Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy

Anastasia Yendiki

doi:10.3389/fninf.2011.00023

Abstract

We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy training subjects can be used to reconstruct the pathways accurately in patients as well as in controls.

Highlights

Diffusion MRI has become an important tool in the study of a wide range of diseases affecting the brain, as it allows us to probe the shape and integrity of the white-matter pathways that connect functionally related cortical and subcortical regions
We have developed TRACULA (TRActs Constrained by UnderLying Anatomy), a method for automated reconstruction of major white-matter pathways that is based on the global probabilistic approach of Jbabdi et al (2007) and utilizes prior information on the anatomy of the pathways from a set of training subjects
Based on uncorrected p-values from a T -test on the difference between groups, we found average fractional anisotropy (FA) to be significantly lower in patients compared to controls in the left Uncinate fasciculus (UNC) (p = 0.005), left Anterior thalamic radiation (ATR) (p = 0.019), left CCG (p = 0.011), left Cingulum – angular (infracallosal) bundle (CAB) (p = 0.006), right Superior longitudinal fasciculus – parietal bundle (SLFP) (p = 0.033), forceps major (FMAJ) (p = 0.00005), and forceps minor (FMIN) (p = 0.034), with trend toward significance in the FA reductions that were observed in the right UNC (p = 0.067), left Inferior longitudinal fasciculus (ILF) (p = 0.059), right ATR (p = 0.061), and right CCG (p = 0.088)

Summary

Introduction

Diffusion MRI has become an important tool in the study of a wide range of diseases affecting the brain, as it allows us to probe the shape and integrity of the white-matter pathways that connect functionally related cortical and subcortical regions. Other deterministic methods were volumetric, modeling the path as a volume, and allowing it to grow in three dimensions (Jones et al, 1999; O’Donnell et al, 2002; Parker et al, 2002; Jackowski et al, 2005; Pichon et al, 2005) Both streamline and volumetric approaches were local, in the sense that the algorithm considered the image data at a single location to determine how to grow the path at each step. Statistical extensions to local streamline tractography were introduced to model uncertainty in the image data by drawing samples from an assumed local distribution of diffusion directions at each voxel (Behrens et al, 2003; Hagmann et al, 2003; Cook et al, 2005; Parker and Alexander, 2005; Friman et al, 2006) or by boot-strapping (Jones and Pierpaoli, 2005; Lazar and Alexander, 2005)

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Discussion

Conclusion