Time-Series Statistical Applications to Mammalian Cerebral Physiology for Understanding Network Relations and Building State-Space Projections

哺乳动物大脑生理学的时间序列统计应用，用于理解网络关系和构建状态空间投影

• 批准号: 576386-2022
• 负责人: Zeiler, FrederickFA
• 金额: $ 1.82万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765810
• 关键词: Time; Series; Statistical; Applications; Mammalian

Cerebral physiologic processes rarely occur in isolation within humans and large mammals. However, our existing knowledge of the inter-relationships between cerebral physiologic systems of pressure-flow dynamics, cerebrovascular control, oxygen and nutrient delivery, autonomic nervous system modulation, neural electrophysiology, and their integration with cardiovascular physiology has suffered from several limitations. In particular, previous work has often occurred in small groups of humans or large mammals, with low-resolution physiologic data, exploring single system relationships without accounting for large systems interactions. This has led to wide knowledge gaps in our understanding of cerebral physiology in humans and large mammals, where comprehensive understanding of the physiologic systems network relationships is absent. By understanding network relationships, taking the entire cerebral and cardiovascular physiome into account in high-resolution, we can improve our understanding of the greater cerebral physiologic system and potentially facilitate the generation of physiology state modelling, with point and interval physiology state forecasting. Leveraging existing globally unique high-fidelity, high-frequency multi-modal cerebral physiologic data sets and expertise from both the University of Manitoba (Canada) and Karolinska Institute (Sweden), this NSERC Alliance International Catalyst project aims to apply time-series, vector and state-space statistical methodologies (commonly applied in market analysis or astrophysics), with the aid of machine learning, to understand the network physiologic relationships and generate novel physiologic state-space forecasting models that can be applied to both human and large mammal cerebral physiology. Discoveries here will advance the natural sciences and engineering (NSE) fields of cerebral physiology, statistical methodologies, data science and application of machine learning, while facilitating the building of long-term international links between two centers of excellence focused on bridging knowledge gaps in our fundamental understanding of cerebral physiology in mammals.

大脑生理过程很少在人类和大型哺乳动物中孤立发生。然而，我们现有的知识之间的相互关系的脑生理系统的压力-血流动力学，脑血管控制，氧气和营养输送，自主神经系统的调制，神经电生理学，和他们的整合与心血管生理受到了一些限制。特别是，以前的工作往往发生在人类或大型哺乳动物的小群体，低分辨率的生理数据，探索单一系统的关系，而不考虑大系统的相互作用。这导致我们对人类和大型哺乳动物大脑生理学的理解存在广泛的知识差距，缺乏对生理系统网络关系的全面理解。通过理解网络关系，以高分辨率考虑整个大脑和心血管生理组，我们可以提高我们对更大的大脑生理系统的理解，并可能促进生理状态建模的生成，以及点和间隔生理状态预测。 利用现有的全球唯一的高保真，高频多模态脑生理数据集和专业知识，从马尼托巴大学（加拿大）和卡罗林斯卡研究所（瑞典），这个NSERC联盟国际催化剂项目旨在应用时间序列，矢量和状态空间统计方法（通常应用于市场分析或天体物理学），在机器学习的帮助下，以了解网络生理关系，并产生新的生理状态空间预测模型，可应用于人类和大型哺乳动物的大脑生理。这里的发现将推进脑生理学，统计方法，数据科学和机器学习应用的自然科学和工程（NSE）领域，同时促进两个卓越中心之间建立长期的国际联系，专注于弥合我们对哺乳动物脑生理学基本理解的知识差距。