Predictors of seizure outcomes in stereo-electroencephalography-guided radio-frequency thermocoagulation for MRI-negative epilepsy.

Background: One-third of intractable epilepsy patients have no visually identifiable focus for neurosurgery based on imaging tests [magnetic resonance imaging (MRI)-negative cases]. Stereo-electroencephalography-guided radio-frequency thermocoagulation (SEEG-guided RF-TC) is utilized in the clinical treatment of epilepsy to lower the incidence of complications post-open surgery. Objective: This study aimed to identify prognostic factors and long-term seizure outcomes in SEEG-guided RF-TC for patients with MRI-negative epilepsy. Design: This was a single-center retrospective cohort study. Methods: We included 30 patients who had undergone SEEG-guided RF-TC at Sanbo Brain Hospital, Capital Medical University, from April 2015 to December 2019. The probability of remaining seizure-free and the plotted survival curves were analyzed. Prognostic factors were analyzed using log-rank tests in univariate analysis and the Cox regression model in multivariate analysis. Results: With a mean time of 31.07 ± 2.64 months (median 30.00, interquartile range: 18.00-40.00 months), 11 out of 30 patients (36.7%) were classified as International League Against Epilepsy class 1 in the last follow-up. The mean time of remaining seizure-free was 21.33 ± 4.55 months [95% confidence interval (CI) 12.41-30.25], and the median time was 3.00 ± 0.54 months (95% CI 1.94-4.06). Despite falling in the initial year, the probability of remaining seizure-free gradually stabilizes in the subsequent years. The patients were more likely to obtain seizure freedom when the epileptogenic zone was located in the insular lobe or with one focus on the limbic system (p = 0.034, hazard ratio 5.019, 95% CI 1.125-22.387). Conclusion: Our findings may be applied to guide individualized surgical interventions and help clinicians make better decisions.

An artificial intelligence-based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography.

Objective.Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence-based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data.Approach.To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients.Main results.The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h).Significance.The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.

Ectopic expression of neuronal adenosine kinase, a biomarker in mesial temporal lobe epilepsy without hippocampal sclerosis.

AIMS: Mesial temporal lobe epilepsy without hippocampal sclerosis (no-HS MTLE) refers to those MTLE patients who have neither magnetic resonance imaging (MRI) lesions nor definite pathological evidence of hippocampal sclerosis. They usually have resistance to antiepileptic drugs, difficulties in precise seizure location and poor surgical outcomes. Adenosine is a neuroprotective neuromodulator that acts as a seizure terminator in the brain. The role of adenosine in no-HS MTLE is still unclear. Further research to explore the aetiology and pathogenesis of no-HS MTLE may help to find new therapeutic targets. METHODS: In surgically resected hippocampal specimens, we examined the maladaptive changes of the adenosine system of patients with no-HS MTLE. In order to better understand the dysregulation of the adenosine pathway in no-HS MTLE, we developed a rat model based on the induction of focal cortical lesions through a prenatal freeze injury. RESULTS: We first examined the adenosine system in no-HS MTLE patients who lack hippocampal neuronal loss and found ectopic expression of the astrocytic adenosine metabolising enzyme adenosine kinase (ADK) in hippocampal pyramidal neurons, as well as downregulation of neuronal A1 receptors (A1 Rs) in the hippocampus. In the no-HS MTLE model rats, the transition of ADK from neuronal expression to an adult pattern of glial expression in the hippocampus was significantly delayed. CONCLUSIONS: Ectopic expression of neuronal ADK might be a pathological hallmark of no-HS MTLE. Maladaptive changes in adenosine metabolism might be a novel target for therapeutic intervention in no-HS MTLE.

Periventricular nodular heterotopia is coupled with the neocortex during resting and task states.

