptogenic tuber using the epileptogenicity index. Epileptogenic zone (EZ) organization was quantitatively separated into focal (isolated DT) and complex (all other patterns). Using a 20-min interictal recording, FC was estimated with nonlinear regression, h² . We calculated (1) intrazone FC within all sampled tubers and normal-appearing cortical zones, respectively; and (2) interzone FC involving connections between DT, other tubers, and normal cortex. The relationship between FC and (1) presence of ES as a current seizure type at the time of SEEG, (2) EZ organization, and (3) epileptogenicity was analyzed using a mixed generalized linear model. Spike rate and distance between zones were considered in the model as covariates.

RESULTS

Six patients had ES as a current seizure type at time of SEEG. ES patients had a greater number of tubers with a fluid-attenuated inversion recovery hypointense center (p < .001), and none had TSC1 mutations. The presence of ES was independently associated with increased FC within both intrazone (p = .033) and interzone (p = .011) networks. Post hoc analyses identified that increased FC was associated with ES across tuber and nontuber networks. EZ organization and epileptogenicity biomarkers were not associated with FC.

SIGNIFICANCE

Increased cortical synchrony among both tuber and nontuber networks is characteristic of patients with ES and independent of both EZ organization and tuber epileptogenicity. This further supports the prospect of FC biomarkers aiding treatment paradigms in TSC.

A personalizable autonomous neural mass model of epileptic seizures

Work in the last two decades has shown that neural mass models (NMM) can realistically reproduce and explain epileptic seizure transitions as recorded by electrophysiological methods (EEG, SEEG). In previous work, advances were achieved by increasing excitation and heuristically varying network inhibitory coupling parameters in the models. Based on these early studies, we provide a laminar NMM capable of realistically reproducing the electrical activity recorded by SEEG in the epileptogenic zone during interictal to ictal states. With the exception of the external noise input into the pyramidal cell population, the model dynamics are autonomous. By setting the system at a point close to bifurcation, seizure-like transitions are generated, including pre-ictal spikes, low voltage fast activity, and ictal rhythmic activity. A novel element in the model is a physiologically motivated algorithm for chloride dynamics: the gain of GABAergic post-synaptic potentials is modulated by the pathological accumulation of chloride in pyramidal cells due to high inhibitory input and/or dysfunctional chloride transport. In addition, in order to simulate SEEG signals for comparison with real seizure recordings, the NMM is embedded first in a layered model of the neocortex and then in a realistic physical model. We compare modeling results with data from four epilepsy patient cases. By including key pathophysiological mechanisms, the proposed framework captures succinctly the electrophysiological phenomenology observed in ictal states, paving the way for robust personalization methods based on NMMs.

Focal non-invasive deep-brain stimulation with temporal interference for the suppression of epileptic biomarkers

Introduction

Neurostimulation applied from deep brain stimulation (DBS) electrodes is an effective therapeutic intervention in patients suffering from intractable drug-resistant epilepsy when resective surgery is contraindicated or failed. Inhibitory DBS to suppress seizures and associated epileptogenic biomarkers could be performed with high-frequency stimulation (HFS), typically between 100 and 165 Hz, to various deep-seated targets, such as the Mesio-temporal lobe (MTL), which leads to changes in brain rhythms, specifically in the hippocampus. The most prominent alterations concern high-frequency oscillations (HFOs), namely an increase in ripples, a reduction in pathological Fast Ripples (FRs), and a decrease in pathological interictal epileptiform discharges (IEDs).

Materials and methods

In the current study, we use Temporal Interference (TI) stimulation to provide a non-invasive DBS (130 Hz) of the MTL, specifically the hippocampus, in both mouse models of epilepsy, and scale the method using human cadavers to demonstrate the potential efficacy in human patients. Simulations for both mice and human heads were performed to calculate the best coordinates to reach the hippocampus.

