Prevalence of Suicidality in Adolescents With Newly Diagnosed Focal Epilepsy at Diagnosis and Over the Following 36 Months.

BACKGROUND AND OBJECTIVES: Individuals with epilepsy have increased risk of suicidal ideation (SI) and behaviors when compared with the general population. This relationship has remained largely unexplored in adolescents. We investigated the prevalence of suicidality in adolescents with newly diagnosed focal epilepsy within 4 months of treatment initiation and over the following 36 months. METHODS: This was a post hoc analysis of the enrollment and follow-up data from the Human Epilepsy Project, an international, multi-institutional study that enrolled participants between 2012 and 2017. Participants enrolled were 11-17 years of age within 4 months of treatment initiation for focal epilepsy. We used data from the Columbia Suicide Severity Rating Scale (C-SSRS), administered at enrollment and over the 36-month follow-up period, along with data from medical records. RESULTS: A total of 66 adolescent participants were enrolled and completed the C-SSRS. At enrollment, 14 (21%) had any lifetime SI and 5 (8%) had any lifetime suicidal behaviors (SBs). Over the following 36 months, 6 adolescents reported new onset SI and 5 adolescents reported new onset SB. Thus, the lifetime prevalence of SI within this population increased from 21% to 30% (14-20 adolescents), and the lifetime prevalence of SB increased from 8% to 15% (5-10). DISCUSSION: The prevalence of suicidality in adolescents with newly diagnosed focal epilepsy reported in our study is consistent with previous findings of significant suicidality observed in epilepsy. We identify adolescents as an at-risk population at the time of epilepsy diagnosis and in the following years.

Epilepsia partialis continua: A review.

Epilepsia partialis continua (EPC) is a rare type of focal motor seizure characterized by continuous, involuntary muscle contractions in a specific part of the body. These contractions usually involve rhythmic, twitching movements and can last for several hours to days. The seizures are usually limited to one part of the body and can be clonic or dystonic. EPC can affect people of all ages but is more common in children and adolescents. The pathophysiology of EPC is complex and depends on the cause. There are several possible causes of EPC including structural brain abnormalities, infections, metabolic and genetic disorders, inflammatory conditions, traumatic brain injury, and vascular causes. The work-up of EPC includes electroencephalography (EEG), magnetic resonance imaging (MRI) of the brain, position emission tomography (PET) scan of the brain, autoimmune antibodies, infection work-up, and metabolic and genetic work-up. The management of EPC can be challenging. Antiseizure medications (ASDs) including benzodiazepines are an integral part of the management of EPC. Immunotherapy trials are recommended in resistant cases. Epilepsy surgery is one of the effective modalities in some surgically amenable cases. This article reviews the topic of EPC and summarizes diagnostic and .treatment recommendations.

Unrecognized Focal Nonmotor Seizures in Adolescents Presenting to Emergency Departments.

BACKGROUND AND OBJECTIVES: Many adolescents with undiagnosed focal epilepsy seek evaluation in emergency departments (EDs). Accurate history-taking is essential to prompt diagnosis and treatment. In this study, we investigated ED recognition of motor vs nonmotor seizures and its effect on management and treatment of focal epilepsy in adolescents. METHODS: This was a retrospective analysis of enrollment data from the Human Epilepsy Project (HEP), an international multi-institutional study that collected data from 34 sites between 2012 and 2017. Participants were 12 years or older, neurotypical, and within 4 months of treatment initiation for focal epilepsy. We used HEP enrollment medical records to review participants' initial diagnosis and management. RESULTS: A total of 83 adolescents were enrolled between 12 and 18 years. Fifty-eight (70%) presented to an ED before diagnosis of epilepsy. Although most ED presentations were for motor seizures (n = 52; 90%), many patients had a history of nonmotor seizures (20/52 or 38%). Adolescents with initial nonmotor seizures were less likely to present to EDs (26/44 or 59% vs 32/39 or 82%, p = 0.02), and nonmotor seizures were less likely to be correctly identified (2/6 or 33% vs 42/52 or 81%, p = 0.008). A history of initial nonmotor seizures was not recognized in any adolescent who presented for a first-lifetime motor seizure. As a result, initiation of treatment and admission from the ED was not more likely for these adolescents who met the definition of epilepsy compared with those with no seizure history. This lack of nonmotor seizure history recognition in the ED was greater than that observed in the adult group (0% vs 23%, p = 0.03) and occurred in both pediatric and nonpediatric ED settings. DISCUSSION: Our study supports growing evidence that nonmotor seizures are often undiagnosed, with many individuals coming to attention only after conversion to motor seizures. We found this treatment gap is exacerbated in the adolescent population. Our study highlights a critical need for physicians to inquire about the symptoms of nonmotor seizures, even when the presenting seizure is motor. Future interventions should focus on improving nonmotor seizure recognition for this population in EDs.

Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures.

Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy diagnosis, but manual seizure classification is time-consuming and requires extensive expertise. This paper presents an automated multi-class seizure classification model using EEG signals from the Temple University Hospital Seizure Corpus ver. 1.5.2. 11 features including time-based correlation, time-based eigenvalues, power spectral density, frequency-based correlation, frequency-based eigenvalues, sample entropy, spectral entropy, logarithmic sum, standard deviation, absolute mean, and ratio of Daubechies D4 wavelet transformed coefficients were extracted from 10-second sliding windows across channels. The model combines multi-head self-attention mechanism with a deep convolutional neural network (CNN) to classify seven subtypes of generalized and focal epileptic seizures. The model achieved 0.921 weighted accuracy and 0.902 weighted F1 score in classifying focal onset non-motor, generalized onset non-motor, simple partial, complex partial, absence, tonic, and tonic-clonic seizures. In comparison, a CNN model without multi-head attention achieved 0.767 weighted accuracy. Ablation studies were conducted to validate the importance of transformer encoders and attention. The promising classification results demonstrate the potential of deep learning for handling EEG complexity and improving epilepsy diagnosis. This seizure classification model could enable timely interventions when translated into clinical practice.

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches.

PURPOSE: Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods. METHODS: We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa. RESULTS: The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use. CONCLUSION: A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging.

Using scalp EEG to predict seizure recurrence and electrical status epilepticus in children with idiopathic focal epilepsy.

PURPOSE: Some individuals with idiopathic focal epilepsy (IFE) experience recurring seizures accompanied by the evolution of electrical status epilepticus during sleep (ESES). Here, we aimed to develop a predictor for the early detection of seizure recurrence with ESES in children with IFE using resting state electroencephalogram (EEG) data. METHODS: The study group included 15 IFE patients who developed seizure recurrence with ESES. There were 17 children in the control group who did not experience seizure recurrence with ESES during at least 2-year follow-up. We used the degree value of the partial directed coherence (PDC) from the EEG data to predict seizure recurrence with ESES via 6 machine learning (ML) algorithms. RESULTS: Among the models, the Xgboost Classifier (XGBC) model achieved the highest specificity of 0.90, and a remarkable sensitivity and accuracy of 0.80 and 0.85, respectively. The CATC showed balanced performance with a specificity of 0.85, sensitivity of 0.73, and an accuracy of 0.80, with an AUC equal to 0.78. For both of these models, F4, Fz and T4 were the overlaps of the top 4 features. CONCLUSIONS: Considering its high classification accuracy, the XGBC model is an effective and quantitative tool for predicting seizure recurrence with ESES evolution in IFE patients. We developed an ML-based tool for predicting the development of IFE using resting state EEG data. This could facilitate the diagnosis and treatment of patients with IFE.

Detection of somatic and germline pathogenic variants in adult cohort of drug-resistant focal epilepsies.

