Dysmature superficial white matter microstructure in developmental focal epilepsy

Benign epilepsy with centrotemporal spikes is a common childhood epilepsy syndrome that predominantly affects boys, characterized by self-limited focal seizures arising from the perirolandic cortex and fine motor abnormalities. Concurrent with the age-specific presentation of this syndrome, the brain undergoes a developmentally choreographed sequence of white matter microstructural changes, including maturation of association u-fibres abutting the cortex. These short fibres mediate local cortico-cortical communication and provide an age-sensitive structural substrate that could support a focal disease process. To test this hypothesis, we evaluated the microstructural properties of superficial white matter in regions corresponding to u-fibres underlying the perirolandic seizure onset zone in children with this epilepsy syndrome compared with healthy controls. To verify the spatial specificity of these features, we characterized global superficial and deep white matter properties. We further evaluated the characteristics of the perirolandic white matter in relation to performance on a fine motor task, gender and abnormalities observed on EEG. Children with benign epilepsy with centrotemporal spikes (n = 20) and healthy controls (n = 14) underwent multimodal testing with high-resolution MRI including diffusion tensor imaging sequences, sleep EEG recordings and fine motor assessment. We compared white matter microstructural characteristics (axial, radial and mean diffusivity, and fractional anisotropy) between groups in each region. We found distinct abnormalities corresponding to the perirolandic u-fibre region, with increased axial, radial and mean diffusivity and fractional anisotropy values in children with epilepsy (P = 0.039, P = 0.035, P = 0.042 and P = 0.017, respectively). Increased fractional anisotropy in this region, consistent with decreased integrity of crossing sensorimotor u-fibres, correlated with inferior fine motor performance (P = 0.029). There were gender-specific differences in white matter microstructure in the perirolandic region; males and females with epilepsy and healthy males had higher diffusion and fractional anisotropy values than healthy females (P ≤ 0.035 for all measures), suggesting that typical patterns of white matter development disproportionately predispose boys to this developmental epilepsy syndrome. Perirolandic white matter microstructure showed no relationship to epilepsy duration, duration seizure free, or epileptiform burden. There were no group differences in diffusivity or fractional anisotropy in superficial white matter outside of the perirolandic region. Children with epilepsy had increased radial diffusivity (P = 0.022) and decreased fractional anisotropy (P = 0.027) in deep white matter, consistent with a global delay in white matter maturation. These data provide evidence that atypical maturation of white matter microstructure is a basic feature in benign epilepsy with centrotemporal spikes and may contribute to the epilepsy, male predisposition and clinical comorbidities observed in this disorder.K23 NS092923 - NINDS NIH HHSPublished versio

EEG source localization analysis in epileptic children during a visual working-memory task

We localize the sources of brain activity of children with epilepsy based on EEG recordings acquired during a visual discrimination working memory task. For the numerical solution of the inverse problem, with the aid of age-specific MRI scans processed from a publicly available database, we use and compare three regularization numerical methods, namely the standarized Low Resolution Electromagnetic Tomography (sLORETA), the weighted Minimum Norm Estimation (wMNE) and the dynamic Statistical Parametric Mapping (dSPM). We show that all three methods provide the same spatio-temporal patterns of differences between epileptic and control children. In particular, our analysis reveals statistically significant differences between the two groups in regions of the Parietal Cortex indicating that these may serve as "biomarkers" for diagnostic purposes and ultimately localized treatment

An updated systematic review and meta-analysis of brain network organization in focal epilepsy: Looking back and forth

Abnormalities of the brain network organization in focal epilepsy have been extensively quantified. However, the extent and directionality of abnormalities are highly variable and subtype insensitive. We conducted meta-analyses to obtain a more accurate and epilepsy type-specific quantification of the interictal global brain network organization in focal epilepsy. By using random-effects models, we estimated differences in average clustering coefficient, average path length, and modularity between patients with focal epilepsy and controls, based on 45 studies with a total sample size of 1,468 patients and 1,021 controls. Structural networks had a significant lower level of integration in patients with epilepsy as compared to controls, with a standardized mean difference of -0.334 (95% confidence interval -0.631 to -0.038; p-value 0.027). Functional networks did not differ between patients and controls, except for the beta band clustering coefficient. Our meta-analyses show that differences in the brain network organization are not as well defined as individual studies often propose. We discuss potential pitfalls and suggestions to enhance the yield and clinical value of network studies

L'épilepsie bénigne à pointes centrotemporales : investigation cognitive et études en imagerie fonctionnelle et structurelle

