The layer composition of the cerebral cortex represents a unique anatomical fingerprint of brain development, function, connectivity, and pathology. Historically, the cortical layers were investigated solely ex-vivo using histological means, but recent magnetic resonance imaging (MRI) studies suggest that T1 relaxation images can be utilized to separate the layers. Despite technological advancements in the field of high-resolution MRI, accurate estimation of whole-brain cortical laminar composition has remained limited due to partial volume effects, leaving some layers far beyond the image resolution. In this study, we offer a simple and accurate method for cortical laminar composition analysis, resolving partial volume effects and cortical curvature heterogeneity. We use a low-resolution 3T MRI echo planar imaging inversion recovery (EPI IR) scan protocol that provides fast acquisition (~ 12 min) and enables extraction of multiple T1 relaxation time components per voxel, which are assigned to types of brain tissue and utilized to extract the subvoxel composition of six T1 layers. While previous investigation of the layers required the estimation of cortical normals or smoothing of layer widths (similar to VBM), here we developed a sphere-based approach to explore the inner mesoscale architecture of the cortex. Our novel algorithm conducts spatial analysis using volumetric sampling of a system of virtual spheres dispersed throughout the entire cortical space. The methodology offers a robust and powerful framework for quantification and visualization of the cortical laminar structure on the cortical surface, providing a basis for quantitative investigation of its role in cognition, physiology and pathology.
Keywords: Brain mapping; Computational biology; Gray matter; Image processing; Neuroimaging.