PT  - JOURNAL ARTICLE
AU  - Shamir, Ittai
AU  - Assaf, Yaniv
AU  - Shamir, Ron
TI  - Clustering multilayered cytoarchitectonic surface-based data in the brain
AID  - 10.1101/2022.10.29.514347
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.10.29.514347
4099  - http://biorxiv.org/content/early/2022/10/30/2022.10.29.514347.short
4100  - http://biorxiv.org/content/early/2022/10/30/2022.10.29.514347.full
AB  - In recent years, multiple neuroimaging studies have utilized magnetic resonance imaging (MRI) relaxometry to visualize and explore the intricate laminar microstructure of the cerebral cortex, which is considered a unique anatomical mark of the development, function, connectivity, and even various pathologies of the brain. T1 relaxometry has successfully delineated laminar components across the cortex, expanding its applicability from solely a direct measure of myelin content to an indirect measure of cortical cytoarchitecture. However, analyzing the resulting laminar dataset is no simple task due to its multidimensionality and geometric complexity: six cortical laminar components, representing the regionally varying microstructure of the cortex, are dispersed across a triangulated surface, representing the intricate and tortuous geometry of the cortex. In this study we implement an adaptation of an algorithm that clusters cell omics profiles using their spatial organization to cluster multidimensional surface-based microstructural data in the cortex. The correspondence of the results with an established atlas of cytoarchitectonic features provides further validation of the T1 imaging framework for cortical laminar composition analysis and highlights the role of MRI neuroimaging as a probe of tissue microstructure.Competing Interest StatementThe authors have declared no competing interest.