Fig 1.
Major, reading-related white matter tracts chosen as regions of interest.
Whole brain tractography was performed via constrained spherical deconvolution, then tracts were segmented using deterministic semi-automated methods in ExploreDTI. Regions of interest were investigated bilaterally, but only the left hemisphere is shown here.
Fig 2.
Processing pipeline to prepare imaging data for principal component analysis.
Preprocessed diffusion-weighted images (A) were registered to T1-weighted anatomical images (B). Measure maps from NODDI, ihMT, and mcDESPOT sequences were registered to diffusion-weighted images in anatomical space (C) to produce all measure maps in anatomical space (D). Next, whole brain tractography was computed from b = 900s/mm2 data using constrained spherical deconvolution (E), and tracts of interest were segmented in a semiautomated fashion in ExploreDTI (F). Measure means were extracted for each tract of interest (G) and along each tract of interest at 20 equidistant segments (H).
Fig 3.
Multimodal imaging of white matter microstructure in the splenium.
Measures from DTI, NODDI, MT, and mcDESPOT imaging can be contrasted to provide a multifaceted understanding of white matter structure.
Fig 4.
Principal components visualized in the left arcuate fasciculus.
Correlations for measures which contribute greater variance than expected by chance (>11.1%) are included for each component. Panel A displays PCA results from all 9 measures. Components in Panel A explained 79.5% of variance in our data (variance explained by each individual component is noted in brackets). Principal components were related to diffusion along a primary axis (PC1), myelin and axonal packing (PC2), and axon diameter (PC3). Panel B shows results from a secondary PCA with FA and MD removed, as they loaded onto multiple components. Principal components in Panel B explain 77.3% of variance.
Table 1.
Parameters for mixed effects models linking principal components to Total Reading (formula: Total Reading ~ PC1 + PC2 + PC3 + Age + (1|Subject)).
Fig 5.
Scatterplots visualizing relationships between principal component 3 (PC3) and Total Reading in the left uncinate fasciculus (A), PC1 and age in the left uncinate (B) and PC2 and age in the left uncinate (C).
Principal components are shown in an example tract for each relationship. Increases in PC1 indicate increased diffusion along a primary axis, while increases in PC2 indicate increased myelin and axon packing, thus relationships depicted in panels A and B could potentially reflect axonal maturation. No significant links between principal components and Total Reading were observed. The relationship between PC3 and Total Reading in the left uncinate was closest to our significance threshold.
Table 2.
Parameters for mixed effects regression models linking principal components to age and gender (formula: PC ~ Age + Gender+ Age*Gender + (1|Subject)).
Table 3.
Bayes factors assessing the likelihood of the null hypothesis condition (no relationship between Total Reading scores and model components) versus the likelihood of the model condition (relationships between included components and Total Reading).