Postoperative seizure outcomes and antiseizure medication utilization based on histopathological diagnosis: A retrospective cohort study.

OBJECTIVE: Analyze the association between histopathology, seizure outcomes, and drug load of antiseizure medications (ASMs) 5-8 years after epilepsy surgery to inform preoperative decision-making and consultation. METHODS: In this retrospective, non-interventional, single-center study, patients who visited the epilepsy clinic at West China Hospital, Sichuan University from Jan 1, 2015 to Dec 31, 2020 were assessed. Patients with postoperative histopathology after epilepsy resection were included and categorized into 13 etiological groups. The primary outcomes were achieving Engel class 1 at 1, 2, 3, 5, and 8 years postoperative. Secondary outcomes included the use of ASMs and comparison of postoperative seizure outcomes between adults and children. Univariate and multivariable analyses were conducted to explore the association between clinical characteristics such as histopathology and seizure outcomes. RESULTS: A total of 315 patients were include. Patients with embryonic dysplastic neuroepithelial tumor (DNT) achieved the best seizure outcomes (84.6 % Engel class 1). DNT (odds ratio, OR=0.103, 95 %CI=0.012-0.899), cavernous hemangiomas (OR=0.140, 95 %CI=0.024-0.819) and meningioma (OR=0.137, 95 %CI=0.021-0.910) were independently associated with a higher probability of seizure-free outcome. The results of epileptic seizures in adult and pediatric groups with different pathologies were significantly different, and the preoperative and postoperative ASM dosages were also different among adult patients with various etiologies. Additionally, multivariate analysis showed that early age at onset (adjusted hazard ratio (HR) = 1.754, 95 % CI=1.049-2.934, P=0.032), late surgical age (HR=0.569, 95 %CI=0.339-0.954, P=0.032), and longer duration from seizure onset to surgery (HR=1.735, 95 % CI=1.028-2.928, P=0.039) were independent predictors of unfavorable outcomes in epileptic seizures. CONCLUSIONS: we demonstrated that the seizure outcomes of focal epilepsy have high pathological specificity, with histopathological diagnosis serving as a crucial and independent determinant of seizure outcome. Surgical assessment should be contemplated for all patients with presumed refractory focal epilepsy, irrespective of their age.

Magnetoencephalography-based approaches to epilepsy classification.

Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures. Not only does epilepsy severely affect the daily life of the patient, but the risk of premature death in patients with epilepsy is three times higher than that of the normal population. Magnetoencephalography (MEG) is a non-invasive, high temporal and spatial resolution electrophysiological data that provides a valid basis for epilepsy diagnosis, and used in clinical practice to locate epileptic foci in patients with epilepsy. It has been shown that MEG helps to identify MRI-negative epilepsy, contributes to clinical decision-making in recurrent seizures after previous epilepsy surgery, that interictal MEG can provide additional localization information than scalp EEG, and complete excision of the stimulation area defined by the MEG has prognostic significance for postoperative seizure control. However, due to the complexity of the MEG signal, it is often difficult to identify subtle but critical changes in MEG through visual inspection, opening up an important area of research for biomedical engineers to investigate and implement intelligent algorithms for epilepsy recognition. At the same time, the use of manual markers requires significant time and labor costs, necessitating the development and use of computer-aided diagnosis (CAD) systems that use classifiers to automatically identify abnormal activity. In this review, we discuss in detail the results of applying various different feature extraction methods on MEG signals with different classifiers for epilepsy detection, subtype determination, and laterality classification. Finally, we also briefly look at the prospects of using MEG for epilepsy-assisted localization (spike detection, high-frequency oscillation detection) due to the unique advantages of MEG for functional area localization in epilepsy, and discuss the limitation of current research status and suggestions for future research. Overall, it is hoped that our review will facilitate the reader to quickly gain a general understanding of the problem of MEG-based epilepsy classification and provide ideas and directions for subsequent research.

Positron emission tomography in autoimmune encephalitis: Clinical implications and future directions.

18 F-fluoro-deoxyglucose position emission tomography (18 F-FDG-PET) has been proven as a sensitive and reliable tool for diagnosis of autoimmune encephalitis (AE). More attention was paid to this kind of imaging because of the shortage of MRI, EEG, and CSF findings. FDG-PET has been assessed in a few small studies and case reports showing apparent abnormalities in cases where MRI does not. Here, we summarized the patterns (specific or not) in AE with different antibodies detected and the clinical outlook for the wide application of FDG-PET considering some limitations. Specific patterns based on antibody subtypes and clinical symptoms were critical for identifying suspicious AE, the most common of which was the anteroposterior gradient in anti- N -methyl- d -aspartate receptor (NMDAR) encephalitis and the medial temporal lobe hypermetabolism in limbic encephalitis. And the dynamic changes of metabolic presentations in different phases provided us the potential to inspect the evolution of AE and predict the functional outcomes. Except for the visual assessment, quantitative analysis was recently reported in some voxel-based studies of regions of interest, which suggested some clues of the future evaluation of metabolic abnormalities. Large prospective studies need to be conducted controlling the time from symptom onset to examination with the same standard of FDG-PET scanning.

