From Covariance Regressions to Nonparametric Dynamic Causal Modelling (CoreDCM)

脑损伤的全球发生率正在增加，其中至少80%被归类为轻度。这些轻微的损伤通常在常规的临床成像如磁共振成像（MRI）和计算机断层扫描（CT）上是看不见的。脑磁图（MEG）成像是目前唯一在检测急性期和慢性期轻度脑损伤方面成功率高的技术。然而，这个速率取决于MEG数据分析的准确性。目前对MEG数据的统计分析存在以下主要局限性：a)假设传感器数据是平稳的，但在实践中这一假设并不成立。b)假设有源的底层位置与时间无关，根据物理原理，这是无效的。c)基于典型微电路对非线性神经微分方程的原始近似的有效连通性分析可能会遗漏一些重要因素。例如，将规范微电路和提出的非参数模型拟合到一些静息状态MEG数据中，发现一些重要项出现在非参数模型中，而没有出现在微电路模型中。d)由于生态谬误，在该领域普遍使用的群体水平分析可能不适用于个体的诊断，即基于群体的汇总数据推断个体的失败。因此，这些局限性强调了加强单例脑磁图数据统计分析的必要性，以进一步提高其对脑部疾病的诊断成功率，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。提出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，直接影响精神卫生保健实践。总之，所提出的MEG数据分析新方法能够：-处理非平稳MEG数据-利用源位置的时变行为-向完全非参数动态因果模型（DCM）移动，以避免传统DCM中发生的重要因素的缺失-有严格的统计理论支持。这些独特的功能代表了超越现有方法的一个步骤变化，并为大规模临床应用铺平了道路。因此，提出的研究具有令人兴奋的前景，可以拯救人类的生命，并具有经济和社会影响。这是及时的，因为随着进一步的研究确定，MEG在脑损伤中的临床作用可能会变得明显，为此已经组建了一支强大的跨学科团队，该研究可以由工业合作伙伴Innovision IP直接实施，并且PDRA将在这个高需求的研究领域得到培训。通过影响活动的途径和与拟议的研究伙伴合作，可以最大限度地提高在医疗保健方案中接触相关最终用户的可能性。拟议的研究与EPSRC当前在优化疾病预测、诊断和干预方面的医疗技术的重大挑战之一密切相关，特别是在通过优化患者特定疾病预测、准确诊断和有效干预的技术来解决身心健康问题。

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.