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.

Psychiatric complications following SEEG-guided radiofrequency thermocoagulations in patients with drug-resistant epilepsy.

SEEG-guided radiofrequency thermocoagulation (RF-TC) in the epileptogenic regions is a therapeutic option for patients with drug-resistant focal epilepsy who may have or not indication for epilepsy surgery. The most common adverse events of RF-TC are seizures, headaches, somatic pain, and sensory-motor deficits. If RF-TC could lead to psychiatric complications is unknown. In the present study, seven out of 164 patients (4.2 %) experienced psychiatric decompensation with or without memory deterioration after RF-TC of bilateral or unilateral amygdala and hippocampus. The appearance of symptoms was either acute, subacute, or chronic and the symptoms were either transient or lasted for several months. Common features among these patients were female sex, mesial temporal epilepsy, and a pre-existing history of psychological distress and memory dysfunction. Our study highlights the possibility of neuropsychiatric deterioration in specific patients following SEEG-guided RF-TC, despite its rarity.

Multiscale neuro-inspired models for interpretation of EEG signals in patients with epilepsy.

OBJECTIVE: The aim is to gain insight into the pathophysiological mechanisms underlying interictal epileptiform discharges observed in electroencephalographic (EEG) and stereo-EEG (SEEG, depth electrodes) recordings performed during pre-surgical evaluation of patients with drug-resistant epilepsy. METHODS: We developed novel neuro-inspired computational models of the human cerebral cortex at three different levels of description: i) microscale (detailed neuron models), ii) mesoscale (neuronal mass models) and iii) macroscale (whole brain models). Although conceptually different, micro- and mesoscale models share some similar features, such as the typology of neurons (pyramidal cells and three types of interneurons), their spatial arrangement in cortical layers, and their synaptic connectivity (excitatory and inhibitory). The whole brain model consists of a large-scale network of interconnected neuronal masses, with connectivity based on the human connectome. RESULTS: For these three levels of description, the fine-tuning of free parameters and the quantitative comparison with real data allowed us to reproduce interictal epileptiform discharges with a high degree of fidelity and to formulate hypotheses about the cell- and network-related mechanisms underlying the generation of fast ripples and SEEG-recorded epileptic spikes and spike-waves. CONCLUSIONS: The proposed models provide valuable insights into the pathophysiological mechanisms underlying the generation of epileptic events. The knowledge gained from these models effectively complements the clinical analysis of SEEG data collected during the evaluation of patients with epilepsy. SIGNIFICANCE: These models are likely to play a key role in the mechanistic interpretation of epileptiform activity.

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.

The Temporal Lobe Club: Newer Approaches to Treat Temporal Lobe Epilepsy.

This brief review summarizes presentations at the Temporal Lobe Club Special Interest Group session held in December 2022 at the American Epilepsy Society meeting. The session addressed newer methods to treat temporal epilepsy, including methods currently in clinical use and techniques under investigation. Brief summaries are provided for each of 4 lectures. Dr Chengyuan Wu discussed ablative techniques such as laser interstitial thermal ablation, radiofrequency ablation, focused ultrasound; Dr Joon Kang reviewed neuromodulation techniques including electrical stimulation and focused ultrasound; Dr Julia Makhalova discussed network effects of the aforementioned techniques; and Dr Derek Southwell reviewed inhibitory interneuron transplantation. These summaries are intended to provide a brief overview and references are provided for the reader to learn more about each topic.

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.

Grading system for assessing the confidence in the epileptogenic zone reported in published studies: A Delphi consensus study.

OBJECTIVE: This study was undertaken to develop a standardized grading system based on expert consensus for evaluating the level of confidence in the localization of the epileptogenic zone (EZ) as reported in published studies, to harmonize and facilitate systematic reviews in the field of epilepsy surgery. METHODS: We conducted a Delphi study involving 22 experts from 18 countries, who were asked to rate their level of confidence in the localization of the EZ for various theoretical clinical scenarios, using different scales. Information provided in these scenarios included one or several of the following data: magnetic resonance imaging (MRI) findings, invasive electroencephalography summary, and postoperative seizure outcome. RESULTS: The first explorative phase showed an overall interrater agreement of .347, pointing to large heterogeneity among experts' assessments, with only 17% of the 42 proposed scenarios associated with a substantial level of agreement. A majority showed preferences for the simpler scale and single-item scenarios. The successive Delphi voting phases resulted in a majority consensus across experts, with more than two thirds of respondents agreeing on the rating of each of the tested single-item scenarios. High or very high levels of confidence were ascribed to patients with either an Engel class I or class IA postoperative seizure outcome, a well-delineated EZ according to all available invasive EEG (iEEG) data, or a well-delineated focal epileptogenic lesion on MRI. MRI signs of hippocampal sclerosis or atrophy were associated with a moderate level of confidence, whereas a low level was ascribed to other MRI findings, a poorly delineated EZ according to iEEG data, or an Engel class II-IV postoperative seizure outcome. SIGNIFICANCE: The proposed grading system, based on an expert consensus, provides a simple framework to rate the level of confidence in the EZ reported in published studies in a structured and harmonized way, offering an opportunity to facilitate and increase the quality of systematic reviews and guidelines in the field of epilepsy surgery.