Predicting the functional outcomes of anti-LGI1 encephalitis using a random forest model.

OBJECTIVES: To establish a model in order to predict the functional outcomes of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis and identify significant predictive factors using a random forest algorithm. METHODS: Seventy-nine patients with confirmed LGI1 antibodies were retrospectively reviewed between January 2015 and July 2020. Clinical information was obtained from medical records and functional outcomes were followed up in interviews with patients or their relatives. Neurological functional outcome was assessed using a modified Rankin Scale (mRS), the cutoff of which was 2. The prognostic model was established using the random forest algorithm, which was subsequently compared with logistic regression analysis, Naive Bayes and Support vector machine (SVM) metrics based on the area under the curve (AUC) and the accuracy. RESULTS: A total of 79 patients were included in the final analysis. After a median follow-up of 24 months (range, 8-60 months), 20 patients (25%) experienced poor functional outcomes. A random forest model consisting of 16 variables used to predict the poor functional outcomes of anti-LGI1 encephalitis was successfully constructed with an accuracy of 83% and an F1 score of 60%. In addition, the random forest algorithm demonstrated a more precise predictive performance for poor functional outcomes in patients with anti-LGI1 encephalitis compared with three other models (AUC, 0.90 vs 0.80 vs 0.70 vs 0.64). CONCLUSIONS: The random forest model can predict poor functional outcomes of patients with anti-LGI1 encephalitis. This model was more accurate and reliable than the logistic regression, Naive Bayes, and SVM algorithm.

The prognosis of late-onset anti-N-methyl-D-aspartate receptor encephalitis in China.

OBJECTIVES: Early-onset anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDARE) differs from late-onset anti-NMDARE regarding clinical characteristics. Until recently, research focusing on prognosis of elder adults has been scarce and showed inconsistent results. This study aims to evaluate the prognosis of late-onset anti-NMDARE in China. MATERIALS & METHODS: One hundred and twelve adults diagnosed as anti-NMDARE in four hospitals in China were reviewed retrospectively. Outcome data were assessed using modified Rankin Scale (mRS) score in short term (3 months after discharge) and long term (≥12 months after discharge). The relapse rate was also computed. Multivariable logistic regression was used to evaluate whether there are substantial differences in functional outcomes and recurrence rate across two groups. RESULTS: Of the 112 patients with anti-NMDARE, 81 (72.3%) were early-onset disease and 31 (27.7%) were late-onset disease. Of these, all had short-term follow-up and 70 completed long-term follow-up. Late-onset anti-NMDARE group showed better short-term (OR 2.70, 95% CI 1.09-6.71) and long-term prognoses (OR 10.25, 95% CI 1.90-55.15). Recurrence rates were statistically different between the groups (OR 4.25, 95% CI 1.22-14.75). CONCLUSION: The prognosis for anti-NMDARE in China was poorer for older adults relative to younger adults. The relapse rates were higher in late-onset group compared to early-onset group.

Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model.

Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80-200 Hz) and fast ripples (FRs, 200-500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner-Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p < 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 ± 1.43% and 77.85 ± 1.61% for ripples and FRs, respectively, p < 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.

The Clinical Value of 18 F-FDG-PET in Autoimmune Encephalitis Associated With LGI1 Antibody.

