White matter but not grey matter predicts change in reading skills after intervention.

This study examined changes in white matter microstructure and grey matter volume, cortical thickness, and cortical surface area before and after reading intervention. Participants included 22 average readers and 13 dyslexic readers (8-9 years old in third grade); the dyslexic readers were enrolled in reading intervention programs at their elementary school. Participants completed scans of diffusion tensor imaging and T1-weighted MRI before and after 3 months of instruction. An a priori region of interest (ROI) analysis was used. Dyslexic readers, compared to average readers, showed higher mean diffusivity in white matter ROIs including bilateral inferior frontal, bilateral insula, left superior temporal, and right supramarginal gyri across time points. Dyslexic readers also had thicker cortex in left fusiform and bilateral supramarginal gyri; whereas, average readers had greater surface area in right fusiform across time. There were no significant changes in white or grey matter following intervention; however, mean diffusivity in the right hemisphere was associated with reading gains over time. White matter organization in the right hemisphere predicts reading changes, and dyslexic readers may have persistent differences in white and grey matter due to ongoing reading deficits.

Comparison of CPU and GPU bayesian estimates of fibre orientations from diffusion MRI.

BACKGROUND: The correct estimation of fibre orientations is a crucial step for reconstructing human brain tracts. Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques (bedpostx) is able to estimate several fibre orientations and their diffusion parameters per voxel using Markov Chain Monte Carlo (MCMC) in a whole brain diffusion MRI data, and it is capable of running on GPUs, achieving speed-up of over 100 times compared to CPUs. However, few studies have looked at whether the results from the CPU and GPU algorithms differ. In this study, we compared CPU and GPU bedpostx outputs by running multiple trials of both algorithms on the same whole brain diffusion data and compared each distribution of output using Kolmogorov-Smirnov tests. RESULTS: We show that distributions of fibre fraction parameters and principal diffusion direction angles from bedpostx and bedpostx_gpu display few statistically significant differences in shape and are localized sparsely throughout the whole brain. Average output differences are small in magnitude compared to underlying uncertainty. CONCLUSIONS: Despite small amount of differences in output between CPU and GPU bedpostx algorithms, results are comparable given the difference in operation order and library usage between CPU and GPU bedpostx.