The brain is in a constant state of dynamic change, for example switching between cognitive and behavioural tasks, and between wakefulness and sleep. The brains of people with epilepsy have ...additional features to their dynamic repertoire, particularly the paroxysmal occurrence of seizures. Substantial effort over decades has produced a detailed description of many human epilepsies and of specific seizure types; in some instances there are known causes, sometimes highly specific such as single gene mutations, but the mechanisms of seizure onset and termination are not known. A large number of in vivo animal models and in vitro models based on animal tissues can generate seizures and seizure-like phenomena. Although in some instances there is much known about the mechanism of seizure onset and termination, across the range of models there is a bewildering range of mechanisms. There is a pressing need to bridge the gap between microscale mechanisms in experimental models and mechanisms of human epilepsies. Computational models of epilepsy have advanced rapidly, allowing dynamic mechanisms to be revealed in a computer model that can then be tested in biological systems. These models are typically simplified, leaving a need to scale up these models to the large scale brain networks in which seizures become manifest. The emerging science of connectomics provides an approach to understanding the large scale brain networks in which normal and abnormal brain functions operate. The stage is now set to couple dynamics with connectomics, to reveal the abnormal dynamics of brain networks which allow seizures to occur.
Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological ...disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.
In recent years, digital technology and wearable devices applied to seizure detection have progressively become available. In this study, we investigated the perspectives of people with epilepsy ...(PWE), caregivers (CG), and healthcare professionals (HP). We were interested in their current use of digital technology as well as their willingness to use wearables to monitor seizures. We also explored the role of factors influencing engagement with technology, including demographic and clinical characteristics, data confidentiality, need for technical support, and concerns about strain or increased workload.
An online survey drawing on previous data collected via focus groups was constructed and distributed via a web link. Using logistic regression analyses, demographic, clinical, and other factors identified to influence engagement with technology were correlated with reported use and willingness to use digital technology and wearables for seizure tracking.
Eighty-seven surveys were completed, fifty-two (59.7%) by PWE, 13 (14.4%) by CG, and 22 (25.3%) by HP. Responders were familiar with multiple digital technologies, including the Internet, smartphones, and personal computers, and the use of digital services was similar to the UK average. Moreover, age and disease-related factors did not influence access to digital technology. The majority of PWE were willing to use a wearable device for long-term seizure tracking. However, only a limited number of PWE reported current regular use of wearables, and nonusers attributed their choice to uncertainty about the usefulness of this technology in epilepsy care. People with epilepsy envisaged the possibility of understanding their condition better through wearables and considered, with caution, the option to send automatic emergency calls. Despite concerns around accuracy, data confidentiality, and technical support, these factors did not limit PWE's willingness to use digital technology. Caregivers appeared willing to provide support to PWE using wearables and perceived a reduction of their workload and anxiety. Healthcare professionals identified areas of application for digital technologies in their clinical practice, pending an appropriate reorganization of the clinical team to share the burden of data reviewing and handling.
Unlike people who have other chronic health conditions, PWE appeared not to be at risk of digital exclusion. This study highlighted a great interest in the use of wearable technology across epilepsy service users, carers, and healthcare professionals, which was independent of demographic and clinical factors and outpaced data security and technology usability concerns.
•Epilepsy service users are interested in the application of wearables in routine care.•Uncertainty about the usefulness of wearable devices limited their current use.•Commonly used, discrete multimodal devices were preferred for long-term use.•An automated device-generated alarm feature should be considered with caution.•Specific epilepsy care team personnel should be dedicated to digital data handling.
