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.

癫痫越来越多地理解为一种疾病，在该疾病中，大型大脑网络特性及其动力学是造成癫痫事件的原因[1,2]。此外，还增加了对计算和理论模型的访问，这些模型可以描述应减少或增加癫痫脑活动的条件，这些条件可以映射到脑电图（EEG; eeg;在头皮或颅内获得）和功能磁共振成像（fMRI）（fMRI）[3] [3]。同时对这两种方法的测量在技术上都具有挑战性，但它们共同提供了对大脑活动的高分辨率，可读取大脑活动，并能够测量监督团队所证明的癫痫病理学脑动力学周期。 E具有多种方式，可以在包括经颅电刺激（TES）[4]，Biofefback [5]和认知任务[6]的连通性和动力学方面改变大脑网络活动。但是，鉴于难以在非常大的参数空间的背景下找到适当的参数，目前几乎没有工作解决了优化这些方法的需求。自动神经科学家（AN）使用贝叶斯（AN）使用贝叶斯的优化（作为实时监督的学习算法是由主管开发的实时监督学习算法），该算法是通过大型任务空间在封闭循环中搜索的封闭循环搜索。该替代框架可能通过构建神经适应性范例与实时分析结合来解决上述问题。该算法可以从逐渐学习的样品空间中自动选择，并可以利用其对空间的知识来优化后续选择。在这项研究中，将对EEG-FMRI数据或IEEG数据进行实时分析，以根据以前任务的实时结果迭代更新认知任务选择。这种方法比测试所有可能的任务同时提供更多信息的速度要快，而不是随机测试。它允许算法通过测试和精炼在高度维空间中产生强大的模型并优化最佳脑动力学的任务建议，以实现强大的模型来建立对功能组织的预先理解。在这里，目标（成本函数）将是对癫痫活性的调节，例如进行癫痫发作的速率作为不同类型的认知任务。