A Framework for Integrating Neurovascular Coupling in Brain Function Models

Thesis Abstract

Neurovascular coupling (NVC) plays a critical role in the regulation of cerebral blood flow in response to neural activity, yet current brain function models inadequately integrate this dynamic relationship, thereby limiting the understanding of neurophysiological processes and impairing the accuracy of neuroimaging interpretations. This study aims to develop a comprehensive theoretical framework that systematically incorporates neurovascular coupling into existing brain function models, facilitating a more holistic representation of neurovascular interactions. The specific objectives include evaluating current models’ inadequacies, identifying key neurovascular mechanisms, synthesizing relevant neurophysiological and vascular theories, and formulating an integrative framework adaptable across diverse neuroimaging modalities and cognitive contexts. Adopting a pragmatic research design rooted in theoretical synthesis, this investigation employs an exploratory qualitative approach complemented by quantitative validation. The study population comprises neuroscientists, neurovascular researchers, and neuroimaging experts, with a purposive sample of 30 participants selected through stratified sampling to ensure diverse expertise across functional neuroanatomy, neurovascular physiology, and computational modeling domains. Data collection involves semi-structured interviews, expert feedback sessions, and review of relevant literature, complemented by a Delphi method to achieve consensus on core neurovascular mechanisms and theoretical constructs. Instruments include interview guides, thematic coding frameworks, and a tailored survey instrument assessing the perceived gaps and practical requirements for model integration. Validity and reliability are maintained via triangulation, member checking, and iterative refinement, ensuring robustness of the developed framework. Data analysis involves thematic analysis of qualitative data using NVivo software to identify recurrent themes related to neurovascular interactions, while quantitative data from the survey are subjected to descriptive statistics and multiple regression analysis using SPSS to examine relationships between model components and expert consensus levels. The theoretical basis draws upon the neurovascular coupling theory and the neurophysiological model by Attwell and colleagues (2010), with an innovative synthesis that emphasizes coupling dynamics and temporal-spatial specificity. Model specification employs systems thinking approaches and computational modeling principles, facilitating simulation of neurovascular interactions under varying physiological and pathological conditions. Expected findings include the identification of key neurovascular mechanisms often underrepresented in current models, such as glial cell involvement, vascular compliance dynamics, and neurochemical signaling pathways. The research anticipates developing an integrative framework that enhances model realism, improves interpretability of neuroimaging data—particularly functional MRI—and provides a basis for subsequent quantitative modeling. The study contributes to existing knowledge by bridging a notable methodological gap and offering a versatile, evidence-based framework adaptable for neuroscience research, clinical diagnostics, and neurotechnology development. The primary conclusion emphasizes that integrating neurovascular coupling significantly enriches brain function models, advancing both theoretical understanding and practical applications. It recommends further empirical validation using computational simulations and neuroimaging experiments, advocating for an interdisciplinary approach involving neurology, vascular biology, and computational neuroscience. Additionally, the study suggests future research directions to explore NVC mechanisms in neurodegenerative diseases and cerebrovascular disorders, emphasizing the importance of dynamic, multi-scale models. This framework aims to serve as a foundational template for ongoing developments in neuroimaging interpretation, neural network modeling, and precision medicine in neuroscience.]