Author Type

Graduate Student

Date of Award

Fall 9-19-2025

Document Type

Dissertation

Publication Status

Version of Record

Submission Date

November 2025

Department

Computer and Electrical Engineering and Computer Science

Degree Name

Doctor of Philosophy (PhD)

Thesis/Dissertation Advisor [Chair]

Ramin Pashaie

Abstract

Cerebral hemodynamic imaging is increasingly used as a non-invasive technique to study brain function and dysfunction in both research and clinical settings. Recent advances in imaging technologies have enabled high-speed, high-resolution, whole-brain measurements of blood flow and oxygenation. Improving the interpretation of these cerebral hemodynamic signals through a mechanistic understanding of their cellular drivers would enable more accurate characterization of brain function and pathology. However, no existing biophysical model fully captures the fine-grained dynamics observed in these signals. This dissertation introduces a biophysically grounded computational framework for multi-scale analysis of the mouse cerebral blood flow regulatory system, which captures mechanistic interactions among its primary components that shape cerebrovascular dynamics.

The tight coupling between neuronal activity and metabolic demand has led to the evolution of complex neurovascular coupling (NVC) mechanisms that dynamically regulate regional cerebral blood flow in response to local energy needs. This process, known as functional hyperemia, ensures the adequate delivery of oxygen and nutrients to active brain regions. Consequently, regional hemodynamic signals correlate with underlying brain activity, forming the foundation of hemodynamic neuroimaging. However, this correlation is complex, as multiple cellular and vascular factors contribute to the cerebrovascular dynamics, complicating the inference of cell-type-specific activity. To address this challenge, we developed a quantitative framework to disentangle the cell-specific drivers of cerebrovascular dynamics and generate a model that links cellular activity to hemodynamic signals. Incorporating the proposed biophysical model into cerebral hemodynamic analysis enhances the accuracy of inverse models that infer regional neuronal and astrocytic activity from hemodynamic signals.

Dysfunction in cerebral blood flow (CBF) regulation is implicated in the pathogenesis of various neurological disorders, including stroke and Alzheimer’s disease. Understanding the physiological mechanisms that support healthy CBF regulation is essential for identifying early markers of pathology and improving the diagnosis of diseases in which cerebrovascular dysfunction plays a role. Current research also aims to identify specific morphological and functional impairments in the cerebral vasculature that most severely disrupt brain health. This work uses computational modeling to establish a systems-level perspective on healthy CBF regulation and to examine how cellular-level disruptions impair key system-level regulatory processes required for maintaining cerebral homeostasis. Furthermore, the model can be used to investigate how these impairments manifest in macroscopic, noninvasively measurable hemodynamic signals, supporting the identification of biomarkers of cerebrovascular dysfunction.

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