Can heart rate variability identify a high-risk state of upcoming seizure?

Heart rate variability (HRV) is an accessible and convenient means to assess the sympathetic/parasympathetic balance. Autonomic dysfunctions may reflect a pro-ictal state and occur before the seizure onset. Previous studies have reported HRV-based models to identify preictal states in continuous electrocardiogram (EKG) monitoring. Here, we evaluated the ability of HRV metrics extracted from daily single resting-state periods to estimate the risk of upcoming seizure(s) using probabilistic forecasts. Daily standardized 10-min vigilance-controlled EKG periods were recorded in 15 patients with drug-resistant focal epilepsy who underwent intracerebral electroencephalography (EEG). Analyses of a total of 156 periods, based on machine learning approaches, suggested that HRV features can identify preictal states with a median AUC of 0.75 [0.68;0.99]. Pseudoprospective daily forecasts yielded a median Brier score of 0.3 [0.18;0.48]. About 60% of preictal days were correctly forecasted, while false positive predictions were noticed in 24% of interictal days. Daily resting HRV seems to capture information on autonomic variations that may reflect a pro-ictal state. The method could be embedded in an ambulatory clinical seizure prediction device, but additional modalities (prodromes, EEG-based features, etc.) should be associated to improve its performance.

Brain Metabolic Alterations in Seropositive Autoimmune Encephalitis: An 18F-FDG PET Study.

INTRODUCTION: Autoimmune encephalitis (AE) diagnosis and follow-up remain challenging. Brain 18F-fluoro-deoxy-glucose positron emission tomography (FDG PET) has shown promising results in AE. Our aim was to investigate FDG PET alterations in AE, according to antibody subtype. METHODS: We retrospectively included patients with available FDG PET and seropositive AE diagnosed in our center between 2015 and 2020. Brain PET Z-score maps (relative to age matched controls) were analyzed, considering metabolic changes significant if |Z-score| ≥ 2. RESULTS: Forty-six patients were included (49.4 yrs [18; 81]): 13 with GAD autoantibodies, 11 with anti-LGI1, 9 with NMDAR, 5 with CASPR2, and 8 with other antibodies. Brain PET was abnormal in 98% of patients versus 53% for MRI. The most frequent abnormalities were medial temporal lobe (MTL) and/or striatum hypermetabolism (52% and 43% respectively), cortical hypometabolism (78%), and cerebellum abnormalities (70%). LGI1 AE tended to have more frequent MTL hypermetabolism. NMDAR AE was prone to widespread cortical hypometabolism. Fewer abnormalities were observed in GAD AE. Striatum hypermetabolism was more frequent in patients treated for less than 1 month (p = 0.014), suggesting a relation to disease activity. CONCLUSION: FDG PET could serve as an imaging biomarker for early diagnosis and follow-up in AE.

Daily resting-state intracranial EEG connectivity for seizure risk forecasts.

Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term electroencephalographic (EEG) recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure-during a vigilance-controlled period-to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide the best prediction performances (area under the curve ≥ .7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.

Semiology, EEG, and neuroimaging findings in temporal lobe epilepsies.

Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. First descriptions of TLE date back in time and detailed portraits of epileptic seizures of temporal origin can be found in early medical reports as well as in the works of various artists and dramatists. Depending on the seizure onset zone, several subtypes of TLE have been identified, each one associated with peculiar ictal semiology. TLE can result from multiple etiological causes, ranging from genetic to lesional ones. While the diagnosis of TLE relies on detailed analysis of clinical as well as electroencephalographic (EEG) features, the lesions responsible for seizure generation can be highlighted by multiple brain imaging modalities or, in selected cases, by genetic investigations. TLE is the most common cause of refractory epilepsy and despite the great advances in diagnostic tools, no lesion is found in around one-third of patients. Surgical treatment is a safe and effective option, requiring presurgical investigations to accurately identify the seizure onset zone (SOZ). In selected cases, presurgical investigations need intracerebral investigations (such as stereoelectroencephalography) or dedicated metabolic imaging techniques (interictal PET and ictal SPECT) to correctly identify the brain structures to be removed.

