Construction of a stereotaxic DTI atlas with full diffusion tensor information for studying white matter maturation from childhood to adolescence using tractography‐based segmentations

Abstract

Reconstruction of white matter (WM) fiber tracts based on diffusion tensor imaging (DTI) is increasingly being used in clinical and research settings to study normal and pathological WM tissue as well as the maturation of this WM tissue. Such fiber tracking (FT) methodology, however, is highly dependent on the manual delineation of anatomical landmarks and the algorithm settings, often rendering the reproducibility and reliability questionable. Predefining these regions of interest on a fractional anisotropy (FA) atlas in standard space has already been shown to improve the reliability of FT results. In this paper, we constructed a new DTI atlas, which contains the complete diffusion tensor information in ICBM152 coordinates. From this high-dimensional DTI atlas, and using robust FT protocols, we reconstructed a large number of WM tracts. Subsequently, we created tract masks from these fiber tract bundles and evaluated the atlas framework by comparing the reproducibility of the results obtained from our standardized tract masks with regions-of-interest labels from the conventional FA-based WM atlas. Finally, we assessed laterality and age-related WM changes in 42 normal subjects aged 0 to 18 years using these tractography-derived tract segmentations. In agreement with previous literature, we observed an FA increase with age, which was mainly due to the decrease of perpendicular diffusivity. In addition, major functional pathways in the language, motor, and limbic system, showed a significant asymmetry in terms of the observed diffusion metrics.

Bimanual Coordination and Corpus Callosum Microstructure in Young Adults with Traumatic Brain Injury: A Diffusion Tensor Imaging Study

“…During normalization, the Jacobian is constrained to reduce the chance of forcing the underlying brain structures in an anatomically non-plausible way. This viscous fluid model was optimized for aligning multiple diffusion tensor components, and has been applied successfully in a wide range of applications, for which adjusting for morphological inter-subject (and inter-group) differences, such as, for instance, ventricle size, is considered to be of paramount importance Hsu et al, 2010;Sage et al, 2009;Verhoeven et al, 2010). Based on a recently developed simulation framework, the non-affine DTI-based co-registration method, in particular, has been shown to provide highly accurate registration results (Van 902 CAEYENBERGHS ET AL.…”

Automated longitudinal intra-subject analysis (ALISA) for diffusion MRI tractography

“…an automated way (Clayden et al, 2007;Hagler et al, 2009;Lebel et al, 2008;Leemans et al, 2006;O'Donnell et al, 2009;Reich et al, 2010;Suarez et al, 2012;Verhoeven et al, 2010;Yendiki et al, 2011;Zhang et al, 2010), there is typically a trade-off that needs to be made between prior knowledge incorporated by the user for the manual approach (e.g., the neuroanatomical expertise regarding the location and extent of tracts-of-interests) and the predefined parameter settings required by the automated technique. In other words, the labor-intensive and Fig.…”

“…Diffusion tensor imaging (DTI) (Basser et al, 1994), in particular, has been widely used to investigate e.g., WM abnormalities in pathological conditions (Caeyenberghs et al, 2010;Ciccarelli et al, 2006;Concha et al, 2005aConcha et al, , b, 2009Deprez et al, 2011;Jones et al, 2006;Price et al, 2008;Sage et al, 2009;Van Hecke et al, 2010b;Yogarajah et al, 2008), WM changes in normal development (Eluvathingal et al, 2007;Lebel et al, 2008Lebel et al, , 2010Verhoeven et al, 2010;Zhang et al, 2007), and aging (Hsu et al, 2008(Hsu et al, , 2010Sullivan and Pfefferbaum, 2007;Van Hecke et al, 2008a). In many of these studies, fiber tractography (FT) (Basser et al, 2000;Conturo et al, 1999;Jones et al, 1999a;Koch et al, 2001;Mori et al, 1999;Parker et al, 2002Parker et al, , 2003Poupon et al, 2000) has been used to identify specific WM fiber bundles, from which diffusion characteristics, such as fractional anisotropy (FA) and mean diffusivity (MD), can be derived (Jones et al, 2005a).…”

“…The major drawback of manual FT segmentations, however, is that placing ROIs for tract selection can be very labor-intensive and time-consuming, whichfor obvious reasons -can become problematic for large-cohort studies. Notwithstanding the existence of methods that can identify WM fiber bundles in an automated way in the absence of gross pathology (Clayden et al, 2007;Hagler et al, 2009;Lebel et al, 2008;Leemans et al, 2006;O'Donnell et al, 2009;Reich et al, 2010;Suarez et al, 2012;Verhoeven et al, 2010;Yendiki et al, 2011;Zhang et al, 2010), manual FT segmentations across multiple subjects performed by a trained rater with neuroanatomical expertise are generally more specific and, therefore, more accurate. For longitudinal DTI analyses, however, it may still be beneficial to automate the FT segmentation across multiple time points, but then for each individual subject separately.…”

A new cover for Neuroradiology

“…Specifically, we see a left lateral view on a whole brain fiber tractography performed on our stereotactic DTI atlas constructed from 42 healthy volunteers nonlinearly registered to the MNI space [1]. Readers will notice superimposed deterministic reconstructions of the uncinate fascicule (in blue), the corticospinal tract (in white), the cingulum bundle (in pink) and the superior longitudinal fascicule (first eigenvector color coded: red = left-right, green = anterior-posterior, blue = cranialcaudal) on the sparse whole brain white matter tractography (in green/yellow).…”