In patients with epilepsy, the potential to prevent seizure‐related injuries and to improve the unreliability of seizure self‐report have fostered the development and marketing of numerous seizure ...detection devices for home use. Understanding the requirements of users (patients and caregivers) is essential to improve adherence and mitigate barriers to the long‐term use of such devices. Here we reviewed the evidence on the needs and preferences of users and provided an overview of currently marketed devices for seizure detection (medically approved or with published evidence for their performance). We then compared devices with known needs. Seizure‐detection devices are expected to improve safety and clinical and self‐management, and to provide reassurance to users. Key factors affecting a device’s usability relate to its design (attractive appearance, low visibility, low intrusiveness), comfort of use, confidentiality of recorded data, and timely support from both technical and clinical ends. High detection sensitivity and low false alarm rates are paramount. Currently marketed devices are focused primarily on the recording of non–electroencephalography (EEG) signals associated with tonic‐clonic seizures, whereas the detection of focal seizures without major motor features remains a clear evidence gap. Moreover, there is paucity of evidence coming from real‐life settings. A joint effort of clinical and nonclinical experts, patients, and caregivers is required to ensure an optimal level of acceptability and usability, which are key aspects for a successful continuous monitoring aimed at seizure detection at home.
Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs. In pre-surgical planning, an array of data modalities, often including intra-cranial ...EEG, is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures. These regions are then resected with the hope that the individual is rendered seizure free as a consequence. However, post-operative seizure freedom is currently sub-optimal, suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks. Herein we use mathematical models to uncover the relative contribution of regions of the brain to seizure generation and consequently which brain regions should be considered for resection. A critical advantage of this modeling approach is that the effect of different surgical strategies can be predicted and quantitatively compared in advance of surgery. Herein we seek to understand seizure generation in networks with different topologies and study how the removal of different nodes in these networks reduces the occurrence of seizures. Since this a computationally demanding problem, a first step for this aim is to facilitate tractability of this approach for large networks. To do this, we demonstrate that predictions arising from a neural mass model are preserved in a lower dimensional, canonical model that is quicker to simulate. We then use this simpler model to study the emergence of seizures in artificial networks with different topologies, and calculate which nodes should be removed to render the network seizure free. We find that for scale-free and rich-club networks there exist specific nodes that are critical for seizure generation and should therefore be removed, whereas for small-world networks the strategy should instead focus on removing sufficient brain tissue. We demonstrate the validity of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients who have undergone epilepsy surgery, revealing rich-club structures within the obtained functional networks. We show that the postsurgical outcome for these patients was better when a greater proportion of the rich club was removed, in agreement with our theoretical predictions.
•The pooled incidence of pre-ictal heart rate increase (HRI) was 36/100 seizures.•Higher HRI estimates were found in TLE, adults and people on AEDs.•Pre-ictal HR reduction incidence was 0.5% in the ...paediatric population.•Bias and methods assessment disclosed limitations in the evidence-base.•Monitoring of HR changes could be attempted to early identify seizures.
To estimate the incidence of pre-ictal heart rate (HR) manifestations and to identify clinical and study-related factors modulating the estimate.
We searched articles recording concurrent pre-ictal EEG and HR in adults and children with epilepsy. Pre-ictal HR changes were classified as HR reduction (HRR) or increase (HRI). Studies reporting the total number of seizures and the number of seizures with pre-ictal HR changes were included in a random-effects meta-analysis. A random-effects meta-regression was used to identify variables affecting study heterogeneity.
Thirty studies, including 1110 participants and 2957 seizures, were included. The meta-analysis showed a pooled incidence of pre-ictal HRI of 36/100 seizures (95% CI 22–50). The pre-ictal HRI incidence was 44/100 seizures (95% CI 33–55) in studies including temporal lobe epilepsy, 55/100 seizures (95% CI 41–68) in studies enrolling adults and 35/100 seizures (95% CI 16–58) when patients on antiepileptic drugs were included. The meta-regression showed that the age group, the length of the pre-ictal period, the incidence of ictal tachycardia and the time of onset of the pre-ictal HRI had a significant impact on estimates variability. The pooled incidence of pre-ictal HRR was 0/100 seizures (95% CI 0–1).
