An Individual Data-Driven Virtual Resection Model Based on Epileptic Network Dynamics in Children with Intractable Epilepsy: A Magnetoencephalography Interictal Activity Application

Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30-70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient\u27s magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient\u27s clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography-MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery

Complete Corpus Callosotomy Using a Frameless Navigation Probe through a Minicraniotomy in Children with Medically Refractory Epilepsy: A Case Series and Technical Note

BACKGROUND: Medically refractory epilepsy constitutes up to one-third of the epilepsy pediatric patients. Corpus callosotomy (CC) has been used for the treatment of medically refractory epilepsy in children with atonic seizures and generalized tonic-clonic (GTC) seizures. In this case series study, we are describing a novel technique for CC using the frameless navigation probe through a minicraniotomy. METHODS: Thirteen pediatric patients with the diagnosis of medically refractory epilepsy predominantly GTC with drop attack who underwent extensive Phase I. An L-shape was done, then through a 4 × 3 cm craniotomy, we were able to open the interhemispheric fissure until the corpus callosum is visualized. The Stealth probe is then used to go down to the midline raphe which is followed anteriorly then traced posteriorly to the anterior border of the vein of Galen. Finally, the Stealth probe is used to confirm the completeness of the callosotomy. RESULTS: The procedure was accomplished successfully with no intraoperative complications; mean surgical time is 3 h:07 m. The mean follow-up was 31.5 months. All patients achieved significant seizure control. No patients experienced worsening of their atonic seizures after surgery compared with their preoperative state; however, six patients achieved Engel Class I, four patients achieved Engel Class II, and three patients achieved Engel Class III. CONCLUSION: Complete CC using a frameless navigation probe is a novel and effective technique for the treatment of medically refractory epilepsy with a very good surgical and seizure outcomes, minimal neurological morbidity, minimal blood loss, and short OR time

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform \u3e70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients

Responsive Neurostimulation for People With Drug-Resistant Epilepsy and Autism Spectrum Disorder

PURPOSE: Individuals with autism spectrum disorder (ASD) have comorbid epilepsy at much higher rates than the general population, and about 30% will be refractory to medication. Patients with drug-resistant epilepsy (DRE) should be referred for surgical evaluation, yet many with ASD and DRE are not resective surgical candidates. The aim of this study was to examine the response of this population to the responsive neurostimulator (RNS) System. METHODS: This multicenter study evaluated patients with ASD and DRE who underwent RNS System placement. Patients were included if they had the RNS System placed for 1 year or more. Seizure reduction and behavioral outcomes were reported. Descriptive statistics were used for analysis. RESULTS: Nineteen patients with ASD and DRE had the RNS System placed at 5 centers. Patients were between the ages of 11 and 29 (median 20) years. Fourteen patients were male, whereas five were female. The device was implanted from 1 to 5 years. Sixty-three percent of all patients experienced a \u3e50% seizure reduction, with 21% of those patients being classified as super responders (seizure reduction \u3e90%). For the super responders, two of the four patients had the device implanted for \u3e2 years. The response rate was 70% for those in whom the device was implanted for \u3e2 years. Improvements in behaviors as measured by the Clinical Global Impression Scale-Improvement scale were noted in 79%. No complications from the surgery were reported. CONCLUSIONS: Based on the authors\u27 experience in this small cohort of patients, the RNS System seems to be a promising surgical option in people with ASD-DRE

Post-Zygotic Rescue of Meiotic Errors Causes Brain Mosaicism and Focal Epilepsy

Somatic mosaicism is a known cause of neurological disorders, including developmental brain malformations and epilepsy. Brain mosaicism is traditionally attributed to post-zygotic genetic alterations arising in fetal development. Here we describe post-zygotic rescue of meiotic errors as an alternate origin of brain mosaicism in patients with focal epilepsy who have mosaic chromosome 1q copy number gains. Genomic analysis showed evidence of an extra parentally derived chromosome 1q allele in the resected brain tissue from five of six patients. This copy number gain is observed only in patient brain tissue, but not in blood or buccal cells, and is strongly enriched in astrocytes. Astrocytes carrying chromosome 1q gains exhibit distinct gene expression signatures and hyaline inclusions, supporting a novel genetic association for astrocytic inclusions in epilepsy. Further, these data demonstrate an alternate mechanism of brain chromosomal mosaicism, with parentally derived copy number gain isolated to brain, reflecting rescue in other tissues during development

