Feasibility of functional magnetic resonance imaging of ocular dominance and orientation preference in primary visual cortex

Abstract

A recent hemodynamic model is extended and applied to simulate and explore the feasibility of detecting ocular dominance (OD) and orientation preference (OP) columns in primary visual cortex by means of functional magnetic resonance imaging (fMRI). The stimulation entails a short oriented bar stimulus being presented to one eye and mapped to cortical neurons with corresponding OD and OP selectivity. Activated neurons project via patchy connectivity to excite other neurons with similar OP in nearby visual fields located preferentially along the direction of stimulus orientation. The resulting blood oxygen level dependent (BOLD) response is estimated numerically via the model’s spatiotemporal hemodynamic response function. The results are then used to explore the feasibility of detecting spatial OD-OP modulation, either directly measuring BOLD or by using Wiener deconvolution to filter the image and estimate the underlying neural activity. The effect of noise is also considered and it is estimated that direct detection can be robust for fMRI resolution of around 0.5 mm, whereas detection with Wiener deconvolution is possible at a broader range from 0.125 mm to 1 mm resolution. The detection of OD-OP features is strongly dependent on hemodynamic parameters, such as low velocity and high damping reduce response spreads and result in less blurring. The short-bar stimulus that gives the most detectable response is found to occur when neural projections are at 45 relative to the edge of local OD boundaries, which provides a constraint on the OD-OP architecture even when it is not fully resolved.

Cortical depth-dependent modeling of visual hemodynamic responses

“…At that time, fMRI resolutions were not fine enough to resolve cortical depths reliably, so averaging over the different depths not only makes the resulting calculations much more tractable but also did not affect the accuracy of the predictions of the model compared to experimental data. Moreover, it was able to produce key findings such as astrocyte-induced hemodynamic time delays (Pang et al, 2017), origins of resting-state fMRI spectrum , and techniques for imaging ocular dominance and orientation preference maps (De Oliveira et al, 2019).…”

Cortical Depth-Dependent Modeling of Visual Hemodynamic Responses

“…At that time, fMRI resolutions were not fine enough to resolve cortical depths reliably, so averaging over the different depths not only made the resulting calculations much more tractable but also did not affect the accuracy of the predictions of the model compared to experimental data. Moreover, it was able to produce key findings such as astrocyte-induced hemodynamic time delays (Pang et al, 2017), origins of resting-state fMRI spectrum (Pang and Robinson, 2019), techniques for imaging ocular dominance and orientation preference maps (De Oliveira et al, 2019), and improvements in population receptive field estimation (Infanti and Schwarzkopf, 2020).…”