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.

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.

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.

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 .

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.

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.

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.

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.

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.

[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.

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.

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.

Seizure onset zone classification based on imbalanced iEEG with data augmentation.

Objective. Identifying the seizure onset zone (SOZ) in patients with focal epilepsy is the critical information required for surgery. However, collecting this information is challenging, time-consuming, and subjective. Some machine learning methods reduce the workload of clinical experts in intracranial electroencephalogram (iEEG) visual diagnosis but face significant challenges because interictal iEEG clinical data often suffer from a significant class imbalance. We aim to generate synthetic data for the minority class.Approach. To make the clinically imbalanced data suitable for machine learning, we introduce an EEG augmentation method (EEGAug). The EEGAug method randomly selects several samples from the minority class and transforms them into the frequency domain. Then, different frequency bands from different samples are used to compose new data. Finally, a synthetic sample is generated after converting the new data back to the time domain.Main results. The imbalanced clinical iEEG data can be balanced and applied to machine learning models using the method. A one-dimensional convolutional neural network model is used to classify the SOZ and non-SOZ data. We compare the EEGAug method with other data augmentation methods and another method of class-balanced focal loss function, which is also used for solving the data imbalance problem by adjusting the weights between the minority and majority classes. The results show that the EEGAug method performs best in most data.Significance. Data imbalance is a widespread clinical problem. The EEGAug method can flexibly generate synthetic data for the minority class, yielding synthetic and raw data with a high distribution similarity. By using the EEGAug method, clinical data can be used in machine learning models.

Satelight: self-attention-based model for epileptic spike detection from multi-electrode EEG.

Objective.Because of the lack of highly skilled experts, automated technologies that support electroencephalogram (EEG)-based in epilepsy diagnosis are advancing. Deep convolutional neural network-based models have been used successfully for detecting epileptic spikes, one of the biomarkers, from EEG. However, a sizeable number of supervised EEG records are required for training.Approach.This study introduces the Satelight model, which uses the self-attention (SA) mechanism. The model was trained using a clinical EEG dataset labeled by five specialists, including 16 008 epileptic spikes and 15 478 artifacts from 50 children. The SA mechanism is expected to reduce the number of parameters and efficiently extract features from a small amount of EEG data. To validate the effectiveness, we compared various spike detection approaches with the clinical EEG data.Main results.The experimental results showed that the proposed method detected epileptic spikes more effectively than other models (accuracy = 0.876 and false positive rate = 0.133).Significance.The proposed model had only one-tenth the number of parameters as the other effective model, despite having such a high detection performance. Further exploration of the hidden parameters revealed that the model automatically attended to the EEG's characteristic waveform locations of interest.

[Reactivation of latent viruses in Neurology].

After establishing latent infection, some viruses can be reactivated by the alteration of host immunological conditions. First, we reviewed viruses that can cause neuronal damage by reactivation. Then we focused on the herpes simplex virus (HSV). The reactivation leads to neuronal damages through two possible mechanisms; "reactivation of a latent herpes virus" by which viruses can cause direct virus neurotoxicity, and "post-infectious immune inflammatory response" by which a focal reactivation of HSV leads to an inflammatory reaction. The former is radiologically characterized by cortical lesions, the latter is characterized by subcortical white matter lesions. We experienced a female, who underwent the right posterior quadrantectomy and then developed recurrent herpes encephalitis caused by herpes simplex reactivation, which pathologically demonstrated inflammation in the white matter, suggesting a post-infectious immune inflammatory response. The patient was successfully treated with immunosuppressants. The reactivation of the HSV is extremely rare in Japan. Neurologists should recognize this condition because this disorder will increase as epilepsy surgery gains more popularity.

Higher phase-amplitude coupling between ripple and slow oscillations indicates the distribution of epileptogenicity in temporal lobe epilepsy with hippocampal sclerosis.