Results

This non-invasive DBS increases physiological ripples, and decreases the number of FRs and IEDs in a mouse model of epilepsy. Similarly, we show the inability of 130 Hz transcranial current stimulation (TCS) to achieve similar results. We therefore further demonstrate the translatability to human subjects via measurements of the TI stimulation vs. TCS in human cadavers. Results show a better penetration of TI fields into the human hippocampus as compared with TCS.

Significance

These results constitute the first proof of the feasibility and efficiency of TI to stimulate at depth an area without impacting the surrounding tissue. The data tend to show the sufficiently focal character of the induced effects and suggest promising therapeutic applications in epilepsy.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Magnetoencephalography can reveal deep brain network activities linked to memory processes

Recording from deep neural structures such as hippocampus noninvasively and yet with high temporal resolution remains a major challenge for human neuroscience. Although it has been proposed that deep neuronal activity might be recordable during cognitive tasks using magnetoencephalography (MEG), this remains to be demonstrated as the contribution of deep structures to MEG recordings may be too small to be detected or might be eclipsed by the activity of large‐scale neocortical networks. In the present study, we disentangled mesial activity and large‐scale networks from the MEG signals thanks to blind source separation (BSS). We then validated the MEG BSS components using intracerebral EEG signals recorded simultaneously in patients during their presurgical evaluation of epilepsy. In the MEG signals obtained during a memory task involving the recognition of old and new images, we identified with BSS a putative mesial component, which was present in all patients and all control subjects. The time course of the component selectively correlated with stereo‐electroencephalography signals recorded from hippocampus and rhinal cortex, thus confirming its mesial origin. This finding complements previous studies with epileptic activity and opens new possibilities for using MEG to study deep brain structures in cognition and in brain disorders.

Startle-Induced Epileptic Spasms: A Clinical and Video-EEG Study

Objective

This study aimed to delineate the detailed characteristics of startle-induced epileptic spasms (ES) and explore the brain regions where startle-induced ES originated.

Methods

Among 581 patients with ES registered in our database, 30 were diagnosed with startle-induced ES according to video-electroencephalogram (EEG) and seizure semiology and were included in this study. Patients' clinical characteristics and ictal high-frequency oscillations (HFOs) were analyzed.

Results

Mean age at the onset of startle-induced ES was 28.1 months. Half of the patients had structural etiology, two of whom were diagnosed with co-existing structural and genetic etiologies. The focal neuroimaging abnormalities were predominant in the frontal cortex (9/15, 60.0%). Fifteen patients (50%) had prominent interictal epileptiform discharges in the frontal and anterior temporal. Ictal HFOs counts of the startle-induced ES in the anterior region were significantly higher than those in the posterior regions (p &lt; 0.05). Five patients (16.7%) became seizure-free ≥6 months, and ten (33.3%) showed startle-induced ES cessation ≥6 months. All patients except one had mild to severe psychomotor developmental delay after the onset of seizures.

Conclusion

Patients with startle-induced ES typically had brain lesions and showed drug-resistant. The neuroimaging and EEG findings, including ictal HFOs, support that startle-induced ES often originates from the frontal cortex.

Virtual Epileptic Patient brain modeling: relationships with seizure onset and surgical outcome

Objective

The virtual epileptic patient (VEP) is a large‐scale brain modeling method based on virtual brain technology, using stereoelectroencephalography (SEEG), anatomical data (magnetic resonance imaging [MRI] and connectivity), and a computational neuronal model to provide computer simulations of a patient's seizures. VEP has potential interest in the presurgical evaluation of drug‐resistant epilepsy by identifying regions most likely to generate seizures. We aimed to assess the performance of the VEP approach in estimating the epileptogenic zone and in predicting surgical outcome.

Methods

VEP modeling was retrospectively applied in a cohort of 53 patients with pharmacoresistant epilepsy and available SEEG, T1‐weighted MRI, and diffusion‐weighted MRI. Precision recall was used to compare the regions identified as epileptogenic by VEP (EZ_VEP) to the epileptogenic zone defined by clinical analysis incorporating the Epileptogenicity Index (EI) method (EZ_C). In 28 operated patients, we compared the VEP results and clinical analysis with surgical outcome.

