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.

Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy

Stereoelectroencephalography is a powerful intracerebral EEG recording method for the presurgical evaluation of epilepsy. It consists in implanting depth electrodes in the patient's brain to record electrical activity and map the epileptogenic zone, which should be resected to render the patient seizure-free. Stereoelectroencephalography has high spatial accuracy and signal-to-noise ratio but remains limited in the coverage of the explored brain regions. Thus, the implantation might provide a suboptimal sampling of epileptogenic regions. We investigate the potential of improving a suboptimal stereoelectroencephalography recording by performing source localization on stereoelectroencephalography signals. We propose combining independent component analysis, connectivity measures to identify components of interest, and distributed source modelling. This approach was tested on two patients with two implantations each, the first failing to characterize the epileptogenic zone and the second giving a better diagnosis. We demonstrate that ictal and interictal source localization performed on the first stereoelectroencephalography recordings matches the findings of the second stereo-EEG exploration. Our findings suggest that independent component analysis followed by source localization on the topographies of interest is a promising method for retrieving the epileptogenic zone in case of suboptimal implantation.

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.

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.

The origin of pleasant sensations: Insight from direct electrical brain stimulation

Research into the neuroanatomical basis of emotions has resulted in a plethora of studies over the last twenty years. However, studies about positive emotions and pleasant sensations remain rare and their anatomical-functional bases are less understood than that of negative emotions. Pleasant sensations can be evoked by electrical brain stimulations (EBS) during stereotactic electroencephalography (SEEG) performed for pre-surgical exploration in patients with drug-resistant epilepsy. We conducted a retrospective analysis of 10 106 EBS performed in 329 patients implanted with SEEG in our epileptology department. We found that 13 EBS in 9 different patients evoked pleasant sensations (.60% of all responses). By contrast we collected 111 emotional responses of negative valence (i.e., 5.13% of all responses). EBS evoking pleasant sensations were applied at 50 Hz with an average intensity of 1.4 ± .55 mA (range .5-2 mA). Pleasant sensations were reported by nine patients of which three patients presented responses to several EBS. We found a male predominance among the patients reporting pleasant sensations and a prominent role of the right cerebral hemisphere. Results show the preponderant role of the dorsal anterior insula and amygdala in the occurrence of pleasant sensations.

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.

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.

Neural bases of the bodily self as revealed by electrical brain stimulation: A systematic review

An increasing amount of recent research has focused on the multisensory and neural bases of the bodily self. This pre-reflective form of self is considered as multifaceted, incorporating phenomenal components, such as self location, body ownership, first-person perspective, agency, and the perceptual body image. Direct electrical brain stimulation (EBS) during presurgical evaluation of epilepsy and brain tumor resection is a unique method to causally relate specific brain areas to the various phenomenal components of the bodily self. We conducted a systematic review of the literature describing altered phenomenal experience of the bodily self evoked by EBS. We included 42 articles and analyzed self reports from 221 patients. Three-dimensional density maps of EBS revealed that stimulation in the middle cingulum, inferior parietal lobule, supplementary motor area, posterior insula, hippocampal complex/amygdala, and precuneus most consistently altered one or several components of the bodily self. In addition, we found that only EBS in the parietal cortex induced disturbances of all five components of the bodily self considered in this review article. These findings inform current neuroscientific models of the bodily self.

Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy

Precise estimates of epileptogenic zone networks (EZNs) are crucial for planning intervention strategies to treat drug-resistant focal epilepsy. Here, we present the virtual epileptic patient (VEP), a workflow that uses personalized brain models and machine learning methods to estimate EZNs and to aid surgical strategies. The structural scaffold of the patient-specific whole-brain network model is constructed from anatomical T1 and diffusion-weighted magnetic resonance imaging. Each network node is equipped with a mathematical dynamical model to simulate seizure activity. Bayesian inference methods sample and optimize key parameters of the personalized model using functional stereoelectroencephalography recordings of patients' seizures. These key parameters together with their personalized model determine a given patient's EZN. Personalized models were further used to predict the outcome of surgical intervention using virtual surgeries. We evaluated the VEP workflow retrospectively using 53 patients with drug-resistant focal epilepsy. VEPs reproduced the clinically defined EZNs with a precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZNs was small. Compared with the resected brain regions of 25 patients who underwent surgery, VEP showed lower false discovery rates in seizure-free patients (mean, 0.028) than in non-seizure-free patients (mean, 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with an expected 356 prospective patients with epilepsy.