Long-term evolution and prognostic factors of epilepsy in limbic encephalitis with LGI1 antibodies.

OBJECTIVE: To characterize the evolution of epilepsy in patients with leucine-rich glioma inactivated 1 antibody-associated (LGI1ab) limbic encephalitis, including factors associated with drug-resistant epilepsy (DRE). METHODS: Retrospective analysis of patients with LGI1 encephalitis managed at two tertiary epilepsy centers between 2005 and 2019 and whose samples were confirmed by the French Reference Center of Paraneoplastic Neurological Syndromes. Raw clinical, biological, EEG, and MRI data were reviewed. Two endpoints were defined: (i) Epilepsy remission: patients seizure free and in whom anti-seizure medications (ASM) have been stopped for at least 1 year at the last follow-up visit (ii) DRE: patients with persistent seizures at the last follow-up despite at least two ASM used at efficacious daily dose. RESULTS: 39 patients with LGI1 encephalitis were included with a median follow-up duration of 42 months (range 13-169). All of them reported seizures at the acute phase, with faciobrachial dystonic seizures (FBDS) in 23 (59%) and other focal seizures in 38 (97%), including 4 patients (10%) with de novo status epilepticus. At the last follow-up visit, 11 patients (28%) achieved epilepsy remission. Among the 28 patients with persistent epilepsy, eight (29%) fulfilled criteria of DRE. The only factor significantly associated with epilepsy remission was the time from clinical onset of the encephalitis to initiation of the first immunomodulatory treatment, with longer delay in patients with persistent epilepsy (7.5 ± 8.9 vs 2.4 ± 1.7 months, p = 0.006). Evolution to DRE was only driven by MRI evolution. Eight of the 15 patients (53%) who developed hippocampal atrophy (p = 0.007) also suffered from drug-resistant seizures at the last follow-up. SIGNIFICANCE: In patients with LGI1 encephalitis, rapid initiation of immunomodulatory treatment favors long-term epilepsy remission. Evolution to DRE might primarily reflect the anatomical lesion of limbic structures. Determining what modalities of immune treatment may alter these outcomes requires prospective studies with long-term follow-up.

Long-term deep intracerebral microelectrode recordings in patients with drug-resistant epilepsy: Proposed guidelines based on 10-year experience.

PURPOSE: Human neuronal activity, recorded in vivo from microelectrodes, may offer valuable insights into physiological mechanisms underlying human cognition and pathophysiological mechanisms of brain diseases, in particular epilepsy. Continuous and long-term recordings are necessary to monitor non predictable pathological and physiological activities like seizures or sleep. Because of their high impedance, microelectrodes are more sensitive to noise than macroelectrodes. Low noise levels are crucial to detect action potentials from background noise, and to further isolate single neuron activities. Therefore, long-term recordings of multi-unit activity remains a challenge. We shared here our experience with microelectrode recordings and our efforts to reduce noise levels in order to improve signal quality. We also provided detailed technical guidelines for the connection, recording, imaging and signal analysis of microelectrode recordings. RESULTS: During the last 10 years, we implanted 122 bundles of Behnke-Fried hybrid macro-microelectrodes, in 56 patients with pharmacoresistant focal epilepsy. Microbundles were implanted in the temporal lobe (74%), as well as frontal (15%), parietal (6%) and occipital (5%) lobes. Low noise levels depended on our technical setup. The noise reduction was mainly obtained after electrical insulation of the patient's recording room and the use of a reinforced microelectrode model, reaching median root mean square values of 5.8 µV. Seventy percent of the bundles could record multi-units activities (MUA), on around 3 out of 8 wires per bundle and for an average of 12 days. Seizures were recorded by microelectrodes in 91% of patients, when recorded continuously, and MUA were recorded during seizures for 75 % of the patients after the insulation of the room. Technical guidelines are proposed for (i) electrode tails manipulation and protection during surgical bandage and connection to both clinical and research amplifiers, (ii) electrical insulation of the patient's recording room and shielding, (iii) data acquisition and storage, and (iv) single-units activities analysis. CONCLUSIONS: We progressively improved our recording setup and are now able to record (i) microelectrode signals with low noise level up to 3 weeks duration, and (ii) MUA from an increased number of wires . We built a step by step procedure from electrode trajectory planning to recordings. All these delicate steps are essential for continuous long-term recording of units in order to advance in our understanding of both the pathophysiology of ictogenesis and the neuronal coding of cognitive and physiological functions.

