Using real-time intracranial EEG and EEG-fMRI to investigate dynamic connectivity and epileptogenic activity in epilepsy disease

使用实时颅内脑电图和脑电图-fMRI 研究癫痫疾病的动态连接和致癫痫活动

• 批准号: 2749273
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2749273
• 关键词: Using; real; time; intracranial; EEG

Epilepsy is increasingly understood as a disease where abnormal large-scale brain network properties and their dynamics are responsible for epileptic events[1,2]. In addition, there is increasing access to computational and theoretical models that can describe conditions that should reduce or increase epileptic brain activity, which can be mapped onto measures of brain activity from Electroencephalography (EEG; obtained on the scalp or intracranially) and functional Magnetic Resonance Imaging (fMRI) [3]. The measurement of both of these simultaneously is technically challenging, but together they provide a high resolutionspatial and dynamic readout of brain activity with the ability to measure periods of pathological brain dynamics in epilepsy as demonstrated by the supervisory team. e have a number of ways in which we can alter brain network activity in terms of connectivity and dynamics including transcranial electrical stimulation (TES) [4], biofeedback [5] and cognitive tasks [6]. However, there is currently very little work that addresses the need to optimise these approaches given the difficulty of finding appropriate parameters that are individual specific in the context of very large parameter spaces.The Automatic Neuroscientist (AN) uses Bayesian optimisation (as a real-time supervised learning algorithm developed by supervisor Leech) which functions on a closed loop search through a large task space [7]. This alternative framework might resolve the problem discussed above by constructing neuroadaptive paradigms combined with real-time analysis. The algorithm can be characterised by automatic selection from a sample space from which it progressively learns and can use its knowledge of the space to optimises the subsequent selection. In this study, the EEG-fMRI data or iEEG data would therefore be analysed in real time to iteratively update the cognitive task selection based upon the real time results from previous tasks. This approach is faster than testing all possible tasks while able to provide more information than simply testing at random. It allows the algorithm to build upon its pre-existing understanding of functional organisations by testing and refining in iterative cycles producing a robust model across a highly dimensional space and optimising task suggestion for optimal brain dynamics.Previously this approach has been used to maximise the activity in a brain region related to a cognitive process. Here, the target (cost function) would instead be a modulation of epileptic activity such as the rate of interictal epileptiform discharges as different types of cognitive tasks are performed.

癫痫越来越多地被理解为一种疾病，其中异常的大规模脑网络特性及其动力学是癫痫事件的原因[1，2]。此外，越来越多的人可以使用计算和理论模型来描述应该减少或增加癫痫脑活动的条件，这些模型可以映射到脑电图（EEG;在头皮或颅内获得）和功能性磁共振成像（fMRI）的脑活动测量值[3]。同时测量这两者在技术上具有挑战性，但它们一起提供了大脑活动的高分辨率空间和动态读数，能够测量癫痫患者的病理性大脑动力学周期，如监督团队所示。我们有很多方法可以改变大脑网络活动的连通性和动力学，包括经颅电刺激（TES）[4]，生物反馈[5]和认知任务[6]。然而，目前很少有工作，以解决需要优化这些方法，因为很难找到适当的参数，是个人特定的上下文中非常大的参数空间。自动神经科学家（AN）使用贝叶斯优化（作为一个实时监督学习算法开发的主管利奇），其功能上的闭环搜索通过一个大的任务空间[7]。这种替代框架可以通过构建与实时分析相结合的神经适应范例来解决上述问题。该算法的特点是从一个样本空间中自动选择，并从中逐步学习，并可以使用其空间知识来优化随后的选择。因此，在本研究中，将在真实的时间中分析EEG-fMRI数据或iEEG数据，以基于来自先前任务的真实的时间结果迭代地更新认知任务选择。这种方法比测试所有可能的任务更快，同时能够提供比简单随机测试更多的信息。它允许该算法建立在其预先存在的功能组织的理解，通过测试和改进迭代周期，在高维空间中产生一个强大的模型，并优化任务建议，以实现最佳的大脑动力学。以前，这种方法已被用于最大限度地提高与认知过程相关的大脑区域的活动。在这里，目标（成本函数）将改为癫痫活动的调制，例如在执行不同类型的认知任务时发作间期癫痫样放电的速率。