Treatment effects in epilepsy:a mathematical framework for understanding response over time

Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom

Treatment effects in epilepsy: a mathematical framework for understanding response over time

Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom

A digital intervention for capturing the real-time health data needed for epilepsy seizure forecasting:formative codesign and the usability study protocol (the ATMOSPHERE study)

Background: Epilepsy is a chronic neurological disorder affecting individuals globally, marked by recurrent and apparently unpredictable seizures that pose significant challenges, including increased mortality, injuries, and diminished quality of life. Despite advancements in treatments, a significant proportion of people with epilepsy continue to experience uncontrolled seizures. The apparent unpredictability of these events has been identified as a major concern for people with epilepsy, highlighting the need for innovative seizure forecasting technologies.Objectives: The ATMOSPHERE study aims to develop and evaluate a digital intervention, using wearable technology and data science, that provides real-time, individualised seizure forecasting for individuals living with epilepsy. This paper reports the protocol for one of the workstreams focusing on the design and testing of a prototype to capture real-time input data needed for predictive modelling. The aims were/are to (1) collaboratively design the prototype (work completed) and (2) conduct an 'in-the-wild' study to assess usability and to refine the prototype (planned research).Methods: This study employs a person-based approach to design and usability test a prototype for real-time seizure precipitant data capture. Phase 1 (work completed) involved co-design with individuals living with epilepsy and healthcare professionals. Sessions explored users’ requirements for the prototype, followed by iterative design of low fidelity, static prototypes. Phase 2 (planned research) will be an 'in-the-wild' usability study involving the deployment of a mid-fidelity, interactive prototype for four weeks, with the collection of mixed-methods usability data to assess the prototype's real-world application, feasibility, acceptability, and engagement. This phase involves primary participants (adults diagnosed with epilepsy) and, optionally, their nominated significant other. The usability study will run in three waves of deployment and data collection, aiming to recruit five participants per wave, with prototype refinement between waves.Results: The phase 1 co-design study engaged 22 individuals, resulting in the development of a mid-fidelity, interactive prototype based on identified requirements, including the tracking of evidence-based and personalised seizure precipitants. The upcoming Phase 2 usability study is expected to provide insights into the prototype's real-world usability, identify areas for improvement, and refine the technology for future development. The estimated completion date of Phase 2 is the last quarter of 2024.Conclusions: The ATMOSPHERE study aims to make a significant step forward in epilepsy management, focusing on the development of a user-centred, non-invasive wearable device for seizure forecasting. Through a collaborative design process and comprehensive usability testing, this research aims to address the critical need for predictive seizure forecasting technologies, offering a promising approach to improving the lives of individuals with epilepsy. By leveraging predictive analytics and personalised machine learning models, this technology seeks to offer a novel approach to managing epilepsy, potentially improving clinical outcomes, including quality of life through increased predictability and seizure management.<br/

A digital intervention for capturing the real-time health data needed for epilepsy seizure forecasting:formative codesign and the usability study protocol (the ATMOSPHERE study)

Background: Epilepsy is a chronic neurological disorder affecting individuals globally, marked by recurrent and apparently unpredictable seizures that pose significant challenges, including increased mortality, injuries, and diminished quality of life. Despite advancements in treatments, a significant proportion of people with epilepsy continue to experience uncontrolled seizures. The apparent unpredictability of these events has been identified as a major concern for people with epilepsy, highlighting the need for innovative seizure forecasting technologies.Objectives: The ATMOSPHERE study aims to develop and evaluate a digital intervention, using wearable technology and data science, that provides real-time, individualised seizure forecasting for individuals living with epilepsy. This paper reports the protocol for one of the workstreams focusing on the design and testing of a prototype to capture real-time input data needed for predictive modelling. The aims were/are to (1) collaboratively design the prototype (work completed) and (2) conduct an 'in-the-wild' study to assess usability and to refine the prototype (planned research).Methods: This study employs a person-based approach to design and usability test a prototype for real-time seizure precipitant data capture. Phase 1 (work completed) involved co-design with individuals living with epilepsy and healthcare professionals. Sessions explored users’ requirements for the prototype, followed by iterative design of low fidelity, static prototypes. Phase 2 (planned research) will be an 'in-the-wild' usability study involving the deployment of a mid-fidelity, interactive prototype for four weeks, with the collection of mixed-methods usability data to assess the prototype's real-world application, feasibility, acceptability, and engagement. This phase involves primary participants (adults diagnosed with epilepsy) and, optionally, their nominated significant other. The usability study will run in three waves of deployment and data collection, aiming to recruit five participants per wave, with prototype refinement between waves.Results: The phase 1 co-design study engaged 22 individuals, resulting in the development of a mid-fidelity, interactive prototype based on identified requirements, including the tracking of evidence-based and personalised seizure precipitants. The upcoming Phase 2 usability study is expected to provide insights into the prototype's real-world usability, identify areas for improvement, and refine the technology for future development. The estimated completion date of Phase 2 is the last quarter of 2024.Conclusions: The ATMOSPHERE study aims to make a significant step forward in epilepsy management, focusing on the development of a user-centred, non-invasive wearable device for seizure forecasting. Through a collaborative design process and comprehensive usability testing, this research aims to address the critical need for predictive seizure forecasting technologies, offering a promising approach to improving the lives of individuals with epilepsy. By leveraging predictive analytics and personalised machine learning models, this technology seeks to offer a novel approach to managing epilepsy, potentially improving clinical outcomes, including quality of life through increased predictability and seizure management.<br/