OBJECTIVE: We assessed the diagnostic utility of the occurrence rate of high-frequency oscillations and modulation index (MI) from intraoperative electrocorticography (ioECoG) in determining the extent of epileptogenicity in mesial temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS). METHODS: We enrolled 17 patients who underwent selective amygdalohippocampectomy (SelAH) for TLE due to HS. We analyzed the occurrence rate of ripples (80-200 Hz) and fast ripples (200-300 Hz); and MI between ripples and 3-4 Hz (MIRipples/3-4 Hz) and fast ripples and 3-4 Hz (MIFRs/3-4 Hz) from the amygdala, hippocampus, and lateral temporal lobe (LTL) pre-SelAH and the LTL post-SelAH, and subsequently categorized the patients into good and poor seizure outcome groups. We compared the occurrence rates and MIs over each region of interest between both groups. Receiver operating characteristic analysis was used to identify the most optimal indicator to predict poor surgical outcomes. RESULTS: In the poor seizure outcome group, an increase in the occurrence rate of ripples was seen in the hippocampus and LTL pre-SelAH and the LTL post-SelAH. The MIRipples/3-4 Hz from the LTL pre-SelAH was the most indicative factor of poor outcome. CONCLUSIONS: High occurrence rate of ripples and MIRipples/3-4 Hz from the LTL showed wide epileptogenicity in TLE patients with poor seizure outcomes after SelAH. Our data suggest that the analysis of the occurrence rate of HFOs and MIHFOs/3-4 Hz from ioECoG, especially from the LTL, can indicate the distribution of epileptogenicity in TLE with HS.

Impact of COVID-19 pandemic on epilepsy care in Japan: A national-level multicenter retrospective cohort study.

OBJECTIVE: The impact of the coronavirus disease 2019 (COVID-19) pandemic on epilepsy care across Japan was investigated by conducting a multicenter retrospective cohort study. METHODS: This study included monthly data on the frequency of (1) visits by outpatients with epilepsy, (2) outpatient electroencephalography (EEG) studies, (3) telemedicine for epilepsy, (4) admissions for epilepsy, (5) EEG monitoring, and (6) epilepsy surgery in epilepsy centers and clinics across Japan between January 2019 and December 2020. We defined the primary outcome as epilepsy-center-specific monthly data divided by the 12-month average in 2019 for each facility. We determined whether the COVID-19 pandemic-related factors (such as year [2019 or 2020], COVID-19 cases in each prefecture in the previous month, and the state of emergency) were independently associated with these outcomes. RESULTS: In 2020, the frequency of outpatient EEG studies (-10.7%, p<0.001) and cases with telemedicine (+2,608%, p=0.031) were affected. The number of COVID-19 cases was an independent associated factor for epilepsy admission (-3.75*10-3 % per case, p<0.001) and EEG monitoring (-3.81*10-3 % per case, p = 0.004). Further, the state of emergency was an independent factor associated with outpatient with epilepsy (-11.9%, p<0.001), outpatient EEG (-32.3%, p<0.001), telemedicine for epilepsy (+12,915%, p<0.001), epilepsy admissions (-35.3%; p<0.001), EEG monitoring (-24.7%: p<0.001), and epilepsy surgery (-50.3%, p<0.001). SIGNIFICANCE: We demonstrated the significant impact that the COVID-19 pandemic had on epilepsy care. These results support those of previous studies and clarify the effect size of each pandemic-related factor on epilepsy care.

Surgery for intractable epilepsy after severe encephalopathy with reversible splenial lesion and new onset hippocampal lesion associated with parechovirus.

We describe a case of severe encephalopathy with reversible splenial lesion associated with parechovirus, followed by intractable temporal lobe epilepsy (TLE), which was improved by epilepsy surgery. A 3-year-old girl was admitted because of fever, consciousness disturbance and generalized tonic clonic seizure. Her seizure lasted for four hours. Fluid-attenuated inversion recovery (FLAIR) showed a hyperintensity in the splenium of the corpus callosum. Electroencephalogram (EEG) demonstarated continuous diffuse epileptic activity represented by synchronous and rhythmic high-amplitude spikes and waves, which led to the diagnosis of status epilepticus. Her consciousness was improved with fosphenytoin, midazolam and methylprednisolone pulse after 3 days. Seven days later, FLAIR hyperintensity in the splenium of the corpus callosum was disappeared; however, a hyperintensity in the right hippocampus was detected. Since the stool examination was positive for parechovirus, her final diagnosis was reversible splenial lesion syndrome (RESLES) associated with parechovirus. At age 8, she experienced epigastric sensation and consciousness disturbance once a week. Based on the scalp EEG and radiological findings, she was diagnosed with intractable right TLE. We performed a right selective amygdalohippocampectomy and anterior temporal disconnection at 10 years of age. One year and 3 months after surgery, she was seizure free. To our knowledge, this is the first report of severe febrile epilepticus status. with RESLES associated with parechovirus, followed by intractable TLE, which was resolved by epilepsy surgery.