Periventricular nodular heterotopia (PVNH) is a well-defined developmental disorder characterized by failed neuronal migration, which forms ectopic neuronal nodules along the ventricular walls. Previous studies mainly focus on clinical symptoms caused by the PVNH tissue, such as seizures. However, little is known about whether and how neurons in the PVNH tissue functionally communicate with neurons in the neocortex. To probe this, we applied magnetoencephalography (MEG) and stereo-electroencephalography (sEEG) recordings to patients with PVNH during resting and task states. By estimating frequency-resolved phase coupling strength of the source-reconstructed neural activities, we found that the PVNH tissue was spontaneously coupled with the neocortex in the α-ß frequency range, which was consistent with the synchronization pattern within the neocortical network. Furthermore, the coupling strength between PVNH and sensory areas effectively modulated the local neural activity in sensory areas. In both MEG and sEEG visual experiments, the PVNH tissue exhibited visual-evoked responses, with a similar pattern and latency as the ipsilateral visual cortex. These findings demonstrate that PVNH is functionally integrated into cognition-related cortical circuits, suggesting a co-development perspective of ectopic neurons after their migration failure.

EMHapp: a pipeline for the automatic detection, localization and visualization of epileptic magnetoencephalographic high-frequency oscillations.

Objective.High-frequency oscillations (HFOs) are promising biomarkers for localizing epileptogenic brain tissue. Previous studies have revealed that HFOs that present concurrence with interictal epileptic discharges (IEDs) better delineate epileptogenic brain tissue, particularly for epilepsy patients with multitype interictal discharges. However, the analysis of noninvasively recorded epileptic HFOs involves many complex procedures, such as data preprocessing, detection and source localization, impeding the translation of this approach to clinical practice.Approach.To address these problems, we developed a graphical user interface (GUI)-based pipeline called EMHapp, which can be used for the automatic detection, source localization and visualization of HFO events concurring with IEDs in magnetoencephalography (MEG) signals by using a beamformer-based virtual sensor (VS) technique. An improved VS reconstruction method was developed to enhance the amplitudes of both HFO and IED VS signals. To test the capability of our pipeline, we collected MEG data from 11 complex focal epilepsy patients with surgical resections or seizure onset zones (SOZs) that were identified by intracranial electroencephalography.Main results.Our results showed that the HFO sources of eight patients were concordant with their resection margins or SOZs. Our proposed VS signal reconstruction approach achieved an 83.2% improvement regarding the number of detected HFO events and a 17.3% improvement in terms of the spatial overlaps between the HFO sources and the resection margins or SOZs in comparison with conventional VS reconstruction approaches.Significance.EMHapp is the first GUI-based pipeline for the analysis of epileptic magnetoencephalographic HFOs, which conveniently obtains HFO source locations using clinical data and enables direct translation to clinical applications.

Analysis of power spectrum and phase lag index changes following deep brain stimulation of the anterior nucleus of the thalamus in patients with drug-resistant epilepsy: A retrospective study.

OBJECTIVES: The mechanisms underlying the anterior nucleus of the thalamus (ANT) deep brain stimulation (DBS) for the treatment of drug-resistant epilepsy (DRE) have not been fully explored. The present study aimed to measure the changes in whole-brain activity generated by ANT DBS using interictal electroencephalography (EEG). MATERIALS AND METHODS: Interictal EEG signals were retrospectively collected in 20 DRE patients who underwent ANT DBS surgery. Patients were classified as responders or non-responders depending on their response to ANT DBS treatment. The power spectrum (PS) and Phase Lag Index (PLI) were determined and data analyzed using a paired sample t-test to evaluate activity differences between pre-and-post-treatment on different frequency categories. Student's t-test, Mann-Whitney test (non-parametric test) and Fisher exact test were used to compare groups in terms of clinical variables and EEG metrics. P values < 0.05 were considered statistically significant, and FDR-corrected values were used for multiple testing. RESULTS: PS analysis revealed that whole-brain spectral power had a significant decrease in the beta (p = 0.005) and gamma (p = 0.037) bands following ANT DBS treatment in responders. The analysis of scalp topographic images of all patients showed that ANT DBS decreases PS in the beta band at the F3, F7 and Cz electrode sites. The findings indicated a decrease in PS in the gamma band at the Fp2, F3, Cz, T3, T5 and Oz electrode sites. After ANT DBS treatment, PLI analysis showed a significant decrease in PLI between Fp1 and T3 in the gamma band in responders. CONCLUSION: The findings showed that ANT DBS induces a decrease in power in the left frontal lobe, left temporal lobe and midline areas in the beta and gamma bands. Lower whole-brain power in the beta and gamma bands can be used as biomarkers for a favorable therapeutic response to ANT DBS, and decreased synchronization between the left frontal pole and temporal lobe in the gamma band can also be used as a biomarker for effective clinical stimulation to guide postoperative programming.

