Clustering multilayered cytoarchitectonic surface-based data in the brain

The laminar microstructure of the cerebral cortex is considered a unique anatomical mark of the development, function, connectivity, and even various pathologies of the brain. In recent years, multiple neuroimaging studies have utilized magnetic resonance imaging (MRI) relaxometry to visualize and explore this intricate microstructure. By successfully delineating the cortical laminar components, the applicability of T1 relaxometry has been expanded beyond solely a direct measure of myeline content. However, validating its applicability as a measure of cortical cytoarchitecture demands analyzing the complex resulting laminar datasets. In this study, we adapt and implement an algorithm for clustering cell omics profiles to cluster these complex microstructural cortical datasets. The resulting clusters correspond with an established atlas of cytoarchitectonic features, providing robust validation of T1 imaging as a tool for exploring cortical laminar composition. Lastly, we demonstrate the applicability of this framework in the exploration of the cytoarchitectonic features behind various unique skillsets.