Brain networks in people after a first unprovoked seizure

Background: A single unprovoked seizure occurs in up to 10% of the population. Some develop epilepsy, but the majority do not. Brain network changes are observed in people with epilepsy, but it is unknown if they are present after this first seizure. This study examines network connectivity after the first seizure to determine if any changes exist. Methods: Twelve patients after a single unprovoked seizure and twelve age- and sex-matched healthy controls were recruited. All underwent 7T resting-state fMRI scanning. Whole brain and limbic, default mode and salience network connectivity were analyzed with graph theory. Results: Baseline characteristics were similar between groups. No network connectivity differences were observed between groups. Conclusions: No network connectivity differences were found between patients and controls. This suggests that there are not inherent connectivity differences predisposing an individual to seizures; however, the small sample size and considerable variability could prevent realization of small group differences

Musical hallucinations and their relation with epilepsy

Musical hallucinations are poorly understood phenomena. Their relation with epilepsy was first described over a century ago, but never systematically explored. We, therefore, reviewed the literature, and assessed all descriptions of musical hallucinations attributed to epileptic activity. Our search yielded 191 articles, which together describe 983 unique patients, with 24 detailed descriptions of musical hallucinations related to epilepsy. We also describe six of our own patients. Based on the phenomenological descriptions and neurophysiological data, we distinguish four subgroups of epilepsy-related musical hallucination, comprising auras/ictal, inter-ictal and post-ictal phenomena, and phenomena related to brain stimulation. The case descriptions suggest that musical hallucinations in epilepsy can be conceptualised as lying on a continuum with other auditory hallucinations, including verbal auditory hallucinations, and—notably—tinnitus. To account for the underlying mechanism we propose a Bayesian model involving top-down and bottom-up prediction errors within the auditory network that incorporates findings from EEG and MEG studies. An analysis of phenomenological characteristics, pharmacological triggers, and treatment effects suggests wider ramifications for understanding musical hallucinations. We, therefore, conclude that musical hallucinations in epilepsy open a window to understanding these phenomena in a variety of conditions.Stress and Psychopatholog

Mapping Functional Architecture in Neocortical Epileptic Networks

Epilepsy is a debilitating brain disorder that causes recurring seizures in approximately 60 million people worldwide. For the one-third of epilepsy patients whose seizures are refractory to medication, effective therapy relies on reliably localizing where seizures originate and spread. This clinical practice amounts to delineating the epileptic network through neural sensors recording the electrocorticogram. Mapping functional architecture in the epileptic network is promising for objectively localizing cortical targets for therapy in cases of neocortical refractory epilepsy, where post-surgical seizure freedom is unfavorable when cortical structures responsible for generating seizures are difficult to delineate. In this work, we develop and apply network models for analyzing and interrogating the role of fine-grain functional architecture during epileptic events in human neocortical networks. We first develop and validate a model for objectively identifying regions of the epileptic network that drive seizure dynamics. We then develop and validate a model for disentangling network pathways traversed during ``normal\u27\u27 function from pathways that drive seizures. Lastly, we devise and apply a novel platform for predicting network response to targeted lesioning of neocortical structures, revealing key control areas that influence the spread of seizures to broader network regions. The outcomes of this work demonstrate network models can objectively identify and predict targets for treating neocortical epilepsy, blueprint potential control strategies to limit seizure spread, and are poised for further validation prior to near-term clinical translation

Interictal Network Dynamics in Paediatric Epilepsy Surgery

Epilepsy is an archetypal brain network disorder. Despite two decades of research elucidating network mechanisms of disease and correlating these with outcomes, the clinical management of children with epilepsy does not readily integrate network concepts. For example, network measures are not used in presurgical evaluation to guide decision making or surgical management plans. The aim of this thesis was to investigate novel network frameworks from the perspective of a clinician, with the explicit aim of finding measures that may be clinically useful and translatable to directly benefit patient care. We examined networks at three different scales, namely macro (whole brain diffusion MRI), meso (subnetworks from SEEG recordings) and micro (single unit networks) scales, consistently finding network abnormalities in children being evaluated for or undergoing epilepsy surgery. This work also provides a path to clinical translation, using frameworks such as IDEAL to robustly assess the impact of these new technologies on management and outcomes. The thesis sets up a platform from which promising computational technology, that utilises brain network analyses, can be readily translated to benefit patient care

Ion Channels in Epilepsy: Blasting Fuse for Neuronal Hyperexcitability

Voltage-gated ion channels (VGICs), extensively distributed in the central nervous system (CNS), are responsible for the generation as well as modulation of neuroexcitability and considered as vital players in the pathogenesis of human epilepsy, with regulating the shape and duration of action potentials (APs). For instance, genetic alterations or abnormal expression of voltage-gated sodium channels (VGSCs), Kv channels, and voltage-gated calcium channels (VGCCs) are proved to be associated with epileptogenesis. This chapter aims to highlight recent discoveries on the mutations in VGIC genes and dysfunction of VGICs in epilepsy, especially focusing on the pathophysiological and pharmacological properties. Understanding the role of epilepsy-associated VGICs might not only contribute to clarify the mechanism of epileptogenesis and genetic modifiers but also provide potential targets for the precise treatment of epilepsy