Results

VEP showed a precision of 64% and a recall of 44% for EZ_VEP detection compared to EZ_C. There was a better concordance of VEP predictions with clinical results, with higher precision (77%) in seizure‐free compared to non‐seizure‐free patients. Although the completeness of resection was significantly correlated with surgical outcome for both EZ_C and EZ_VEP, there was a significantly higher number of regions defined as epileptogenic exclusively by VEP that remained nonresected in non‐seizure‐free patients.

Significance

VEP is the first computational model that estimates the extent and organization of the epileptogenic zone network. It is characterized by good precision in detecting epileptogenic regions as defined by a combination of visual analysis and EI. The potential impact of VEP on improving surgical prognosis remains to be exploited. Analysis of factors limiting the performance of the actual model is crucial for its further development.

Non-thermal Electroporation Ablation of Epileptogenic Zones Stops Seizures in Mice While Providing Reduced Vascular Damage and Accelerated Tissue Recovery

In epilepsy, the most frequent surgical procedure is the resection of brain tissue in the temporal lobe, with seizure-free outcomes in approximately two-thirds of cases. However, consequences of surgery can vary strongly depending on the brain region targeted for removal, as surgical morbidity and collateral damage can lead to significant complications, particularly when bleeding and swelling are located near delicate functional cortical regions. Although focal thermal ablations are well-explored in epilepsy as a minimally invasive approach, hemorrhage and edema can be a consequence as the blood-brain barrier is still disrupted. Non-thermal irreversible electroporation (NTIRE), common in many other medical tissue ablations outside the brain, is a relatively unexplored method for the ablation of neural tissue, and has never been reported as a means for ablation of brain tissue in the context of epilepsy. Here, we present a detailed visualization of non-thermal ablation of neural tissue in mice and report that NTIRE successfully ablates epileptic foci in mice, resulting in seizure-freedom, while causing significantly less hemorrhage and edema compared to conventional thermal ablation. The NTIRE approach to ablation preserves the blood-brain barrier while pathological circuits in the same region are destroyed. Additionally, we see the reinnervation of fibers into ablated brain regions from neighboring areas as early as day 3 after ablation. Our evidence demonstrates that NTIRE could be utilized as a precise tool for the ablation of surgically challenging epileptogenic zones in patients where the risk of complications and hemorrhage is high, allowing not only reduced tissue damage but potentially accelerated recovery as vessels and extracellular matrix remain intact at the point of ablation.

BrainQuake: An Open-Source Python Toolbox for the Stereoelectroencephalography Spatiotemporal Analysis

Intracranial stereoelectroencephalography (SEEG) is broadly used in the presurgical evaluation of intractable epilepsy, due to its high temporal resolution in neural activity recording and high spatial resolution within suspected epileptogenic zones. Neurosurgeons or technicians face the challenge of conducting a workflow of post-processing operations with the multimodal data (e.g., MRI, CT, and EEG) after the implantation surgery, such as brain surface reconstruction, electrode contact localization, and SEEG data analysis. Several software or toolboxes have been developed to take one or more steps in the workflow but without an end-to-end solution. In this study, we introduced BrainQuake, an open-source Python software for the SEEG spatiotemporal analysis, integrating modules and pipelines in surface reconstruction, electrode localization, seizure onset zone (SOZ) prediction based on ictal and interictal SEEG analysis, and final visualizations, each of which is highly automated with a user-friendly graphical user interface (GUI). BrainQuake also supports remote communications with a public server, which is facilitated with automated and standardized preprocessing pipelines, high-performance computing power, and data curation management to provide a time-saving and compatible platform for neurosurgeons and researchers.

