Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.

Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the underlying Electrophysiological Source Imaging (ESI) problem. To guarantee a unique solution, most existing ESI methods pay more attention to solve this inverse problem by imposing physiological constraints. This paper proposes an efficient ESI approach based on simulation-driven deep learning. Epileptic High-resolution 256-channels scalp EEG (Hr-EEG) signals are simulated in a realistic manner to train the proposed patient-specific model. More particularly, a computational neural mass model developed in our team is used to generate the temporal dynamics of the activity of each dipole while the forward problem is solved using a patient-specific three-shell realistic head model and the boundary element method. A Temporal Convolutional Network (TCN) is considered in the proposed model to capture local spatial patterns. To enable the model to observe the EEG signals from different scale levels, the multi-scale strategy is leveraged to capture the overall features and fine-grain features by adjusting the convolutional kernel size. Then, the Long Short-Term Memory (LSTM) is used to extract temporal dependencies among the computed spatial features. The performance of the proposed method is evaluated through three different scenarios of realistic synthetic interictal Hr-EEG data as well as on real interictal Hr-EEG data acquired in three patients with drug-resistant partial epilepsy, during their presurgical evaluation. A performance comparison study is also conducted with two other deep learning-based methods and four classical ESI techniques. The proposed model achieved a Dipole Localization Error (DLE) of 1.39 and Normalized Hamming Distance (NHD) of 0.28 in the case of one patch with SNR of 10 dB. In the case of two uncorrelated patches with an SNR of 10 dB, obtained DLE and NHD were respectively 1.50 and 0.28. Even in the more challenging scenario of two correlated patches with an SNR of 10 dB, the proposed approach still achieved a DLE of 3.74 and an NHD of 0.43. The results obtained on simulated data demonstrate that the proposed method outperforms the existing methods for different signal-to-noise and source configurations. The good behavior of the proposed method is also confirmed on real interictal EEG data. The robustness with respect to noise makes it a promising and alternative tool to localize epileptic brain areas and to reconstruct their electrical activities from EEG signals.

A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials.

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies. Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics. To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings.

Adaptive behavior and psychiatric comorbidities in KCNB1 encephalopathy.

AIM: KCNB1 encephalopathy encompasses a broad phenotypic spectrum associating intellectual disability, behavioral disturbances, and epilepsies of various severity. Using standardized parental questionnaires, we aimed to capture the heterogeneity of the adaptive and behavioral features in a series of patients with KCNB1 pathogenic variants. METHODS: We included 25 patients with a KCNB1 encephalopathy, aged from 3.2 to 34.1 years (median = 10 years). Adaptive functioning was assessed in all patients using the French version of the Vineland Adaptive Behavior Scales, Second Edition (VABS-II) questionnaire. We screened global behavior with the Childhood Behavioral Check-List (CBCL, Achenbach) and autism spectrum disorder (ASD) with the Social Communication Questionnaire (SCQ). We used a cluster analysis to identify subgroups of adaptive profiles. RESULTS: VABS-II questionnaire showed pathological adaptive behavior in all participants with a severity of adaptive deficiency ranging from mild in 8/20 to severe in 7/20. Eight out of 16 were at risk of Attention Problems at the CBCL and 13/18 were at risk of autism spectrum disorder (ASD). The adaptive behavior composite score significantly decreased with age (Spearman's Rho=-0.72, p<0.001) but not the equivalent ages, suggesting stagnation and slowing but no regression over time. The clustering analysis identified two subgroups of patients, one showing more severe adaptive behavior. The severity of the epilepsy phenotype predicted the severity of the behavioral profile with a sensitivity of 70% and a specificity of 90.9%. CONCLUSION: This study confirms the deleterious consequences of early-onset epilepsy in addition to the impact of the gene dysfunction in patients with KCNB1 encephalopathy. ASD and attention disorders are frequent. Parental questionnaires should be considered as useful tools for early screening and care adaptation.

