Abstract
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 Statement
The authors have declared no competing interest.
Footnotes
↵† The authors wish it to be known that, in their opinion, the last two authors should be regarded as Joint Last Authors
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