An MRI-Based, Data-Driven Model of Cortical Laminar Connectivity

Over the past two centuries, great scientific efforts have been spent on deciphering the structure and function of the cerebral cortex using a wide variety of methods. Since the advent of MRI neuroimaging, significant progress has been made in imaging of global white matter connectivity (connectomics), followed by promising new studies regarding imaging of grey matter laminar compartments. Despite progress in both fields, there still lacks mesoscale information regarding cortical laminar connectivity that could potentially bridge the gap between the current resolution of connectomics and the relatively higher resolution of cortical laminar imaging. Here, we systematically review a sample of prominent published articles regarding cortical laminar connectivity, in order to offer a simplified data-driven model that integrates white and grey matter MRI datasets into a novel way of exploring whole-brain tissue-level connectivity. Although it has been widely accepted that the cortex is exceptionally organized and interconnected, studies on the subject display a variety of approaches towards its structural building blocks. Our model addresses three principal cortical building blocks: cortical layer definitions (laminar grouping), vertical connections (intraregional, within the cortical microcircuit and subcortex) and horizontal connections (interregional, including connections within and between the hemispheres). While cortical partitioning into layers is more widely accepted as common knowledge, certain aspects of others such as cortical columns or microcircuits are still being debated. This study offers a broad and simplified view of histological and microscopical knowledge in laminar research that is applicable to the limitations of MRI methodologies, primarily regarding specificity and resolution.