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.

全球脑损伤的发生率正在增加，至少80％被归类为轻度。这些轻度伤害通常在常规临床成像（例如磁共振成像（MRI）和计算机断层扫描（CT））上看不见。磁脑摄影（MEG）成像目前是唯一在检测急性和慢性期轻度脑损伤方面取得成功率很高的技术。但是，此速率取决于MEG数据分析的准确性。 MEG数据的当前统计分析受到以下主要局限性的影响：a）传感器数据是固定的，但在实践中，该假设并非如此。 b）主动源的基本位置假定时间无关，根据物理原理是无效的。 c）基于原始近似值（例如针对非线性神经微分方程的规范微电路）的有效连通性分析可能会错过一些重要因素。例如，将两个规范的微电路和提出的非参数模型拟合到某些静止状态MEG数据中，发现了一些重要的术语出现在非参数模型中，但在微电路模型中却没有出现。 d）由于生态谬误性，基于组的骨料数据，在该领域普遍使用的小组级分析可能不适用于诊断个体的诊断。因此，这些局限性强调了对MEG数据的增强统计分析的需求，以进一步提高其脑部疾病诊断和差异不同脑部疾病的成功率。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局限性。拟议的研究将产生一种灵活，准确的统计推理工具，用于诊断大脑状况，直接影响心理保健实践。总之，提出的用于MEG数据分析的新方法能够： - 应对非固定MEG数据 - 利用源位置的时间变化行为，以朝着完全非参数的动态因果模型（DCM）迈进，以避免在常规的DCMS中遇到的重要因素，从而避免了重要的DCMS，而超过了一项独特的统计特征，这些统一的特征是这些独特的特征，这些特征是这些独特的特征，这些特征是这些独特的一定的特征，这些特征是这些独特的特征，这些特征是这些独特的特征，这些特征是这些独特的特征，这些是这些统一的特征，这些特征是这些统一性的一定程度的范围。大规模临床应用的方式。因此，具有令人兴奋的承诺的拟议研究可以挽救人类的生命，并具有经济和社会影响。这是及时的，因为梅格在脑损伤中的临床作用可能会变得明显，并通过进一步的调查而变得明显，为此，已经组装了一个强大的跨学科团队，该研究可以由工业合作伙伴Innovision IP直接实施，并且PDRA将在这个高端研究领域进行培训。可以通过影响活动并与拟议的研究伙伴合作的途径最大化与相关最终用户接触相关最终用户的可能性。拟议的研究与当前的EPSRC医疗保健技术的盛大挑战之一紧密一致，以优化疾病预测，诊断和尤其是在技术健康方面​​与技术诊断的良好诊断和精神诊断，以挑战疾病的诊断和精神疾病，以预测有效地进行精确诊断。