L’épilepsie bénigne à pointes centrotemporales (EPCT) est la forme la plus fréquente des épilepsies idiopathiques chez l’enfant (Fastenau et al., 2009). Le pronostic de ces patients est bon, notamment en raison de la rémission spontanée de cette épilepsie à l’adolescence; toutefois plusieurs études suggèrent la présence de troubles cognitifs et de spécificités neuroanatomiques. Il n’existe pas actuellement de consensus sur les liens entre leurs troubles cognitifs et leurs particularités neuroanatomiques et neurofonctionnelles. Dans cette thèse, notre but est de préciser le profil des enfants ayant une épilepsie bénigne à pointes centro-temporales, en investiguant les caractéristiques des patients à plusieurs niveaux: cognitif, fonctionnel, structurel. La thèse est composée de quatre articles, dont deux articles empiriques. Notre premier article a pour objectif de recenser les difficultés cognitives et affectives rapportées par les études s’intéressant aux caractéristiques des enfants ayant une épilepsie bénigne. Bien qu’une certaine variabilité soit retrouvée dans la littérature, cette revue démontre qu’une histoire d’épilepsie, même bénigne, peut être un facteur de risque pour le développement cognitif et socio-affectif des enfants. Notre revue de littérature a indiqué des troubles particuliers du langage chez ces enfants, mais aucune étude n’avait auparavant investigué spécifiquement la compréhension de lecture chez les enfants ayant une EPCT, une compétence essentielle dans le cheminement scolaire des enfants. Ainsi, nous avons développé une tâche novatrice de compréhension de lecture de phrases en imagerie par résonnance magnétique fonctionnelle (IRMf), adaptée à la population pédiatrique. Dans notre second article, nous avons validé cette tâche auprès d’enfants sains et nous avons mis en évidence une mobilisation des régions cérébrales généralement engagées dans des tâches langagières chez l’enfant sain, y compris les régions impliquées dans le traitement sémantique (Berl et al., 2010; Blumenfeld, Booth et Burman, 2006). Le troisième article de cette thèse rapporte notre investigation du réseau cérébral activé durant cette nouvelle tâche de compréhension de lecture de phrases en IRMf chez les enfants ayant une EPCT. Nos résultats suggèrent que ces derniers ont recours à l’activation d’un réseau cérébral plus large, présentant des similarités avec celui retrouvé chez les enfants dyslexiques. Par ailleurs, l’activation du striatum gauche, structure généralement associée à la réalisation de processus cognitifs complexes est uniquement retrouvée chez les enfants épileptiques. Étant donné que les enfants ayant une EPCT obtiennent des performances à la tâche d’IRMf équivalentes à celles des enfants sains, il est possible d’émettre l’hypothèse que ces différences d’activations cérébrales soient adaptatives. L’étude des relations entre les résultats neuropsychologiques, la performance à la tâche et les activations cérébrales a mis en évidence des prédicteurs différents entre les deux groupes d’enfants, suggérant qu’ils ne s’appuient pas exactement sur les mêmes processus cognitifs pour réussir la tâche. De plus, nous avons réalisé un travail d’intégration des diverses méthodologies utilisées dans les études en imagerie pondérée en diffusion chez l’enfant épileptique, ce qui constitue le quatrième article de cette thèse. Nous rapportons les diverses applications de cette méthode dans la caractérisation des anomalies structurelles subtiles de la matière blanche chez les enfants épileptiques en général. Les différentes méthodologies employées, les enjeux, et les biais potentiels relatifs aux traitements des données de diffusion y sont discutés. Enfin, pour mieux comprendre l’origine et les marqueurs de cette épilepsie, nous avons étudié les spécificités structurelles des cerveaux des enfants ayant une EPCT à l’aide d’analyses sur les données d’imagerie par résonnance magnétique. Aucune différence n’a été mise en évidence au niveau de la matière grise entre les cerveaux d’enfants sains et ceux ayant une EPCT. À l’inverse, nous rapportons des différences subtiles au niveau de la matière blanche dans notre population d’enfants épileptiques, avec une diminution de l’anisotropie fractionnelle (FA) au niveau temporal inférieur/moyen de l’hémisphère gauche, ainsi que dans l’hémisphère droit dans les régions frontales moyennes et occipitales inférieures. Ces résultats suggèrent la présence d’altérations de la matière blanche subtiles et diffuses dans le cerveau des enfants ayant une EPCT et concordent avec ceux d’autres études récentes (Ciumas et al., 2014).Benign epilepsy with centrotemporal spikes (BECTS) is the most common idiopathic epilepsy in children. Owing to its spontaneous remission during adolescence, prognosis is usually good. However, studies have recently demonstrated cognitive deficits and neuroanatomical abnormalities in BECTS. To date, the relationship between cognitive impairment and brain function and structure in BECTS is unclear owing to a lack of consensus in the literature. The aim of the present thesis was to establish a multidimensional profile of children with BECTS, investigating impairments on a cognitive, functional and structural level as compared to healthy controls. The present thesis is composed of four articles, including two review articles and two empirical articles. Our first review article summarizes the cognitive and behavioural impairments reported by studies investigating the characteristics of children with BECTS. Although some variability was found in the literature, our review demonstrates that a history of childhood epilepsy, even if benign, can be considered as a risk factor for both cognitive and socio-affective development in children. More precisely, our review showed that children with BECTS present specific language impairments. However, none of the studies reviewed investigated reading comprehension in these children, which represent an essential academic skill. Thus, we developed an innovative functional magnetic resonance imaging (fMRI) sentence reading comprehension task, adapted to a pediatric population. In our first empirical article, we validated this task in healthy children. Our results were concordant with those of other studies investigating semantic processing in children (Berl et al., 2010; Blumenfeld et al., 2006). The second empirical article aimed at investigating cerebral reading networks activated in BECTS children during our fMRI sentence-reading comprehension task. Our results suggest that these children activate a larger cerebral reading network as compared to healthy controls, showing similar activation patterns to children with dyslexia while performing a similar task. Moreover, activations in the left striatum, a region generally associated with complex cognitive processes, were found in BECTS children. Given that the performance of these children on the fMRI task was similar to that of healthy controls, we hypothesize that these differences in brain reading network activations are adaptive. We further studied relationships between neuropsychological results, task performance and brain activations. Our results suggest that BECTS children rely on slightly different cognitive processes during task performance as compared to healthy controls. In addition, we performed an integrated review of various methodologies used in diffusion magnetic resonance imaging (dMRI) studies of epileptic children, which constitutes the second review article in the present thesis. We reported various applications of this method in the characterisation of subtle white matter abnormalities in epileptic children in general. The different methodologies utilized, the challenges and the potential biases associated with data processing were discussed. Finally, we investigated structural abnormalities in children with BECTS using MRI data in order to better understand the origin and markers of this epilepsy. No grey matter differences were found between BECTS children and healthy controls. However, we reported subtle white matter differences between groups, such that children with BECTS demonstrated a decreased fractional anisotropy (FA) in the middle/superior temporal region of the left hemisphere as well as in the middle frontal and inferior occipital regions of the right hemisphere. These results suggest subtle and diffuse white matter alterations in BECTS, supporting recently reported results (Ciumas et al., 2014)

