Investigation of White Matter Networks in Resting State BOLD fMRI

Abstract

White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that there are intrinsic low-frequency signals which create a default white matter network. Identifying default white matter networks can provide insight into brain physiology and potentially be used as early markers for neurological changes. To identify white matter networks, three different fMRI imaging datasets were used with a total of 2,095 scans. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the time series data. The stability of clustering between subjects within a dataset and across datasets with different subject populations and acquisition sites was investigated. Unique functional clusters were mapped within a dataset (P < 0.001). Across datasets, functional clusters are not entirely reproducible, but clusters were identified that are similar between datasets, with Dice Coefficients greater than 0.60. Comparing the identified similar dataset clusters to the JHU-DTI-SS Type 1 Atlas found that the most similar clusters correspond with the internal capsules, corpus callosum, and middle fronto-orbital gyrus with Dice Coefficients of 0.5402, 0.4665, and 0.4800 respectively.