OBJECTIVE: This study investigates the prevalence of pathogenic variants in the mechanistic target of rapamycin (mTOR) pathway in surgical specimens of malformations of cortical development (MCDs) and cases with negative histology. The study also aims to evaluate the predictive value of genotype-histotype findings on the surgical outcome. METHODS: The study included patients with drug-resistant focal epilepsy who underwent epilepsy surgery. Cases were selected based on histopathological diagnosis, focusing on MCDs and negative findings. We included brain tissues both as formalin-fixed, paraffin-embedded (FFPE) or fresh frozen (FF) samples. Single-molecule molecular inversion probes (smMIPs) analysis was conducted, targeting the MTOR gene in FFPE samples and 10 genes within the mTOR pathway in FF samples. Correlations between genotype-histotype and surgical outcome were examined. RESULTS: We included 78 patients for whom we obtained 28 FFPE samples and 50 FF tissues. Seventeen pathogenic variants (22 %) were identified and validated, with 13 being somatic within the MTOR gene and 4 germlines (2 DEPDC5, 1 TSC1, 1 TSC2). Pathogenic variants in mTOR pathway genes were exclusively found in FCDII and TSC cases, with a significant association between FCD type IIb and MTOR genotype (P = 0.003). Patients carrying mutations had a slightly better surgical outcome than the overall cohort, however it results not significant. The FCDII diagnosed cases more frequently had normal neuropsychological test, a higher incidence of auras, fewer multiple seizure types, lower occurrence of seizures with awareness impairment, less ictal automatisms, fewer Stereo-EEG investigations, and a longer period long-life of seizure freedom before surgery. SIGNIFICANCE: This study confirms that somatic MTOR variants represent the primary genetic alteration detected in brain specimens from FCDII/TSC cases, while germline DEPDC5, TSC1/TSC2 variants are relatively rare. Systematic screening for these mutations in surgically treated patients' brain specimens can aid histopathological diagnoses and serve as a biomarker for positive surgical outcomes. Certain clinical features associated with pathogenic variants in mTOR pathway genes may suggest a genetic etiology in FCDII patients.

Comprehensive review of status gelasticus: Diagnostic challenges and therapeutic insights.

Status gelasticus is a rare form of status epilepticus characterized by prolonged and/or clustered gelastic seizures. The review encompasses an analysis of cases reported in the literature, focusing on causes, clinical-electroencephalographic features, and therapeutic interventions. The study reveals the challenges in defining and understanding status gelasticus due to its diverse etiologies and limited reported cases. The association with hypothalamic hamartomas and other brain abnormalities underscores the importance of thorough evaluations. The review also discusses new treatments, including medications and less invasive surgeries. While progress has been made, the study points out challenges in diagnosing and managing this complex condition, highlighting the importance of ongoing research.

Specific consistency score for rational selection of epilepsy resection surgery candidates.

OBJECTIVE: Degree of indication for epilepsy surgery is determined by taking multiple factors into account. This study aimed to investigate the usefulness of the Specific Consistency Score (SCS), a proposed score for focal epilepsy to rate the indication for epilepsy focal resection. METHODS: This retrospective cohort study included patients considered for resective epilepsy surgery in Kyoto University Hospital from 2011 to 2022. Plausible epileptic focus was tentatively defined. Cardinal findings were scored based on specificity and consistency with the estimated laterality and lobe. The total points represented SCS. The association between SCS and the following clinical parameters was assessed by univariate and multivariate analysis: (1) probability of undergoing resective epilepsy surgery, (2) good postoperative seizure outcome (Engel I and II or Engel I only), and (3) lobar concordance between the noninvasively estimated focus and intracranial electroencephalographic (EEG) recordings. RESULTS: A total of 131 patients were evaluated. Univariate analysis revealed higher SCS in the (1) epilepsy surgery group (8.4 [95% confidence interval (CI) = 7.8-8.9] vs. 4.9 [95% CI = 4.3-5.5] points; p < .001), (2) good postoperative seizure outcome group (Engel I and II; 8.7 [95% CI = 8.2-9.3] vs. 6.4 [95% CI = 4.5-8.3] points; p = .008), and (3) patients whose focus defined by intracranial EEG matched the noninvasively estimated focus (8.3 [95% CI = 7.3-9.2] vs. 5.4 [95% CI = 3.5-7.3] points; p = .004). Multivariate analysis revealed areas under the curve of .843, .825, and .881 for Parameters 1, 2, and 3, respectively. SIGNIFICANCE: SCS provides a reliable index of good indication for resective epilepsy surgery and can be easily available in many institutions not necessarily specializing in epilepsy.

Pitfalls of using video-EEG for a trial endpoint in children aged <4 years with focal seizures.

