David Abramian

The paper introduces voxel-wise brain graphs from diffusion MRI, showing that functional MRI data can be approximated by low-frequency graph Laplacian eigenmodes.

This paper develops a deep learning method to enhance the spatial resolution of diffusion MRI data, achieving better results than spline interpolation on Human Connectome Project data.

This paper introduces an anisotropic spatial filtering method for smoothing fMRI data in white matter, showing improved detection of anisotropic activations compared to isotropic Gaussian filters.

This study uses diffusion-informed white matter harmonics to analyze spatial dynamics of fMRI data, revealing subtle spatial modulations under functional load.

This paper introduces anatomically informed Bayesian spatial models for fMRI analysis that improve posterior probability maps by incorporating anatomical structure.

This study uses CycleGAN to reconstruct facial features from anonymized MRI data, showing face blurring is insufficient while face removal offers better but still reversible anonymization.