Epileptogenic networks in drug-resistant epilepsy with amygdala enlargement: Assessment with stereo-EEG and 7 T MRI

OBJECTIVE

Amygdala enlargement is increasingly described in association with temporal lobe epilepsies. Its significance, however, remains uncertain both in terms of etiology and its link with psychiatric disorders and of its involvement in the epileptogenic zone. We assessed the epileptogenic networks underlying drug-resistant epilepsy with amygdala enlargement and investigated correlations between clinical features, epileptogenicity and morphovolumetric amygdala characteristics.

METHODS

We identified 12 consecutive patients suffering from drug-resistant epilepsy with visually suspected amygdala enlargement and available stereoelectroencephalographic recording. The epileptogenic zone was defined using the Connectivity Epileptogenicity Index. Morphovolumetric measurements were performed using automatic segmentation and co-registration on the 7TAMIbrain Amygdala atlas.

RESULTS

The epileptogenic zone involved the enlarged amygdala in all but three cases and corresponded to distributed, temporal-insular, temporal-insular-prefrontal or prefrontal-temporal networks in ten cases, while only two were temporo-mesial networks. Morphovolumetrically, amygdala enlargement was bilateral in 75% of patients. Most patients presented psychiatric comorbidities (anxiety, depression, posttraumatic stress disorder). The level of depression defined by screening questionnaire was positively correlated with the extent of amygdala enlargement.

CONCLUSIONS

Drug-resistant epilepsy with amygdala enlargement is heterogeneous; most cases implied "temporal plus" networks.

SIGNIFICANCE

The enlarged amygdala could reflect an interaction of stress-mediated limbic network alterations and mechanisms of epileptogenesis.

Changes in epileptogenicity biomarkers after stereotactic thermocoagulation

OBJECTIVE

Stereo-electroencephalography (SEEG)-guided radiofrequency thermocoagulation (RF-TC) aims at modifying epileptogenic networks to reduce seizure frequency. High-frequency oscillations (HFOs), spikes, and cross-rate are quantifiable epileptogenic biomarkers. In this study, we sought to evaluate, using SEEG signals recorded before and after thermocoagulation, whether a variation in these markers is related to the therapeutic effect of this procedure and to the outcome of surgery.

METHODS

Interictal segments of SEEG signals were analyzed in 38 patients during presurgical evaluation. We used an automatized method to quantify the rate of spikes, rate of HFOs, and cross-rate (a measure combining spikes and HFOs) before and after thermocoagulation. We analyzed the differences both at an individual level with a surrogate approach and at a group level with analysis of variance. We then evaluated the correlation between these variations and the clinical response to RF-TC and to subsequent resective surgery.

RESULTS

After thermocoagulation, 19 patients showed a clinical improvement. At the individual level, clinically improved patients more frequently had a reduction in spikes and cross-rate in the epileptogenic zone than patients without clinical improvement (p = .002, p = .02). At a group level, there was a greater decrease of HFOs in epileptogenic and thermocoagulated zones in patients with clinical improvement (p < .05) compared to those with no clinical benefit. Eventually, a significant decrease of all the markers after RF-TC was found in patients with a favorable outcome of resective surgery (spikes, p = .026; HFOs, p = .03; cross-rate, p = .03).

SIGNIFICANCE

Quantified changes in the rate of spikes, rate of HFOs, and cross-rate can be observed after thermocoagulation, and the reduction of these markers correlates with a favorable clinical outcome after RF-TC and with successful resective surgery. This may suggest that interictal biomarker modifications after RF-TC can be clinically used to predict the effectiveness of the thermocoagulation procedure and the outcome of resective surgery.

Parieto-premotor functional connectivity changes during parietal lobe seizures are associated with motor semiology

OBJECTIVES

Parietal lobe seizures (PLS) are characterized by multiple clinical manifestations including motor signs. The mechanisms underlying the occurrence of motor signs are poorly understood. The main objective of this work was to estimate the functional coupling of brain regions associated with this clinical presentation.