OBJECTIVE: Double-blind, randomized, and placebo-controlled trial SP0967 (NCT02477839/2013-000717-20) did not demonstrate superior efficacy of lacosamide versus placebo in patients aged ≥1 month to <4 years with uncontrolled focal seizures, per ≤72 h video-electroencephalogram (video-EEG)-based primary endpoints (reduction in average daily frequency of focal seizures at end-of-maintenance [EOM] versus end-of-baseline [EOB], patients with ≥50% response). This was unexpected because randomized controlled trial SP0969 (NCT01921205) showed efficacy of lacosamide in patients aged ≥4 to <17 years with uncontrolled focal seizures. SP0969's primary endpoint was based on seizure diary instead of video-EEG, an issue with the latter being inter-reader variability. We evaluated inter-reader agreement in video-EEG interpretation in SP0967, which to our knowledge, are the first such data for very young children with focal seizures from a placebo-controlled trial. METHODS: Local investigator and central reader agreement in video-EEG interpretation was analyzed post hoc. RESULTS: Analysis included 105 EOB and 98 EOM video-EEGs. Local investigators and central reader showed poor agreement based on ≥2 focal seizures at EOB (Kappa = 0.01), and fair agreement based on ≥2 focal seizures at EOM (Kappa = 0.23). Local investigator and central reader seizure count interpretations varied substantially, particularly for focal seizures, but also primary generalized and unclassified epileptic seizures, at both timepoints. INTERPRETATION: High inter-reader variability and low inter-reader reliability of the interpretation of seizure types and counts prevent confident conclusion regarding the lack of efficacy of lacosamide in this population. We recommend studies in very young children do not employ video-EEGs exclusively for accurate study inclusion or as an efficacy measure.

Ictal Central Apnea Is Predictive of Mesial Temporal Seizure Onset: An Intracranial Investigation.

OBJECTIVE: Ictal central apnea (ICA) is a semiological sign of focal epilepsy, associated with temporal and frontal lobe seizures. In this study, using qualitative and quantitative approaches, we aimed to assess the localizational value of ICA. We also aimed to compare ICA clinical utility in relation to other seizure semiological features of focal epilepsy. METHODS: We analyzed seizures in patients with medically refractory focal epilepsy undergoing intracranial stereotactic electroencephalographic (SEEG) evaluations with simultaneous multimodal cardiorespiratory monitoring. A total of 179 seizures in 72 patients with reliable artifact-free respiratory signal were analyzed. RESULTS: ICA was seen in 55 of 179 (30.7%) seizures. Presence of ICA predicted a mesial temporal seizure onset compared to those without ICA (odds ratio = 3.8, 95% confidence interval = 1.3-11.6, p = 0.01). ICA specificity was 0.82. ICA onset was correlated with increased high-frequency broadband gamma (60-150Hz) activity in specific mesial or basal temporal regions, including amygdala, hippocampus, and fusiform and lingual gyri. Based on our results, ICA has an almost 4-fold greater association with mesial temporal seizure onset zones compared to those without ICA and is highly specific for mesial temporal seizure onset zones. As evidence of symptomatogenic areas, onset-synchronous increase in high gamma activity in mesial or basal temporal structures was seen in early onset ICA, likely representing anatomical substrates for ICA generation. INTERPRETATION: ICA recognition may help anatomoelectroclinical localization of clinical seizure onset to specific mesial and basal temporal brain regions, and the inclusion of these regions in SEEG evaluations may help accurately pinpoint seizure onset zones for resection. ANN NEUROL 2024;95:998-1008.

Epilepsy with eyelid myoclonia (Jeavons syndrome): Generalized, focal, or combined generalized and focal epilepsy syndrome?

Epilepsy with eyelid myoclonia (EM) or Jeavons syndrome (JS) is an epileptic syndrome related to the spectrum of genetic generalized epilepsies (GGE). We report two untreated children on which EEGs were performed several hours after a generalized tonic-clonic seizure (GTCS). These showed a unilateral, nearly continuous posterior slowing. This slow-wave activity was associated with contralateral epileptiform activity in one case, while in the second case, it was associated with an ipsilateral activity. However, in the latter child, a few months later an independent focus on the contralateral side was observed. A diagnosis of focal occipital lobe epilepsy was proposed in both cases, and one child underwent a left occipital lobectomy at 3.5 years of age. Despite surgery, absences with EM persisted in this child, and a marked photosensitivity to photic stimulation was observed two years later. The focal slow wave activity of one occipital lobe several hours after a GTCS in these two subjects was in favor of a focal onset preceding the generalization. The EEG evidence for independent left and right posterior focus in these two cases, the persistence of EM, and the development of a marked photosensitivity to photic stimulation in the child who underwent an occipital lobectomy, allow us to suggest that JS is associated with a network of bi-occipital hyperexcitability that rapidly engages bilaterally to produce generalized seizures.