Mechanical Ventilation Associated Pneumomediastinum - A Rising Incidence of Cases in an Emergency Hospital During the COVID-19 Pandemics.

Introduction: Mechanical ventilation is a last resort solution for patients presenting with acute respiratory distress syndrome produced by SARS-CoV-2. Spontaneous pneumomediastinum is a rare pathology associated with invasive mechanical ventilation. The objective of our research was to highlight the increased incidence of spontaneous pneumomediastinum during the COVID-19 pandemics in our hospital. Material and method: A retrospective review of the cases in our hospital requiring surgical evaluation was performed. Electronic health records from our institution were searched for nontraumatic pneumomediastinum from October to November 2021. All patients that were identified with pneumomediastinum were included in the review. Results: We identified 12 mechanical ventilated patients that presented with free air in the mediastinum on a computed tomography during the study period. All of the patients had SARS-CoV-2 bronchopneumonia with extensive pulmonary involvement. The mortality rate among these patients was 58.33%. Conclusions: The main take-home message of our study is that the incidence of mechanically ventilation-associated pneumomediastinum was exponentially higher during the fourth wave of COVID-19. There is a need for multicenter data in this pathology for a better approach and to define surgical management options for these patients.

Surgical Stasis: Anomalies Scarcities in Surgical Residents Training in the Covid-19 Period.

Introduction: The COVID-19 pandemic has led to a marked decrease in surgical procedures performed worldwide and to numerous other changes in medical practice. We investigated the effect of these changes on surgical trainees. Method: We searched medical records and asked a total of 67 surgical residents from different specialities (general surgery, obstetrics-gynecology, orthopedics-traumatology, neurosurgery) to answer a questionnaire investigating how the total number of surgical procedures and operative role varied for each respondent during the pandemic, the number of medical congresses and handson courses they attended during this time, how much study time was available to them and how the changes in their training modules affected them. Results: Most respondents reported a marked decrease in the number of surgical procedures performed, performing key operative steps with a lower frequency. Most of them believed that their training stagnated or suffered a setback. However, most residents consider the changes in their training during the pandemic a useful experience. Conclusion: The real effect of the COVID-19 pandemic on surgical training should be further studied. Future prospective studies should identify threshold values for each surgical procedure and the most effective compensatory strategies.

Expanding the genetic and phenotypic relevance of KCNB1 variants in developmental and epileptic encephalopathies: 27 new patients and overview of the literature.

Developmental and epileptic encephalopathies (DEE) refer to a heterogeneous group of devastating neurodevelopmental disorders. Variants in KCNB1 have been recently reported in patients with early-onset DEE. KCNB1 encodes the α subunit of the delayed rectifier voltage-dependent potassium channel Kv 2.1. We review the 37 previously reported patients carrying 29 distinct KCNB1 variants and significantly expand the mutational spectrum describing 18 novel variants from 27 unreported patients. Most variants occur de novo and mainly consist of missense variants located on the voltage sensor and the pore domain of Kv 2.1. We also report the first inherited variant (p.Arg583*). KCNB1-related encephalopathies encompass a wide spectrum of neurodevelopmental disorders with predominant language difficulties and behavioral impairment. Eighty-five percent of patients developed epilepsies with variable syndromes and prognosis. Truncating variants in the C-terminal domain are associated with a less-severe epileptic phenotype. Overall, this report provides an up-to-date review of the mutational and clinical spectrum of KCNB1, strengthening its place as a causal gene in DEEs and emphasizing the need for further functional studies to unravel the underlying mechanisms.

Developmental and epilepsy spectrum of KCNB1 encephalopathy with long-term outcome.

