Multimodal principal component analysis to identify major features of white matter structure and links to reading

Abstract

The role of white matter fibers in reading has been established by diffusion tensor imaging (DTI), but DTI cannot identify specific microstructural features driving these relationships. Neurite orientation dispersion and density imaging (NODDI), inhomogeneous magnetization transfer (ihMT) and multicomponent driven equilibrium single-pulse observation of T1/T2 (mcDESPOT) can be used to link more specific aspects of white matter microstructure and reading due to their sensitivity to axonal packing and fiber coherence (NODDI) and myelin (ihMT and mcDESPOT). We applied principal component analysis (PCA) to combine DTI, NODDI, ihMT and mcDESPOT measures (10 in total), identify major features of white matter structure, and link these features to both reading and age. Analysis was performed for nine reading-related tracts in 46 neurotypical 6-16 year olds. We identified three principal components (PCs) which explained 79.5% of variance in our dataset. PC1 probed tissue complexity, PC2 described myelin and axonal packing, while PC3 was related to axonal diameter. Mixed effects models regressions did not identify any relationships between principal components and reading skill. Further Bayes factor analysis revealed that absence of relationships was not due to low power. PC1 suggested increases in tissue complexity with age in the left arcuate fasciculus, while PC2 suggested increases in myelin and axonal packing with age in the bilateral arcuate, inferior longitudinal, inferior fronto-occipital fasciculi, and splenium. Multimodal white matter imaging and PCA produce microstructurally informative, powerful principal components which can be used by future studies of development and cognition.