  High-resolution fMRI provides a window into the brain's mesoscale
organization. Yet, higher spatial resolution increases scan times, to
compensate for the low signal and contrast-to-noise ratio. This work introduces
a deep learning-based 3D super-resolution (SR) method for fMRI. By
incorporating a resolution-agnostic image augmentation framework, our method
adapts to varying voxel sizes without retraining. We apply this innovative
technique to localize fine-scale motion-selective sites in the early visual
areas. Detection of these sites typically requires a resolution higher than 1
mm isotropic, whereas here, we visualize them based on lower resolution (2-3mm
isotropic) fMRI data. Remarkably, the super-resolved fMRI is able to recover
high-frequency detail of the interdigitated organization of these sites
(relative to the color-selective sites), even with training data sourced from
different subjects and experimental paradigms -- including non-visual
resting-state fMRI, underscoring its robustness and versatility. Quantitative
and qualitative results indicate that our method has the potential to enhance
the spatial resolution of fMRI, leading to a drastic reduction in acquisition
time.