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.

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.

Correspondence between scalp-EEG and stereoelectroencephalography seizure-onset patterns in patients with MRI-negative drug-resistant focal epilepsy.

OBJECTIVE: Our objective was to evaluate the relationship between scalp-EEG and stereoelectroencephalography (SEEG) seizure-onset patterns (SOP) in patients with MRI-negative drug-resistant focal epilepsy. METHODS: We analyzed retrospectively 41 patients without visible lesion on brain MRI who underwent video-EEG followed by SEEG. We defined five types of SOPs on scalp-EEG and eight types on SEEG. We examined how various clinical variables affected scalp-EEG SOPs. RESULTS: The most prevalent scalp SOPs were rhythmic sinusoidal activity (56.8%), repetitive epileptiform discharges (22.7%), and paroxysmal fast activity (15.9%). The presence of paroxysmal fast activity on scalp-EEG was always seen without delay from clinical onset and correlated with the presence of low-voltage fast activity in SEEG (sensitivity = 22.6%, specificity = 100%). The main factor explaining the discrepancy between the scalp and SEEG SOPs was the delay between clinical and scalp-EEG onset. There was a correlation between the scalp and SEEG SOPs when the scalp onset was simultaneous with the clinical onset (p = 0.026). A significant delay between clinical and scalp discharge onset was observed in 25% of patients and featured always with a rhythmic sinusoidal activity on scalp, corresponding to similar morphology of the discharge on SEEG. The presence of repetitive epileptiform discharges on scalp was associated with an underlying focal cortical dysplasia (sensitivity = 30%, specificity = 90%). There was no significant association between the scalp SOP and the epileptogenic zone location (deep or superficial), or surgical outcome. SIGNIFICANCE: In patients with MRI-negative focal epilepsy, scalp SOP could suggest the SEEG SOP and some etiology (focal cortical dysplasia) but has no correlation with surgical prognosis. Scalp SOP correlates with the SEEG SOP in cases of simultaneous EEG and clinical onset; otherwise, scalp SOP reflects the propagation of the SEEG discharge. PLAIN LANGUAGE SUMMARY: We looked at the correspondence between the electrical activity recorded during the start of focal seizure using scalp and intracerebral electrodes in patients with no visible lesion on MRI. If there is a fast activity on scalp, it reflects similar activity inside the brain. We found a good correspondence between scalp and intracerebral electrical activity for cases without significant delay between clinical and scalp electrical onset (seen in 75% of the cases we studied). Visualizing repetitive epileptic activity on scalp could suggest a particular cause of the epilepsy: a subtype of brain malformation called focal cortical dysplasia.

Helium Optically Pumped Magnetometers Can Detect Epileptic Abnormalities as Well as SQUIDs as Shown by Intracerebral Recordings.

Magnetoencephalography based on superconducting quantum interference devices (SQUIDs) has been shown to improve the diagnosis and surgical treatment decision for presurgical evaluation of drug-resistant epilepsy. Still, its use remains limited because of several constraints such as cost, fixed helmet size, and the obligation of immobility. A new generation of sensors, optically pumped magnetometers (OPMs), could overcome these limitations. In this study, we validate the ability of helium-based OPM (4He-OPM) sensors to record epileptic brain activity thanks to simultaneous recordings with intracerebral EEG [stereotactic EEG (SEEG)]. We recorded simultaneous SQUIDs-SEEG and 4He-OPM-SEEG signals in one patient during two sessions. We show that epileptic activities on intracerebral EEG can be recorded by OPMs with a better signal-to noise ratio than classical SQUIDs. The OPM sensors open new venues for the widespread application of magnetoencephalography in the management of epilepsy and other neurologic diseases and fundamental neuroscience.

Stress and Epilepsy: Towards Understanding of Neurobiological Mechanisms for Better Management.

