Surgical treatment of epilepsy

Robot-assisted insular stereoelectroencephalography in pediatric drug-resistant epilepsy: accuracy and diagnostic value

BACKGROUND

Insular epilepsy is a well-known cause of drug-resistant epilepsy (DRE) in the pediatric population. It can be a source of surgical epilepsy treatment failures when not ruled out pre-operatively. Non-invasive methods often provide limited information about its existence, being the invasive methods necessary to diagnose it in the vast majority of cases. The most used is stereoelectroencephalography (SEEG). We report a series of DRE pediatric patients in which insular SEEG was performed to rule out insular epilepsy.

METHOD

We performed a retrospective review of pediatric DRE patients operated on SEEG including insular electrodes between April 2016 and September 2022. We described the different trajectories used (orthogonal or oblique) and surgical techniques. After implantation, we assessed electrodes' precision using three measures: entry point location error (EPLE), target point location error (TPLE), and target deviation (TD). We also reported complications that occurred with this technique as well as the diagnostic information provided.

RESULTS

Overall, 32 DRE patients were operated on SEEG including insular electrodes. Four hundred one electrodes were implanted, 148 (39.91%) of whom were directed to the insula. One hundred twelve followed an orthogonal trajectory, and 36 were oblique. The mean EPLE was 1.45 mm, TPLE was 1.88 mm and TD was 0.71 mm. Three patients suffered from frontal hematoma, two of them diagnosed on post-operative MRI and one who required surgery, with no sequelae. One patient suffered from meningitis treated with antibiotics with no permanent sequelae. Nine patients (28.13%) had the insula included in the epileptogenic zone.

CONCLUSIONS

Insular epilepsy has to be ruled out in DRE patients when little suspicion is obtained after non-invasive testing. This is especially important in the pediatric population, in which seizure semiology is more difficult to characterize and failures to control epilepsy have devastating consequences in neurocognitive development and scholarship. Given its relative low rate of relevant complications and potential benefits, we should consider widening the inclusion criteria for insular SEEG monitoring.

Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy

Epilepsy is one of the most common clinical diseases of the nervous system. The occurrence of epilepsy will bring many serious consequences, and some patients with epilepsy will develop drug-resistant epilepsy. Surgery is an effective means to treat this kind of patients, and lesion localization can provide a basis for surgery. The purpose of this study was to explore the functional types and connectivity evolution patterns of relevant regions of the brain during seizures. We used intracranial EEG signals from patients with epilepsy as the research object, and the method used was GRU-GC. The role of the corresponding area of each channel in the seizure process was determined by the introduction of group analysis. The importance of each area was analysed by introducing the betweenness centrality and PageRank centrality. The experimental results show that the classification method based on effective connectivity has high accuracy, and the role of the different regions of the brain could also change during the seizures. The relevant methods in this study have played an important role in preoperative assessment and revealing the functional evolution patterns of various relevant regions of the brain during seizures.

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 .

Remote effects of temporal lobe epilepsy surgery: Long-term morphological changes after surgical resection

OBJECTIVE

Epilepsy surgery is an effective treatment for drug-resistant patients. However, how different surgical approaches affect long-term brain structure remains poorly characterized. Here, we present a semiautomated method for quantifying structural changes after epilepsy surgery and compare the remote structural effects of two approaches, anterior temporal lobectomy (ATL), and selective amygdalohippocampectomy (SAH).

METHODS

We studied 36 temporal lobe epilepsy patients who underwent resective surgery (ATL = 22, SAH = 14). All patients received same-scanner MR imaging preoperatively and postoperatively (mean 2 years). To analyze postoperative structural changes, we segmented the resection zone and modified the Advanced Normalization Tools (ANTs) longitudinal cortical pipeline to account for resections. We compared global and regional annualized cortical thinning between surgical treatments.

RESULTS

Across procedures, there was significant cortical thinning in the ipsilateral insula, fusiform, pericalcarine, and several temporal lobe regions outside the resection zone as well as the contralateral hippocampus. Additionally, increased postoperative cortical thickness was seen in the supramarginal gyrus. Patients treated with ATL exhibited greater annualized cortical thinning compared with SAH cases (ATL: -0.08 ± 0.11 mm per year, SAH: -0.01 ± 0.02 mm per year, t = 2.99, P = 0.006). There were focal postoperative differences between the two treatment groups in the ipsilateral insula (P = 0.039, corrected). Annualized cortical thinning rates correlated with preoperative cortical thickness (r = 0.60, P < 0.001) and had weaker associations with age at surgery (r = -0.33, P = 0.051) and disease duration (r = -0.42, P = 0.058).

SIGNIFICANCE

Our evidence suggests that selective procedures are associated with less cortical thinning and that earlier surgical intervention may reduce long-term impacts on brain structure.

Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification

Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154.

Low-intensity focused ultrasound, a novel approach to epilepsy treatment in developing countries

Approximately 80% of patients with epilepsy reside in poor resource settings. Despite the continued advancements and development of new treatment approaches, epilepsy remains a major health problem in developing countries. Consistent findings of epidemiologic studies reflect that both prevalence and treatment gap are higher in the developing world. The objective of this short review was to evaluate current treatment options and low-intensity, pulsed-focused ultrasound (FUS) as a potential new treatment option for epilepsy. Although some of the patients could be candidates for surgery, many factors, including poor health-care infrastructure, socioeconomic status, risks and complications associated with the surgery, and patients' preferences and attitudes toward the surgical procedure, limit the adherence to get surgical therapies. Low-intensity FUS, a novel and noninvasive therapeutic approach, has the potential to be approved by regulatory bodies and added to the list of standard treatment options for epilepsy. Improved understanding of epilepsy's prevalence and incidence in developing worlds, identification of potential new therapeutic options, and their evaluation through continuous studies and clinical trials are needed to reduce the burden of epilepsy and the treatment gap.

