TBSS uses statistical methods to compare groups of subjects and can thus identify common features of WM microstructure inside a group of subjects. This is likely a confounding factor in the analysis of mTBI, where the patients have been subjects of different blunt head traumas. Generally, these head traumas are inherently varying (not controlled) in nature, and thus the variation in the trauma mechanics is a factor to be considered in patient grouping. Pooling together patients with different trauma mechanics may distort the results and even average out the findings in an inhomogeneous patient group (Kenzie et al., 2017). Because some WM regions, e.g., the corpus callosum (Aoki et al., 2012), are more prone to the effects of mTBI, it is possible TBSS can detect the abnormalities in these areas, but cannot distinguish individual differences dependent on trauma mechanisms and patient characteristics. Thus, analysing mTBI patients with TBSS may lead to a loss of sensitivity by averaging out possible focal abnormalities in different anatomical regions.
An important factor in the study of mTBI is the time of imaging, which can cause confusion and heterogeneity between mTBI study results. The heterogeneity in the reported mTBI study results can at least be partly explained by the variation in the time of imaging of the patients. The effect of mTBI on the WM is not linear through
time, and DTI metrics can fluctuate between high and low depending on the time since injury (Hasan et al., 2014; Lancaster et al., 2016). Our results are thus valid only for mTBI patients imaged in the acute stage.
Our subject data were limited in numbers, which creates limitations to the statistical power of the analyses and can directly impact the quality of inferences drawn from the results. While our patient pools are reasonably large, for more reliable results, the number of control subjects should be larger. In some cases, age- and gender matching can alleviate the deficiency, but overall the analyses would benefit from a larger control pool. In general, the same limitations apply to our SCI study as were noted for the mTBI TBSS study. In addition to these limitations, the possibility of a concomitant mTBI cannot be ruled out when dealing with traumatic spinal injuries (Wei et al., 2008). In defence of our study, the abnormal findings in the post-SCI brain WM were located in regions less frequently associated with TBIs (Hulkower et al., 2013). The time of imaging in our sample was not an optimal one, and for more clinically relevant results the imaging should be done in the acute phase of the injury.
The limited control subject data used to create our normal model led to a few prior conclusions concerning the modelling process. First, instead of more advanced nonlinear or piecewise regression models, we felt compelled to use linear regression in the age-dependency model, which, nevertheless, has been postulated to be a sufficient predictor of age-dependency (Kodiweera et al., 2016; Salat et al., 2005; Sullivan, Rohlfing, & Pfefferbaum, 2010). Second, the only demographical parameter taken into account in the model was age, while variables, such as gender and education, may have a significant effect on DTI metrics (Hsu et al., 2008; Kanaan et al., 2012; Noble, Korgaonkar, Grieve, & Brickman, 2013). A considerably larger control pool would be needed to create a prospective normal population model. In their study, Knofczynski and Mundfrom (2007) concluded that a minimum sample size of approximately 300 subjects is required for a good prediction level in multiple regression analysis. Collecting a control sample of this magnitude is, however, too expensive and time-consuming.
Additionally, there are multiple universal limitations in DTI that need to be taken into account when considering the results of any type of quantitative DTI analysis. The first and one of the most important restriction is the imaging process. While the acquisition of DTI data using EPI minimises motion artefacts, the collected data suffers from low spatial resolution and a medium to low signal-to-noise ratio due to current hardware limitations (Tournier, Mori, & Leemans, 2011). The use of low spatial resolution in an application where the objective is to examine microstructural
qualities is, in my opinion, a sort of paradox. The DWI EPI sequence is also highly susceptible to several imaging artefacts, such as magnetic field inhomogeneity induced geometric distortions, eddy current artefacts, chemical shift artefacts, T2 shine-through artefact and point spread function artefacts (Jones & Cercignani, 2010; Denis Le Bihan, Poupon, Amadon, & Lethimonnier, 2006; Soares, Marques, Alves, & Sousa, 2013; Tournier et al., 2011). If the image artefacts cannot be avoided, or if dealing with retrospective data, a possible solution is to exclude the affected volume from the analysis (Soares et al., 2013). The applied imaging parameters will affect the accuracy and quality of the collected diffusion data, some of the most important of which are the number of diffusion gradient directions, applied diffusion weighting (b-value), physical size of the imaging matrix (field of view, acquisition matrix) and the used TE and TR (Soares et al., 2013). In addition, the acquisition parameters need to be identical for the images to be comparable, for a slight change in any of the parameters will cause a deviation in the quantitative DTI scalars. Diffusion data obtained with identical parameters from different scanners are also not comparable.
In addition to the standard image quality control steps, there is also a number of things to account for in the DTI image processing stage. The first real issue with the image processing is that no consensus has yet been reached for a standardized DTI processing pipeline. This is an issue similar to the variation in imaging parameters, and in practice leads to the fact that most current studies are not directly comparable with each other. Possible sources of bias in the results include different diffusion tensor estimation algorithms, varying pre-processing methods, various registration- based differences and distortions, and the possible spatial and quantitative distortion caused by lesions (Jones & Cercignani, 2010). A significant source of bias specific to DTI are crossing fibres, which are partly countered by acquiring diffusion data with a high diffusion gradient direction count (HARDI, high angular resolution diffusion imaging), and by using an appropriate q-ball imaging reconstruction (Tuch, 2004). Crossing fibres are neural pathways that cross inside an image voxel, causing the diffusion to seem isotropic inside the voxel. Crossing fibres are a notable source of error, especially in tractography where they can potentially cause disconnections in the visualised tracts (Denis Le Bihan et al., 2006; Soares et al., 2013).
While TBSS claims to solve some of the registration-based issues in DTI analysis, it also adds some specific issues to the analysis process. One of the issues with TBSS is that it includes a vast number of image processing and mathematical calculation steps, which should be understood before interpreting the results. While the skeletonisation step reduces registration bias, it is only a coarse simplification of the
fibre bundle it represents and, due to its wireframe model property, the majority of diffusion information in thick fibre bundles is ignored by the analysis (Bach et al., 2014). Brain lesions may produce invalid FA skeleton geometry or give rise to false low FA values in the skeleton (Jones & Cercignani, 2010). For more detailed information on possible diffusion MRI analysis pitfalls, I suggest the reader takes a look at the publications by Jones and Cercigani (2010), Le Bihan et al. (2006), Soares et al. (2013), and Bach et al. (2014).
The diffusion data used in this thesis may be considered dated. For example, the spatial resolution and the number of diffusion-encoding gradient directions are lower than in current state-of-the-art studies. The slice thickness of 3 mm can be considered to be one of the main issues with the data. The slice thickness also means that the voxels are non-isotropic. In addition, the slices include a small 0.9 mm gap, which would cause possible issues with tractography (Soares et al., 2013). The use of non-isotropic voxels has benefits in terms of higher SNR and lower imaging time, but can also lead to issues with partial volume averaging and the possible orientation dependence of FA values (Jones, Knösche, & Turner, 2013; Oouchi et al., 2007). The quality of the diffusion data is a potential limitation and a possible source of error that likely resulted in a loss of sensitivity in the analyses.