Review of bias evaluation and methods assessment disclosed several major limitations in the evidence-base. HR monitoring could be valuable to identify seizures prior to their apparent onset, opening the possibility to early interventions. Additional effort is necessary to delineate the target population who might benefit from its use and the mechanisms sustaining the pre-ictal cardiac changes.
Summary
The longstanding dichotomy between the concepts of “focal” and “primary generalized” epilepsy has become increasingly blurred, raising fundamental questions about the nature of ictal onset in ...localized brain regions versus large‐scale brain networks. We hypothesize that whether an EEG discharge appears focal or generalized is driven by the pattern of connections in brain networks, irrespective of the presence of focal brain abnormality. Using a computational model of a simple “brain” consisting of four regions and the connections between them, we explored the effects of altering connectivity structure versus the effects of introducing an “abnormal” brain region, and the interactions between these factors. Computer simulations demonstrated that electroencephalography (EEG) discharges representing either generalized or focal seizures arose purely as a consequence of subtle changes in network structure, without the requirement for any localized pathologic brain region. Furthermore we found that introducing a pathologic region gave rise to focal, secondary generalized, or primary generalized seizures depending on the network structure. Counterintuitively, we found that decreasing connectivity between regions of the brain increased the frequency of seizure‐like activity. Our findings may enlighten current controversies surrounding the concepts of focal and generalized epilepsy, and help to explain recent observations in genetic animal models and human epilepsies, where loss of white matter pathways was associated with the occurrence of seizures.
Juvenile myoclonic epilepsy is the most common idiopathic generalized epilepsy, characterized by frequent myoclonic jerks, generalized tonic-clonic seizures and, less commonly, absences. ...Neuropsychological and, less consistently, anatomical studies have indicated frontal lobe dysfunction in the disease. Given its presumed thalamo–cortical basis, we investigated thalamo–cortical structural connectivity, as measured by diffusion tensor imaging, in a cohort of 28 participants with juvenile myoclonic epilepsy and detected changes in an anterior thalamo–cortical bundle compared with healthy control subjects. We then investigated task-modulated functional connectivity from the anterior thalamic region identified using functional magnetic resonance imaging in a task consistently shown to be impaired in this group, phonemic verbal fluency. We demonstrate an alteration in task-modulated connectivity in a region of frontal cortex directly connected to the thalamus via the same anatomical bundle, and overlapping with the supplementary motor area. Further, we show that the degree of abnormal connectivity is related to disease severity in those with active seizures. By integrating methods examining structural and effective interregional connectivity, these results provide convincing evidence for abnormalities in a specific thalamo–cortical circuit, with reduced structural and task-induced functional connectivity, which may underlie the functional abnormalities in this idiopathic epilepsy.
Video‐electroencephalographic (EEG) monitoring is an essential tool in epileptology, conventionally carried out in a hospital epilepsy monitoring unit. Due to high costs and long waiting times for ...hospital admission, coupled with technological advances, several centers have developed and implemented video‐EEG monitoring in the patient's home (home video‐EEG telemetry HVET). Here, we review the history and current status of three general approaches to HVET: (1) supervised HVET, which entails setting up video‐EEG in the patient's home with daily visiting technologist support; (2) mobile HVET (also termed ambulatory video‐EEG), which entails attaching electrodes in a health care facility, supplying the patient and carers with the hardware and instructions, and then asking the patient and carer to set up recording at home without technologist support; and (3) cloud‐based HVET, which adds to either of the previous models continuous streaming of video‐EEG from the home to the health care provider, with the option to review data in near real time, troubleshoot hardware remotely, and interact remotely with the patient. Our experience shows that HVET can be highly cost‐effective and is well received by patients. We note limitations related to long‐term electrode attachment and correct camera placing while the patient is unsupervised at home, and concerns related to regulations regarding data privacy for cloud services. We believe that HVET opens significant new opportunities for research, especially in the field of understanding the many influences in seizure occurrence. We speculate that in the future HVET may merge into innovative new multisensor approaches to continuously monitoring people with epilepsy.
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