Improved Assessment of Reading Networks in the Brain Using Diffusion MRI

Abstract

Reading is a complex cognitive behavior, which relies on the incorporation of a network of brain regions. White matter is the information transfer pathway between distant brain regions, and thus plays an important role in mediating reading ability. Diffusion Tensor Imaging (DTI) is an MR technique to characterize white matter microstructure by probing the propensity of water molecules¡¯ diffusion in in vivo tissues. This dissertation seeks to investigate the reading network in the brain using diffusion MRI. The first part of the dissertation studies the cortical network with a focus on the putative visual word form area (VWFA), which is reproducibly found to be selectively recruited by visual orthographic conversion. We studied the structural connectivity patterns of the VWF-system in children with typically developing (TD) reading ability and with reading difficulty (RD). We found that the architecture of the VWFA connectivity is fundamentally different between TD and RD groups, with TD showing greater connectivity to linguistic regions than RD, and RD showing greater connectivity to visual regions than TD. The second part of the dissertation studies subcortical-cortical network, with a focus on the thalamus, the way-station of information transfer in white matter. Abnormal thalamo-cortical connectivity was found in the RD group in sensorimotor, orbital frontal and insula cortices. These results suggest that the thalamus plays a key role in reading behavior by mediating the functions of task specific cortical regions. Despite the valuable information DTI can provide, it suffers from fundamental limitations, especially when multiple fiber bundles are present. To address this problem, the third part of the dissertation proposes a new method to study complex white matter structures. It improves the current spherical deconvolution method by relaxing the assumption that all fiber bundles share the same response kernel. The in vivo experiments show that this Multiple Kernel Spherical Deconvolution (MKSD) approach can identify crossing fiber bundles and simultaneously provide an estimate of the diffusion properties intrinsic to each fiber bundle.