A subject-specific framework for in vivo myeloarchitectonic analysis using high resolution quantitative MRI.

Structural magnetic resonance imaging can now resolve laminar features within the cerebral cortex in vivo. A variety of intracortical contrasts have been used to study the cortical myeloarchitecture with the purpose of mapping cortical areas in individual subjects. In this article, we first briefly review recent advances in MRI analysis of cortical microstructure to portray the potential and limitations of the current state-of-the-art. We then present an integrated framework for the analysis of intracortical structure, composed of novel image processing tools designed for high resolution cortical images. The main features of our framework are the segmentation of quantitative T1 maps to delineate the cortical boundaries (Bazin et al., 2014), and the use of an equivolume layering model to define an intracortical coordinate system that follows the anatomical layers of the cortex (Waehnert et al., 2014). We evaluate the framework with 150µm isotropic post mortem T2(∗)-weighted images and 0.5mm isotropic in vivo T1 maps, a quantitative index of myelin content. We study the laminar structure of the primary visual cortex (Brodmann area 17) in the post mortem and in vivo data, as well as the central sulcus region in vivo, in particular Brodmann areas 1, 3b and 4. We also investigate the impact of the layering models on the relationship between T1 and cortical curvature. Our experiments demonstrate that the equivolume intracortical surfaces and transcortical profiles best reflect the laminar structure of the cortex in areas of curvature in comparison to the state-of-the-art equidistant and Laplace implementations. This framework generates a subject specific intracortical coordinate system, the basis for subsequent architectonic analyses of the cortex. Any structural or functional contrast co-registered to the T1 maps, used to segment the cortex, can be sampled on the curved grid for analysis. This work represents an important step towards in vivo structural brain mapping of individual subjects.

Multi-contrast multi-scale surface registration for improved alignment of cortical areas.

The position of cortical areas can be approximately predicted from cortical surface folding patterns. However, there is extensive inter-subject variability in cortical folding patterns, prohibiting a one-to-one mapping of cortical folds in certain areas. In addition, the relationship between cortical area boundaries and the shape of the cortex is variable, and weaker for higher-order cortical areas. Current surface registration techniques align cortical folding patterns using sulcal landmarks or cortical curvature, for instance. The alignment of cortical areas by these techniques is thus inherently limited by the sole use of geometric similarity metrics. Magnetic resonance imaging T1 maps show intra-cortical contrast that reflects myelin content, and thus can be used to improve the alignment of cortical areas. In this article, we present a new symmetric diffeomorphic multi-contrast multi-scale surface registration (MMSR) technique that works with partially inflated surfaces in the level-set framework. MMSR generates a more precise alignment of cortical surface curvature in comparison to two widely recognized surface registration algorithms. The resulting overlap in gyrus labels is comparable to FreeSurfer. Most importantly, MMSR improves the alignment of cortical areas further by including T1 maps. As a first application, we present a group average T1 map at a uniquely high-resolution and multiple cortical depths, which reflects the myeloarchitecture of the cortex. MMSR can also be applied to other MR contrasts, such as functional and connectivity data.

A histology-based model of quantitative T1 contrast for in-vivo cortical parcellation of high-resolution 7 Tesla brain MR images.

A conclusive mapping of myeloarchitecture (myelin patterns) onto the cortical sheet and, thus, a corresponding mapping to cytoarchitecture (cell configuration) does not exist today. In this paper we present a generative model which can predict, on the basis of known cytoarchitecture, myeloarchitecture in different primary and non-primary cortical areas, resulting in simulated in-vivo quantitative T1 maps. The predicted patterns can be used in brain parcellation. Our model is validated using a similarity distance metric which enables quantitative comparison of the results with empirical data measured using MRI. The work presented may provide new perspectives for this line of research, both in imaging and in modelling the relationship with myelo- and cytoarchitecture, thus leading the way towards in-vivo histology using MRI.