Epilepsy surgery without lipoma removal for temporal lobe epilepsy associated with lipoma in the Sylvian fissure.

Epileptic seizure is the common symptom associated with lipomas in the Sylvian fissure (Sylvian lipomas). Removal of these lipomas carries risks of hemorrhage and brain damage. We report a surgical strategy of not removing the lipoma in a case of intractable temporal lobe epilepsy associated with Sylvian lipoma. We performed anterior temporal lobectomy with preservation of the pia mater of the Sylvian fissure and achieved seizure freedom. Focal cortical dysplasia type 1 of the epileptic neocortex adjacent to the Sylvian lipoma was pathologically diagnosed. We recommend our surgical procedure in similar cases to avoid complications and achieve adequate seizure control.

[Implanted Electrical Stimulation].

Neuromodulation therapy for epilepsy is the third treatment option after medical treatment with antiepileptic drugs and surgical treatment, such as epileptic focal resection. In addition to vagus nerve stimulation(VNS), deep brain stimulation(DBS)and responsive neurostimulation(RNS)have been approved in several countries. These therapies consist of an implantable device and stimulating electrodes. These therapies have great potential to reduce seizure frequency and severity, improve patients' quality of life, and maintain therapeutic efficacy. When VNS was first introduced, electrical stimulation was set at regular intervals. However, current devices have introduced closed-loop therapy, in which stimulation is performed by detecting seizures. Multi-mode stimulation settings have also been introduced in VNS to adjust patient's seizure characteristics based on the time of the day when seizures are most likely to occur. This review describes the third therapeutic approach for the treatment of epilepsy based on recent research reports.

Connectome analysis of male world-class gymnasts using probabilistic multishell, multitissue constrained spherical deconvolution tracking.

In athletes, long-term intensive training has been shown to increase unparalleled athletic ability and might induce brain plasticity. We evaluated the structural connectome of world-class gymnasts (WCGs), as mapped by diffusion-weighted magnetic resonance imaging probabilistic tractography and a multishell, multitissue constrained spherical deconvolution method to increase the precision of tractography at the tissue interfaces. The connectome was mapped in 10 Japanese male WCGs and in 10 age-matched male controls. Network-based statistic identified subnetworks with increased connectivity density in WCGs, involving the sensorimotor, default mode, attentional, visual, and limbic areas. It also revealed a significant association between the structural connectivity of some brain structures with functions closely related to the gymnastic skills and the D-score, which is used as an index of the gymnasts' specific physical abilities for each apparatus. Furthermore, graph theory analysis demonstrated the characteristics of brain anatomical topology in the WCGs. They displayed significantly increased global connectivity strength with decreased characteristic path length at the global level and higher nodal strength and degree in the sensorimotor, default mode, attention, and limbic/subcortical areas at the local level as compared with controls. Together, these findings extend the current understanding of neural mechanisms that distinguish WCGs from controls and suggest brain anatomical network plasticity in WCGs resulting from long-term intensive training. Future studies should assess the contribution of genetic or early-life environmental factors in the brain network organization of WCGs. Furthermore, the indices of brain topology (i.e., connection density and graph theory indices) could become markers for the objective evaluation of gymnastic performance.

Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy.

The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features of high-frequency components (HFCs) in interictal intracranial electroencephalograms (iEEGs) to identify the possible seizure onset zone (SOZ) channels. It is known that the activity of HFCs in interictal iEEGs, including ripple and fast ripple bands, is associated with epileptic seizures. This paper proposes to decompose multi-channel interictal iEEG signals into a number of subbands. For every 20 s segment, twelve features are computed from each subband. A mutual information (MI)-based method with grid search was applied to select the most prominent bands and features. A gradient-boosting decision tree-based algorithm called LightGBM was used to score each segment of the channels and these were averaged together to achieve a final score for each channel. The possible SOZ channels were localized based on the higher value channels. The experimental results with eleven epilepsy patients were tested to observe the efficiency of the proposed design compared to the state-of-the-art methods.

