Ittai Shamir

This study integrates a neuroimaging model of laminar-level connectomics into a biologically-inspired RNN for working memory tasks, improving brain topology representation without sacrificing performance.

The paper clusters cortical microstructural datasets using an algorithm adapted from cell omics profiling, validating T1 relaxometry as a tool for exploring cortical laminar composition.

The paper models laminar connectivity in the macaque brain using MRI data and validates it against historical findings with 83% accuracy.

This paper introduces a method to analyze cortical layer composition using low-resolution T1 MRI images, resolving partial volume effects and enabling subvoxel composition estimation.