Stress has been identified as a major contributor to human disease and is postulated to play a substantial role in epileptogenesis. In a significant proportion of individuals with epilepsy, sensitivity to stressful events contributes to dynamic symptomatic burden, notably seizure occurrence and frequency, and presence and severity of psychiatric comorbidities [anxiety, depression, posttraumatic stress disorder (PTSD)]. Here, we review this complex relationship between stress and epilepsy using clinical data and highlight key neurobiological mechanisms including the hypothalamic-pituitary-adrenal (HPA) axis dysfunction, altered neuroplasticity within limbic system structures, and alterations in neurochemical pathways such as brain-derived neurotrophic factor (BNDF) linking epilepsy and stress. We discuss current clinical management approaches of stress that help optimize seizure control and prevention, as well as psychiatric comorbidities associated with epilepsy. We propose that various shared mechanisms of stress and epilepsy present multiple avenues for the development of new symptomatic and preventative treatments, including disease modifying therapies aimed at reducing epileptogenesis. This would require close collaborations between clinicians and basic scientists to integrate data across multiple scales, from genetics to systems biology, from clinical observations to fundamental mechanistic insights. In future, advances in machine learning approaches and neuromodulation strategies will enable personalized and targeted interventions to manage and ultimately treat stress-related epileptogenesis.

Human thalamic low-frequency oscillations correlate with expected value and outcomes during reinforcement learning.

Reinforcement-based adaptive decision-making is believed to recruit fronto-striatal circuits. A critical node of the fronto-striatal circuit is the thalamus. However, direct evidence of its involvement in human reinforcement learning is lacking. We address this gap by analyzing intra-thalamic electrophysiological recordings from eight participants while they performed a reinforcement learning task. We found that in both the anterior thalamus (ATN) and dorsomedial thalamus (DMTN), low frequency oscillations (LFO, 4-12 Hz) correlated positively with expected value estimated from computational modeling during reward-based learning (after outcome delivery) or punishment-based learning (during the choice process). Furthermore, LFO recorded from ATN/DMTN were also negatively correlated with outcomes so that both components of reward prediction errors were signaled in the human thalamus. The observed differences in the prediction signals between rewarding and punishing conditions shed light on the neural mechanisms underlying action inhibition in punishment avoidance learning. Our results provide insight into the role of thalamus in reinforcement-based decision-making in humans.

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.

Post-traumatic stress disorder (PTSD), cognitive control, and perceived seizure control in patients with epilepsy: An exploratory study.

Epilepsy is often linked to various psychiatric symptoms, with anxiety, depression, and interictal dysphoric disorders being the most prevalent. Few studies have investigated posttraumatic stress disorder (PTSD) in epilepsy, but they suggest a notable prevalence of PTSD. PTSD is known to be associated with cognitive impairments, particularly memory and executive functions. Our proposed exploratory study aims to investigate executive attentional control and emotional inhibition in patients with drug-resistant epilepsy (DRE) who exhibit PTSD symptoms compared with a healthy control group. Additionally, some PWE can manage their seizures using emotional and cognitive strategies, we find it relevant to explore the connection between their regulation abilities, cognitive control performance, and PTSD symptoms. We included 54 PWE and 60 healthy participants. They completed anxiety and depression scales as well as two questionnaires assessing PTSD symptoms and a questionnaire that measured the perceived self-control of seizures. We measured executive control using an executive control task (Attention Network Test, ANT) and an emotional Go/No-Go task. We found a positive correlation between PTSD scores (PDS-5) and performance at the ANT task. In contrast, in the emotional inhibition (Go/No-Go) task, behavioral inhibition errors were positively correlated with PTSD scores, specifically with hypervigilance symptoms in PTSD+ patients. There was a positive correlation between response reaction times in an aversive condition and PTSD scores: the more severe the PTSD symptoms, the faster the PWE identified stimuli in the angry face condition of the Go/No-Go task. Regarding perceived seizure control, we found correlations between alertness and PTSD symptoms associated with seizure anticipation during the inter- and peri-ictal periods. Patients with PTSD symptoms reported better seizure control. Our findings suggest that epilepsy patients with PTSD experience cognitive changes such as heightened executive attentional control, weakened emotional inhibition, and improved seizure control perception.

BDNF as potential biomarker of epilepsy severity and psychiatric comorbidity: pitfalls in the clinical population.

BACKGROUND: Several studies implicate brain-derived neurotrophic factor (BDNF) in the pathophysiology of epilepsy. In particular, preclinical data suggest that lower serum BDNF is a biomarker of epilepsy severity and psychiatric comorbidities. We tested this prediction in clinical epilepsy cohorts. METHODS: Patients with epilepsy were recruited from 4 epilepsy centers in France and serum BDNF was quantified. Clinical characteristics including epilepsy duration, classification, localization, etiology, seizure frequency and drug resistance were documented. Presence of individual anti-seizure medications (ASM) was noted. Screening for depression and anxiety symptoms was carried out in all patients using the NDDI-E and the GAD-7 scales. In patients with positive screening for anxiety and/or depression, detailed psychiatric testing was performed including the Mini International Neuropsychiatric Interview (MINI), STAI-Y, Holmes Rahe Stressful Events Scale and Beck Depression Interview. Descriptive analysis was applied. Spearman's test and Pearson's co-efficient were used to assess the association between BDNF level and continuous variables. For discrete variables, comparison of means (Student's t-test, Mann-Whitney u-test) was used to compare mean BDNF serum level between groups. Multivariate analysis was performed using a regression model. RESULTS: No significant correlation was found between serum BDNF level and clinical features of epilepsy or measures of depression. The main group-level finding was that presence of any ASM at was associated with increased BDNF; this effect was particularly significant for valproate and perampanel. CONCLUSION: Presence of ASM affects serum BDNF levels in patients with epilepsy. Future studies exploring BDNF as a possible biomarker of epilepsy severity and/or psychiatric comorbidity must control for ASM effects.