Aberrant myelination in patients with Sturge-Weber syndrome analyzed using synthetic quantitative magnetic resonance imaging.

PURPOSE: Accelerated myelination in the affected hemisphere has been demonstrated previously in patients with Sturge-Weber syndrome (SWS). This prospective study investigated myelin-related changes in patients with unilateral SWS using synthetic quantitative magnetic resonance imaging (qMRI). METHODS: Fourteen children with unilateral SWS were categorized according to age, i.e., ≤ 2 years (group A, n = 5, mean age 1.1 years, 3 males) and > 2 years (group B, n = 9, mean age 3.9 years, 4 males). All children underwent two-dimensional synthetic qMRI. The myelin volume in the cerebral hemisphere and white matter (WM) myelin volume fraction (MVF), proton density (PD), R1 and R2 relaxation rates ipsilateral to the leptomeningeal enhancement, and/or a port-wine birthmark were compared with the corresponding values in the contralateral hemisphere. RESULTS: In group A, 3 patients had a higher myelin volume in the ipsilateral hemisphere and a higher MVF, R1, and R2 and lower PD in the ipsilateral WM than on the contralateral side; the findings were the opposite in the remaining two patients. All patients in group B had a significantly lower myelin volume in the ipsilateral hemisphere (P < 0.05) and a lower MVF and R1 and higher PD in the ipsilateral WM than on the contralateral side (P < 0.0125). CONCLUSION: Higher estimated myelin was observed on the ipsilateral side in some patients aged ≤ 2 years and lower myelin on the ipsilateral side in all older patients. Synthetic qMRI might be useful for showing myelin-related abnormalities in SWS.

Review of synthetic MRI in pediatric brains: Basic principle of MR quantification, its features, clinical applications, and limitations.

Quantitative magnetic resonance imaging (MRI) with multislice, multi-echo, and multi-delay acquisition enables simultaneous quantification of R1 and R2 relaxation rates, proton density, and the B1 field in a single acquisition, and requires only about 6 minutes for full-head coverage. Using dedicated SyMRI software, radiologists can generate any contrast-weighted image by manipulating the acquisition parameters, including repetition time, echo time, and inversion time. Moreover, automatic brain tissue segmentation, volumetry, and myelin measurement can also be performed. Using the SyMRI approach, a shorter scan time, an objective examination, and personalized MR imaging parameters can be obtained in daily clinical pediatric imaging. Here we summarize and review the use of SyMRI in imaging of the pediatric brain, including the basic principles of MR quantification along with its features, clinical applications, and limitations.

The somatic GNAQ mutation c.548G>A (p.R183Q) is consistently found in Sturge-Weber syndrome.

Sturge-Weber syndrome (SWS) is a neurocutaneous disorder characterized by capillary malformation (port-wine stains), and choroidal and leptomeningeal vascular malformations. Previously, the recurrent somatic mutation c.548G>A (p.R183Q) in the G-α q gene (GNAQ) was identified as causative in SWS and non-syndromic port-wine stain patients using whole-genome sequencing. In this study, we investigated somatic mutations in GNAQ by next-generation sequencing. We first performed targeted amplicon sequencing of 15 blood-brain-paired samples in sporadic SWS and identified the recurrent somatic c.548G>A mutation in 80% of patients (12 of 15). The percentage of mutant alleles in brain tissues of these 12 patients ranged from 3.6 to 8.9%. We found no other somatic mutations in any of the seven GNAQ exons in the remaining three patients without c.548G>A. These findings suggest that the recurrent somatic GNAQ mutation c.548G>A is the major determinant genetic factor for SWS and imply that other mutated candidate gene(s) may exist in SWS.

Posterior quadrant disconnection surgery for Sturge-Weber syndrome.