METHODS

We retrospectively selected patients affected by drug-resistant epilepsy who underwent Stereoelectroencephalography (SEEG) for pre-surgical evaluation and in whom the seizure onset zone (SOZ) was located in the parietal cortex. The SOZ was defined visually and quantitatively by the epileptogenicity index (EI) method. Two groups of seizures were defined according to the presence ("motor seizures") or the absence ("non-motor seizures") of motor signs. Functional connectivity (FC) estimation was based on pairwise nonlinear regression analysis (h² coefficient). To study FC changes between parietal, frontal and temporal regions, for each patient, z-score values of 16 cortico-cortical interactions were obtained comparing h² coefficients of pre-ictal, seizure onset and seizure propagation periods.

RESULTS

We included 22 patients, 13 with "motor seizures" and 9 with "non-motor seizures". Resective surgery was performed in 14 patients, 8 patients had a positive surgical outcome (Engel's class I and II). During seizure onset period, a decrease of FC was observed and was significantly more important (in comparison with background period) in "motor" seizures. This was particularly observed between parietal operculum/post-central gyrus (OP/PoCg) and mesial temporal areas. During seizure propagation, a FC increase was significantly more important (in comparison with seizure onset) in "motor seizures", in particular between lateral pre-motor (pmL) area and precuneus, pmL and superior parietal lobule (SPL) and between inferior parietal lobule (IPL) and supplementary motor area (SMA).

CONCLUSIONS

Our study shows that motor semiology in PLS is accompanied by an increase of FC between parietal and premotor cortices, significantly different than what is observed in PLS without motor semiology.

SIGNIFICANCE

Our results indicate that preferential routes of coupling between parietal and premotor cortices are responsible for the prominent motor presentation during PLS.

Distinction of Physiologic and Epileptic Ripples: An Electrical Stimulation Study

Ripple oscillations (80-250 Hz) are a promising biomarker of epileptic activity, but are also involved in memory consolidation, which impairs their value as a diagnostic tool. Distinguishing physiologic from epileptic ripples has been particularly challenging because usually, invasive recordings are only performed in patients with refractory epilepsy. Here, we identified 'healthy' brain areas based on electrical stimulation and hypothesized that these regions specifically generate 'pure' ripples not coupled to spikes. Intracranial electroencephalography (EEG) recorded with subdural grid electrodes was retrospectively analyzed in 19 patients with drug-resistant focal epilepsy. Interictal spikes and ripples were automatically detected in slow-wave sleep using the publicly available Delphos software. We found that rates of spikes, ripples and ripples coupled to spikes ('spike-ripples') were higher inside the seizure-onset zone (p < 0.001). A comparison of receiver operating characteristic curves revealed that spike-ripples slightly delineated the seizure-onset zone channels, but did this significantly better than spikes (p < 0.001). Ripples were more frequent in the eloquent neocortex than in the remaining non-seizure onset zone areas (p < 0.001). This was due to the higher rates of 'pure' ripples (p < 0.001; median rates 3.3/min vs. 1.4/min), whereas spike-ripple rates were not significantly different (p = 0.87). 'Pure' ripples identified 'healthy' channels significantly better than chance (p < 0.001). Our findings suggest that, in contrast to epileptic spike-ripples, 'pure' ripples are mainly physiological. They may be considered, in addition to electrical stimulation, to delineate eloquent cortex in pre-surgical patients. Since we applied open source software for detection, our approach may be generally suited to tackle a variety of research questions in epilepsy and cognitive science.

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

BACKGROUND

We developed CEPS as an open access MATLAB^® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements.

METHODS

Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system.

RESULTS

The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing.

CONCLUSIONS

We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.

