Investigation of the confounding effects of vasculature and metabolism on computational anatomy studies.

Computational anatomy studies typically use T1-weighted magnetic resonance imaging contrast to look at local differences in cortical thickness or grey matter volume across time or subjects. This type of analysis is a powerful and non-invasive tool to probe anatomical changes associated with neurodevelopment, aging, disease or experience-induced plasticity. However, these comparisons could suffer from biases arising from vascular and metabolic subject- or time-dependent differences. Differences in blood flow and volume could be caused by vasodilation or differences in vascular density, and result in a larger signal contribution of the blood compartment within grey matter voxels. Metabolic changes could lead to differences in dissolved oxygen in brain tissue, leading to T1 shortening. Here, we analyze T1 maps and T1-weighted images acquired during different breathing conditions (ambient air, hypercapnia (increased CO2) and hyperoxia (increased O2)) to evaluate the effect size that can be expected from changes in blood flow, volume and dissolved O2 concentration in computational anatomy studies. Results show that increased blood volume from vasodilation during hypercapnia is associated with an overestimation of cortical thickness (1.85%) and grey matter volume (3.32%), and that both changes in O2 concentration and blood volume lead to changes in the T1 value of tissue. These results should be taken into consideration when interpreting existing morphometry studies and in future study design. Furthermore, this study highlights the overlap in structural and physiological MRI, which are conventionally interpreted as two independent modalities.

A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI.

This work presents a novel approach for modelling laminar myelin patterns in the human cortex in brain MR images on the basis of known cytoarchitecture. For the first time, it is possible to estimate intracortical contrast visible in quantitative ultra-high resolution MR images in specific primary and secondary cytoarchitectonic areas. The presented technique reveals different area-specific signatures which may help to study the spatial distribution of cortical T1 values and the distribution of cortical myelin in general. It may lead to a new discussion on the concordance of cyto- and myeloarchitectonic boundaries, given the absence of such concordance atlases. The modelled myelin patterns are quantitatively compared with data from human ultra-high resolution in-vivo 7T brain MR images (9 subjects). In the validation, the results are compared to one post-mortem brain sample and its ex-vivo MRI and histological data. Details of the analysis pipeline are provided. In the context of the increasing interest in advanced methods in brain segmentation and cortical architectural studies, the presented model helps to bridge the gap between the microanatomy revealed by classical histology and the macroanatomy visible in MRI.