OBJECTIVE: Some patients with Sturge-Weber syndrome (SWS) need epilepsy surgery for adequate seizure control and prevention of psychomotor deterioration. The majority of patients with SWS have leptomeningeal angioma located over the temporal, parietal, and occipital lobes. We applied posterior quadrant disconnection surgery for this type of SWS with intractable seizure. We evaluated the efficacy of this procedure in seizure control and psychomotor development. METHODS: Ten patients who were surgically treated using the posterior quadrantectomy (PQT) were enrolled in this study. Surgical outcome was analyzed as seizure-free or not at 2 years after surgery. Psychomotor development was evaluated by the scores of mental developmental index (MDI) and psychomotor developmental index (PDI) in the Bayley Scales of Infant Development II preoperatively, and at 6 and 12 months after the PQT. RESULTS: Eight of 10 patients were seizure-free. Patients without complete elimination of the angiomatous areas had residual seizures. Average MDI and PDI scores before the surgery were 64.8 and 71.6, respectively. Scores of MDI at 6 and 12 months after the PQT in seizure-free patients were 80.5 and 84.5, respectively (p < 0.01). PDI scores at these postoperative intervals were 87.3 and 86.4, respectively (p < 0.05). Patients with residual seizures did not improve in either MDI or PDI. SIGNIFICANCE: The PQT achieved good seizure control and improved psychomotor development in patients with SWS. The complete deafferentation of angiomatous areas is required for seizure-free results and psychomotor developmental improvement.

Causal Connectivity Network Analysis of Ictal Electrocorticogram With Temporal Lobe Epilepsy Based on Dynamic Phase Transfer Entropy.

Temporallobe epilepsy (TLE) has been conceptualized as a brain network disease, which generates brain connectivity dynamics within and beyond the temporal lobe structures in seizures. The hippocampus is a representative epileptogenic focus in TLE. Understanding the causal connectivity in terms of brain network during seizures is crucial in revealing the triggering mechanism of epileptic seizures originating from the hippocampus (HPC) spread to the lateral temporal cortex (LTC) by ictal electrocorticogram (ECoG), particularly in high-frequency oscillations (HFOs) bands. In this study, we proposed the unified-epoch dynamic causality analysis method to investigate the causal influence dynamics between two brain regions (HPC and LTC) at interictal and ictal phases in the frequency range of 1-500 Hz by introducing the phase transfer entropy (PTE) out/in-ratio and sliding window. We also proposed PTE-based machine learning algorithms to identify epileptogenic zone (EZ). Nine patients with a total of 26 seizures were included in this study. We hypothesized that: 1) HPC is the focus with the stronger causal connectivity than that in LTC in the ictal state at gamma and HFOs bands. 2) Causal connectivity in the ictal phase shows significant changes compared to that in the interictal phase. 3) The PTE out/in-ratio in the HFOs band can identify the EZ with the best prediction performance.

Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech.

Several attempts for speech brain-computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech and covert speech of eight Japanese sentences, each consisting of three tokens. In particular, Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing, and then evaluated the model's performance when trained with overt task for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a TER of 46.3% ( p > 0.05 ; d = 0.07 ) . Therefore, the challenge of collecting training data for covert speech can be addressed using overt speech. The performance of covert speech can improve by employing several overt speeches.

Text and image generation from intracranial electroencephalography using an embedding space for text and images.

Objective.Invasive brain-computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex. However, BCIs based on motor cortical activities may experience signal deterioration in users with motor cortical degenerative diseases such as amyotrophic lateral sclerosis. An alternative approach to using iEEG of the motor cortex is necessary to support patients with such conditions.Approach. In this study, a multimodal embedding of text and images was used to decode visual semantic information from iEEG signals of the visual cortex to generate text and images. We used contrastive language-image pretraining (CLIP) embedding to represent images presented to 17 patients implanted with electrodes in the occipital and temporal cortices. A CLIP image vector was inferred from the high-γpower of the iEEG signals recorded while viewing the images.Main results.Text was generated by CLIPCAP from the inferred CLIP vector with better-than-chance accuracy. Then, an image was created from the generated text using StableDiffusion with significant accuracy.Significance.The text and images generated from iEEG through the CLIP embedding vector can be used for improved communication.

Epileptic seizure detection by using interpretable machine learning models.

