Elsevier

NeuroImage

Volume 59, Issue 4, 15 February 2012, Pages 3227-3242
NeuroImage

Along-tract statistics allow for enhanced tractography analysis

https://doi.org/10.1016/j.neuroimage.2011.11.004Get rights and content

Abstract

Diffusion imaging tractography is a valuable tool for neuroscience researchers because it allows the generation of individualized virtual dissections of major white matter tracts in the human brain. It facilitates between-subject statistical analyses tailored to the specific anatomy of each participant. There is prominent variation in diffusion imaging metrics (e.g., fractional anisotropy, FA) within tracts, but most tractography studies use a “tract-averaged” approach to analysis by averaging the scalar values from the many streamline vertices in a tract dissection into a single point-spread estimate for each tract. Here we describe a complete workflow needed to conduct an along-tract analysis of white matter streamline tract groups. This consists of 1) A flexible MATLAB toolkit for generating along-tract data based on B-spline resampling and compilation of scalar data at different collections of vertices along the curving tract spines, and 2) Statistical analysis and rich data visualization by leveraging tools available through the R platform for statistical computing. We demonstrate the effectiveness of such an along-tract approach over the tract-averaged approach in an example analysis of 10 major white matter tracts in a single subject. We also show that these techniques easily extend to between-group analyses typically used in neuroscience applications, by conducting an along-tract analysis of differences in FA between 9 individuals with fetal alcohol spectrum disorders (FASDs) and 11 typically-developing controls. This analysis reveals localized differences between FASD and control groups that were not apparent using a tract-averaged method. Finally, to validate our approach and highlight the strength of this extensible software framework, we implement 2 other methods from the literature and leverage the existing workflow tools to conduct a comparison study.

Introduction

Since the late 1990s, diffusion magnetic resonance imaging (MRI) tractography methods have developed into a powerful set of techniques to investigate white matter connectivity in the human brain (Basser et al., 2000, Conturo et al., 1999, Jones et al., 1999, Le Bihan, 2003, Mori et al., 1999). By generating virtual dissections of different white matter tracts for each individual, tractography has proved valuable in a variety of applications — including pre- and intra-operative mapping of fiber tracts (Duncan, 2010, Maruyama et al., 2005, Prabhu et al., 2011, Young et al., 2010), and connectivity analyses of anatomical and functional brain networks (Aron et al., 2007, Behrens et al., 2003, Bullmore and Sporns, 2009, Ramnani et al., 2004). In addition to providing information about tract geometry, tractography can provide individualized volumes of interest for the investigation of white matter microstructural qualities in the context of development and aging (Asato et al., 2010, Davis et al., 2009, Eluvathingal et al., 2007, Huang et al., 2006, Lebel et al., 2008b, Lebel et al., 2010, Liston et al., 2006, Penke et al., 2010, Sala et al., in press, Schmithorst and Yuan, 2010, Verhoeven et al., 2010, Voineskos et al., in press), numerous diseases (Ashtari et al., 2007, Kumar et al., 2010, Kunimatsu et al., 2003, Lebel et al., 2008a, Zarei et al., 2009), and the relation of brain structure to functional, cognitive, and psychiatric differences between individuals (Boorman et al., 2007, Dougherty et al., 2007, Glenn et al., 2007, Lebel and Beaulieu, 2009, Luck et al., 2011, Schulte et al., 2010, Tsang et al., 2009). As tract dissections are personalized to each individual, and do not rely on any between-subject warping to a common template space, analogous regions can be compared between individuals even when there are large differences in brain morphology. This is valuable in clinical studies, in which patients might have gross structural brain abnormalities through alterations in neurodevelopment, or in white matter microstructure as a result of disease.

Direct voxelwise comparison of diffusion imaging data is challenging, as the high-contrast edges of diffusion imaging volumes (e.g., FA maps) make them more susceptible to small misregistration errors, as well as to anatomical variability of tract position in health and disease. Even so, traditional voxelwise brain mapping is an important complement to tractography — especially now that analysis methods have advanced beyond a generic voxel-based statistical approach to include more optimized strategies tuned specifically for the analysis of white matter and diffusion imaging data (Smith et al., 2006, Smith et al., 2007). Furthermore, the inherent voxel-to-voxel independence of voxelwise processing allows these methods to see beyond the type of small focal disruptions that could potentially derail the streamline tractography algorithms. In general, these voxelwise studies have been in broad agreement with their tract-based counterparts (Schmithorst and Yuan, 2010, Sullivan and Pfefferbaum, 2006, Wozniak and Lim, 2006). Additionally, they have demonstrated a remarkable degree of regional heterogeneity – even within a given tract – in the diffusion imaging indices and observed relationships with other variables (Barnea-Goraly et al., 2010, Bava et al., 2010, Bengtsson et al., 2005, Hsu et al., 2010, Keller and Just, 2009).

