Diffusion Tensor Based White Matter Tract Atlases for Pediatric Populations

Abstract

Diffusion Tensor Imaging (DTI) is a non-invasive neuroimaging method that has become the most widely employed MRI modality for investigations of white matter fiber pathways. DTI has proven especially valuable for improving our understanding of normative white matter maturation across the life span and has also been used to index clinical pathology and cognitive function. Despite its increasing popularity, especially in pediatric research, the majority of existing studies examining infant white matter maturation depend on regional or white matter skeleton-based approaches. These methods generally lack the sensitivity and spatial specificity of more advanced functional analysis options that provide information about microstructural properties of white matter along fiber bundles. DTI studies of early postnatal brain development show that profound microstructural and maturational changes take place during the first two years of life. The pattern and rate of these changes vary greatly throughout the brain during this time compared to the rest of the life span. For this reason, appropriate image processing of infant MR imaging requires the use of age-specific reference atlases. This article provides an overview of the pre-processing, atlas building, and the fiber tractography procedures used to generate two atlas resources, one for neonates and one for 1- to 2-year-old populations. Via the UNC-NAMIC DTI Fiber Analysis Framework, our pediatric atlases provide the computational templates necessary for the fully automatic analysis of infant DTI data. To the best of our knowledge, these atlases are the first comprehensive population diffusion fiber atlases in early pediatric ages that are publicly available.

Identifying interindividual variability of social perception and associated brain anatomical correlations in children with autism spectrum disorder using eye-tracking and diffusion tensor imaging MRI (DTI-MRI)

“…Indeed, although the traditional tensor model offers readily accessible tensor-derived measures, employing sequences with greater encoding directions and b-values could effectively tackle the issue of crossing fibers and facilitate exploration of more advanced diffusion measures and should be considered in future studies. Further studies could also beneficiate from using age specific tractography atlas, that are currently under investigation in the literature ( Short et al. 2022 ; Spencer et al.…”

Language exposure during infancy is negatively associated with white matter microstructure in the arcuate fasciculus

“…The displacement fields created in this procedure mapped the study-specific atlas space to individual subject spaces through a nonlinear, invertible transformation. Tensor maps were transformed into the atlas space and averaged to produce the final DTI atlas ( Short et al, 2022 , Verde et al, 2014 ). In this study, each infant’s DTI data was processed as described above and mapped into the final DTI atlas, which served as a reference space for tractography and tract parameterization ( Verde et al, 2014 , Swanson et al, 2018 ).…”

Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting

“…Spencer et al [17] created age-specific white matter atlases for young children aged 6-8 years old, motivated by the fact that there are developmental changes and adult brain atlases may not be representative of younger populations. Publicly available white matter atlases have also been created for neonates and 1-2 year old populations by Short et al [18]. In addition, atlases sometimes require tens to hundreds of subjects, and those manually segmented by trained neuroanatomists results in lower sample size, overall making the process time consuming.…”

“…Publicly available white matter atlases have also been created for neonates and 1-2 year old populations by Short et al . [18]. In addition, atlases sometimes require tens to hundreds of subjects, and those manually segmented by trained neuroanatomists results in lower sample size, overall making the process time consuming.…”