Objective.Accurate detection of epileptic seizures using electroencephalogram (EEG) data is essential for epilepsy diagnosis, but the visual diagnostic process for clinical experts is a time-consuming task. To improve efficiency, some seizure detection methods have been proposed. Regardless of traditional or machine learning methods, the results identify only seizures and non-seizures. Our goal is not only to detect seizures but also to explain the basis for detection and provide reference information to clinical experts.Approach.In this study, we follow the visual diagnosis mechanism used by clinical experts that directly processes plotted EEG image data and apply some commonly used models of LeNet, VGG, deep residual network (ResNet), and vision transformer (ViT) to the EEG image classification task. Before using these models, we propose a data augmentation method using random channel ordering (RCO), which adjusts the channel order to generate new images. The Gradient-weighted class activation mapping (Grad-CAM) and attention layer methods are used to interpret the models.Main results.The RCO method can balance the dataset in seizure and non-seizure classes. The models achieved good performance in the seizure detection task. Moreover, the Grad-CAM and attention layer methods explained the detection basis of the model very well and calculate a value that measures the seizure degree.Significance.Processing EEG data in the form of images can flexibility to use a variety of machine learning models. The imbalance problem that exists widely in clinical practice is well solved by the RCO method. Since the method follows the visual diagnosis mechanism of clinical experts, the model interpretation results can be presented to clinical experts intuitively, and the quantitative information provided by the model is also a good diagnostic reference.

Epileptic seizure focus detection from interictal electroencephalogram: a survey.

Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.

Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram.

Automatic seizure onset zone (SOZ) localization using interictal electrocorticogram (ECoG) improves the diagnosis and treatment of patients with medically refractory epilepsy. This study aimed to investigate the characteristics of phase-amplitude coupling (PAC) extracted from interictal ECoG and the feasibility of PAC serving as a promising biomarker for SOZ identification. We employed the mean vector length modulation index approach on the 20-s ECoG window to calculate PAC features between low-frequency rhythms (0.5-24 Hz) and high frequency oscillations (HFOs) (80-560 Hz). We used statistical measures to test the significant difference in PAC between the SOZ and non-seizure onset zone (NSOZ). To overcome the drawback of handcraft feature engineering, we established novel machine learning models to learn automatically the characteristics of the obtained PAC features and classify them to identify the SOZ. Besides, to handle imbalanced dataset classification, we introduced novel feature-wise/class-wise re-weighting strategies in conjunction with classifiers. In addition, we proposed a time-series nest cross-validation to provide more accurate and unbiased evaluations for this model. Seven patients with focal cortical dysplasia were included in this study. The experiment results not only showed that a significant coupling at band pairs of slow waves and HFOs exists in the SOZ when compared with the NSOZ, but also indicated the effectiveness of the PAC features and the proposed models in achieving better classification performance .

Identification of Seizure Onset Zone from Intracranial EEG Using Source Selection-Based Domain Adaptation.

For focal epilepsy patients, correctly identifying the seizure onset zone (SOZ) is essential for surgical treatment. In automated realistic SOZ identification, it is necessary to identify the SOZ of an unknown patient using another patient's electroencephalogram (EEG). However, in such cases, the influence of individual differences in EEG becomes a bottleneck. In this paper, we propose the method with domain adaptation and source patient selection to address the issue of individual differences in EEG and improve performance. The proposed method was evaluated on intracranial EEG data from 11 patients with epilepsy caused by focal cortical dysplasia. The results showed that the proposed method significantly improved SOZ identification performance compared to existing methods without domain adaptation and source patient selection. In addition, it was suggested that data from residual-seizure patients may have adversely affected estimation performance. Visualization of the prediction on MRI images showed that the proposed method might detect SOZs missed by epileptologists.

Chronological changes in phase-amplitude coupling during epileptic seizures in temporal lobe epilepsy.