Network perspectives on epilepsy using EEG/MEG source connectivity

The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience

Unravelling Molecular Genetic Causes and Disease Mechanisms in Landau Kleffner Syndrome

The cost of epilepsy to an individual lies not just in the burden of having recurrent seizures but also in the condition’s neurodevelopmental, cognitive, psychological and social co-morbidities. Presently, our understanding of the pathophysiological mechanisms underlying epilepsy and its neurocognitive co-morbidities remains severely limited, translating to our current lack of targeted treatment options. This PhD study aims to better understand the pathophysiological mechanisms underlying epilepsy and its neurocognitive co-morbidities through the clinical and molecular genetic study of a cohort of patients with Landau Kleffner syndrome (LKS), an epilepsy syndrome characterised by seizures, and neurodevelopmental regression in the form of loss of speech and language skills. Patients were recruited from a database of children referred for LKS at Great Ormond Street Hospital’s Developmental Epilepsy Clinic. Clinical data was extracted through case note review. As mutations in GRIN2A, a gene encoding the N2A subunit of the Nmethyl-D-Aspartate (NMDA) receptor have previously been described in 8-20% of individuals with LKS and related disorders, recruited individuals were screened for GRIN2A mutations via Sanger Sequencing and multiplex-ligation probe amplification. Functional investigations exploring gene/protein expression, protein localisation and channel function were carried out on missense GRIN2A mutations identified. Individuals who screened negative for GRIN2A variants underwent whole exome sequencing or whole genome sequencing to identify novel genes associated with LKS. This study has drawn conclusions that LKS is a neurodevelopmental disorder and clinical features influencing prognosis include age at onset of regression, non-verbal intelligence, and the presence of motor difficulties. GRIN2A mutations are likely to lead to LKS through overall NMDA receptor loss of function effects. Nonetheless, LKS may be a complex disorder with multi-factorial or oligogenic aetiology. Lastly, the long term potentiation pathway, important for learning and memory mechanisms, features strongly in the pathogenesis of LKS

Biomarker discovery and statistical modeling with applications in childhood epilepsy and Angelman syndrome

