As tractography finds increasing use in the clinical evaluation of white matter abnormalities, there is growing interest in reducing its operator-dependence. To the clinician, automated tractography methods offer the possibility of a more efficient clinical workflow. To the researcher, automated tractography methods offer the possibility of a reducing bias and improving sensitivity in group comparisons.
However, the problem of validation has long challenged tractographers, and automated tractography methods compound this problem. Very few studies have directly compared user-driven tractography to automated methods. Zhang et al.
developed a standardized set of seed regions consisting of uniform white matter located in the body of white matter tracts of interest, similar to those chosen in typical user-driven tractography. These seeds were applied to registered subjects and used for automated deterministic tractography.27, 28
Spatial normalization to a labeled white matter atlas has also been used as an alternative to tractography.29, 30
Both of these methods compared favorably to user-driven seed placement. Nevertheless, these methods may be susceptible to misregistration error if there is anatomic variability or pathologic distortion of the tract of interest.
In contrast, the large size of tract termini relative to the body of the tract may improve robustness in the setting of minor displacements occurring as a result of pathology. This approach has been used in automated tractography in diseases with unpredictable white matter anatomy (e.g schizophrenia and Huntington’s disease) as well as complicated anatomy (e.g. Meyer’s loop in optic neuritis).11, 14, 15
It is important to note that automated tractography in these pathologic states has not yet been validated against a reference standard, and future studies will be needed to extended the scope of our understanding of white matter pathology.
We chose tract termini from regions defined in an established brain atlas. Although effective for our tracts of interest, this technique may be less suitable for tracts that project outside the brain (such as the corticospinal tract) or commissural tracts (such as the corpus callosum). However, any labeling scheme could be used, including one making use of advances in white matter anatomy. As tractography increasingly informs our knowledge of anatomy, this approach raises the possibility of directly correlating standardized cortical templates with evolving white matter atlases.10, 31, 32
Because we chose white matter termini containing both gray matter and subjacent white matter, we used a probabilistic tractography algorithm that performs more robustly in voxels with low FA. With this algorithm, however, we lost some of the advantages of deterministic tractography. For example, probabilistic tractography does not provide a voxel-by-voxel depiction of the course of the fiber, as commonly found in deterministic depictions. Instead, it aggregates the voxels traversed by multiple fibers into a directionless probability score. We expect such maps to be suitable for evaluating clinical issues regarding white matter displacement or destruction, but specific questions regarding fiber directionality may require user-driven tractography.
In automated tractography, thresholding criteria are used as a substitute for expert visual inspection to reject output not belonging to the tract of interest. We found that tractography output varies widely with small changes in thresholding and smoothing criteria. In this work, postprocessing criteria were standardized over all tracts of interest. This might be problematic if each white matter tract had substantially different features, but we did not find this to be the case in an earlier study. When postprocessing criteria were determined independently for each of these tracts, the optimal values for the tracts we evaluated were found to be identical.23
Naturally this uniformity may not extend to all white matter tracts, but comparable values have been described for automated tractography of the optic radiations.33
The mean FA values reported by our method are slightly less than those described elsewhere in the literature.34–36
However, FA is typically measured over a small segment of the tract rather than averaged over its entirety. If the most prominent segments of a tract also have the highest FA, then we would expect whole-tract measurements to be lower. Using geometric methods to measure mean FA over the entire cingulum, O’Donnell et al
. have also reported slightly lower FA values than other groups.37
Morris et al.
have noted that probabilistic tractography can under-represent tract termini and overrepresent areas of a white matter tract that are close to the seed.38
This effect was mitigated in our work by choosing terminal seeds, so that the distance of a tract segment from one seed was inversely related to its distance from the other seed. In addition, threshold masks were used to reduce the effect of small variations in probability over the course of a tract. As a result, we did not observe any gross “flare” in our results.
Finally, we note that the relative advantages of automated tractography may depend on the tract of interest. In a careful series of measurements, Wakana et al. found that inter-rater reproducibility of manual tractography of cingulum could achieve a kappa value as high as 0.97 when raters were from the same institution.5
However, the kappa values varied considerably for other tracts. The corresponding kappa value was 0.89 for the inferior fronto-occipital fasciculus and 0.69 for the inferior longitudinal fasciculus. In this context, automated tractography is perhaps best viewed as one of many tools that can be used in the study of white matter, each of which is best-suited for a different purpose.