Purpose: The metabolic patterns of 18F-fluoro-2-deoxy-d-glucose positron emission tomography (18F-FDG-PET) in autoimmune encephalitis associated with leucine-rich glioma-inactivated 1 antibody (LGI1 AE) are still unclear. We performed a cohort study to investigate the clinical metabolic characteristics and diagnostic value based on 18F-FDG-PET in patients with LGI1 AE. Materials and Methods: A total of 34 patients including 18 patients (53%) in the acute phase and 16 patients (47%) in the chronic phase who were diagnosed with LGI1 AE were retrospectively analyzed from October 2014 to June 2018 at the Department of Neurology in Beijing Tiantan Hospital, the Capital Medical University. The clinical data were collected by searching through electronic medical records. Results: The initial 18F-FDG-PET scan indicated a significant abnormal metabolic pattern in 31 LGI1 AE patients (91%), whereas only 20 patients (59%) showed an abnormal MRI signal (P < 0.05). The 18F-FDG-PET metabolic pattern was reversible after treatment; most of the patients showed an almost normal uptake of 18F-FDG-PET after discharge. Regarding the spatial distribution, the abnormal metabolic pattern in LGI1 AE subjects exhibiting hypermetabolism was specifically located in the basal ganglia (BG) and medial temporal lobe (MTL). BG hypermetabolism was observed in 28 subjects (82%), and 68% of patients showed MTL hypermetabolism. A total of 17 patients (50%) exhibited faciobrachial dystonic seizures (FBDS), and the remaining subjects showed non-FBDS symptoms (50 and 50%). BG-only hypermetabolism was detected in seven subjects in the FBDS subgroup (7/16) but in only one subject in the non-FBDS subgroup (1/15) (44 vs. 7%, P < 0.05). Conclusion: 18F-FDG-PET imaging was more sensitive than MRI in the diagnosis of LGI1 AE. Isolated BG hypermetabolism was more frequently observed in subjects with FBDS, suggesting the potential involvement of the BG.

Different patterns of white matter changes after successful surgery of mesial temporal lobe epilepsy.

OBJECTIVES: To explore the dynamic changes of white matters following anterior temporal lobectomy (ATL) in mesial temporal lobe epilepsy (MTLE) patients who achieved seizure-free at two-year follow-up. METHODS: Diffusion tensor imaging (DTI) was obtained in ten MTLE patients at five serial time points: before surgery, three months, six months, 12 months and 24 months after surgery, as well as in 11 age- and sex-matched healthy controls at one time point. Regions with significant postoperative fractional anisotropy (FA) changes and their dynamic changes were confirmed by comparing all preoperative and postoperative data using Tract-Based Spatial Statistics (TBSS). RESULTS: After successful ATL, significant FA changes were found in widespread ipsilateral and contralateral white matter regions (P <.05, FWE correction). Ipsilateral external capsule, cingulum, superior corona radiate, body of corpus callosum, inferior longitudinal fasciculus, optic radiation and contralateral inferior cerebellar peduncle, inferior longitudinal fasciculus showed significant FA decrease at three months after surgery, without further changes. Ipsilateral superior cerebellar peduncle and contralateral corpus callosum, anterior corona radiate, external capsule, optic radiation showed significant FA decrease at three months follow up but increase later. Ipsilateral cerebral peduncle and contralateral middle cerebellar peduncle showed significant FA decrease at three months follow up, with further decrease after that. While ipsilateral posterior limb of internal capsule, retrolenticular part of internal capsule and contralateral posterior corona radiate showed significant FA increase after surgery. CONCLUSIONS: FA changes after successful ATL presented as four distinct patterns, reflecting different structural adaptions following epilepsy surgery. Some FA increases indicated the reversibility of preoperative diffusion abnormalities and the possibility of structural reorganization, especially in the contralateral hemisphere.

Assessment of Utilization and Cost-Effectiveness of Telemedicine Program in Western Regions of China: A 12-Year Study of 249 Hospitals Across 112 Cities.

BACKGROUND: The imbalance in healthcare between urban and rural areas is still a problem in China. In recent decades, China has aimed to develop telemedicine. We assessed the implementation, utilization, and cost-effectiveness of a large telemedicine program across western China. MATERIALS AND METHODS: In 2002-2013, a government-sponsored major telemedicine program was established by West China Hospital of Sichuan University (hub), covering 249 spoke hospitals in 112 cities throughout western China and in 40 medical expertise areas. We analyzed the cross-sectional data from 11,987 consultations conducted at West China Hospital using the telemedicine network over a 12-year period. The types of diseases as well as the diagnosis and treatment changes were assessed. We also performed a cost-savings analysis and a one-way sensitivity analysis. RESULTS: Of the 11,987 teleconsultations, we noted that neoplasms (19.4%), injuries (13.9%), and circulatory diseases (10.3%) were the three most common diagnoses. Teleconsultations resulted in a change of diagnosis in 4,772 (39.8%) patients, and 3,707 (77.7%) of them underwent major diagnosis changes. Moreover, it led to a change of treatment in 6,591 (55.0%) patients, including 3,677 (55.8%) changes not linked to diagnosis changes. The telemedicine network resulted in an estimated net saving of $2,364,525 (if the patients traveled to the hub) or $3,759,014 (if the specialists traveled to the spoke hospitals). CONCLUSIONS: The introduction of telemedicine in China, linking highly specialized major hospitals (hub) with hundreds of small rural hospitals (spoke), can greatly improve the quality, efficiency, and cost-effectiveness of healthcare delivery and utilization. This new Internet-based healthcare model should be utilized more widely in developing countries.