Transfer, Collection and Organisation of Electrophysiological and Imaging Data for Multicentre Studies

Multicentre studies are of utmost importance to confirm hypotheses. The lack of established standards and the ensuing complexity of their data management often hamper their implementation. The Brain Imaging Data Structure (BIDS) is an initiative for organizing and describing neuroimaging and electrophysiological data. Building on BIDS, we have developed two software programs: BIDS Manager and BIDS Uploader. The former has been designed to collect, organise and manage the data and the latter has been conceived to handle their transfer and anonymisation from the partner centres. These two programs aim at facilitating the implementation of multicentre study by providing a standardised framework.

Extracting seizure onset from surface EEG with independent component analysis: Insights from simultaneous scalp and intracerebral EEG

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Independent component analysis (ICA) is able to identify seizure generators.

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Simultaneous long-term scalp-SEEG allows validation of the ICA results.

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Ability to record seizure onset patterns on scalp depends on generator depth.

The success of stereoelectroencephalographic (SEEG) investigations depends crucially on the hypotheses on the putative location of the seizure onset zone. This information is derived from non-invasive data either based on visual analysis or advanced source localization algorithms. While source localization applied to interictal spikes recorded on scalp is the classical method, it does not provide unequivocal information regarding the seizure onset zone. Raw ictal activity contains a mixture of signals originating from several regions of the brain as well as EMG artifacts, hampering direct input to the source localization algorithms. We therefore introduce a methodology that disentangles the various sources contributing to the scalp ictal activity using independent component analysis and uses equivalent current dipole localization as putative locus of ictal sources. We validated the results of our analysis pipeline by performing long-term simultaneous scalp – intracerebral (SEEG) recordings in 14 patients and analyzing the wavelet coherence between the independent component encoding the ictal discharge and the SEEG signals in 8 patients passing the inclusion criteria. Our results show that invasively recorded ictal onset patterns, including low-voltage fast activity, can be captured by the independent component analysis of scalp EEG. The visibility of the ictal activity strongly depends on the depth of the sources. The equivalent current dipole localization can point to the seizure onset zone (SOZ) with an accuracy that can be as high as 10 mm for superficially located sources, that gradually decreases for deeper seizure generators, averaging at 47 mm in the 8 analyzed patients. Independent component analysis is therefore shown to have a promising SOZ localizing value, indicating whether the seizure onset zone is neocortical, and its approximate location, or located in mesial structures. That may contribute to a better crafting of the hypotheses used as basis of the stereo-EEG implantations.

Is beta band desynchronization related to skin conductance biofeedback effectiveness in drug resistant focal epilepsy?

Skin Conductance Biofeedback (SCB) is a non-invasive behavioral treatment for epilepsy based on modulation of Galvanic Skin Response (GSR). We evaluated changes in functional connectivity occurring after SCB. Six patients with drug-resistant temporal lobe epilepsy underwent monthly SCB sessions. For each patient, 10 min of resting-state magnetoencephalographic (MEG) recording were acquired before and after the first and the last SCB session. For each recording we computed the mean weighted phase lag index (WPLI) across all pair of MEG sensors. After SCB, two patients had consistent reduction of seizure frequency (>50 %). Connectivity analysis revealed a decrease of WPLI-beta band in the two responders and an increase of WPLI-alpha connectivity in all patients regardless of the clinical effect. Results suggest that reduction of WPLI-beta-low connectivity is related to the clinical response after SCB.

Prefrontal seizure classification based on stereo-EEG quantification and automatic clustering

PURPOSE

Frontal seizures are organized according to anatomo-functional subdivisions of the frontal lobe. Prefrontal seizures have been the subject of few detailed studies to date. The objective of this study was to identify subcategories of prefrontal seizures based on seizure onset quantification and to look for semiological differences.

METHODS

Consecutive patients who underwent stereoelectroencephalography (SEEG) for drug-resistant prefrontal epilepsy between 2000 and 2018 were included. The different prefrontal regions investigated in our patients were dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), dorsomedial prefrontal cortex (DMPFC), ventromedial prefrontal cortex (VMPFC), and orbitofrontal cortex (OFC). The seizure onset zone (SOZ) was determined from one or two seizures in each patient, using the epileptogenicity index (EI) method. The presence or absence of 16 clinical ictal manifestations was analyzed. Classification of prefrontal networks was performed using the k-means automatic classification method.

