Fiber-specific structural properties relate to reading skills in children and adolescents

Abstract

Recent studies suggest that the cross-sectional relationship between reading skills and white matter microstructure, as indexed by fractional anisotropy, is not as robust as previously thought. Fixel-based analyses yield fiber-specific micro- and macrostructural measures, overcoming several shortcomings of the traditional diffusion tensor model. We ran a whole-brain analysis investigating whether the product of fiber density and cross-section (FDC) related to single-word reading skills in a large, open, quality-controlled data set of 983 children and adolescents ages 6-18. We also compared FDC between participants with (n = 102) and without (n = 570) reading disabilities. We found that FDC positively related to reading skills throughout the brain, especially in left temporoparietal and cerebellar white matter, but did not differ between reading proficiency groups. Exploratory analyses revealed that among metrics from other diffusion models - DTI, DKI, and NODDI - only the orientation dispersion and neurite density indexes from NODDI were associated (inversely) with reading skills. The present findings further support the importance of left-hemisphere dorsal temporoparietal white matter tracts in reading. Additionally, these results suggest future DWI studies of reading and dyslexia should be designed to benefit from advanced diffusion models, include cerebellar coverage, and consider continuous analyses that account for individual differences in reading skill.

White Matter Microstructural Plasticity Associated with Educational Intervention in Reading Disability

“…First, the only test that survived multiple comparison correction was the one that suggested decreasing leftward laterality of MD in the ILF was related to improving SIT scores (and the analogous model concerning only the left ILF was marginally significant at p FDR = 0.056). Despite this, we believe the strong effect sizes achieved by many of the models, even those that did not survive FDR correction, are noteworthy given the typically small effect sizes observed in analogous cross-sectional analyses (e.g., ~3% variance explained, as observed in Meisler & Gabrieli, 2022b). The small amount of withinparticipant data (two time points) precluded us from running more statistically sophisticated models, such as linear mixed-effect models as in (Huber et al, 2018).…”

“…While higher FA and lower MD are often thought to reflect more “healthy” white matter, these metrics are biologically unspecific due to the variety of factors that can influence diffusion of water in voxel-sized regions and the confounding impact of crossing fibers (De Santis et al, 2014; Jones et al, 2013), which can impact as many as 90% of white matter voxels (Behrens et al, 2007; Jeurissen et al, 2013). FIber-specific measures such as quantitative anisotropy (Yeh et al, 2013) and fixel-based metrics (Raffelt et al, 2017), and multicompartmental models such as NODDI (Zhang et al, 2012), can provide higher biological specificity and have shown promise in better resolving brain-behavior relationships in studies of reading abilities (Koirala et al, 2021; Meisler & Gabrieli, 2022b; Sihvonen et al, 2021). Unfortunately, the low angular resolution and weak single-shelled diffusion weighting of the present DWI acquisition scheme were not well-suited for these more novel approaches (Genc et al, 2020), effectively limiting us to using DTI metrics.…”