Seizures and the Brain: The Role of Perturbed Dynamic Networks

癫痫发作和大脑：扰动动态网络的作用

• 批准号: EP/T027703/1
• 负责人: John Terry
• 金额: $ 243.61万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT027703%2F1
• 关键词: Seizures; Brain; Role; Perturbed; Dynamic

Accurate diagnosis, prognosis and management of epilepsy is a significant unmet medical need. Epilepsy is a serious brain condition whereby susceptible individuals have recurrent seizures. It affects almost 1% of the UK population at some point in their lives. Every year 125,000 people attend first seizure clinics across the UK. Of these, 40,000 eventually receive a confirmed diagnosis of epilepsy, typically following an average delay of a year (Joint Epilepsy Council, September 2011). Whilst two-thirds of these confirmed cases can ultimately be controlled by anti-epilepsy drugs (AED), approximately 50% remain uncontrolled a year after commencing treatment, with no clear indicators of choice of AED or dose. Treatment response is currently a case of "watchful waiting" to see whether further seizures occur and adjusting choice of medication and dose accordingly. For those who do not respond they continue to have seizures, apparently at random, which leads to a significant reduction in quality and quantity of life. Every year over 1,000 people die in the UK as a direct consequence of epilepsy.I propose an exciting programme of research in which mathematical models and computer algorithms will be developed to improve our understanding of how seizures occur. These models will describe how different regions of the brain interact with each other and how internal and external stimuli can influence these interactions. The interplay between neural activity within brain regions and the connections between regions critically determines whether seizures can occur and how frequently they are likely to happen. Having developed this fundamental understanding, computer algorithms can be developed to inform key parameters of these models directly from clinical recordings collected from people with epilepsy. This makes the models personalised and therefore suitable to address key questions for people with (suspected) epilepsy: - is the diagnosis accurate?- are the drugs being prescribed effective?- will exposure to stress make seizures more likely?- can I know when my seizures are most likely to happen? - will surgery stop my seizures happening? I will work with a software engineer and people with epilepsy to translate this understanding into a prototype tools to address these questions. For example, developing a tool that can aid a multi-disciplinary clinical team in determining where in the brain seizures originate from and using this information to better plan for surgery. Alternatively, a smart-phone based app that receives electrical brain recordings from a wireless headset to provide a forecast of seizure risk. Co-designing and developing these prototypes with people with lived experience and clinicians will maximise the likelihood of their leading to impact of the fundamental research.

癫痫的准确诊断、预后和治疗是一项重大的未满足的医疗需求。癫痫是一种严重的脑部疾病，易感人群会反复发作。近 1% 的英国人口在其生命中的某个阶段受到影响。每年有 125,000 人到英国各地的首次癫痫诊所就诊。其中，40,000 人最终得到癫痫确诊，通常平均延迟一年（联合癫痫委员会，2011 年 9 月）。虽然这些确诊病例中的三分之二最终可以通过抗癫痫药物 (AED) 得到控制，但大约 50% 的病例在开始治疗一年后仍未得到控制，且没有明确的 AED 或剂量选择指标。目前的治疗反应是“观察等待”，看看是否会进一步发生癫痫发作，并相应地调整药物选择和剂量。对于那些没有反应的人来说，他们会继续癫痫发作，显然是随机的，这导致生活质量和数量显着下降。在英国，每年有超过 1,000 人死于癫痫症。我提出了一项令人兴奋的研究计划，其中将开发数学模型和计算机算法，以提高我们对癫痫发作如何发生的理解。这些模型将描述大脑的不同区域如何相互作用，以及内部和外部刺激如何影响这些相互作用。大脑区域内的神经活动和区域之间的连接之间的相互作用至关重要地决定癫痫发作是否会发生以及癫痫发作的频率。有了这种基本的理解，就可以开发计算机算法，直接从癫痫患者收集的临床记录中获取这些模型的关键参数。这使得模型个性化，因此适合解决（疑似）癫痫患者的关键问题： - 诊断准确吗？ - 所开的药物是否有效？ - 暴露在压力下会增加癫痫发作的可能性吗？ - 我能知道什么时候最有可能发生癫痫发作吗？ - 手术可以阻止我的癫痫发作吗？ 我将与软件工程师和癫痫患者合作，将这种理解转化为原型工具来解决这些问题。例如，开发一种工具，可以帮助多学科临床团队确定癫痫发作的大脑部位，并利用这些信息更好地制定手术计划。或者，基于智能手机的应用程序可以从无线耳机接收脑电记录，以提供癫痫风险的预测。与有生活经验的人和临床医生共同设计和开发这些原型将最大限度地提高他们对基础研究产生影响的可能性。

项目成果

The impact of paediatric epilepsy and co-occurring neurodevelopmental disorders on functional brain networks in wake and sleep

小儿癫痫和同时发生的神经发育障碍对清醒和睡眠状态下功能性大脑网络的影响

• DOI: 10.1101/2023.03.15.530959
• 发表时间: 2023
• 作者: Junges L

Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery.

• DOI: 10.1038/s41467-022-28640-x
• 发表时间: 2022-02-22
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Cao M;Galvis D;Vogrin SJ;Woods WP;Vogrin S;Wang F;Woldman W;Terry JR;Peterson A;Plummer C;Cook MJ
• 通讯作者: Cook MJ

Circadian distribution of epileptiform discharges in epilepsy: Candidate mechanisms of variability.

• DOI: 10.1371/journal.pcbi.1010508
• 发表时间: 2023-10
• 期刊: PLoS computational biology
• 影响因子: 4.3

Classification of human chronotype based on fMRI network-based statistics.

• DOI: 10.3389/fnins.2023.1147219
• 发表时间: 2023
• 期刊: FRONTIERS IN NEUROSCIENCE
• 影响因子: 4.3
• 作者: Mason, Sophie L. L.;Junges, Leandro;Woldman, Wessel;Facer-Childs, Elise R. R.;de Campos, Brunno M.;Bagshaw, Andrew P. P.;Terry, John R. R.
• 通讯作者: Terry, John R. R.

Analysis of wearable time series data in endocrine and metabolic research.

• DOI: 10.1016/j.coemr.2022.100380
• 发表时间: 2022-08
• 期刊: Current opinion in endocrine and metabolic research