How valid are proxy assessment of mental health and sleep comorbidities of patients with epilepsy using standardized questionnaires?

BACKGROUND: The study aimed to determine the level of agreement between patients with epilepsy and their proxies when assessing psychiatric comorbidities, sleep disorders, and medication adherence using standardized questionnaires. METHODS: This agreement study is an ancillary analysis of the PRERIES study, a matched case-control study exploring SUDEP risk factors. Controls aged 15 years and older, with active epilepsy or in remission for less than 5 years were recruited between 01/01/2011 and 03/31/2019. An interview was carried out by a trained psychologist on both the patient and a proxy-respondent. During these independent interviews, the following comorbidities were explored: psychiatric comorbidities using the MINI, the STAI- Y2 and NDDI-E scales, sleep disorders with the SDQ-SA and Epworth scales and medication adherence. Level of agreement between patient and their proxy was estimated using Gwet's AC1&2. RESULTS: Among the 107 patient-proxy dyads recruited, proxy respondents were mainly family members (65.4%) or spouses (30.8%). Exploration of present major depression showed excellent agreement at 0.81 [0.65;0.97], as well as exploration of dysthymia at 0.96 [0.61;1]. Suicidal risk evaluation had a lesser agreement at 0.77 [0.60;0.94]. Agreement on anxiety was moderate 0.5 [0.38;0.62]. For sleep disorder, SDQ-SA presented a better agreement than the Epworth questionnaire with respectively 0.73 [0.51;0.95] and 0.45 [0.26;0.63]. For medication adherence, the overall agreement rate was excellent (0.90 [0.78;1]). CONCLUSION: Exploration of potential risk factors through families can give valuable and relatively robust information, especially if the respondent lives with the patient, and should be retrieved, when possible, in usual clinical setting.

Multi-scale structural alterations of the thalamus and basal ganglia in focal epilepsy using 7T MRI.

Focal epilepsy is characterized by repeated spontaneous seizures that originate from cortical epileptogenic zone networks (EZN). Analysis of intracerebral recordings showed that subcortical structures, and in particular the thalamus, play an important role in seizure dynamics as well, supporting their structural alterations reported in the neuroimaging literature. Nonetheless, between-patient differences in EZN localization (e.g., temporal vs. non-temporal lobe epilepsy) as well as extension (i.e., number of epileptogenic regions) might impact the magnitude as well as spatial distribution of subcortical structural changes. Here we used 7 Tesla MRI T1 data to provide an unprecedented description of subcortical morphological (volume, tissue deformation, and shape) and longitudinal relaxation (T1 ) changes in focal epilepsy patients and evaluate the impact of the EZN and other patient-specific clinical features. Our results showed variable levels of atrophy across thalamic nuclei that appeared most prominent in the temporal lobe epilepsy group and the side ipsilateral to the EZN, while shortening of T1 was especially observed for the lateral thalamus. Multivariate analyses across thalamic nuclei and basal ganglia showed that volume acted as the dominant discriminator between patients and controls, while (posterolateral) thalamic T1 measures looked promising to further differentiate patients based on EZN localization. In particular, the observed differences in T1 changes between thalamic nuclei indicated differential involvement based on EZN localization. Finally, EZN extension was found to best explain the observed variability between patients. To conclude, this work revealed multi-scale subcortical alterations in focal epilepsy as well as their dependence on several clinical characteristics.

Ictal Fear during parietal seizures.

Ictal fear is characterized by subjective fear sensation and consistent clinical manifestations during seizures. This phenomenon is rarely observed in parietal seizures. We report anatomical electroclinical correlations of an SEEG-recorded seizure with prominent fear semiology. Seizure onset zone was quantified using the Connectivity Epileptogenicity Index method (cEI). Occurrence of fear during seizures was related to the involvement of the left inferior parietal cortex and the superior temporal gyrus without amygdala involvement. Our case confirms that parietal seizure can produce ictal fear without concomitant involvement of the limbic temporal network.