RESULTS

A total of 51 seizures from 31 patients were analyzed. The optimal clustering was 4 subgroups of prefrontal seizures: a "pure DLPF" group, a "pure VMPF" group, a "pure OFC" group, and a "global prefrontal" group. The first 3 groups showed a mean EI considered epileptogenic (>0.4) only in one predominant structure, while the fourth group showed a high mean EI in almost all prefrontal structures. The median number of epileptogenic structures per seizure (prefrontal or extrafrontal) was 5 for the "global prefrontal" group and 2 for the other groups. We found that the most common signs were altered consciousness, automatisms/stereotypies, integrated gestural motor behavior, and hyperkinetic motor behavior. We found no significant difference in the distribution of ictal signs between the different groups.

CONCLUSION

Our study showed that although most prefrontal seizures manifest as a network of several anatomically distinct structures, we were able to determine a sublobar organization of prefrontal seizure onset with four groups.

The Ictal Signature of Thalamus and Basal Ganglia in Focal Epilepsy: A SEEG Study

OBJECTIVE

To determine the involvement of subcortical regions in human epilepsy by analyzing direct recordings from these regions during epileptic seizures using stereo-EEG (SEEG).

METHODS

We studied the SEEG recordings of a large series of patients (74 patients, 157 seizures) with an electrode sampling the thalamus and in some cases also the basal ganglia (caudate nucleus, 22 patients; and putamen, 4 patients). We applied visual analysis and signal quantification methods (Epileptogenicity Index [EI]) to their ictal recordings and compared electrophysiologic with clinical data.

RESULTS

We found that in 86% of patients, thalamus was involved during seizures (visual analysis) and 20% showed high values of epileptogenicity (EI >0.3). Basal ganglia may also disclose high values of epileptogenicity (9% in caudate nucleus) but to a lesser degree than thalamus (p < 0.01). We observed different seizure onset patterns including low voltage high frequency activities. We found high values of thalamic epileptogenicity in different epilepsy localizations, including opercular and motor epilepsies. We found no difference between epilepsy etiologies (cryptogenic vs malformation of cortical development, p = 0.77). Thalamic epileptogenicity was correlated with the extension of epileptogenic networks (p = 0.02, ρ 0.32). We found a significant effect (p < 0.05) of thalamic epileptogenicity regarding the postsurgical outcome (higher thalamic EI corresponding to higher probability of surgical failure).

CONCLUSIONS

Thalamic involvement during seizures is common in different seizure types. The degree of thalamic epileptogenicity is a possible marker of the epileptogenic network extension and of postsurgical prognosis.

Epileptogenic network of focal epilepsies mapped with cortico-cortical evoked potentials

OBJECTIVE

The goal of this study was to investigate the spatial extent and functional organization of the epileptogenic network through cortico-cortical evoked potentials (CCEPs) in patients being evaluated with intracranial stereoelectroencephalography.

METHODS

We retrospectively included 25 patients. We divided the recorded sites into three regions: epileptogenic zone (EZ); propagation zone (PZ); and noninvolved zone (NIZ). The root mean square of the amplitudes was calculated to reconstruct effective connectivity network. We also analyzed the N1/N2 amplitudes to explore the responsiveness influenced by epileptogenicity. Prognostic analysis was performed by comparing intra-region and inter-region connectivity between seizure-free and non-seizure-free groups.

RESULTS

Our results confirmed that stimulation of the EZ caused the strongest responses on other sites within and outside the EZ. Moreover, we found a hierarchical connectivity pattern showing the highest connectivity strength within EZ, and decreasing connectivity gradient from EZ, PZ to NIZ. Prognostic analysis indicated a stronger intra-EZ connection in the seizure-free group.

