Accessing Mesoscale Functional Organization from Standard Functional MRI Leveraging AI-Based Super-Resolution

Access to the fine-scale functional organization of the human brain is essential for understanding cognition and neurological disorders yet remains beyond the reach of conventional functional MRI (fMRI) because of its limited spatial resolution and contrast-to-noise ratio. To overcome this limitation, we developed f-LENNS (functional Localization Enhancement via Neural-Net-Sharpening), an artificial intelligence (AI)-based framework that enhances the effective spatial resolution of reconstructed fMRI magnitude images. The model was trained exclusively on high-resolution synthetic MRI data using extensive domain randomization and no adversarial or generative components. This approach enables robust out-of-the-box generalization (without retraining) to experimental datasets obtained with diverse acquisitions, while effectively curbing hallucination. Our experiments show that our publicly available model substantially improves the spatial fidelity of functional activation maps across multiple imaging paradigms and generalizes to a cohort of individuals with atypical brain function caused by amblyopia (lazy eye). F-LENNS consistently outperforms conventional resolution enhancement approaches, including interpolation-based upsampling and sharpening methods. These findings demonstrate that AI-driven super-resolution combined with domain randomization can overcome domain shifts arising from differences in MRI acquisition, enabling scalable, fine-scale functional brain mapping with widely available MRI hardware.