From Covariance Regressions to Nonparametric Dynamic Causal Modelling (CoreDCM)

从协方差回归到非参数动态因果建模 (CoreDCM)

• 批准号: EP/X038297/1
• 负责人: Jian Zhang
• 金额: $ 56.49万
• 依托单位: University of Kent
• 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%2FX038297%2F1
• 关键词: Covariance; Regressions; Nonparametric; Dynamic; Causal

The global incidence of brain injuries is increasing, with at least 80% being classified as mild. These mild injuries are often not visible on routine clinical imaging such as magnetic resonance imaging (MRI) and computed tomography (CT). Magnetoencephalography (MEG) imaging is currently the only technique that has a high rate of success in detecting mild brain injuries both in the acute and chronic phase. However, this rate depends on the accuracy of MEG data analysis. The current statistical analysis of MEG data suffers from the following major limitations: a) The sensor data are assumed stationary but this assumption is not true in practice. b) The underlying locations of active sources are assumed time-independent, which is invalid according to physical principles. c) The effective connectivity analysis, based on a raw approximation such as canonical microcircuits to nonlinear neural differential equations, may miss some important factors. For example, fitting both canonical microcircuits and the proposed nonparametric model to some resting state MEG data, a few significant terms were found to appear in the nonparametric model but not in the microcircuit model. d) Group-level analyses prevalently used in the field may not be applicable for diagnosis of individuals due to the ecological fallacy, failure in inference about an individual based on aggregate data for a group. Therefore, these limitations underscore the need of an enhanced statistical analysis of MEG data on single-case basis to further improve its success rate of diagnosis of brain diseases and to differential different brain disorders. In this project, we propose to tackle these limitations by advancing MEG data modelling in the context of dynamic source localisation and nonparametric causal modelling. The proposed research will produce a flexible and accurate statistical inference tool for the diagnosis of brain conditions with a direct impact on mental healthcare practices. In conclusion, the proposed new method for MEG data analysis is able:- To cope with non stationary MEG data- To exploit time-varying behaviour of source locations- To move toward a fully nonparametric dynamic causal modelling (DCM) to avoid the missing of important factors as occurred in the conventional DCMs- To have supports from a rigorous statistical theoryCollectively, these unique features represent a step change beyond the methods available today and help pave a way for a large scale clinical applications. Therefore, the proposed research with exciting promise could save human life and have both economic and social impacts. It is timely because a clinical role for MEG in brain injury could become evident with further investigation identified, for which a strong interdisciplinary team has been assembled, the research can be directly implemented by the industrial partner Innovision IP, and a PDRA will be trained in this high-demand research area. The possibility of exposure to relevant end users in healthcare scenarios can be maximised through pathways to impact activities and working with the proposed research partners.The proposed research is closely aligned to one of the current EPSRC grand challenges of healthcare technology in optimising disease prediction, diagnosis and intervention and particularly in addressing both physical and mental health with techniques that optimise patient-specific illness prediction, accurate diagnosis and effective intervention.

脑损伤的全球发病率正在增加，至少80%被归类为轻度。这些轻度损伤通常在常规临床成像中不可见，例如磁共振成像（MRI）和计算机断层扫描（CT）。脑磁图（MEG）成像是目前唯一的技术，具有较高的成功率，在检测轻度脑损伤的急性和慢性阶段。然而，这一比率取决于MEG数据分析的准确性。目前的MEG数据的统计分析受到以下主要限制：a）传感器数据被假设为静止的，但这种假设在实践中是不正确的。B）假定活动源的基本位置与时间无关，这根据物理原理是无效的。c）有效的连接性分析，基于原始的近似，如典型的微电路到非线性神经微分方程，可能会错过一些重要的因素。例如，拟合规范的微电路和建议的非参数模型的一些静息态脑磁图数据，一些显着的条款被发现出现在非参数模型，但不是在微电路模型。d）由于生态学谬误，不能根据群体的总体数据对个体进行推断，因此，在该领域中使用的群体水平分析可能不适用于个体诊断。因此，这些局限性强调了需要在单个病例的基础上增强MEG数据的统计分析，以进一步提高其诊断脑部疾病的成功率并区分不同的脑部疾病。在这个项目中，我们建议通过在动态源定位和非参数因果建模的背景下推进MEG数据建模来解决这些限制。拟议的研究将产生一个灵活和准确的统计推断工具，用于诊断大脑状况，对精神卫生保健实践产生直接影响。总之，所提出的用于MEG数据分析的新方法能够：-科普非平稳MEG数据-利用源位置的时变行为-向完全非参数动态因果建模（DCM）移动以避免如在常规DCM中发生的重要因素的缺失-从严格的统计理论得到支持。总的来说，这些独特的特征代表了超越当今可用方法的一步变化，并有助于为大规模临床应用铺平道路。因此，这项具有令人兴奋的前景的研究可以挽救人类生命，并产生经济和社会影响。这是及时的，因为MEG在脑损伤中的临床作用可能会随着进一步的研究而变得明显，为此已经组建了一支强大的跨学科团队，该研究可以直接由工业合作伙伴Innovision IP实施，并且PDRA将在这个高需求的研究领域进行培训。通过影响活动的途径以及与拟议的研究合作伙伴的合作，可以最大限度地提高在医疗保健场景中接触相关最终用户的可能性。拟议的研究与EPSRC当前医疗保健技术在优化疾病预测、诊断和干预方面面临的重大挑战之一密切相关。干预，特别是通过优化患者特定疾病预测的技术来解决身体和心理健康问题，准确诊断和有效干预。