Characterising Neurological Disorders with Nonlinear System Identification and Network Analysis

随着人口日益老龄化，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和世界上最大的残疾调整生命年的原因。目前，无法治愈的ND对个人和家庭造成破坏性影响，并给社会带来沉重的经济负担。ND的早期诊断和纵向监测，如AD，对于他们的治疗，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（获得生物样品），或神经成像扫描（例如正电子发射断层扫描，磁共振成像），往往是非常主观和不舒服，或非常资本密集和耗时。在这个项目中，我们提出了一个新的计算框架，集成了新的非线性系统工程和网络分析的诊断和表征ND的基础上脑电图（EEG）记录。EEG通过附着在头皮上的小电极（每个电极称为EEG通道）测量脑电活动。EEG具有相对低的成本（即，与磁共振成像的数百万英镑相比，100英镑至10，000英镑）、更好的可访问性和便携性、用户友好性以及重要的上级时间分辨率（即，具有毫秒精度的高采样率）的优点。当前的EEG方法主要采用单个EEG通道的分析或使用简单（线性）方法的通道对的分析，所述方法不能捕获信息的全部复杂性，并且集中于所选择的局部脑区域。我们的新方法的新奇在于，通过使用非线性（交叉频率）方法将大脑作为一个网络进行分析，从而实现脑电图。新出现的证据表明，交叉频率耦合（CFC），不同频带之间，是在整合的关键机制，本项目旨在研究大脑在不同时空尺度上的（局部和整体）交流，因此，本项目旨在研究其在神经退行性疾病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（WP）的成果实现，即：（1）发展（第一次）建立一个统一的框架，从系统工程的角度查明和量化氟氯化碳（即非线性系统识别）;（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）识别用于非线性动力学分析的重要大脑区域，以及;（4）结合局部非线性CFC特征和全局网络特征进行诊断。与当前的机器/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）以及哪些大脑区域（EEG通道）参与神经变性。这些信息对于开发可解释的，准确的诊断以及最终的ND管理至关重要。例如，了解特定的CFC和所涉及的大脑区域不仅有助于PD的诊断，而且还可以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善治疗（即深部脑刺激）。我们将在我们的项目合作伙伴（包括NHS皇家德文郡和埃克塞特医院的临床神经科医生）的密切合作和指导下，根据对从AD和PD患者以及健康对照组收集的（匿名）EEG数据的分析，开发方法并评估我们方法的可行性。和谢菲尔德大学。

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.