Vagus nerve stimulation for drug-resistant epilepsy induced by tuberous sclerosis complex.

OBJECTIVE: This study investigated the dynamic and long-term efficacy of vagus nerve stimulation (VNS) in patients with drug-resistant epilepsy (DRE) induced by tuberous sclerosis complex (TSC). In addition, the impact of VNS on cognition and emotion after a one-year follow-up was evaluated. METHODS: A total of 17 patients diagnosed with DRE induced by TSC were retrospectively recruited between 2008 and 2019. Dynamic changes in seizure frequency were observed in the responders (≥50% reduction of seizure frequency at last follow-up) and non-responders. Clinical characteristics and seizure outcomes were comprehensively analyzed to determine factors associated with seizure outcomes. The Wechsler intelligence scale was applied in a subgroup of six pediatric patients, whereas the Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS) were assessed in a subgroup of nine patients to determine the impact of VNS therapy on cognitive performance and emotional state. RESULTS: The follow-up duration for the 17 patients who underwent VNS treatment ranged from 0.5 to 10 years (mean ±â€¯SD: 4.1 ±â€¯3.2 years). Monthly seizures decreased significantly from three months to four years post-treatment (p < 0.05). At the last follow-up, 70.6% of the patients achieved at least a 50% reduction in seizure frequency, and three patients were completely seizure free. Comparatively, non-responder patients experienced deterioration of seizure frequency after the first year. Notably, after one-year follow-up the mean standard score of full-scale intelligence quotient increased from 67.33 to 69.5 (p = 0.078) while the mean, standard score of SDS decreased from 49.22 to 45.67 (p = 0.003) compared to preoperative neuropsychological evaluation results. CONCLUSION: VNS is a safe and effective treatment for patients with DRE caused by TSC. Although early outcomes were encouraging, a follow-up of at least one-year was required to predict long-term outcomes in patients receiving VNS treatment. Moreover, VNS may improve depressive mood in patients with DRE caused by TSC. Further investigations are needed to validate the present results.

Magnetoencephalography STOUT Method Adapted to Radiofrequency Thermocoagulation for MR-Negative Insular Epilepsy: A Case Report.

Epilepsy is one of the most challenging neurologic diseases confronted by human society. Approximately 30-40% of the worldwide epilepsy patients are diagnosed with drug-resistant epilepsy and require pre-surgery evaluation. Magnetoencephalography (MEG) is a unique technology that provides optimal spatial-temporal resolution and has become a powerful non-invasive imaging modality that can localize the interictal spikes and guide the implantation of intracranial electrodes. Currently, the most widely used MEG source estimation method for clinical applications is equivalent current dipoles (ECD). However, ECD has difficulties in precisely locating deep sources such as insular lobe. In contrast to ECD, another MEG source estimation method named spatio-temporal unifying tomography (STOUT) with spatial sparsity has particular advantages in locating deep sources. In this case study, we recruited a 5 year-old female patient with insular lobe epilepsy and her seizure recurred in 1 year after receiving the radiofrequency thermocoagulation (RF-TC) therapy. The STOUT method was adopted to locate deep sources for identifying the epileptic foci in epilepsy evaluation. MEG STOUT method strongly supported a stereo-electroencephalographic (SEEG)-guided RF-TC operation, and the patient reported a satisfactory therapeutic effect. This case raises the possibility that STOUT method can be used particularly for the localization of deep sources, and successfully conducted RF-TC under the guidance of MEG STOUT results.