Epilepsy related to focal neuronal lipofuscinosis: extra-frontal localization, EEG signatures and GABA involvement.

Focal neuronal lipofuscinosis (FNL) is an uncommon epileptic disorder related to an excess of lipofuscin accumulation within dysmorphic-appearing neurons (DANs), whose epileptogenic mechanisms are still poorly understood. It shares some clinical and neuroimaging similarities with focal cortical dysplasia of type IIb (FCDIIb), but it represents a different pathological entity. Here, we identified two patients with FNL among a 10-year cohort of 323 patients who underwent neurosurgery for a focal pharmacoresistant epilepsy. We describe the electroclinical, metabolic and neuropathological features of both patients with FNL who benefited from a comprehensive presurgical investigation. While the previous reports showed frontal lobe localization of the lesion, FNL was identified in the temporal lobe, in one of our patients. EEG investigations in both patients showed striking focal and rich interictal activity resembling that described in FCDIIb. Besides focal intraneuronal lipofuscin accumulation, the neuropathological analysis demonstrated that somata of DANs were surrounded by a large amount of GABAergic presynaptic buttons, suggesting the involvement of interneurons in the epileptogenicity of FNL. To further explore the role of GABAergic transmission in the generation of epileptiform activity in FNL, we performed in vitro multi-electrode array recordings on the post-surgery tissue from one patient. Spontaneous interictal-like discharges (IILDs) were identified only in the restricted area displaying the highest density of lipofuscin-containing DANs, suggesting a close correlation between the density of lipofuscin-containing neurons and epileptogenicity. Moreover, IILDs were blocked by the GABAA receptor antagonist gabazine. All together, these findings showed how GABA signaling may contribute to the generation of interictal-like activity in FNL tissue.

Kv1.1 channels inhibition in the rat motor cortex recapitulates seizures associated with anti-LGI1 encephalitis.

Autoimmune encephalitis associated with antibodies directed against the leucine-rich glioma inactivated 1 (LGI1) protein is responsible for specific tonic-dystonic motor seizures. Although dysfunctions in neuronal excitability have been associated with anti-LGI1 autoantibodies, their relation to seizures remain inconclusive. We developed a new in vivo experimental rat model to determine whether inhibition of Kv1.1 channels by dentrotoxin-K (DTX) in the primary motor cortex (M1) could recapitulate the human seizures and to elucidate their subtending cortical mechanisms. Comparing electro-clinical features of DTX-induced seizures in rats with those recorded from a cohort of anti-LGI1 encephalitis patients revealed striking similarities in their electroencephalographic (EEG) signature, frequency of recurrence and semiology. By combining multi-site extracellular and intracellular recordings of M1 pyramidal neurons in DTX rats, we demonstrated that the blockade of Kv1.1 channels induced a sequence of changes in neuronal excitability and synaptic activity, leading to massive suprathreshold membrane depolarizations underlying the paroxysmal EEG activity. Our results suggest the central role of Kv1.1 channels disruption in the emergence of anti-LGI1-associated seizures and suggest that this new rodent model could serve future investigations on ictogenesis in autoimmune encephalitis.

Neuron Specific Enolase, S100-beta protein and progranulin as diagnostic biomarkers of status epilepticus.

