Pediatric epilepsy surgery and sudden unexpected death epilepsy: the contribution of a Brazilian epilepsy surgery program

Individuals with epilepsy are at higher risk of death than those from the general population, and sudden unexpected death in epilepsy (SUDEP) is the most important direct epilepsy-related cause of death. Epilepsies in the pediatric group are more frequently associated with known potentially risk factors for SUDEP, and a treatment resulting in an improved seizure control may also decrease mortality. The aim of this study is to identify the incidence of SUDEP in a group of operated-on children and adolescents. We analyzed 267 patients up to 18 years old, with medically intractable epilepsy submitted to surgery. We considered the age at surgery, the seizure type, the pathological findings, and the seizure outcome. Data were prospectively collected, according to the protocols of our institution`s ethics committee. The percentage of boys was 58.05. A good outcome was achieved in 72.6% of the cases and a bad outcome in 27.4%. Nine patients died during follow-up, six from clinical complications, and one from SUDEP. All patients who died during the long-term follow-up had persisted with refractory postoperative seizures. The patient who died from SUDEP died during a generalized tonic-clonic seizure. Of the patients, 72.6% had excellent postoperative outcome, and one patient died of SUDEP. All patients who died had had disabling seizures` persistence. The surgical treatment of epilepsy in children and adolescents is an efficient therapy for the medically intractable symptomatic epilepsies and also for the reduction of mortality and SUDEP risks.

Role of surgery in pediatric epilepsy.

Twenty five percent of patients with intractable epilepsy have surgically remediable epilepsy syndromes. This article reviews the treatment paradigm for pediatric epilepsy and also the indications, methods, and surgical options for the subgroup of patients with surgically remediable epileptic disorders based on our experience in the management of these children. The article also discusses the rationale for offering surgery and the timing of surgery in these patients. The study of surgically remediable epilepsy can best be divided into focal, sub hemispheric, hemispheric and multifocal epileptic syndromes. These syndromes have both acquired and congenital etiologies and can be treated by resective or disconnective surgery. The surgical management of these conditions (with the exception of multifocal epilepsy) provides Engel's Class 1 outcome(complete seizure freedom) in approximately 80% of children. The consequences of seizure freedom leads to a marked improvement in the quality of life of these children. The benefits to society, of allowing a child to grow to adulthood with normal cognition to earn a livelihood and contribute actively to society, cannot be understated.

Impact of pediatric epilepsy surgery on intellectual efficiency

Introduction. Epilepsy surgery may be a promising alternative therapy for seizure control in patients with refractory seizures, resistant to medication. Cognitive outcome is another important factor in favor of the surgical decision. Aim. To investigate the correlation between seizure outcome and cognitive outcome after epilepsy surgery in a pediatric population. Patients and methods. A total of 59 pediatric patients were retrospectively assessed with the WISC-III (Full Scale, Verbal Scale and Performance Scale) before and, at least, 6 months after surgery. Patients were divided into two groups according whether or not improvement of seizure control after surgery. Data collected for each child included: epileptic syndrome, etiology, age at epilepsy onset, duration of epilepsy and seizure frequency. Results. Comparison using a MANOVA test revealed significant differences across pre-operative Full Scale, Verbal Scale and Performance Scale (p = 0.01) with seizure reduction group performing better than no seizure reduction group. Seizure improvement group achieved significant Performance Scale improvement (p = 0.01) and no seizure improvement group showed significant Verbal Scale worsened after surgery (p = 0.01). Conclusions. Our results suggest that the success of the epilepsy surgery in childhood when the seizure control is achieved may also provide an improvement in the Performance Scale whereas the seizure maintenance may worsen the Verbal Scale.

Design and implementation of a multisite data repository for pediatric epilepsy

This dissertation established a software-hardware integrated design for a multisite data repository in pediatric epilepsy. A total of 16 institutions formed a consortium for this web-based application. This innovative fully operational web application allows users to upload and retrieve information through a unique human-computer graphical interface that is remotely accessible to all users of the consortium. A solution based on a Linux platform with My-SQL and Personal Home Page scripts (PHP) has been selected. Research was conducted to evaluate mechanisms to electronically transfer diverse datasets from different hospitals and collect the clinical data in concert with their related functional magnetic resonance imaging (fMRI). What was unique in the approach considered is that all pertinent clinical information about patients is synthesized with input from clinical experts into 4 different forms, which were: Clinical, fMRI scoring, Image information, and Neuropsychological data entry forms. A first contribution of this dissertation was in proposing an integrated processing platform that was site and scanner independent in order to uniformly process the varied fMRI datasets and to generate comparative brain activation patterns. The data collection from the consortium complied with the IRB requirements and provides all the safeguards for security and confidentiality requirements. An 1-MR1-based software library was used to perform data processing and statistical analysis to obtain the brain activation maps. Lateralization Index (LI) of healthy control (HC) subjects in contrast to localization-related epilepsy (LRE) subjects were evaluated. Over 110 activation maps were generated, and their respective LIs were computed yielding the following groups: (a) strong right lateralization: (HC=0%, LRE=18%), (b) right lateralization: (HC=2%, LRE=10%), (c) bilateral: (HC=20%, LRE=15%), (d) left lateralization: (HC=42%, LRE=26%), e) strong left lateralization: (HC=36%, LRE=31%). Moreover, nonlinear-multidimensional decision functions were used to seek an optimal separation between typical and atypical brain activations on the basis of the demographics as well as the extent and intensity of these brain activations. The intent was not to seek the highest output measures given the inherent overlap of the data, but rather to assess which of the many dimensions were critical in the overall assessment of typical and atypical language activations with the freedom to select any number of dimensions and impose any degree of complexity in the nonlinearity of the decision space.