To improve the localizability in deterministic tractography, there is a growing interest in methods that can provide greater within-tract detail. While previous work has included efforts to examine DTI metrics along tract lengths (Corouge et al., 2006, Goodlett et al., 2008, Goodlett et al., 2009, Jones et al., 2005, O'Donnell et al., 2009, Zhu et al., 2010), as well as more generic within-tract methods that can accommodate variability along even more dimensions within tracts (Yushkevich et al., 2008, Zhang et al., 2010), it has typically been focused on individual aspects of the along-tract workflow or specific customized applications. It remains true that the large majority of streamline tractography analyses still rely on a simpler tract-averaged methodology. Therefore, there is a need for a higher-level integrated along-tract processing workflow — for an intuitive set of tools that makes it easy for applications researchers to start incorporating along-tract detail into existing tractography analyses, while facilitating statistical analysis of these data in a general linear model (GLM) framework, visualization of raw data and statistical results, and straightforward customization of all aspects of this process. To help fill this gap in the methods landscape, in this manuscript we: 1) Describe the rationale for conducting a tractography study with enhanced within-tract detail, as it relates to common tractography applications within the neuroimaging community, 2) Lay out a straightforward workflow for conducting one type of along-tract analysis, which is able to attain a useful balance between accessibility and improved modeling ability, 3) Demonstrate some advantages of this approach over traditional tract-averaged methods by looking at both within-subject and between-group examples, 4) Validate this approach against existing methods, while highlighting the extensible nature of this workflow toolset, and 5) Make this generic toolset available for others to use as building blocks for their own future analyses (http://www.github.com/johncolby/along-tract-stats).

Section snippets

Rationale

When standard tractography methods collapse tract groups, they yield only a single mean DTI metric and variance estimate for each tract and for each subject. This processing step ignores the potentially rich anatomical variation in diffusion imaging metrics along the tracts, and reduces the effectiveness of this technique. To see that this added detail exists, one can browse through an FA map, or look at a histogram of its contents. FA varies widely throughout the white matter — with very low

Preprocessing

Before calculating along-tract statistics, the tract groups must be delineated. There are many available software platforms to do this. We used tract groups delineated manually for each subject in TrackVis (http://www.trackvis.org). Because these tools are modular functions written in plain text in MATLAB, this framework can be straightforwardly adapted to operate on streamline data from a variety of sources. First, a tensor or alternative diffusion model is fit to the raw data, and the

Data acquisition and preprocessing protocol

For the within-subject and between-group analyses below (Within-subject analysis and Between-group analysis sections), whole brain diffusion weighted imaging data were acquired on a 3 T Siemens Trio MRI scanner. Each DTI acquisition included diffusion weighted volumes (30 directions, b = 1000 s/mm2, 240 mm field of view, 96 × 96 in-plane matrix, 55 axial slices of 2.5 mm thickness, resulting in 2.5 × 2.5 × 2.5 mm3 isotropic voxels), and one non-diffusion-weighted volume (b = 0 s/mm2). A tensor model of

Processing workflow

The main goal of this work was to generate a simple, flexible end-to-end workflow for along-tract processing and statistical analysis. We focused on the portion of the within-tract variability that exists along tracts because: 1) Previous studies show that the largest component of the within-tract variability exists along this axis, and 2) Tract groups already have longitudinal structure built in, due to the connectivity of adjacent vertices in each streamline. Clearly this decision is more

Conclusion

It is clear from inspection of deterministic tractography dissections that there are prominent variations in scalar diffusion imaging metrics (like FA) within the major white matter tracts in the human brain. However, the majority of diffusion tractography analyses still rely on a whole-tract-averaged approach for analyzing differences in these scalar metrics. Moreover, despite excellent work on individual topics related to along-tract processing, and promising advancements towards even more

Acknowledgments

This work was performed in conjunction with the Collaborative Initiative on Fetal Alcohol Spectrum Disorders (CIFASD), which is funded by grants from the National Institute on Alcohol and Alcohol Abuse (NIAAA). Additional information about CIFASD can be found at www.cifasd.org. This work was also supported by the following organizations: National Institute of Child Health and Human Development (NICHD; R01 HD053893), National Center for Research Resources (NCRR; , ), National Institute of

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  • Cited by (0)

    1

    Director, Developmental Cognitive Neuroimaging Laboratory.

    2

    Professor of Pediatrics, Keck School of Medicine, University of Southern California.

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