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.

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.

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.

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.

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.

Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.

VEP atlas: An anatomic and functional human brain atlas dedicated to epilepsy patients

BACKGROUND

Several automated parcellation atlases of the human brain have been developed over the past decades, based on various criteria, and have been applied in basic and clinical research.

NEW METHOD

Here we present the Virtual Epileptic Patient (VEP) atlas that offers a new automated brain region parcellation and labeling, which has been developed for the specific use in the domains of epileptology and functional neurosurgery and is able to apply at individual patient's level.

RESULTS

It comprises 162 brain regions, including 73 cortical and 8 subcortical regions per hemisphere. We demonstrate the successful application of the VEP atlas in a cohort of 50 retrospective patients. The structural organization is complemented by the functional variation of stereotactic intracerebral EEG (SEEG) signal data features establishing brain region-specific 3d-maps.

COMPARISON WITH EXISTING METHODS

The VEP atlas integrates both anatomical and functional definitions in the same atlas, adapted to applications for epilepsy patients and individualizable.

CONCLUSION

The covariation of structural and functional organization is the basis for current efforts of patient-specific large-scale brain network modeling exploiting virtual brain technologies for the identification of the epileptogenic regions in an ongoing prospective clinical trial EPINOV.

Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies

Drug-refractory focal epilepsies are network diseases associated with functional connectivity alterations both during ictal and interictal periods. A large majority of studies on the interictal/resting state have focused on functional MRI-based functional connectivity. Few studies have used electrophysiology, despite its high temporal capacities. In particular, stereotactic-EEG is highly suitable to study functional connectivity because it permits direct intracranial electrophysiological recordings with relative large-scale sampling. Most previous studies in stereotactic-EEG have been directed towards temporal lobe epilepsy, which does not represent the whole spectrum of drug-refractory epilepsies. The present study aims at filling this gap, investigating interictal functional connectivity alterations behind cortical epileptic organization and its association with post-surgical prognosis. To this purpose, we studied a large cohort of 59 patients with malformation of cortical development explored by stereotactic-EEG with a wide spatial sampling (76 distinct brain areas were recorded, median of 13.2 per patient). We computed functional connectivity using non-linear correlation. We focused on three zones defined by stereotactic-EEG ictal activity: the epileptogenic zone, the propagation zone and the non-involved zone. First, we compared within-zone and between-zones functional connectivity. Second, we analysed the directionality of functional connectivity between these zones. Third, we measured the associations between functional connectivity measures and clinical variables, especially post-surgical prognosis. Our study confirms that functional connectivity differs according to the zone under investigation. We found: (i) a gradual decrease of the within-zone functional connectivity with higher values for epileptogenic zone and propagation zone, and lower for non-involved zones; (ii) preferential coupling between structures of the epileptogenic zone; (iii) preferential coupling between epileptogenic zone and propagation zone; and (iv) poorer post-surgical outcome in patients with higher functional connectivity of non-involved zone (within- non-involved zone, between non-involved zone and propagation zone functional connectivity). Our work suggests that, even during the interictal state, functional connectivity is reinforced within epileptic cortices (epileptogenic zone and propagation zone) with a gradual organization. Moreover, larger functional connectivity alterations, suggesting more diffuse disease, are associated with poorer post-surgical prognosis. This is consistent with computational studies suggesting that connectivity is crucial in order to model the spatiotemporal dynamics of seizures.10.1093/brain/awy214_video1awy214media15833456182001.