Automated longitudinal intra-subject analysis (ALISA) for diffusion MRI tractography
Introduction
To date, diffusion magnetic resonance imaging (MRI) is the only non-invasive method for probing soft tissue microstructure and its 3D architectural organization in vivo, offering the possibility of exploring the microstructural organization and architectural configuration of distinct anatomical fiber networks within the brain white matter (WM) (Jones, 2008, Tournier et al., 2011). 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., 2005a, Concha et al., 2005b, Concha et al., 2009, Deprez 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., 2008, Lebel et al., 2010, Verhoeven et al., 2010, Zhang et al., 2007), and aging (Hsu et al., 2008, Hsu et al., 2010, Sullivan 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., 2002, Parker et al., 2003, Poupon 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). Other approaches for investigating DTI data include region-of-interest (ROI) (e.g., Madsen et al., 2011, Snook et al., 2005), histogram (e.g., Cercignani et al., 2001), voxel-based (e.g., Giorgio et al., 2010) including TBSS (Smith et al., 2006), network-based (e.g., Hagmann et al., 2008, Reijmer et al., 2013) and atlas-based (e.g., Faria et al., 2011) analyses. A detailed description of these techniques is considered beyond the scope of this article — the interested reader is referred to surveys by Cercignani (2010) and Hasan et al. (2011).
With the ability to observe diffusion changes over time in the same population of subjects, longitudinally designed DTI studies can provide more specific insights into the microstructural dynamics of brain WM tissue compared to studies with a cross-sectional population setup (Beaulieu, 2002, Johansen-Berg, 2010, Lebel and Beaulieu, 2011). Boosted by the advent of more stable and high performance MR equipment, the interest to perform longitudinal DTI studies for capturing such subject-specific changes in microstructural organization is increasing rapidly (e.g., Concha et al., 2007, Deprez et al., 2012, Gong et al., 2008, Keller and Just, 2009, Kumar et al., 2009, Ljungqvist et al., 2011, Schlaug et al., 2009, Scholz et al., 2009, Sullivan et al., 2010, Yogarajah et al., 2010). Although there are several well-established methods for analyzing cross-sectional DTI data sets, they may not be ‘optimal’ for longitudinal studies. In voxel-based DTI analyses, for instance, which are notorious for their high sensitivity with respect to the amount/type of filtering (Jones et al., 2005b, Van Hecke et al., 2009, Van Hecke et al., 2010a) and choice of template/atlas (Sage et al., 2009, Van Hecke et al., 2008b, Van Hecke et al., 2011), subtle intra-subject changes may not be detected due to the much larger residual inter-subject misalignments. In this case, and if there is also a clear hypothesis regarding a specific WM fiber bundle (or a segment thereof — Colby et al., 2012), FT may be preferred over the voxel-based approach.
By combining objective protocols for extracting WM fiber pathways of interest on the one hand (Catani and Thiebaut de Schotten, 2008, Wakana et al., 2004, Wakana et al., 2007) and incorporating subjective prior knowledge from the neuroanatomical expert on the other hand, many studies have already demonstrated the high inter- and intra-rater reliability and scan–rescan reproducibility of manual FT segmentations (Ciccarelli et al., 2003, Danielian et al., 2010, Heiervang et al., 2006, Kristo et al., 2013a, Kristo et al., 2013b, Malykhin et al., 2008, Pfefferbaum et al., 2003, Wakana et al., 2007). The major drawback of manual FT segmentations, however, is that placing ROIs for tract selection can be very labor-intensive and time-consuming, which – for 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. In doing so, the adverse effect of inter-subject variability on the reliability of the FT results may be circumvented, while maintaining the main advantages of the automated approach, i.e., higher efficiency and objectivity.
In this work, we developed an automated longitudinal intra-subject analysis (ALISA) for investigating FT segmentations and compared its performance in terms of precision and accuracy to the “bronze standard”, i.e. the manual FT segmentations. In addition, we compared these results to those obtained with FT segmentations obtained in an automated way over all time points and all subjects (Lebel et al., 2008). Sixty DTI data sets, which are part of the HUBU cohort database (“Hjernens Udvikling hos Børn og Unge”: Brain maturation in children and adolescents; see Madsen et al., 2010, Madsen et al., 2011 for more details), were included in this study: five acquisitions at six-month intervals for ten healthy subjects and a set of ten repeats of one control subject scanned at weekly intervals. Important to note here is that these automated methods are not intended for case-based clinical use in cases with gross pathology, but rather for the use of longitudinal group studies of healthy development and aging, and pathologies without large displacing lesions.
Without loss of generality, the analyses were evaluated with tractography results from four WM fiber bundles: (i) the superior segment of the cingulum (SSCing) bundle, part of the collection of WM fibers that interconnect limbic structures, relevant for the regulation of emotional processes (e.g., Karaus et al., 2009); (ii) the cortico-spinal tracts (CST), running from the spinal cord to the motor cortex; (iii) the uncinate fasciculus (UF), connecting the frontal and temporal lobes, which has been shown to be important in the interaction between cognition and emotion (e.g., Price et al., 2008); and (iv) the forceps major (FM), or splenium of the corpus callosum, providing interhemispheric occipital connections that are affected in, for instance, schizophrenia (Catani and Thiebaut de Schotten, 2008, Clark et al., 2011).
Section snippets
Subjects and data acquisition
Sixty DTI data sets were acquired for this study, five acquisitions at six-month intervals for ten healthy subjects (8 F/2 M) aged 7.6 to 8.6 years (mean age of 8.1 ± 0.4 years) at the first acquisition date and a set of ten repeats of one control subject (female, age 12.2 years) scanned on four occasions, with respectively two, three, two, and three separate scan sessions. These four occasions were separated by two, two, and seven weeks, respectively. The data sets are part of the HUBU cohort
Subject-specific template construction
The reliability of automating fiber tractography segmentations for serial DTI data depends heavily on the coregistration quality and, consequently, the construction of the subject-specific template that was used in the ALISA approach. Indicating the quality of the applied normalization procedure (Jones et al., 2002), Fig. 3 shows the resulting template derived from the five serial DTI scans of a representative subject. Overlaid on the FA skeleton of this template, the dominant diffusion
Discussion
With the advent of large-cohort DTI studies, there is an increased demand for data analyses that require minimal user input. While several methods have been developed to analyze WM fiber bundles in 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
Acknowledgments
The HUBU project is supported by the Danish Medical Research Council, and the Lundbeck Foundation.
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These authors contributed equally.