How do children with focal epilepsies perceive the moment they realize that they are about to have a seizure? A pilot study.

PROBLEM: A seizure is a challenging situation for children with epilepsy. Little is known regarding the experience of children who perceive in advance that they are about to have a seizure. METHODS: From September 2020 to February 2021, we invited children with focal epilepsies aged 6-18 years to participate in a semi-structured interview. RESULTS: Of 52 children with focal epilepsies, 22 (42 %) said they perceive in advance that they are about to experience a seizure [11 with self-limited epilepsy with centro-temporal spikes (SELECTs), 11 with other focal epilepsies]. All 22/22 (100 %) children described physical symptoms such as headache or a numb feeling in one half of the body. Of those children, 17/22 (77 %) stated they try to do something about the seizure. Those strategies were perceived as helpful by 0/11 (0 %) children with SELECTs and 9/11 (86 %) children with other focal epilepsies (p < 0.001). Of the children with SELECTs 5/11 (45 %), and of those with other focal epilepsies 9/11 (86 %) stated they would like to know in the morning if they are to experience a seizure that day (n.s.). CONCLUSION: Children who perceive in advance that they are about to have a seizure are well able to describe their experience. Most children take measures to manage their seizures. Those measures were regarded as helpful by most children with other focal epilepsies, but by no child with SELECTs. Larger studies are necessary to determine the factors contributing to the child's perception as well as the nature of the support that they require.

Standardized hierarchical adaptive Lp regression for noise robust focal epilepsy source reconstructions.

OBJECTIVE: To investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L1- and L2-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy. METHODS: Description of the standardization methodology relying on the Hierarchical Bayesian framework, referred to as the Standardized Hierarchical Adaptive Lp-norm Regularization (SHALpR). The performance was tested using real data from two focal epilepsy patients. Simulated data that resembled the available real data was constructed for further localization and noise robustness investigation. RESULTS: The proposed algorithms were compared to their non-standardized counterparts, Standardized low-resolution brain electromagnetic tomography, Standardized Shrinking LORETA-FOCUSS, and Dynamic statistical parametric maps. Based on the simulations, the standardized Hierarchical adaptive algorithm using L2-norm was noise robust for 10 dB signal-to-noise ratio (SNR), whereas the L1-norm prior worked robustly also with 5 dB SNR. The accuracy of the standardized L1-normed methodology to localize focal activity was under 1 cm for both patients. CONCLUSIONS: Numerical results of the proposed methodology display improved localization and noise robustness. The proposed methodology also outperformed the compared methods when dealing with real data. SIGNIFICANCE: The proposed standardized methodology, especially when employing the L1-norm, could serve as a valuable assessment tool in surgical decision-making.

Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in focal epilepsy.

OBJECTIVE: We aimed to develop a new approach for identifying the localization of the seizure onset zone (SOZ) based on corticocortical evoked potentials (CCEPs) and to compare the connectivity patterns in patients with different clinical phenotypes. METHODS: Fifty patients who underwent stereoelectroencephalography and CCEP procedures were included. Logistic regression was used in the model, and six CCEP metrics were input as features: root mean square of the first peak (N1RMS) and second peak (N2RMS), peak latency, onset latency, width duration, and area. RESULTS: The area under the curve (AUC) for localizing the SOZ ranged from 0.88 to 0.93. The N1RMS values in the hippocampus sclerosis (HS) group were greater than that of the focal cortical dysplasia (FCD) IIa group (p < 0.001), independent of the distance between the recorded and stimulated sites. The sensitivity of localization was higher in the seizure-free group than in the non-seizure-free group (p = 0.036). CONCLUSIONS: This new method can be used to predict the SOZ localization in various focal epilepsy phenotypes. SIGNIFICANCE: This study proposed a machine-learning approach for localizing the SOZ. Moreover, we examined how clinical phenotypes impact large-scale abnormality of the epileptogenic networks.

EEG microstates show different features in focal epilepsy and psychogenic nonepileptic seizures.