Predictors and Long-term Outcome of Resective Epilepsy Surgery in Patients with Tuberous Sclerosis Complex: A Single-centre Retrospective Cohort Study.

OBJECTIVE: To identify prognostic factors and long-term seizure outcomes for patients with tuberous sclerosis complex (TSC) who underwent resective treatment for epilepsy. METHODS: We enrolled 81 patients with TSC who had undergone resective epilepsy surgery at Sanbo Brain Hospital, Capital Medical University, between April 2004 and June 2019. We estimated cumulative probability of remaining seizure-free and plotted survival curves. Variables were compared using Mann-Whitney U, Pearson's correlation, continuity correction, and Fisher's exact chi-square tests. Prognostic predictors were analysed using log-rank (Mantel-Cox) tests and Cox regression models. RESULTS: At the last follow-up, 48 (59.3%) patients were classified as International League Against Epilepsy Class 1 (including 14 patients who had seizures <3 times postoperatively on the same or different day and were seizure-free at all other times). The estimated cumulative probability of remaining seizure-free postoperatively was 69.0% (95% confidence interval [CI] 58.8-79.2%), 61.9% (95% CI 51.1-72.7%), and 55.0% (95% CI 42.8-67.2%) at 2, 5, and 10 years, respectively. The mean time of remaining seizure-free was 7.24 ± 0.634 years (95% CI 6.00-8.49); en bloc resection was an essential positive predictor of postoperative seizure freedom, as was age at seizure onset, regional interictal video-electroencephalography pattern, and temporal lobe surgery. The longer the seizure-free time, the less likely a relapse. Patients who postoperatively experienced seizures remained likely to recover. CONCLUSIONS: We demonstrated the efficacy of surgical treatment for patients with TSC and intractable epilepsy. Detailed perioperative tests are a reliable predictor of postoperative seizure freedom.

Enhanced Fast-VESTAL for Magnetoencephalography Source Imaging: From Theory to Clinical Application in Epilepsy.

A novel magnetoencephalography source imaging approach called Fast Vector-based Spatio-Temporal Analysis (Fast-VESTAL) has been successfully applied in creating source images from evoked and resting-state data from both healthy subjects and individuals with neurological and/or psychiatric disorders, but its reconstructed source images may show false-positive activations, especially under low signal-to-noise ratio conditions. Here, to effectively reduce false-positive artifacts, we introduced an enhanced Fast-VESTAL (eFast-VESTAL) approach that adopts generalized second-order cone programming. We compared the spatiotemporal characteristics of the eFast-VESTAL approach to those of the popular distributed source approaches (e.g., the minimum L2-norm/ mixed-norm methods) using computer simulations and auditory experiments. More importantly, we applied eFast-VESTAL to the presurgical evaluation of epilepsy. Our results demonstrated that eFast-VESTAL exhibited a lower dipole localization error and/or a higher correlation coefficient (CC) between the estimated source time series and ground truth under various conditions of source waveforms. Experimentally, eFast-VESTAL displayed more focal activation maps and a higher CC between the raw and predicted sensor data in response to auditory stimulation. Notably, eFast-VESTAL was the most accurate method for noninvasively detecting the epileptic zones determined using more invasive stereo-electroencephalography in the comparison.

EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes.

Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients' physical, psychological and social health. A practical approach to assist with the clinical assessment and treatment planning for patients is to process magnetoencephalography (MEG) data to identify epileptogenic zones. As a widely accepted biomarker of epileptic foci, epileptic MEG spikes need to be precisely detected. Given that the visual inspection of spikes is time consuming, an automatic and efficient system with adequate accuracy for spike detection is valuable in clinical practice. However, current approaches for MEG spike autodetection are dependent on hand-engineered features. Here, we propose a novel multiview Epileptic MEG Spikes detection algorithm based on a deep learning Network (EMS-Net) to accurately and efficiently recognize the spike events from MEG raw data. The results of the leave-k-subject-out validation tests for multiple datasets (i.e., balanced and realistic datasets) showed that EMS-Net achieved state-of-the-art classification performance (i.e., accuracy: 91.82% - 99.89%; precision: 91.90% - 99.45%; sensitivity: 91.61% - 99.53%; specificity: 91.60% - 99.96%; f1 score: 91.70% - 99.48%; and area under the curve: 0.9688 - 0.9998).