OBJECTIVE: We aimed to delineate the phenotypic spectrum and long-term outcome of individuals with KCNB1 encephalopathy. METHODS: We collected genetic, clinical, electroencephalographic, and imaging data of individuals with KCNB1 pathogenic variants recruited through an international collaboration, with the support of the family association "KCNB1 France." Patients were classified as having developmental and epileptic encephalopathy (DEE) or developmental encephalopathy (DE). In addition, we reviewed published cases and provided the long-term outcome in patients older than 12 years from our series and from literature. RESULTS: Our series included 36 patients (21 males, median age = 10 years, range = 1.6 months-34 years). Twenty patients (56%) had DEE with infantile onset seizures (seizure onset = 10 months, range = 10 days-3.5 years), whereas 16 (33%) had DE with late onset epilepsy in 10 (seizure onset = 5 years, range = 18 months-25 years) and without epilepsy in six. Cognitive impairment was more severe in individuals with DEE compared to those with DE. Analysis of 73 individuals with KCNB1 pathogenic variants (36 from our series and 37 published individuals in nine reports) showed developmental delay in all with severe to profound intellectual disability in 67% (n = 41/61) and autistic features in 56% (n = 32/57). Long-term outcome in 22 individuals older than 12 years (14 in our series and eight published individuals) showed poor cognitive, psychiatric, and behavioral outcome. Epilepsy course was variable. Missense variants were associated with more frequent and more severe epilepsy compared to truncating variants. SIGNIFICANCE: Our study describes the phenotypic spectrum of KCNB1 encephalopathy, which varies from severe DEE to DE with or without epilepsy. Although cognitive impairment is worse in patients with DEE, long-term outcome is poor for most and missense variants are associated with more severe epilepsy outcome. Further understanding of disease mechanisms should facilitate the development of targeted therapies, much needed to improve the neurodevelopmental prognosis.

Stereoelectroencephalography (SEEG) and epilepsy surgery in posttraumatic epilepsy: A multicenter retrospective study.

PURPOSE: Posttraumatic epilepsy (PTE) is a common cause of drug-resistant epilepsy, especially in young adults. Nevertheless, such patients are not common candidates for intracranial presurgical evaluation. We investigated the role of stereoelectroencephalography (SEEG) in defining epileptogenicity and surgical strategy in patients with PTE. METHODS: We analyzed ictal SEEG recordings from 18 patients. We determined the seizure onset zone (SOZ) by quantifying the epileptogenicity of the sampled structures, using the "epileptogenicity index" (EI). We also identified seizure onset patterns (SOPs) through visual and frequency analysis. Postsurgical outcome was assessed by Engel's classification. RESULTS: The SOZ in PTE was most often located in temporal lobes, followed by frontal lobes. The SOZ was network-organized in the majority of the cases. Half of the SOP did not contain fast discharges. Half of the recordings showed SOZ that were less extensive than the posttraumatic lesions seen on brain magnetic resonance imaging (MRI). All but one operated patient benefited from tailored cortectomy. Only 3 patients were contraindicated for surgical resection due to bilateral epileptogenicity. The overall surgical outcome was good in majority of patients (67% Engel I). CONCLUSION: Despite the potential risk of bilateral or multifocal epilepsy, patients with PTE may benefit from presurgical assessment in well-selected cases. In this context, SEEG allows guidance of tailored resections adapted to the SOZ.

Acute Colonic Pseudoobstruction (Ogilvie Syndrome) - A Severe Complication in the Evolution of the Hospitalized or Institutionalized Patients.

Introduction: Ogilvie syndrome, or acute colonic pseudo-obstruction (ACPO), represents a pathological entity, potentially with a severe outcome, due to the acute important dilation of the large bowel, in the absence of a mechanical luminal obstruction. Usually, it occurs in patients admitted in intensive care unit, that associate severe surgical or medical pathologies. The mechanism of the ACPO has not been completely explained, but it is assumed that the motor function of the colon may be affected, as a result of autonomic regulation disturbance. Early diagnosis and treatment help reduce the risk of severe outcome, such as ischemia or perforation. Material and Method: In addition to our experience, a literature search was elaborated in order to evaluate the incidence, the etiology, the clinical presentation and the diagnosis of the ACPO. Results and Conclusions: The present study may be of help in the process of guiding the optimal management of a critically ill patient is at high risk of developing colonic pseudo-obstruction.