Post-zygotic Rescue of Meiotic Errors Causes Brain Mosaicism and Focal Epilepsy

Somatic mosaicism is a known cause of neurological disorders, including developmental brain malformations and epilepsy. Brain mosaicism is traditionally attributed to post-zygotic genetic alterations arising in fetal development. Here we describe post-zygotic rescue of meiotic errors as an alternate origin of brain mosaicism in patients with focal epilepsy who have mosaic chromosome 1q copy number gains. Genomic analysis showed evidence of an extra parentally derived chromosome 1q allele in the resected brain tissue from five of six patients. This copy number gain is observed only in patient brain tissue, but not in blood or buccal cells, and is strongly enriched in astrocytes. Astrocytes carrying chromosome 1q gains exhibit distinct gene expression signatures and hyaline inclusions, supporting a novel genetic association for astrocytic inclusions in epilepsy. Further, these data demonstrate an alternate mechanism of brain chromosomal mosaicism, with parentally derived copy number gain isolated to brain, reflecting rescue in other tissues during development

Ultra-rare genetic variation in common epilepsies: a case-control sequencing study

BACKGROUND:Despite progress in understanding the genetics of rare epilepsies, the more common epilepsies have proven less amenable to traditional gene-discovery analyses. We aimed to assess the contribution of ultra-rare genetic variation to common epilepsies. METHODS:We did a case-control sequencing study with exome sequence data from unrelated individuals clinically evaluated for one of the two most common epilepsy syndromes: familial genetic generalised epilepsy, or familial or sporadic non-acquired focal epilepsy. Individuals of any age were recruited between Nov 26, 2007, and Aug 2, 2013, through the multicentre Epilepsy Phenome/Genome Project and Epi4K collaborations, and samples were sequenced at the Institute for Genomic Medicine (New York, USA) between Feb 6, 2013, and Aug 18, 2015. To identify epilepsy risk signals, we tested all protein-coding genes for an excess of ultra-rare genetic variation among the cases, compared with control samples with no known epilepsy or epilepsy comorbidity sequenced through unrelated studies. FINDINGS:We separately compared the sequence data from 640 individuals with familial genetic generalised epilepsy and 525 individuals with familial non-acquired focal epilepsy to the same group of 3877 controls, and found significantly higher rates of ultra-rare deleterious variation in genes established as causative for dominant epilepsy disorders (familial genetic generalised epilepsy: odd ratio [OR] 2·3, 95% CI 1·7-3·2, p=9·1 × 10-8; familial non-acquired focal epilepsy 3·6, 2·7-4·9, p=1·1 × 10-17). Comparison of an additional cohort of 662 individuals with sporadic non-acquired focal epilepsy to controls did not identify study-wide significant signals. For the individuals with familial non-acquired focal epilepsy, we found that five known epilepsy genes ranked as the top five genes enriched for ultra-rare deleterious variation. After accounting for the control carrier rate, we estimate that these five genes contribute to the risk of epilepsy in approximately 8% of individuals with familial non-acquired focal epilepsy. Our analyses showed that no individual gene was significantly associated with familial genetic generalised epilepsy; however, known epilepsy genes had lower p values relative to the rest of the protein-coding genes (p=5·8 × 10-8) that were lower than expected from a random sampling of genes. INTERPRETATION:We identified excess ultra-rare variation in known epilepsy genes, which establishes a clear connection between the genetics of common and rare, severe epilepsies, and shows that the variants responsible for epilepsy risk are exceptionally rare in the general population. Our results suggest that the emerging paradigm of targeting of treatments to the genetic cause in rare devastating epilepsies might also extend to a proportion of common epilepsies. These findings might allow clinicians to broadly explain the cause of these syndromes to patients, and lay the foundation for possible precision treatments in the future. FUNDING:National Institute of Neurological Disorders and Stroke (NINDS), and Epilepsy Research UK

Splits in fruitfly Hox gene complexes

The homeotic genes are strikingly conserved between invertebrates and vertebrates. There is conservation, not only of the homeobox sequences, but also of their collinear order on the chromosome. The genes are found in clusters or 'complexes', and are arranged on the chromosome in the order of their function along the anteroposterior body axis (for review, see ref. 1). In the fruitfly Drosophila melanogaster, the homeotic genes are split into two separate clusters, the Antennapedia complex (ANTP-C) and the Bithorax complex (BX-C), which direct development of the anterior and posterior segments, respectively. We show that in Drosophila virilis, a closely related species, the homeotic genes are also in two clusters, but the split occurs within the BX-C. The existence of two independent splits in the Drosophila lineage suggests that these flies lack the molecular constraint responsible for the ordered clusters in other animals

An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application

Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30–70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient’s magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography–MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery.Depto. de Radiología, Rehabilitación y FisioterapiaFac. de MedicinaTRUEpu