The impact of MEG results on surgical outcomes in patients with drug-resistant epilepsy associated with focal encephalomalacia: a single-center experience.

PURPOSE: To analyze the impact of magnetoencephalography (MEG) results on surgical outcomes in patients with drug-resistant epilepsy secondary to encephalomalacia. METHODS: We retrospectively reviewed 121 patients with drug-resistant epilepsy associated with encephalomalacia who underwent MEG followed by resection surgery. Patients were subdivided into concordant MEG group and dis-concordant MEG group for analysis based on whether the MEG results were in concordance with epileptogenic zones or not. RESULTS: 121 patients were included in the present study. The MEG spike sources of 73 (60.33%) patients were in concordance with epileptogenic zones while the MEG spike sources of the other 48 (39.67%) were in dis-concordance with epileptogenic zones. Favorable seizure outcomes were achieved in 79.45% (58 of 73) of patients with concordant MEG results while only 62.50% (30 of 48) of patients with dis-concordant MEG results were seizure free with a follow-up of 2-10 years. The differences of seizure-free rate between patients with concordant MEG results and dis-concordant MEG results were statistically significant. For patients with concordant MEG results, bilateral lesions on MRI are the only independent predictor of unfavorable seizure outcomes. For patients with discordant MEG results, duration of seizures is the only independent predictor of unfavorable seizure outcomes. CONCLUSIONS: Concordant MEG results are associated with favorable seizure outcomes. Bilateral lesions on MRI independently predict unfavorable seizure outcomes in patients with concordant MEG results while longer seizure durations independently predict unfavorable seizure outcomes in patients with dis-concordant MEG results.

Dynamic Correlations between Intrinsic Connectivity and Extrinsic Connectivity of the Auditory Cortex in Humans.

The arrival of sound signals in the auditory cortex (AC) triggers both local and inter-regional signal propagations over time up to hundreds of milliseconds and builds up both intrinsic functional connectivity (iFC) and extrinsic functional connectivity (eFC) of the AC. However, interactions between iFC and eFC are largely unknown. Using intracranial stereo-electroencephalographic recordings in people with drug-refractory epilepsy, this study mainly investigated the temporal dynamic of the relationships between iFC and eFC of the AC. The results showed that a Gaussian wideband-noise burst markedly elicited potentials in both the AC and numerous higher-order cortical regions outside the AC (non-auditory cortices). Granger causality analyses revealed that in the earlier time window, iFC of the AC was positively correlated with both eFC from the AC to the inferior temporal gyrus and that to the inferior parietal lobule. While in later periods, the iFC of the AC was positively correlated with eFC from the precentral gyrus to the AC and that from the insula to the AC. In conclusion, dual-directional interactions occur between iFC and eFC of the AC at different time windows following the sound stimulation and may form the foundation underlying various central auditory processes, including auditory sensory memory, object formation, integrations between sensory, perceptional, attentional, motor, emotional, and executive processes.

Magnetoencephalography in Preoperative Epileptic Foci Localization: Enlightenment from Cognitive Studies.

Over 30% epileptic patients are refractory to medication, who are amenable to neurosurgical treatment. Non-invasive brain imaging technologies including video-electroencephalogram (EEG), magnetic resonance imaging (MRI), and magnetoencephalography (MEG) are widely used in presurgical assessment of epileptic patients. This review mainly discussed the current development of clinical MEG imaging as a diagnose approach, and its correlations with the golden standard intracranial electroencephalogram (iEEG). More importantly, this review discussed the possible applications of functional networks in preoperative epileptic foci localization in future studies.