Status epilepticus (SE) is a life-threatening prolonged epileptic seizure. A rapid diagnosis is fundamental to initiate antiepileptic treatment and to prevent the development of neurological sequels. Several serum and cerebrospinal fluid biomarkers have been proposed to help in the diagnosis of SE. Nevertheless, previous studies were conducted on too small patient cohorts, precluding the utilization of interesting biomarkers for the SE diagnosis. Here, we aimed to assess the ability of Neuron Specific Enolase (NSE), S100-beta protein (S100B) and progranulin to help in the diagnosis of SE in a large cohort of patients (36 control patients, 56 patients with pharmacoresistant epilepsy and 82 SE patients). Blood NSE, S100B and progranulin levels were higher in SE patients when compared with control patients or patients with pharmacoresistant epilepsy. Both NSE and progranulin levels were higher in cerebrospinal fluid from SE patients when compared with control patients. The receiver-operating characteristics curves revealed good accuracy at detecting SE for serum S100B (AUC 0.748) and plasma progranulin (AUC 0.756). The performances were lower for serum NSE (AUC 0.624). Eighty-four percent of patients with serum S100B levels above 0.09 ng/mL presented with a SE, whereas 90% of patients without SE had serum S100B levels lower than 0.09 ng/mL. Serum S100B levels were not significantly different according to SE etiology, SE semiology or SE refractoriness. Our results confirm that NSE, S100B and progranulin levels are increased after SE. We suggest that serum S100B levels might be added to clinical evaluation and electroencephalogram to identify difficult-to-diagnose form of SE.

When should we test patients with epilepsy for autoimmune antibodies? Results from a French retrospective single center study.

BACKGROUND AND PURPOSE: Seizures represent a core symptom of autoimmune encephalitides with specific therapeutic issues. To date, patients with new-onset seizures or established epilepsy are not systematically tested for autoimmune antibodies. We aimed to identify clinical and paraclinical criterion that could help to select patients requiring additional autoimmune antibodies serum and cerebrospinal fluid (CSF) detection. METHODS: In this retrospective single center study from the French Salpêtrière Hospital, data from 286 adult patients with epilepsy who received an autoantibody assay for the first time were analyzed. All patients were evaluated at our institution between January 2007 and December 2018 for assessment of new-onset epilepsy (n = 90) or established epilepsy (n = 196). We only analyzed patients that were screened for autoimmune antibodies. Demographic, clinical and neuroimaging measures were compared between patients with and without autoimmune encephalitis using Fisher's exact test for categorical variables and Welch's t test for continuous variables. Our primary goal was to identify significant factors that differentiated patients with and without autoimmune encephalitis. RESULTS: We identified 27 patients with autoimmune epilepsy (9.4% of the patients who had been tested for autoantibodies). The significant factors differentiating patients with and without autoimmune encephalitis were: (i) the existence of a new-onset focal epilepsy + (e.g., newly diagnosed epilepsy < 6 months associated with additional symptoms, mainly cognitive or psychiatric symptoms), (ii) the presence of faciobrachial dystonic seizures very suggestive of anti- Leucine-rich glioma inactivated 1 (LGI1) encephalitis, and (iii) the presence of magnetic resonance imaging (MRI) abnormalities suggestive of encephalitis. CONCLUSION: New-onset focal seizures combined with cognitive or psychiatric symptoms support the test for autoimmune antibodies. Further clinical already known red flags for an autoimmune origin are the presence of faciobrachial dystonic seizures and MRI signal changes consistent with encephalitis. On the other hand, isolated new-onset seizures and chronic epilepsy, even with associated symptoms, seem rarely linked to autoimmune encephalitis and should not lead to systematic testing.

The LGI1 protein: molecular structure, physiological functions and disruption-related seizures.

Leucine-rich, glioma inactivated 1 (LGI1) is a secreted glycoprotein, mainly expressed in the brain, and involved in central nervous system development and physiology. Mutations of LGI1 have been linked to autosomal dominant lateral temporal lobe epilepsy (ADLTE). Recently auto-antibodies against LGI1 have been described as the basis for an autoimmune encephalitis, associated with specific motor and limbic epileptic seizures. It is the second most common cause of autoimmune encephalitis. This review presents details on the molecular structure, expression and physiological functions of LGI1, and examines how their disruption underlies human pathologies. Knock-down of LGI1 in rodents reveals that this protein is necessary for normal brain development. In mature brains, LGI1 is associated with Kv1 channels and AMPA receptors, via domain-specific interaction with membrane anchoring proteins and contributes to regulation of the expression and function of these channels. Loss of function, due to mutations or autoantibodies, of this key protein in the control of neuronal activity is a common feature in the genesis of epileptic seizures in ADLTE and anti-LGI1 autoimmune encephalitis.