High-frequency oscillations are not better biomarkers of epileptogenic tissues than spikes.

OBJECTIVE: High-frequency oscillations (HFOs) in intracerebral EEG (stereoelectroencephalography; SEEG) are considered as better biomarkers of epileptogenic tissues than spikes. How this can be applied at the patient level remains poorly understood. We investigated how well HFOs and spikes can predict epileptogenic regions with a large spatial sampling at the patient level. METHODS: We analyzed non-REM sleep SEEG recordings sampled at 2,048Hz of 30 patients. Ripples (Rs; 80-250Hz), fast ripples (FRs; 250-500Hz), and spikes were automatically detected. Rates of these markers and several combinations-spikes co-occurring with HFOs or FRs and cross-rate (Spk⊗HFO)-were compared to a quantified measure of the seizure onset zone (SOZ) by performing a receiver operating characteristic analysis for each patient individually. We used a Wilcoxon signed-rank test corrected for false-discovery rate to assess whether a marker was better than the others for predicting the SOZ. RESULTS: A total of 2,930 channels was analyzed (median of 100 channels per patient). The HFOs or any of its variants were not statistically better than spikes. Only one feature, the cross-rate, was better than all the other markers. Moreover, fast ripples, even though very specific, were not delineating all epileptogenic tissues. INTERPRETATION: At the patient level, the performance of HFOs is weakened by the presence of strong physiological HFO generators. Fast ripples are not sensitive enough to be the unique biomarker of epileptogenicity. Nevertheless, combining HFOs and spikes using our proposed measure-the cross-rate-is a better strategy than using only one marker. Ann Neurol 2018;83:84-97.

Probabilistic functional tractography of the human cortex revisited.

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.

Stereoelectroencephalography and surgical outcome in polymicrogyria-related epilepsy: A multicentric study.

OBJECTIVE: We aimed to (1) assess the concordance between various polymicrogyria (PMG) types and the associated epileptogenic zone (EZ), as defined by stereoelectroencephalography (SEEG), and (2) determine the postsurgical seizure outcome in PMG-related drug-resistant epilepsy. METHODS: We retrospectively analyzed 58 cases: 49 had SEEG and 39 corticectomy or hemispherotomy. RESULTS: Mean age at SEEG or surgery was 28.3 years (range, 2-50). PMG was bilateral in 9 (16%) patients and unilateral in 49, including 17 (29%) unilobar, 12 (21%) multilobar, 15 (26%) perisylvian, and only 5 (9%) hemispheric. Twenty-eight (48%) patients additionally had schizencephaly, heterotopia, or focal cortical dysplasia. The SEEG-determined EZ was fully concordant with the PMG in only 8 (16%) cases, partially concordant in 74%, and discordant in 10%. The EZ included remote cortical areas in 21 (43%) cases and was primarily localized in those in 5 (10%), all related to the mesial temporal structures. All but 1 PMG patient with corticectomy or hemispherotomy had a unilateral PMG. At last follow-up (mean, 4.6 years; range, 1-16), 28 (72%) patients remained seizure free. Shorter epilepsy duration to surgery was an independent predictor of seizure freedom. INTERPRETATION: PMG-related drug-resistant epilepsy warrants a comprehensive presurgical evaluation, including SEEG investigations in most cases, given that the EZ may only partially overlap with the PMG or include solely remote cortical areas. Seizure freedom is feasible in a large proportion of patients. PMG extent should not deter from exploring the possibility of epilepsy surgery. Our data support the early consideration of epilepsy surgery in this patient group. Ann Neurol 2017;82:781-794.

Identification of Interictal Epileptic Networks from Dense-EEG.