OBJECTIVE: To analyze chronological changes in phase-amplitude coupling (PAC) and verify whether PAC analysis can diagnose epileptogenic zones during seizures. METHODS: We analyzed 30 seizures in 10 patients with mesial temporal lobe epilepsy who had ictal discharges with preictal spiking followed by low-voltage fast activity patterns on intracranial electroencephalography. We used the amplitude of two high-frequency bands (ripples: 80-200 Hz, fast ripples: 200-300 Hz) and the phase of three slow wave bands (0.5-1 Hz, 3-4 Hz, and 4-8 Hz) for modulation index (MI) calculation from 2 minutes before seizure onset to seizure termination. We evaluated the accuracy of epileptogenic zone detection by MI, in which a combination of MI was better for diagnosis and analyzed patterns of chronological changes in MI during seizures. RESULTS: MIRipples/3-4 Hz and MIRipples/4-8 Hz in the hippocampus were significantly higher than those in the peripheral regions from seizure onset. Corresponding to the phase on intracranial electroencephalography, MIRipples/3-4 Hz decreased once and subsequently increased again. MIRipples/4-8 Hz showed continuously high values. CONCLUSIONS: Continuous measurement of MIRipples/3-4 Hz and MIRipples/4-8 Hz could help identify epileptogenic zones. SIGNIFICANCE: PAC analysis of ictal epileptic discharges can help epileptogenic zone identification.

Classification of the Epileptic Seizure Onset Zone Based on Partial Annotation.

Epilepsy is a chronic disorder caused by excessive electrical discharges. Currently, clinical experts identify the seizure onset zone (SOZ) channel through visual judgment based on long-time intracranial electroencephalogram (iEEG), which is a very time-consuming, difficult and experience-based task. Therefore, there is a need for high-accuracy diagnostic aids to reduce the workload of clinical experts. In this article, we propose a method in which, the iEEG is split into the 20-s segment and for each patient, we ask clinical experts to label a part of the data, which is used to train a model and classify the remaining iEEG data. In recent years, machine learning methods have been successfully applied to solve some medical problems. Filtering, entropy and short-time Fourier transform (STFT) are used for extracting features. We compare them to wavelet transform (WT), empirical mode decomposition (EMD) and other traditional methods with the aim of obtaining the best possible discriminating features. Finally, we look for their medical interpretation, which is important for clinical experts. We achieve high-performance results for SOZ and non-SOZ data classification by using the labeled iEEG data and support vector machine (SVM), fully connected neural network (FCNN) and convolutional neural network (CNN) as classification models. In addition, we introduce the positive unlabeled (PU) learning to further reduce the workload of clinical experts. By using PU learning, we can learn a binary classifier with a small amount of labeled data and a large amount of unlabeled data. This can greatly reduce the amount and difficulty of annotation work by clinical experts. All together, we show that using 105 minutes of labeled data we achieve a classification result of 91.46% on average for multiple patients.

Proper Therapy Selection Improves Epilepsy Outcomes in Patients With Multilobar Sturge-Weber Syndrome.

BACKGROUND: Hemispherectomy is an optimal treatment for patients with Sturge-Weber syndrome (SWS) affecting the whole hemisphere; however, a consensus has not been reached regarding therapeutic choices for those with involvement of two to three lobes. In this study, we compared seizure and cognitive outcomes between medical and surgical treatment groups in patients with multilobar involvement. METHODS: We evaluated 50 patients with multilobar involvement. Surgical indications included (1) antiepileptic drug (AED)-resistant seizures; (2) developmental delay; and (3) cortical atrophy. Twenty-nine patients were classified in the medical treatment group (MTG), and 21 patients were in the surgical treatment group (STG). Seizure type and frequency, SWS electroencephalography score (SWS-EEGS), and pretherapeutic and posttherapeutic SWS neurological scores (SWS-NS) were compared between groups. Median ages at the initial evaluation of the MTG and STG were 4 and 2 years, and at the final evaluation were 13 and 17 years, respectively. RESULTS: The STG had a higher incidence (76.2%) of focal to bilateral tonic-clonic seizures and status epilepticus, although no difference in SWS-EEGS. Seizure and cognitive subcategories of SWS-NS at initial evaluation were worse in the STG (P = 0.025 and P = 0.007). The seizure subcategory in MTG and STG improved after therapy (P = 0.002 and P = 0.001). Cognition was maintained in MTG and improved in STG (P = 0.002). The seizure-free rates in MTG and STG were 58.6% and 85.7%, respectively. CONCLUSIONS: Appropriate therapeutic choices improved seizure outcomes. Although patients who required surgery had more severe epilepsy and cognitive impairment, surgery improved both.

