# Functional Magnetic Resonance Imaging

f-LENNS super-resolves conventional-resolution fMRI magnitude images to 1 mm isotropic, letting mesoscale functional organization be recovered without an ultra-high-field scanner. The model is trained only on domain-randomized synthetic MRI, with no adversarial or generative components, so it applies to experimental data across field strengths, sequences and resolutions without retraining. In visual cortex, color-, motion- and disparity-selective sites localized from super-resolved maps came closer to the original high-resolution acquisitions than interpolation upsampling and sharpening did.

f-LENNS super-resolves conventional-resolution fMRI magnitude images to 1 mm isotropic, letting mesoscale functional organization be recovered without an ultra-high-field scanner. The model is trained only on domain-randomized synthetic MRI, with no adversarial or generative components, so it applies to experimental data across field strengths, sequences and resolutions without retraining. In visual cortex, color-, motion- and disparity-selective sites localized from super-resolved maps came closer to the original high-resolution acquisitions than interpolation upsampling and sharpening did.

The individualized AI-driven fMRI neuromodulation platform iNM abandons a single fixed set of brain targets and instead builds a neurofunctional twin from each participant's own anatomy and function, reinforcing the tongue sensorimotor network in a closed loop at 2-second resolution. In healthy participants, an unsupervised hidden Markov model showed target-state occupancy rising from 82% under control to 97%, swallow-related interference falling from 18% to 3%, and connectivity among motor, sensory and attention networks strengthening by 53% to 74%, with effects persisting after modulation stopped.

The individualized AI-driven fMRI neuromodulation platform iNM abandons a single fixed set of brain targets and instead builds a neurofunctional twin from each participant's own anatomy and function, reinforcing the tongue sensorimotor network in a closed loop at 2-second resolution. In healthy participants, an unsupervised hidden Markov model showed target-state occupancy rising from 82% under control to 97%, swallow-related interference falling from 18% to 3%, and connectivity among motor, sensory and attention networks strengthening by 53% to 74%, with effects persisting after modulation stopped.

MAAS replaces the text-embedding conditioning of a pretrained audio diffusion transformer with region-of-interest fMRI responses, removing the decoder that previously predicted an external stimulus representation such as CLAP or YamNet. On a 7T fMRI dataset it improves acoustic and semantic metrics, including CLAP similarity, pairwise identification accuracy and Mel-SSIM, over baselines such as two-stage fMRI-to-latent conditioning. The framework also uses a fidelity metric grounded in a computational model of auditory cortex to quantify how individual auditory regions contribute to the reconstructed audio.

MAAS replaces the text-embedding conditioning of a pretrained audio diffusion transformer with region-of-interest fMRI responses, removing the decoder that previously predicted an external stimulus representation such as CLAP or YamNet. On a 7T fMRI dataset it improves acoustic and semantic metrics, including CLAP similarity, pairwise identification accuracy and Mel-SSIM, over baselines such as two-stage fMRI-to-latent conditioning. The framework also uses a fidelity metric grounded in a computational model of auditory cortex to quantify how individual auditory regions contribute to the reconstructed audio.

The work proposes Brain Hierarchical Organization Learning (BrainHO), which uses learnable subgraph and graph tokens to aggregate brain regions bottom-up via hierarchical attention driven by node feature affinity, combined with a subgraph orthogonality constraint and a hierarchical consistency constraint; on ABIDE (N=1009, 516 ASD/493 healthy controls) and REST-meta-MDD (N=2380, 1276 MDD/1104 healthy controls) it attains the highest accuracy (69.68% and 64.71%) and sensitivity (73.11% and 67.43%) using only the static PCC connectivity matrix, while visualizing disease-related subgraphs that partly overlap predefined networks such as the SMN and DAN and partly span multiple predefined networks.

The work proposes Brain Hierarchical Organization Learning (BrainHO), which uses learnable subgraph and graph tokens to aggregate brain regions bottom-up via hierarchical attention driven by node feature affinity, combined with a subgraph orthogonality constraint and a hierarchical consistency constraint; on ABIDE (N=1009, 516 ASD/493 healthy controls) and REST-meta-MDD (N=2380, 1276 MDD/1104 healthy controls) it attains the highest accuracy (69.68% and 64.71%) and sensitivity (73.11% and 67.43%) using only the static PCC connectivity matrix, while visualizing disease-related subgraphs that partly overlap predefined networks such as the SMN and DAN and partly span multiple predefined networks.

The work proposes KD-Brain, a prior-informed graph learning framework that injects disorder-specific semantic priors into the attention Query (Semantic-Conditioned Interaction) and aligns learned subnetwork interaction distributions with clinical priors via a KL-divergence Pathology-Consistent Constraint, achieving better performance than 12 baselines on the ABIDE (NYU) ASD task and single-center BD and MDD tasks while producing functional pathways and critical brain regions consistent with psychiatric pathophysiology.

The work proposes KD-Brain, a prior-informed graph learning framework that injects disorder-specific semantic priors into the attention Query (Semantic-Conditioned Interaction) and aligns learned subnetwork interaction distributions with clinical priors via a KL-divergence Pathology-Consistent Constraint, achieving better performance than 12 baselines on the ABIDE (NYU) ASD task and single-center BD and MDD tasks while producing functional pathways and critical brain regions consistent with psychiatric pathophysiology.