OBJECTIVE: Electroencephalography (EEG) microstate analysis seeks to cluster the scalp's electric field into semistable topographical EEG activity maps at different time points. Our study aimed to investigate the features of EEG microstates in subjects with focal epilepsy and psychogenic nonepileptic seizures (PNES). METHODS: We included 62 adult subjects with focal epilepsy or PNES who received video-EEG monitoring at the epilepsy monitoring unit. The subjects (mean age = 42.8 ± 21.2 years) were distributed equally between epilepsy and PNES groups. We extracted microstates from a 4.4 ± 1.0-min, 21-channel resting-state EEG. We excluded subjects with interictal epileptiform discharges during resting-state EEGs. After preprocessing, we derived five main EEG microstates-MS1 to MS5-for the full frequency band (1-30 Hz) and frequency subbands (delta, 1-4 Hz; theta, 4-8 Hz; alpha, 8-12 Hz; beta, 12-30 Hz), using the MATLAB-based EEGLAB toolkit. Statistical features of microstates (duration, occurrence, contribution, global field power [GFP]) were compared between the groups, using logistic regression corrected for age and sex. RESULTS: We detected no differences in microstate parameters in the full frequency band. We found a longer duration (delta: B = -7.680, p = .046; theta: B = -16.200, p = .043) and a higher contribution (delta: B = -7.414, p = .035; theta: B = -7.509, p = .031) of MS4 in lower frequency bands in the epilepsy group. The PNES group showed a higher occurrence of MS5 in the delta subband (B = 3.283, p = .032). In the theta subband, a higher GFP of MS1 was associated with the PNES group (B = 5.674, p = .025), whereas a higher GFP of MS2 was associated with the epilepsy group (B = -6.579, p = .026). SIGNIFICANCE: Microstate features show differences between patients with focal epilepsy and PNES. EEG microstates could be a promising parameter, helping to understand changes in brain dynamics in subjects with epilepsy, and should be explored as a potential biomarker.

Utility of genetic testing in the pre-surgical evaluation of children with drug-resistant epilepsy.

We evaluated the utility of genetic testing in the pre-surgical evaluation of pediatric patients with drug-resistant focal epilepsy. This single-center retrospective study reviewed the charts of all pediatric patients referred for epilepsy surgery evaluation over a 5-year period. We extracted and analyzed results of genetic testing as well as clinical, EEG, and neuroimaging data. Of 125 patients referred for epilepsy surgical evaluation, 86 (69%) had some form of genetic testing. Of these, 18 (21%) had a pathogenic or likely pathogenic variant identified. Genes affected included NPRL3 (3 patients, all related), TSC2 (3 patients), KCNH1, CHRNA4, SPTAN1, DEPDC5, SCN2A, ARX, SCN1A, DLG4, and ST5. One patient had ring chromosome 20, one a 7.17p12 duplication, and one a 15q13 deletion. In six patients, suspected epileptogenic lesions were identified on brain MRI that were thought to be unrelated to the genetic finding. A specific medical therapy choice was allowed due to genetic diagnosis in three patients who did not undergo surgery. Obtaining a molecular diagnosis may dramatically alter management in pediatric patients with drug-resistant focal epilepsy. Genetic testing should be incorporated as part of standard investigations in the pre-surgical work-up of pediatric patients with drug-resistant focal epilepsy.

The seizure classification of focal epilepsy based on the network motif analysis.

Due to the complexity of focal epilepsy and its risk for transiting to the generalized epilepsy, the development of reliable classification methods to accurately predict and classify focal and generalized seizures is critical for the clinical management of patients with epilepsy. In order to holistically understand the seizure propagation behavior of focal epilepsy, we propose a three-node motif reduced network by respectively simplifying the focal region, surrounding healthy region and their critical regions as the single node. Because three-node motif can richly characterize information evolutions, the motif analysis method could comprehensively investigate the seizure behavior of focal epilepsy. Firstly, we define a new seizure propagation marker value to capture the seizure onsets and intensity. Based on the three-node motif analysis, it is shown that the focal seizure and spreading can be categorized as inhibitory seizure, focal seizure, focal-critical seizure and generalized seizures, respectively. The four types of seizures correspond to specific modal types respectively, reflecting the strong correlation between seizure behavior and information flow evolution. In addition, it is found that the intensity difference of outflow and inflow information from the critical node (connection heterogeneity) and the excitability of the critical node significantly affected the distribution and transition of the four seizure types. In particular, the method of local linear stability analysis also verifies the effectiveness of four types of seizures classification. In sum, this paper computationally confirms the complex dynamic behavior of focal seizures, and the study of criticality is helpful to propose novel seizure control strategies.