Campbell, J. S. W. | Search bioRxiv

bioRxiv2024

Mapping the aggregate g-ratio of white matter tracts using multi-modal MRI

The g-ratio of a myelinated axon is defined as the ratio of the inner-to-outer diameter of the myelin sheath and modulates conduction speed of action potentials along axons. This g-ratio can be mapped in vivo at the macroscopic scale across the entire human brain using multi-modal MRI and sampled along white matter streamlines reconstructed from diffusion-weighted images to derive the g-ratio of a white matter tract. This tractometry approach has shown spatiotemporal variations in myelin g-ratio across white matter tracts and networks. However, tractometry is biased by partial volume effects where voxels contain multiple fiber populations. To address this limitation, we used the Convex Optimization Modeling for Microstructure-Informed Tractography (COMMIT) framework to derive tract-specific axonal and myelin volumes, which are used to compute the tract-specific aggregate g-ratio. We compare our novel COMMIT-based tract-specific g-ratio mapping approach to conventional tractometry in a group of 10 healthy adults. Our findings demonstrate that the tract-specific g-ratio mapping approach preserves the overall spatial distribution observed in tractometry and enhances contrast between tracts. Additionally, our scan-rescan data shows high repeatability for medium to large caliber tracts. We show that short and large caliber tracts have a lower g-ratio, whereas tractometry results show the opposite trends. This technique advances tract-specific analysis by reducing biases introduced by the complex network of crossing white matter fibers.

neuroscience↗

bioRxiv2023

The Human Brain Connectome Weighted by the Myelin Content and Total Intra-Axonal Cross-Sectional Area of White Matter Tracts

A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features. Computational models support in vivo investigation of the mechanisms mediating this relationship but currently lack the requisite biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features to assess their potential joint utilization in computational models. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, tractometry-derived networks weighted by either tensor-based metrics or neurite density show no relationship with whole-brain functional connectivity. We conclude that structure-function brain models are likely to be improved by the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content. We anticipate that the proposed microstructure-weighted computational modeling approach will support mechanistic understanding of the structure-function relationship of the human brain. AUTHOR SUMMARYFor computational network models to provide mechanistic links between brain structure and function, they must be informed by networks in which edge weights quantify structural features relevant to brain function. Here, we characterized several weighted structural networks capturing multiscale features of white matter connectivity. We describe these networks in terms of edge weight distribution, variance and network topology, as well as their relationships with each other, edge length and function. Overall, these findings support the joint use of structural networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts in structure-function models. This thorough characterization serves as a benchmark for future investigations of weighted structural brain networks.

neuroscience↗