CONCLUSION

The EZ showed highest excitability and dominantly influenced other regions. Quantitative CCEPs can be useful in mapping epileptic networks and predicting surgical outcome.

SIGNIFICANCE

The generated computational connectivity model may enhance our understanding of epileptogenic networks and provide useful information for surgical planning and prognosis prediction.

Brain-scale cortico-cortical functional connectivity in the delta-theta band is a robust signature of conscious states: an intracranial and scalp EEG study

Long-range cortico-cortical functional connectivity has long been theorized to be necessary for conscious states. In the present work, we estimate long-range cortical connectivity in a series of intracranial and scalp EEG recordings experiments. In the two first experiments intracranial-EEG (iEEG) was recorded during four distinct states within the same individuals: conscious wakefulness (CW), rapid-eye-movement sleep (REM), stable periods of slow-wave sleep (SWS) and deep propofol anaesthesia (PA). We estimated functional connectivity using the following two methods: weighted Symbolic-Mutual-Information (wSMI) and phase-locked value (PLV). Our results showed that long-range functional connectivity in the delta-theta frequency band specifically discriminated CW and REM from SWS and PA. In the third experiment, we generalized this original finding on a large cohort of brain-injured patients. FC in the delta-theta band was significantly higher in patients being in a minimally conscious state (MCS) than in those being in a vegetative state (or unresponsive wakefulness syndrome). Taken together the present results suggest that FC of cortical activity in this slow frequency band is a new and robust signature of conscious states.

Transient cortico-cortical disconnection during psychogenic nonepileptic seizures (PNES)

Psychogenic nonepileptic seizures (PNES) are paroxysmal clinical events that are often misdiagnosed as epileptic seizures, but which are not associated with electrographic discharge. Brain connectivity changes occurring during PNES are not known. We studied functional connectivity (Fc) in two patients with drug-resistant epilepsy, explored by stereotactic electroencephalography (EEG), in whom we recorded both epileptic seizures (ES) and PNES. Functional connectivity using pair-wise nonlinear correlation was computed between signals from seven brain areas: amygdala, hippocampus, lateral temporal cortex, anterior insula, orbitofrontal cortex, prefrontal cortex, and lateral parietal cortex. We assessed changes in global Fc during PNES in comparison with a background period. During PNES, a global decrease of Fc occurred between the different brain regions studied, compared with the interictal period. In both patients, decreased Fc was prominent in connections involving the anterior insula and parietal cortex. In conclusion, some PNES are associated with ictal functional disconnection between brain areas, particularly involving the parietal cortices and the anterior insula.

Ictal blinking in focal seizures: Insights from SEEG recordings

PURPOSE

Ictal blinking may be observed in various forms of epilepsies. In the context of presurgical assessment of drug-resistant focal epilepsies, its semiological value is poorly understood. Our aims were to determine the prevalence and localizing value of ictal blinking.

METHODS

We reviewed our cohort of more than 300 patients explored by SEEG, searching for ictal blinking. We defined seizure onset zone (SOZ) using visual analysis complemented by a quantified method (epileptogenicity index). We analysed the features of ictal blinking and the associated signs. We tested for statistically significant associations with the underlying SOZ.

RESULTS

We found that about 8% of our patients exhibited ictal blinking, mostly bilateral. Ictal blinking was seen mostly in four types of SOZ: occipital, occipito-temporal, temporal mesial, and insulo-opercular. It was significantly over-represented in occipito-temporal and occipital SOZ. Eye blinking was fastest in insulo-opercular SOZ and slowest in temporal mesial SOZ. Nystagmus and tonic eye deviation were associated with SOZ involving the occipital lobe.

CONCLUSION

Ictal blinking is not uncommon in the population of patients with drug-resistant focal epilepsies. It is mostly associated with four types of SOZ: occipital, occipito-temporal, temporal mesial, and insulo-opercular. Some features of ictal blinking, as well as the analysis of the associated signs, allow to orient clinical hypotheses toward some specific SOZ.