In vivo measurements of lamination patterns in the human cortex

Abstract

The laminar composition of the cerebral cortex is tightly connected to the development and connectivity of the brain, as well as to function and pathology. Although most of the research on the cortical layers is done with the aid of ex vivo histology, there have been recent attempts to use magnetic resonance imaging (MRI) with potential in vivo applications. However, the high‐resolution MRI technology and protocols required for such studies are neither common nor practical. In this article, we present a clinically feasible method for assessing the laminar properties of the human cortex using standard pulse sequence available on any common MRI scanner. Using a series of low‐resolution inversion recovery (IR) MRI scans allows us to calculate multiple T1 relaxation time constants for each voxel. Based on the whole‐brain T1‐distribution, we identify six different gray matter T1 populations and their variation across the cortex. Based on this, we show age‐related differences in these population and demonstrate that this method is able to capture the difference in laminar composition across varying brain areas. We also provide comparison to ex vivo high‐resolution MRI scans. We show that this method is feasible for the estimation of layer variability across large population cohorts, which can lead to research into the links between the cortical layers and function, behavior and pathologies that was heretofore unexplorable.

Clustering the cortical laminae: in vivo parcellation

“…In 2019, a complete automated framework was formed for analyzing the cortical laminar composition using low-resolution multi-T1 mapping and a simple surface-based volumetric sampling system (Shamir et al 2019 ). What followed were several studies using multi-T1 imaging to explore the role of the cortical laminar composition in different pathologies, such as epilepsy (Lotan et al 2021 ), as well as in healthy aging (Tomer et al 2022 ). Other studies modeled and explored patterns of cortical connectivity on the laminar level (Shamir and Assaf 2021a , b ; Shamir et al 2022 ).…”

“…Use of the framework for cortical laminar composition analysis (Shamir et al 2019 , 2022 ; Shamir and Assaf 2021a , b ) results in a multilayered, surface-based dataset representing the regionally varying laminar composition across the cortical surfaces of both hemispheres. Despite the established correspondence between T1 layers and the actual cortical layers (Clark et al 1992 ; Barbier et al 2002 ; Bridge and Clare 2006 ; Duyn et al 2007 ; Barazany and Assaf 2012 ; Deistung et al 2013 ; Glasser et al 2014 ; Lutti et al 2014 ; Shafee et al 2015 ; Lifshits et al 2018 ; Assaf 2019 ; Shamir et al 2019 , 2022 ; Van Essen et al 2019 ; Lotan et al 2021 ; Shamir and Assaf 2021a , b ; Tomer et al 2022 ), T1 is still thought to be more of a direct measure of myeloarchitecture (myelin content) than of cytoarchitecture (Glasser et al 2016 ; Shamir and Assaf 2023 ). This dataset is both multidimensional and geometrically complex: up to six cortical laminar components, representing the regionally varying microstructure of the cortex, are measured across vertices of a Delaunay triangulation, delineating the intricate geometry of the cortex.…”

Clustering laminar cytoarchitecture: in vivo parcellation based on cortical granularity

“…In 2019, a complete automated framework was formed for analyzing the cortical laminar composition using low resolution multi-T1 mapping and a simple surface-based volumetric sampling system (Shamir et al 2019). What followed were several studies using multi-T1 imaging to explore the role of the cortical laminar composition in different pathologies, such as epilepsy (Lotan et al 2021), as well as in healthy aging (Tomer et al 2022). Other studies modeled and explored patterns of cortical connectivity on the laminar level (Shamir and Assaf 2021a, Shamir and Assaf 2021b, Shamir et al 2022).…”