Probabilistic atlases of default mode, executive control and salience network white matter tracts: an fMRI-guided diffusion tensor imaging and tractography study.

Navdeep Bhullar,Susan M. Courtney,Teresa D. Figley,Chase R. Figley

doi:10.3389/fnhum.2015.00585

Abstract

Diffusion tensor imaging (DTI) is a powerful MRI technique that can be used to estimate both the microstructural integrity and the trajectories of white matter pathways throughout the central nervous system. This fiber tracking (aka, “tractography”) approach is often carried out using anatomically-defined seed points to identify white matter tracts that pass through one or more structures, but can also be performed using functionally-defined regions of interest (ROIs) that have been determined using functional MRI (fMRI) or other methods. In this study, we performed fMRI-guided DTI tractography between all of the previously defined nodes within each of six common resting-state brain networks, including the: dorsal Default Mode Network (dDMN), ventral Default Mode Network (vDMN), left Executive Control Network (lECN), right Executive Control Network (rECN), anterior Salience Network (aSN), and posterior Salience Network (pSN). By normalizing the data from 32 healthy control subjects to a standard template—using high-dimensional, non-linear warping methods—we were able to create probabilistic white matter atlases for each tract in stereotaxic coordinates. By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses.

Highlights

Stereotaxic brain atlases play an important role in neuroscience and neuroimaging research
This disparity—in our ability to localize cortical regions, but not the underlying white matter structures associated with these functional brain networks—imposes several limitations on the interpretation of Diffusion tensor imaging (DTI) and other quantitative white matter imaging data. It makes direct comparisons between structural and functional connectivity extremely difficult, and completely prevents group-wise or regression analyses from ascribing region-of-interest (ROI) or voxel-wise white matter changes to a particular brain network or group of networks. To address these fundamental issues with the analysis and interpretation of diffusion and other quantitative white matter imaging data, the goals of the current study were to perform functional MRI (fMRI)-guided DTI tractography on data acquired from a group of healthy adults to: (1) identify the specific white matter regions that are most likely to contain tracts between the nodes of six previously established and functionally-connected cortical networks— the dorsal and ventral default mode networks, the left and right executive control networks, as well as the anterior and posterior salience networks—and; (2) generate probabilistic white matter atlases based on these findings
In order to evaluate the efficacy of the two-stage linear (12parameter affine) and non-linear (LDDMM) normalization approach, we compared each subject’s warped mean b = 0 s/mm2 image and calculated coefficient of variation maps across all 32 subjects after each step (Figure 2)

Summary

Introduction

Stereotaxic brain atlases play an important role in neuroscience and neuroimaging research. Shortly thereafter a group of researchers from Canada, The United States, and Germany formed the International Consortium for Brain Mapping (ICBM), which set out to create standardized human brain atlases that were based on high-resolution anatomical MRI data from large populations of healthy control subjects (Evans et al, 1992, 1993; Collins et al, 1994; Mazziotta et al, 1995) These templates have since been adopted by neuroimaging researchers around the world for normalizing individual data for group analyses, and to this day are distributed with many popular image processing and fMRI analysis software packages (c.f., Brett et al, 2002; Lancaster et al, 2007). By examining white matter connectivity between various anatomically-defined seed regions, diffusion tensor imaging (DTI) and fiber tracking (or “tractography”) methods have been used to generate both probabilistic (Hua et al, 2008; Zhang et al, 2010) and non-probabilistic (Catani and Thiebaut de Schotten, 2008; Catani et al, 2012) white matter atlases

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