Seizures in autoimmune encephalitis: specific features based on a systematic comparative study.

OBJECTIVE: To highlight specific characteristics of seizure semiology and EEG features associated with different subtypes of autoimmune encephalitis (AE). METHODS: We systematically reviewed the seizure semiology and all the EEG recordings from patients with AE managed in a tertiary referral centre for epilepsy and a neuro-intensive care unit. Each characteristic across the different subtypes of AE was compared by post hoc analysis. RESULTS: We identified 66 patients with anti-neuronal antibody-mediated AE or Rasmussen's encephalitis (RE) experiencing seizures, which were the most frequent symptom at onset. Anti-NMDAR and anti-LGI1 AE accounted for the majority of patients; 41% and 24%, respectively. We isolated specific semiological features, such as early tonic-clonic seizures (TCS) in anti-NMDAR AE, early mesial temporal lobe seizures with emotional symptoms in anti-GAD AE, somatosensory seizures in RE, and a lower frequency of TCS in anti-LGI1 AE. EEG analysis also provided additional insights into distinguishing the subtypes based on: (1) generalized rhythmic delta activity, which was more sensitive than extreme delta brush in identifying anti-NMDAR AE among all subtypes; and (2) temporal interictal epileptiform activity and temporal seizures on EEG in anti-GAD AE. We identified a new EEG pattern consisting of temporal low-voltage and periodic spikes associated with ipsilateral hippocampal abnormalities on MRI, which could be a sign of inflammatory mesial temporal involvement. SIGNIFICANCE: Specific clinical and EEG features can be useful in guiding the diagnosis of a subtype of AE with acute symptomatic seizures, particularly before the results of anti-neuronal antibody testing are available.

Outliers in clinical symptoms as preictal biomarkers.

Previous findings have suggested that a preictal state might precede the epileptic seizure onset, which is the basis for seizure prediction attempts. Preictal states can be apprehended as outliers that differ from an interictal baseline and display clinical changes. We collected daily clinical scores from patients with epilepsy who underwent continuous video-EEG and assessed the ability of several outlier detection methods to identify preictal states. Results from 24 patients suggested that outlying clinical features were suggestive of preictal states and can be identified by statistical methods: AUC = 0.71, 95 % CI = [0.63 - 0.79]; PPV = 0.77, 95 % CI = [0.70 - 0.84]; FPR = 0.31, 95 % CI = [0.21 - 0.44]); and F1 score = 0.74, 95 % CI = [0.64 - 0.81]. Such algorithms could be straightforwardly implemented in a mobile device (e.g., tablet or smartphone), which would allow a longer data collection that could improve prediction performances. Additional clinical - and even multimodal - parameters could identify more subtle physiological modifications.

Association of Clinical, Biological, and Brain Magnetic Resonance Imaging Findings With Electroencephalographic Findings for Patients With COVID-19.