Energy landscape of resting magnetoencephalography reveals frontoparietal network impairments in epilepsy

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterised statistical regularities in MEG resting-state networks and their differences between JME patients and controls, by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the frontoparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta and gamma-bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multi-stability in selective functional networks and frequency bands in the frontoparietal cortices

An alternative approach for assessing drug induced seizures, using non-protected larval zebrafish

As many as 9% of epileptic seizures occur as a result of drug toxicity. Identifying compounds with seizurogenic side effects is imperative for assessing compound safety during drug development, however, multiple marketed drugs still have clinical associations with seizures. Moreover, current approaches for assessing seizurogenicity, namely rodent EEG and behavioural studies, are highly resource intensive. This being the case, alternative approaches have been postulated for assessing compound seizurogenicity, including in vitro, in vivo, and in silico methods. In this thesis, experimental work is presented supporting the use of larval zebrafish as a candidate model organism for developing new seizure liability screening approaches. Larval zebrafish are translucent, meaning they are highly amenable to imaging approaches while offering a more ethical alternative to mammalian research. Zebrafish are furthermore highly fecund facilitating capacity for both high replication and high throughput. The primary goal of this thesis was to identify biomarkers in larval zebrafish, both behavioural and physiological, of compounds that increase seizure liability. The efficacy of this model organism for seizure liability testing was assessed through exposure of larval zebrafish to a mechanistically diverse array of compounds, selected for their varying degrees of seizurogenicity. Their central nervous systems were monitored using a variety of different techniques including light sheet microscopy, local field potential recordings, and behavioural monitoring. Data acquired from these measurements were then analysed using a variety of techniques including frequency domain analysis, clustering, functional connectivity, regression, and graph theory. Much of this analysis was exploratory in nature and is reflective of the infancy of the field. Experimental findings suggest that larval zebrafish are indeed sensitive to a wide range of pharmacological mechanisms of action and that drug actions are reflected by behavioural and direct measurements of brain activity. For example, local field potential recordings revealed electrographic responses akin to pre-ictal, inter-ictal and ictal events identified in humans. Ca2+ imaging using light sheet microscopy found global increases in fluorescent intensity and functional connectivity due to seizurogenic drug administration. In addition, [2] further functional connectivity and graph analysis revealed macroscale network changes correlated with drug seizurogenicity and mechanism of action. Finally, analysis of swimming behaviour revealed a strong correlation between high speed swimming behaviours and administration of convulsant compounds. In conclusion, presented herein are data demonstrating the power of functional brain imaging, LFP recordings, and behavioral monitoring in larval zebrafish for assessing the action of neuroactive drugs in a highly relevant vertebrate model. These data help us to understand the relevance of the 4 dpf larval zebrafish for neuropharmacological studies and reveal that even at this early developmental stage, these animals are highly responsive to a wide range of neuroactive compounds across multiple primary mechanisms of action. This represents compelling evidence of the potential utility of larval zebrafish as a model organism for seizure liability testing

Topological Changes in the Functional Brain Networks Induced by Isometric Force Exertions Using a Graph Theoretical Approach: An EEG-based Neuroergonomics Study

Neuroergonomics, the application of neuroscience to human factors and ergonomics, is an emerging science focusing on the human brain concerning performance at work and in everyday settings. The advent of portable neurophysiological methods, including electroencephalography (EEG), has enabled measurements of real-time brain activity during physical tasks without restricting body movements. However, the EEG signatures of different physical exertion activity levels that involve the musculoskeletal system in everyday settings remain poorly understood. Furthermore, the assessment of functional connectivity among different brain regions during different force exertion levels remains unclear. One approach to investigating the brain connectome is to model the underlying mechanism of the brain as a complex network. This study applied employed a graph-theoretical approach to characterize the topological properties of the functional brain network induced by predefined force exertion levels, namely extremely light (EL), light (L), somewhat hard (SWH), hard (H), and extremely hard (EH) in two frequency bands, i.e., alpha and beta. Twelve female participants performed an isometric force exertion task and rated their perception of physical comfort at different physical exertion levels. A CGX-Mobile-64 EEG was used for recording spontaneous brain electrical activity. After preprocessing the EEG data, a source localization method was applied to study the functional brain connectivity at the source level. Subsequently, the alpha and beta networks were constructed by calculating the coherence between all pairs of 84 brain regions of interests that were selected using Brodmann Areas. Graph -theoretical measures were then employed to quantify the topological properties of the functional brain networks at different levels of force exertions at each frequency band. During an \u27extremely hard\u27 exertion level, a small-world network was observed for the alpha coherence network, whereas an ordered network was observed for the beta coherence network. The results suggest that high-level force exertions are associated with brain networks characterized by a more significant clustering coefficient, more global and local efficiency, and shorter characteristic path length under alpha coherence. The above suggests that brain regions are communicating and cooperating to a more considerable degree when the muscle force exertions increase to meet physically challenging tasks. The exploration of the present study extends the current understanding of the neurophysiological basis of physical efforts with different force levels of human physical exertion to reduce work-related musculoskeletal disorders

Fluctuations of attention network activation during the resting state reflect fluctuations in subjective attentional state.

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