Effect of vagus nerve stimulation against generalized seizure and status epilepticus recurrence.

Objective: Vagus nerve stimulation (VNS) is a palliative surgery for drug-resistant epilepsy. The two objectives of this study were to (1) determine the seizure type most responsive to VNS and (2) investigate the preventive effect on status epilepticus (SE) recurrence. Methods: We retrospectively reviewed 136 patients with drug-resistant epilepsy who underwent VNS implantation. We examined seizure outcomes at 6, 12, and 24 months following implantation of VNS as well as at the last visit to the Juntendo Epilepsy Center. Univariate analysis and multivariate logistic regression models were used to estimate the prognostic factors. Results: 125 patients were followed up for at least 1 year after VNS implantation. The percentage of patients with at least a 50% reduction in seizure frequency compared with prior to VNS implantation increased over time at 6, 12, and 24 months after VNS implantation: 28, 41, and 52%, respectively. Regarding overall seizure outcomes, 70 (56%) patients responded to VNS. Of the 40 patients with a history of SE prior to VNS implantation, 27 (67%) showed no recurrence of SE. The duration of epilepsy, history of SE prior to VNS implantation and seizure type were correlated with seizure outcomes after VNS implantation in univariate analysis (p = 0.05, p < 0.01, and p = 0.03, respectively). In multivariate logistic regression analysis, generalized seizure was associated with VNS response [odds ratio (OR): 4.18, 95% CI: 1.13-15.5, p = 0.03]. A history of SE prior to VNS implantation was associated with VNS non-responders [(OR): 0.221, 95% CI: 0.097-0.503, p < 0.01]. The duration of epilepsy, focal to bilateral tonic-clonic seizure and epileptic spasms were not significantly associated with VNS responders (p = 0.07, p = 0.71, and p = 0.11, respectively). Conclusion: Following 125 patients with drug-resistant epilepsy for an average of 69 months, 56% showed at least 50% reduction in seizure frequency after VNS implantation. This study suggests that generalized seizure is the most responsive to VNS, and that VNS may reduce the risk of recurrence of SE. VNS was shown to be effective against generalized seizure and also may potentially influence the risk of further events of SE, two marker of disease treatment that can lead to improved quality of life.

An integrated genetic analysis of epileptogenic brain malformed lesions.

Focal cortical dysplasia is the most common malformation during cortical development, sometimes excised by epilepsy surgery and often caused by somatic variants of the mTOR pathway genes. In this study, we performed a genetic analysis of epileptogenic brain malformed lesions from 64 patients with focal cortical dysplasia, hemimegalencephy, brain tumors, or hippocampal sclerosis. Targeted sequencing, whole-exome sequencing, and single nucleotide polymorphism microarray detected four germline and 35 somatic variants, comprising three copy number variants and 36 single nucleotide variants and indels in 37 patients. One of the somatic variants in focal cortical dysplasia type IIB was an in-frame deletion in MTOR, in which only gain-of-function missense variants have been reported. In focal cortical dysplasia type I, somatic variants of MAP2K1 and PTPN11 involved in the RAS/MAPK pathway were detected. The in-frame deletions of MTOR and MAP2K1 in this study resulted in the activation of the mTOR pathway in transiently transfected cells. In addition, the PTPN11 missense variant tended to elongate activation of the mTOR or RAS/MAPK pathway, depending on culture conditions. We demonstrate that epileptogenic brain malformed lesions except for focal cortical dysplasia type II arose from somatic variants of diverse genes but were eventually linked to the mTOR pathway.