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: (i) the algorithm used in the solution of the EEG inverse problem and (ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms (dSPM, wMNE, sLORETA and cMEM) and four connectivity measures (r 2, h 2, PLV, and MI) on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods (nonlinear correlation coefficient, phase synchronization and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.

Appendiceal Diverticulitis - A Case Report.

Appendiceal diverticulitis is a very rare cause for pain in the right iliac fossa. Whether it is simptomatic or discovered randomly during an appendectomy or barium enema, understanding its clinical evolution is important for having a good management. In this report we present the case of a 50 year old female who underwent an open appendectomy during which we discovered appendiceal diverticulitis.

Somatomotor or somatosensory facial manifestations in patients with temporobasal epilepsies.

OBJECTIVE: The semiology of temporo-basal epilepsy has rarely been analysed in the literature. In this paper, we report three patients with proven basal temporal epilepsy with somatomotor or somatosensory facial ictal semiology, highly suggestive of insulo-opercular onset. METHODS: The three patients had a temporobasal lesion and their drugresistant epilepsy was cured with resection of the lesion (follow-up duration: 7-17 years). We reviewed the medical charts, non-invasive EEG data as well as the stereoelectroencephalography (SEEG) performed in two patients. Quantitative analysis of ictal fast gamma activity was performed for one patient. RESULTS: Early ictal features were orofacial, either somatomotor in two patients or ipsilateral somatosensory in one. The three patients had prior sensations compatible with a temporal lobe onset. Interictal and ictal EEG pointed to the temporal lobe. The propagation of the discharge to the insula and operculum before the occurrence of facial features was seen on SEEG. Facial features occurred 7-20 seconds after electrical onset. Quantitative analysis of six seizures in one patient confirmed the visual analysis, showing statistically significant fast gamma activity originating from basal areas and then propagating to insuloopercular regions after a few seconds. SIGNIFICANCE: We report three cases of lesional temporo-basal epilepsy responsible for orofacial semiology related to propagation of insulo-opercular ictal discharge. In MRI-negative patients with facial manifestations, this origin should be suspected when EEG is suggestive. These observations may contribute to our understanding of brain networks.

Intracerebral mechanisms explaining the impact of incidental feedback on mood state and risky choice.

Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. Here, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of subjects (n=30) while they were performing interleaved quiz and choice tasks that were designed to examine how a series of unrelated feedbacks affect decisions between safe and risky options. Neural baseline activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedbacks received in the quiz task, and then to the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that (1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) was respectively signaling periods of high and low mood, (2) increased vmPFC and daIns BGA respectively promoted and tempered risk taking by overweighting gain vs. loss prospects. Thus, incidental feedbacks induce brain states that correspond to different moods and bias the evaluation of risky options. More generally, these findings might explain why people experiencing positive (or negative) outcome in some part of their life tend to expect success (or failure) in any other.

Dynamic brain effective connectivity analysis based on low-rank canonical polyadic decomposition: application to epilepsy.

In this paper, a new method to track brain effective connectivity networks in the context of epilepsy is proposed. It relies on the combination of partial directed coherence with a constrained low-rank canonical polyadic tensor decomposition. With such combination being established, the most dominating directed graph structures underlying each time window of intracerebral electroencephalographic signals are optimally inferred. Obtained time and frequency signatures of inferred brain networks allow respectively to track the time evolution of these networks and to define frequency bands on which they are operating. Besides, the proposed method allows also to track brain connectivity networks through several epileptic seizures of the same patient. Understanding the most dominating directed graph structures over epileptic seizures and investigating their behavior over time and frequency plans are henceforth possible. Since only few but the the most important directed connections in the graph structure are of interest and also for a meaningful interpretation of obtained signatures to be guaranteed, the low-rank canonical polyadic tensor decomposition is prompted respectively by the sparsity and the non-negativity constraints on the tensor loading matrices. The main objective of this contribution is to propose a new way of tracking brain networks in the context of epileptic iEEG data by identifying the most dominant effective connectivity patterns underlying the observed iEEG signals at each time window. The performance of the proposed method is firstly evaluated on simulated data imitating brain activities and secondly on real intracerebral electroencephalographic signals obtained from an epileptic patient. The partial directed coherence-based tensor has been decomposed into space, time, and frequency signatures in accordance with the expected ground truth for each consecutive sequence of the simulated data. The method is also in accordance with the clinical expertise for iEEG epileptic signals, where the signatures were investigated through a simultaneous multi-seizure analysis.

