# Biophysical modeling of the functional MRI signal through parametric variations in neuronal activation and blood vessel anatomy using realistic synthetic microvascular networks > **NIH NIH F32** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $71,024 ## Abstract The most widespread tool for measuring brain activity noninvasively in humans is functional magnetic resonance imaging (fMRI), which typically tracks changes in blood flow and oxygenation using the blood-oxygenation-level- dependent (BOLD) signal. Although BOLD is an indirect measure of neural firing, it has been shown to be a faithful measure of brain activation, yet the details of brain vascular anatomy and physiology are known to influence all fMRI signals including BOLD. Recently, invasive optical imaging studies in animals demonstrated that the changes in blood flow regulation occurring alongside neuronal activity are far more precise than previously believed, indicating fMRI can be a faithful representation of neuronal activity at fine spatial and temporal scales. Recent biophysical simulations have further demonstrated how the microvascular network, and the vascular response to neural activity, can influence fMRI signals in humans, suggesting that modeling can help improve fMRI interpretation. We propose to extend this work through a series of biophysical simulations in which we will parametrically vary vascular anatomy, neuronal activity, and the vascular response to neuronal activity then simulate the resulting BOLD responses to characterize these influences on fMRI. We hypothesize that the specifics of the vascular anatomy and neuronal activity patterns will both have measurable effects on the fMRI signal and that our modeling framework can predict these influences—which can improve inferences of neural activity from fMRI. This approach is only now possible due to the availability of sufficiently-large-scale microscopy data, our highly efficient computational framework, and our novel vascular synthesis algorithm. For this work we will extend our new blood flow and oxygen transport framework to simulate vasomotive responses to neuronal activity, then incorporate MR physics to generate the corresponding BOLD signals. Our modeling platform provides unique capabilities: synthesis of realistic, large-scale vascular networks with fully controllable geometry, density, and topology; and robust simulations of vascular systems far larger than ever attempted. This will allow for accurate, efficient calculations at a sufficient scale to generate meaningful BOLD responses that can be related to human fMRI data. We will test whether other aspects of the hemodynamic response may provide more faithful representations of neuronal activity. Finally, we will test our model predictions against empirical data with a simple, high-resolution human fMRI experiment. This work spans four Aims. In Aim 1 we compare four candidate “scenarios” describing the vascular response to neural activity. In Aim 2 we test the dependence BOLD on vascular anatomy by synthesizing large-scale vascular networks. In Aim 3 we test dependence of patterns of neuronal activity on BOLD by simulating systematically varying spatiotemporal patterns of neuronal activity. In Aim 4 we test m... ## Key facts - **NIH application ID:** 10156061 - **Project number:** 1F32MH125599-01 - **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL - **Principal Investigator:** Grant Hartung - **Activity code:** F32 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $71,024 - **Award type:** 1 - **Project period:** 2020-12-01 → 2023-11-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10156061 ## Citation > US National Institutes of Health, RePORTER application 10156061, Biophysical modeling of the functional MRI signal through parametric variations in neuronal activation and blood vessel anatomy using realistic synthetic microvascular networks (1F32MH125599-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10156061. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*