Importance: There is evidence of central nervous system impairments associated with coronavirus disease 2019 (COVID-19) infection, including encephalopathy. Multimodal monitoring of patients with COVID-19 may delineate the specific features of COVID-19-related encephalopathy and guide clinical management. Objectives: To investigate clinical, biological, and brain magnetic resonance imaging (MRI) findings in association with electroencephalographic (EEG) features for patients with COVID-19, and to better refine the features of COVID-19-related encephalopathy. Design, Setting, and Participants: This retrospective cohort study conducted in Pitié-Salpêtrière Hospital, Paris, France, enrolled 78 hospitalized adults who received a diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-Cov2) and underwent EEG between March 30 and June 11, 2020. Exposures: Detection of SARS-CoV-2 from a nasopharyngeal specimen using a reverse transcription-polymerase chain reaction assay or, in the case of associated pneumonia, on a computed tomography scan of the chest. Main Outcomes and Measures: Data on the clinical and paraclinical features of the 78 patients with COVID-19 were retrieved from electronic patient records. Results: Of 644 patients who were hospitalized for COVID-19, 78 (57 men [73%]; mean [SD] age, 61 [12] years) underwent EEG. The main indications for EEG were delirium, seizure-like events, and delayed awakening in the intensive care unit after stopping treatment with sedatives. Sixty-nine patients showed pathologic EEG findings, including metabolic-toxic encephalopathy features, frontal abnormalities, periodic discharges, and epileptic activities. Of 57 patients who underwent brain MRI, 41 showed abnormalities, including perfusion abnormalities, acute ischemic lesions, multiple microhemorrhages, and white matter-enhancing lesions. Fifty-five patients showed biological abnormalities, including dysnatremia, kidney failure, and liver dysfunction, the same day as the EEG. The results of cerebrospinal fluid analysis were negative for SARS-Cov-2 for all tested patients. Nine patients who had no identifiable cause of brain injury outside COVID-19 were further isolated; their brain injury was defined as COVID-19-related encephalopathy. They represented 1% (9 of 644) of patients with COVID-19 requiring hospitalization. Six of these 9 patients had movement disorders, 7 had frontal syndrome, 4 had brainstem impairment, 4 had periodic EEG discharges, and 3 had MRI white matter-enhancing lesions. Conclusions and Relevance: The results from this cohort of patients hospitalized with COVID-19 suggest there are clinical, EEG, and MRI patterns that could delineate specific COVID-19-related encephalopathy and guide treatment strategy.

Preictal state detection using prodromal symptoms: A machine learning approach.

A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML) approach. Twenty-four patients with drug-resistant epilepsy were admitted for continuous video-electroencephalographic monitoring and filled out a daily four-point questionnaire on prodromal symptoms. Data were then classified into (1) a preictal group for questionnaires completed in a 24-h period prior to at least one seizure (n1  = 58) and (2) an interictal group for questionnaires completed in a 24-h period without seizures (n2  = 190). Our prediction model was based on a support vector machine classifier and compared to a Fisher's linear classifier. The combination of all the prodromal symptoms yielded a good prediction performance (area under the curve [AUC] = .72, 95% confidence interval [CI] = .61-.81). This performance was significantly enhanced by selecting a subset of the most relevant symptoms (AUC = .80, 95% CI = .69-.88). In comparison, the linear classifier systematically failed (AUCs < .6). Our findings indicate that the ML analysis of prodromal symptoms is a promising approach to identifying preictal states prior to seizures. This could pave the way for development of clinical strategies in seizure prevention and even a noninvasive alarm system.

[How and when to stop antiepileptic drugs?] / Quand et comment arrêter un médicament antiépileptique ?

How and when to stop antiepileptic drugs? Antiepileptic drugs withdrawal can be considered in seizure-free patients with a 2-year minimum complete remission. Risk of seizure recurrence, which is related both to the epilepsy syndrome and individual characteristics, must be evaluated and may rely on the use of a computer-based risk calculator. Identifying epileptic abnormalities on EEG and cortical lesions on brain MRI could lead to reconsider drug discontinuation. Drug tapering must be progressive over 2 to 3 months, with a dose reduction every 2 weeks. Driving is not allowed during tapering and several months after withdrawal. Relapses mainly occur in the first year of withdrawal. A 2-year minimum follow-up of patients is recommended.

Quand et comment arrêter un médicament antiépileptique ? L'arrêt d'un traitement antiépileptique se discute après une rémission complète des crises durant une période d'au moins 2 ans. Une évaluation du risque de récidive, inhérent au syndrome épileptique mais également aux caractéristiques propres de chaque patient, est nécessaire et peut s'appuyer sur l'utilisation d'un calculateur de risque. En cas de persistance d'anomalies épileptiques sur l'électroencéphalogramme et d'identification d'une lésion corticale sur l'imagerie par résonance magnétique cérébrale, il faut éviter de sevrer un médicament antiépileptique. S'il est possible, le sevrage doit être progressif, en 2 à 3 mois, voire plus selon la molécule, en procédant par paliers de 15 jours. La conduite automobile est contre-indiquée durant le sevrage et les mois qui suivent. Les récidives de crises surviennent principalement durant la première année d'arrêt, et un suivi des patients d'au moins 2 ans est préconisé.