SEEG re-exploration in a patient with complex frontal epilepsy with rapid perisylvian propagation and mixed "startle - reflex" seizures.

The SEEG International Course, organised in 2017, focused on the investigation and surgery of insulo-perisylvian epilepsies. We present one representative complex case that was discussed. The patient had seizures displaying startle/reflex components. He was MRI negative, while other non-invasive investigations offered only partially concordant data. Initial SEEG exploration resulted in an incomplete definition of the epileptogenic zone. A second SEEG followed, which led to a thorough assessment of the seizure onset zone and the epileptic network, localised to the lateral inferior premotor cortex, explaining the incongruent data obtained beforehand. This was the basis of a tailored resection with a favourable outcome. The patient has been seizure-free for five years without any motor nor cognitive deficits, but with pharmacodependence to one AED. The electroclinical reasoning is presented, accompanied by relevant commentaries and recommendations from the tutors [Published with video sequences].

Cardiac Autonomic Dysfunction and Risk of Sudden Unexpected Death in Epilepsy.

OBJECTIVE: We aimed to test whether patients who died of sudden unexpected death in epilepsy (SUDEP) had an abnormal cardiac autonomic response to sympathetic stimulation by hyperventilation. METHODS: We conducted a retrospective, observational, case-control study of a group of patients who died of SUDEP and controls who were matched to the patients for epilepsy type, drug resistance, sex, age at EEG recording, age at onset of epilepsy, and duration of epilepsy. We analyzed the heart rate (HR) and HR variability (HRV) at rest and during and after hyperventilation performed during the patient's last EEG recording before SUDEP. In each group, changes over time in HRV indexes were analyzed with linear mixed models. RESULTS: Twenty patients were included in each group. In the control group, the HR increased and the root mean square of successive RR-interval differences (RMSSD) decreased during the hyperventilation and then returned to the baseline values. In the SUDEP group, however, the HR and RMSSD did not change significantly during or after hyperventilation. A difference in HR between the end of the hyperventilation and 4 minutes after its end discriminated well between patients with SUDEP and control patients (area under the receiver operating characteristic curve 0.870, sensitivity 85%, specificity 75%). CONCLUSION: Most of patients with subsequent SUDEP have an abnormal cardiac autonomic response to sympathetic stimulation through hyperventilation. An index reflecting the change in HR on hyperventilation might be predictive of the risk of SUDEP and could be used to select patients at risk of SUDEP for inclusion in trials assessing protective measures.

Burnout syndrome in Romanian medical residents in time of the COVID-19 pandemic.

Burnout is a state of physical or mental collapse caused by overwork or stress. Burnout during residency training has gained significant attention secondary to concerns regarding job performance and patient care. The new COVID-19 pandemic has raised public health problems around the world and required a reorganization of health services. In this context, burnout syndrome and physical exhaustion have become even more pronounced. Resident doctors, and especially those in certain specialties, seem even more exposed due to the higher workload, prolonged exposure and first contact with patients. This article is a short review of the literature and a presentation of some considerations regarding the activity of the medical residents in a non-Covid emergency hospital in Romania, based on the responses obtained via a questionnaire. Burnout prevalence is not equal in different specialties. We studied its impact and imagine the potential steps that can be taken in order to reduce the increasing rate of burnout syndrome in the pandemics.