Characterising Neurological Disorders with Nonlinear System Identification and Network Analysis

通过非线性系统识别和网络分析来表征神经系统疾病

• 批准号: EP/X020193/1
• 负责人: Fei He
• 金额: $ 38.68万
• 依托单位: Coventry University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX020193%2F1
• 关键词: Characterising; Neurological; Disorders; Nonlinear; System

With an increasingly ageing population, neurological disorders (ND), including Alzheimer's and Parkinson's disease (AD and PD), are becoming the second leading cause of death and the world's largest cause of disability-adjusted life years. Currently, incurable ND have a devastating impact on individuals, families and a heavy economic burden on societies. Early diagnosis and longitudinal monitoring of ND, such as for AD, is extremely important for their treatment, care and on-going research. However, current ND diagnosis approaches, such as cognitive and physical assessment, invasive tests (obtaining biological samples), or neuroimaging scans (e.g. positron emission tomography, magnetic resonance imaging), are often either very subjective and uncomfortable, or very capital intensive and time-consuming. In this project, we propose a new computational framework that integrates novel nonlinear systems engineering and network analysis for the diagnosis and characterisation of ND based on electroencephalography (EEG) recordings. EEG measures brain electrical activity through small electrodes attached to the scalp (with each electrode called an EEG channel). EEG has the advantage of a relatively low cost (i.e. £100's-£10,000's compared to millions of pounds for magnetic resonance imaging), better accessibility and portability, user-friendliness and, importantly, superior temporal resolution (i.e. high sampling rate with millisecond precision). Current EEG approaches predominantly employ either the analysis of a single EEG channel or the analysis of pairs of channels using simple (linear) methods that cannot capture the full complexity of the information, and focus on a selected local brain region. The novelty of our new approach will be to characterise ND by analysing the brain as a network using non-linear (cross-frequency) methods. Emerging evidence suggests that cross-frequency coupling (CFC), between different frequency bands, is the key mechanism in the integration of (local and global) communication in the brain across spatial-temporal scales, and thus this project seeks to investigate its role in the development and progression of ND.Our goal will be realised through the deliverables from four technical work packages (WPs), namely: (1) development (for the first time) of a unified framework to identify and quantify CFC from a systems engineering approach (i.e. nonlinear system identification); (2) development of a novel multi-layer cross-frequency network approach and extraction of global network features; (3) identification of important brain regions for nonlinear dynamic analysis, and; (4) the integration of both local nonlinear CFC features and global network features for diagnostic purposes.Compared with current machine/deep learning techniques (e.g. recurrent or graph neural networks), our proposed novel approach will provide human interpretable results in addition to the standard classification performance metrics. It will uncover whether linear or nonlinear interactions, the type and variation of nonlinear interactions (e.g. CFC, energy transfer) and which brain regions (EEG channels), are involved in neurodegeneration. Such information can be crucial for developing an interpretable, accurate diagnosis and, eventually, the management of ND. For example, knowing the specific CFC and brain regions involved will not only facilitate the diagnosis of PD, but may also help improve the treatment (i.e. deep brain stimulation) through a more accurate stimulation at specific frequency ranges and brain regions. We will develop the methodology and evaluate the feasibility of our approach based on the analysis of (anonymised) EEG data collected from AD and PD patients and healthy controls, through the close collaboration and guidance from our project partners, including clinical neurologists at NHS Royal Devon and Exeter Hospital and the University of Sheffield.

随着人口老龄化，神经系统疾病（ND），包括阿尔茨海默病和帕金森病（AD 和 PD），正在成为第二大死亡原因，也是世界上导致残疾调整生命年的最大原因。目前，无法治愈的 ND 具有毁灭性的影响。 ND（例如 AD）的早期诊断和纵向监测对于他们的治疗、护理和正在进行的研究极其重要，但是，当前的 ND 诊断方法（例如认知）非常重要。和身体上的评估、侵入性测试（获取生物样本）或神经影像扫描（例如正电子发射断层扫描、磁共振成像）通常要么非常主观且不舒服，要么非常资本密集且耗时。在这个项目中，我们提出了一种新的计算方法。该框架集成了新颖的非线性系统工程和网络分析，用于基于脑电图 (EEG) 记录的 ND 诊断和表征，通过连接到头皮的小电极（每个电极称为 EEG）测量脑电活动。脑电图的优点是成本相对较低（即 100 至 10,000 英镑，而磁共振成像则需要数百万英镑）、更好的可访问性和便携性、用户友好性以及重要的是卓越的时间分辨率（即当前的脑电图方法主要采用单个 EEG 通道的分析或使用无法捕获信息的全部复杂性的简单（线性）方法对通道对进行分析，并重点关注我们新方法的新颖之处在于，通过使用非线性（交叉频率）方法将大脑分析为网络来表征 ND。新出现的证据表明，不同频率之间存在交叉频率耦合 (CFC)。带，是大脑跨时空尺度（局部和全局）通信整合的关键机制，因此该项目旨在研究其在 ND 的发展和进展中的作用。我们的目标将通过可交付成果来实现从四个技术工作包 (WP)，即： (1) 开发（首次）通过系统工程方法（即非线性系统识别）识别和量化 CFC 的统一框架；层跨频网络方法和全局网络特征的提取；（3）识别重要的大脑区域以进行非线性动态分析；（4）集成局部非线性CFC特征和全局网络特征以用于诊断目的。机器/深度学习技术（例如循环或图形神经网络），除了标准分类性能指标之外，我们提出的新方法还将提供人类可解释的结果，它将揭示线性或非线性相互作用、非线性相互作用的类型和变化（例如 CFC、能量转移）。哪些大脑区域（脑电图通道）参与神经退行性变，这些信息对于制定可解释的、准确的诊断以及最终的 ND 管理至关重要。这诊断帕金森病，但也可能有助于通过在特定频率范围和大脑区域进行更准确的刺激来改善治疗（即深部脑刺激），我们将根据（匿名）的分析开发方法并评估我们方法的可行性。通过我们的项目合作伙伴（包括 NHS 皇家德文郡和埃克塞特医院以及谢菲尔德大学的临床神经科医生）的密切合作和指导，从 AD 和 PD 患者以及健康对照组收集脑电图数据。