A research platform for artificial neural networks with applications in pediatric epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that (1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and (2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.

Outcomes of disconnective surgery in intractable pediatric hemispheric and subhemispheric epilepsy.

Objectives: To study the outcome of disconnective epilepsy surgery for intractable hemispheric and sub-hemispheric pediatric epilepsy. Methods: A retrospective analysis of the epilepsy surgery database was done in all children (age <18 years) who underwent a peri-insular hemispherotomy (PIH) or a peri-insular posterior quadrantectomy (PIPQ) from April 2000 to March 2011. All patients underwent a detailed pre surgical evaluation. Seizure outcome was assessed by the Engel's classification and cognitive skills by appropriate measures of intelligence that were repeated annually. Results: There were 34 patients in all. Epilepsy was due to Rasmussen's encephalitis (RE), Infantile hemiplegia seizure syndrome (IHSS), Hemimegalencephaly (HM), Sturge Weber syndrome (SWS) and due to post encephalitic sequelae (PES). Twenty seven (79.4%) patients underwent PIH and seven (20.6%) underwent PIPQ. The mean follow up was 30.5 months. At the last follow up, 31 (91.1%) were seizure free. The age of seizure onset and etiology of the disease causing epilepsy were predictors of a Class I seizure outcome. Conclusions: There is an excellent seizure outcome following disconnective epilepsy surgery for intractable hemispheric and subhemispheric pediatric epilepsy. An older age of seizure onset, RE, SWS and PES were good predictors of a Class I seizure outcome.

Prenatal environmental exposure and neurodevelopmentally important gene expression in malformed brain tissue from pediatric intractable epilepsy patients

The primary objective of this proposal was to determine whether mitochondrial oxidative stress and variation in a particular mtDNA lineage contribute to the risk of developing cortical dysplasia and are potential contributing factors in epileptogenesis in children. The occurrence of epilepsy in children is highly associated with malformations of cortical development (MCD). It appears that MCD might arise from developmental errors due to environmental exposures in combination with inherited variation in response to environmental exposures and mitochondrial function. Therefore, it is postulated that variation in a particular mtDNA lineage of children contributes to the effects of mitochondrial DNA damage on MCD phenotype. Quantitative PCR and dot blot were used to examine mitochondrial oxidative damage and single nucleotide polymorphism (SNP) in the mitochondrial genome in brain tissue from 48 pediatric intractable epilepsy patients from Miami Children’s Hospital and 11 control samples from NICHD Brain and Tissue Bank for Developmental Disorders. Epilepsy patients showed higher mtDNA copy number compared to normal health subjects (controls). Oxidative mtDNA damage was lower in non-neoplastic but higher in neoplastic epilepsy patients compared to controls. There was a trend of lower mtDNA oxidative damage in the non-neoplastic (MCD) patients compared to controls, yet, the reverse was observed in neoplastic (MCD and Non-MCD) epilepsy patients. The presence of mtDNA SNP and haplogroups did not show any statistically significant relationships with epilepsy phenotypes. However, SNPs G9804A and G9952A were found in higher frequencies in epilepsy samples. Logistic regression analysis showed no relationship between mtDNA oxidative stress, mtDNA copy number, mitochondrial haplogroups and SNP variations with epilepsy in pediatric patients. The levels of mtDNA copy number and oxidative mtDNA damage and the SNPs G9952A and T10010C predicted neoplastic epilepsy, however, this was not significant due to a small sample size of pediatric subjects. Findings of this study indicate that an increase in mtDNA content may be compensatory mechanisms for defective mitochondria in intractable epilepsy and brain tumor. Further validation of these findings related to mitochondrial genotypes and mitochondrial dysfunction in pediatric epilepsy and MCD may lay the ground for the development of new therapies and prevention strategies during embryogenesis.

In Adolescents With Epilepsy, High Scores Of Anxiety And Depression Are Associated With Occurrence Of Seizures In Public Places.

Objective To assess depression and anxiety symptoms of adolescents with epilepsy compared with adolescents without epilepsy. Method The study sample consisted of: case participants (50 subjects) attending the pediatric epilepsy clinic of a tertiary hospital and control participants (51 subjects) from public schools. The instruments utilized were: identification card with demographic and epilepsy data, Beck Depression Inventory and State-Trait Anxiety Inventory. Results No significant differences were founded between the groups regarding scores for depression and anxiety symptoms but both groups presented moderate scores of anxiety. A correlation was found between low scores anxiety and not frequent seizures, low scores anxiety and perception of seizure control, high scores of anxiety and depression and occurrence of seizures in public places. Conclusion Low scores of anxiety are associated with not frequent seizures; high scores of anxiety and depression are associated with occurrence of seizures in public places.

Technological challenges of pediatric MEG and potential solutions:the Aston experience

Magnetoencephalography (MEG) offers significant opportunities for the localization and characterization of focal and generalized epilepsies, but its potential has so far not been fully exploited, as the evidence for its effectiveness is still anecdotal. This is particularly true for pediatric epilepsy. MEG recordings on school-age children typically rely on the use of MEG systems that were designed for adults and children's smaller head-size and stature can cause significant problems. Reduced signal-to-noise ratio when recording from smaller heads, increased movement, reduced sensor coverage of anterior temporal regions and incomplete insertion into the MEG helmet can all reduce the quality of data collected from children. We summarize these challenges and suggest some practical solutions.