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.

随着人口日益老龄化，包括阿尔茨海默氏症和帕金森氏病(AD和PD)在内的神经疾病(ND)正成为第二大死亡原因和世界上最大的残疾原因(调整后的生命年)。目前，不治之症对个人、家庭造成了毁灭性的影响，给社会带来了沉重的经济负担。ND的早期诊断和纵向监测，如AD，对其治疗、护理和正在进行的研究非常重要。然而，目前的ND诊断方法，如认知和身体评估、侵入性测试(获取生物样本)或神经成像扫描(如正电子发射断层扫描、磁共振成像)，往往要么非常主观和不舒服，要么非常耗费资金和时间。在这个项目中，我们提出了一种新的计算框架，该框架集成了新的非线性系统工程和网络分析，用于基于脑电记录的ND的诊断和特征描述。EEG通过连接在头皮上的小电极(每个电极被称为EEG通道)来测量大脑的电活动。EEG的优势在于成本相对较低(例如，GB 100‘S-GB 10,000’S，而磁共振成像的成本高达数百万英镑)，更好的可及性和便携性，用户友好性，以及更重要的是，更高的时间分辨率(即高采样率和毫秒精度)。目前的EEG方法主要使用单个EEG通道的分析或使用简单(线性)方法的通道对的分析，这些方法不能捕获信息的全部复杂性，并集中在选定的局部大脑区域。我们新方法的新颖性将是通过使用非线性(交叉频率)方法将大脑分析为一个网络来表征ND。新出现的证据表明，不同频段之间的交叉频率耦合(CFC)是大脑中跨时空尺度整合(局部和全球)通信的关键机制，因此本项目试图研究其在ND的发展和进展中的作用。我们的目标将通过四个技术工作包(WPS)的成果来实现，即：(1)开发(首次)从系统工程方法(即非线性系统识别)识别和量化CFC的统一框架；(2)开发一种新的多层交叉频率网络方法并提取全局网络特征；(3)识别用于非线性动态分析的重要脑区；(4)结合局部非线性CFC特征和全局网络特征用于诊断目的。与现有的机器/深度学习技术(例如递归或图神经网络)相比，我们提出的新方法除了提供标准的分类性能指标外，还将提供人类可解释的结果。它将揭示是线性还是非线性相互作用，非线性相互作用的类型和变化(例如，cfc，能量转移)，以及哪些大脑区域(脑电通道)涉及神经退化。这些信息对于制定可解释的、准确的诊断以及最终的ND管理是至关重要的。例如，了解特定的CFC和涉及的大脑区域不仅有助于帕金森病的诊断，还可能通过在特定频率范围和大脑区域进行更准确的刺激来帮助改进治疗(即脑深部刺激)。我们将通过项目合作伙伴的密切合作和指导，基于从AD和PD患者以及健康对照组收集的(匿名)脑电数据，开发方法并评估我们方法的可行性，这些合作伙伴包括NHS皇家德文和埃克塞特医院和谢菲尔德大学的临床神经学家。