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

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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.