Today, the most widespread tool for measuring whole-brain activity noninvasively is functional magnetic resonance imaging (fMRI). Although fMRI tracks neural activity indirectly through measuring the associated changes in blood flow, volume and oxygenation, recent evidence has suggested that these active hemodynamic changes in the brain are far more precisely coordinated than previously believed, perhaps at the fine spatial scale of the basic modules of functional architecture: cerebral cortical columns and layers. If true, this could enable new studies of brain computation and circuitry as several cortical layers are the well- known inputs and outputs along canonical feedforward and feedback pathways of brain communication. The main challenge faced by this emerging field of “laminar fMRI” is how to interpret the complex hemodynamic signals to infer the underlying patterns of neural activity. Motivated by this, our overall goal is to improve our ability to measure neural activity from distinct cortical layers with human fMRI through detailed biophysical modeling of the underlying hemodynamic response. We will develop a new computational framework to simulate the fMRI signals using realistic microvascular networks and dynamics of associated blood flow, volume, and oxygenation changes that accompany neural activity. This framework has been validated using optical microscopy measures of the microvascular anatomy and dynamics from small animal models, and here we extend it for the first time to the human cortex. We will combine ultra-high-resolution in vivo vascular anatomical imaging data collected at 9.4 Tesla with our validated algorithm for synthesizing realistic microvascular networks to generate human vascular models specific to individual volunteers, and use these to simulate fMRI responses to motor tasks designed to activate specific cortical layers. We will then simulate responses of several forms of fMRI contrast—that are each sensitive to different aspects of the complex hemodynamic response—and compare our predictions to high-resolution fMRI measurements. Finally, to gain insight into whether fMRI can be used correctly to infer neural activity within cortical layers, we will quantify the discriminability of laminar fMRI by simulating various patterns of neural activity across layers and then comparing the computed fMRI activation profiles. This will tell us which neural activity patterns can be distinguished from one another, and which cannot, to help quantify the ability of laminar fMRI to decipher human brain circuitry. We address a fundamental gap in our knowledge regarding the limits of human fMRI: whether fMRI can accurately report on activation within distinct cortical layers. Our approach will allow us to quantify how fMRI sees the neural activity through the “filter” of the vascular response, and provide insight into the origins of newly-available fMRI contrasts. This will aid in the interpretability of fMRI for both neuroscience ...