Liquid biopsies in epilepsy: biomarkers for etiology, diagnosis, prognosis, and therapeutics

Epilepsy is one of the most common diseases of the central nervous system, impacting nearly 50 million people around the world. Heterogeneous in nature, epilepsy presents in children and adults alike. Currently, surgery is one treatment approach that can completely cure epilepsy. However, not all individuals are eligible for surgical procedures or have successful outcomes. In addition to surgical approaches, antiepileptic drugs (AEDs) have also allowed individuals with epilepsy to achieve freedom from seizures. Others have found treatment through nonpharmacologic approaches such as vagus nerve stimulation, or responsive neurostimulation. Difficulty in accessing samples of human brain tissue along with advances in sequencing technology have driven researchers to investigate sampling liquid biopsies in blood, serum, plasma, and cerebrospinal fluid within the context of epilepsy. Liquid biopsies provide minimal or non-invasive sample collection approaches and can be assayed relatively easily across multiple time points, unlike tissue-based sampling. Various efforts have investigated circulating nucleic acids from these samples including microRNAs, cell-free DNA, transfer RNAs, and long non-coding RNAs. Here, we review nucleic acid-based liquid biopsies in epilepsy to improve understanding of etiology, diagnosis, prediction, and therapeutic monitoring.

Neuromodulation in Drug Resistant Epilepsy

Epilepsy affects approximately 70 million people worldwide, and it is a significant contributor to the global burden of neurological disorders. Despite the advent of new AEDs, drug resistant-epilepsy continues to affect 30-40% of PWE. Once identified as having drug-resistant epilepsy, these patients should be referred to a comprehensive epilepsy center for evaluation to establish if they are candidates for potential curative surgeries. Unfortunately, a large proportion of patients with drug-resistant epilepsy are poor surgical candidates due to a seizure focus located in eloquent cortex, multifocal epilepsy or inability to identify the zone of ictal onset. An alternative treatment modality for these patients is neuromodulation. Here we present the evidence, indications and safety considerations for the neuromodulation therapies in vagal nerve stimulation (VNS), responsive neurostimulation (RNS), or deep brain stimulation (DBS).

Improved Detection of Subtle Mesial Temporal Sclerosis: Validation of a Commercially Available Software for Automated Segmentation of Hippocampal Volume

BACKGROUND AND PURPOSE

Identification of mesial temporal sclerosis is critical in the evaluation of individuals with temporal lobe epilepsy. Our aim was to assess the performance of FDA-approved software measures of hippocampal volume to identify mesial temporal sclerosis in patients with medically refractory temporal lobe epilepsy compared with the initial clinical interpretation of a neuroradiologist.

MATERIALS AND METHODS

Preoperative MRIs of 75 consecutive patients who underwent a temporal resection for temporal lobe epilepsy from 2011 to 2016 were retrospectively reviewed, and 71 were analyzed using Neuroreader, a commercially available automated segmentation and volumetric analysis package. Volume measures, including hippocampal volume as a percentage of total intracranial volume and the Neuroreader Index, were calculated. Radiologic interpretations of the MR imaging and pathology from subsequent resections were classified as either mesial temporal sclerosis or other, including normal findings. These measures of hippocampal volume were evaluated by receiver operating characteristic curves on the basis of pathologic confirmation of mesial temporal sclerosis in the resected temporal lobe. Sensitivity and specificity were calculated for each method and compared by means of the McNemar test using the optimal threshold as determined by the Youden J point.

RESULTS

Optimized thresholds of hippocampal percentage of a structural volume relative to total intracranial volume (<0.19%) and the Neuroreader Index (≤-3.8) were selected to optimize sensitivity and specificity (89%/71% and 89%/78%, respectively) for the identification of mesial temporal sclerosis in temporal lobe epilepsy compared with the initial clinical interpretation of the neuroradiologist (50% and 87%). Automated measures of hippocampal volume predicted mesial temporal sclerosis more accurately than radiologic interpretation (McNemar test, P < .0001).

CONCLUSIONS

Commercially available automated segmentation and volume analysis of the hippocampus accurately identifies mesial temporal sclerosis and performs significantly better than the interpretation of the radiologist.

Intraoperative video-rate hemodynamic response assessment in human cortex using snapshot hyperspectral optical imaging

Using light, we are able to visualize the hemodynamic behavior of the brain to better understand neurovascular coupling and cerebral metabolism. In vivo optical imaging of tissue using endogenous chromophores necessitates spectroscopic detection to ensure molecular specificity as well as sufficiently high imaging speed and signal-to-noise ratio, to allow dynamic physiological changes to be captured, isolated, and used as surrogate of pathophysiological processes. An optical imaging system is introduced using a 16-bands on-chip hyperspectral camera. Using this system, we show that up to three dyes can be imaged and quantified in a tissue phantom at video-rate through the optics of a surgical microscope. In vivo human patient data are presented demonstrating brain hemodynamic response can be measured intraoperatively with molecular specificity at high speed.