Biomarker discovery and statistical modeling reveals the brain activity that supports brain function and dysfunction. Detecting abnormal brain activity is critical for developing biomarkers of disease, elucidating disease mechanisms and evolution, and ultimately improving disease course. In my thesis, we develop statistical methodology to characterize neural activity in disease from noisy electrophysiological recordings. First, we develop a modification of a classic statistical modeling approach - multivariate Granger causality - to infer coordinated activity between brain regions. Assuming the signaling dependencies vary smoothly, we propose to write the history terms in autoregressive models of the signals using a lower dimensional spline basis. This procedure requires fewer parameters than the standard approach, thus increasing the statistical power. we show that this procedure accurately estimates brain dynamics in simulations and examples of physiological recordings from a patient with pharmacoresistant epilepsy. This work provides a statistical framework to understand alternations in coordinated brain activity in disease. Second, we demonstrate that sleep spindles, thalamically-driven neural rhythms (9-15 Hz) associated with sleep-dependent learning, are a reliable biomarker for Rolandic epilepsy. Rolandic epilepsy is the most common form of childhood epilepsy and characterized by nocturnal focal epileptic discharges as well as neurocognitive deficits. We show that sleep spindle rate is reduced regionally across cortex and correlated with poor cognitive performance in epilepsy. These results provide evidence for a regional disruption to the thalamocortical circuit in Rolandic epilepsy, and a potential mechanistic explanation for the cognitive deficits observed. Finally, we develop a procedure to utilize delta rhythms (2-4 Hz), a sensitive biomarker for Angelman syndrome, as a non-invasive measure of treatment efficacy in clinical trials. Angelman syndrome is a rare neurodevelopmental disorder caused by reduced expression of the UBE3A protein. Many disease-modifying treatments are being developed to reinstate UBE3A expression. To aid in clinical trials, we propose a procedure that detects therapeutic improvements in delta power outside of the natural variability over age by developing a longitudinal natural history model of delta power. These results demonstrate the utility of biomarker discovery and statistical modeling for elucidating disease course and mechanisms with the long-term goal of improving patient outcomes

A Review of Network and Computer Analysis of Epileptiform Discharge Free EEG to Characterize and Detect Epilepsy.

Objectives. There is emerging evidence that network/computer analysis of epileptiform discharge free electroencephalograms (EEGs) can be used to detect epilepsy, improve diagnosis and resource use. Such methods are automated and can be performed on shorter recordings of EEG. We assess the evidence and its strength in the area of seizure detection from network/computer analysis of epileptiform discharge free EEG. Methods. A scoping review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance was conducted with a literature search of Embase, Medline and PsychINFO. Predesigned inclusion/exclusion criteria were applied to selected articles. Results. The initial search found 3398 articles. After duplicate removal and screening, 591 abstracts were reviewed, 64 articles were selected and read leading to 20 articles meeting the requisite inclusion/exclusion criteria. These were 9 reports and 2 cross-sectional studies using network analysis to compare and/or classify EEG. One review of 17 reports and 10 cross-sectional studies only aimed to classify the EEGs. One cross-sectional study discussed EEG abnormalities associated with autism. Conclusions. Epileptiform discharge free EEG features derived from network/computer analysis differ significantly between people with and without epilepsy. Diagnostic algorithms report high accuracies and could be clinically useful. There is a lack of such research within the intellectual disability (ID) and/or autism populations, where epilepsy is more prevalent and there are additional diagnostic challenges

Hybrid Machine Learning Scheme for Classification of BECTS and TLE Patients Using EEG Brain Signals

Approximately 50 million people have epilepsyworldwide. Prognosis may vary among patients depending on their seizure semiology, age of onset, seizure onset location, and features of electroencephalogram (EEG). Several researchers have focused on EEG patterns and demonstrated that EEG patterns of individuals with epilepsy can be used to predict prognosis and treatment responses. However, accurate EEG analysis requires an experienced epileptologist with several years of training, who are often unavailable in small or medium sized hospitals. In this paper, a novel machine learning (ML) model that accurately distinguishes Benign Epilepsy with Centrotemporal Spikes (BECTS) from Temporal Lobe Epilepsy (TLE) is proposed. BECTS and TLE show different seizure types and age of onset, but differential diagnosis can be challenging due to the similar location and patterns of the EEG spikes. The proposed hybrid machine learning (HML) model processes the diagnosis in the order of (1) creating feature matrices using statistical indexes after signal decomposition, (2) processing feature selection using Support Vector Machine (SVM) technology, and (3) classifying the results through ensemble learning based on decision trees. Simulation was performed using real patient data of 112 BECTS and 112 TLE EEG signals, where training was performed using 80% of the data and 20% of the data was used in the performance analysis comparison with the actual labeled data based on the diagnosis of medical doctors. The performance of the hybrid classi cation model is compared with other representative ML algorithms, which include logistic regression, KNN, SVM, and ensemble learning based decision tree. The model proposed in this paper shows an accuracy performance exceeding 99%, which is higher than the performance obtainable from the other ML classi cation models. The purpose of this study is to introduce a novel EEG diagnostic system that shows maximum ef ciency to support clinical real-time diagnosis that can accurately distinguish epilepsy types. Future research will focus on expanding this ML model to categorize other types of epilepsies beyond BECTS and TLE and implement the HML diagnostic blockchain database into the hospital system.ope