Tipos de tratamientos protectores

This study compared clinical scores and imaging parameters in terms of sensitivity to change in a unique cohort of patients with SCA1, SCA2, SCA3 and SCA7. We identified high effect sizes for volumetric and tractography parameters compared to clinical scores. Likewise, we advocate to use these imaging parameters in upcoming clinical trials in SCAs. In addition, we report a new approach (FBA) to probe white matter fiber integrity in SCAs. FBA allows the analysis of individual fibers in each voxel making it more sensitive and robust compared to FA, the conventional DTI metric. We showed that FA may not be a reliable metric for use in clinical trials as it detected no microstructural change in SCA7. Instead, using FBA analyses, we identified white matter fiber changes in the CST of all SCAs, including SCA7.

Volumetric studies performed in SCA have reported smaller brain regions compared to controls, including the cerebellum, brainstem and cerebellar peduncles (Yamada et al., 2008; Durr, 2010; Schulz et al., 2010; Rüb et al., 2013). The rate of volumetric change, though, has only been reported by Reetz et al. (Reetz et al., 2013) who compared the rate of atrophy across patients with SCA1, SCA3 and SCA6, but not with controls. They reported greater atrophy in the brainstem and left cerebellar hemisphere in patients with SCA1 compared to SCA3 (Reetz et al., 2013). In patients with SCA1 and SCA3, Reetz et al. also reported large effect sizes for the SARA scores (>1.2) and the pons atrophy (>0.8) but low effect sizes (<0.8) for the cerebellum (Reetz et al., 2013). In contrast, we reported atrophy rates in patients with SCA1, SCA2, SCA3 and SCA7 in reference to healthy controls. Our data were acquired on a 3 T MR system that gives twice the image resolution and contrast compared to the 1.5 T system used in the study by Reetz et al. In our estimation of brain volumes, we also used a fully automated method without user interference whilst Reetz et al. used a semi-automated method that included user defined regions of interest. Although our findings need to be confirmed in an independent cohort using the same methodologies and high spatial resolution, we found high effect sizes for the rate of atrophy of the cerebellum and the pons compared to the SARA and CCFS. The discrepancy between the rate of clinical progression and brain atrophy was especially striking for patients with SCA7. This may be, in part, explained by the small sample size of this patient group (n = 10) and their relative early symptomatic stage. Nonetheless, SCA7 is a severe disease, as outlined by the common occurrence of additional non-neurological symptoms (retina, heart), so that a fast rate of brain atrophy is expected.

Using diffusion tensor metrics, we observed decreased FA across several tracts, with the exception of SCA7, associated with increased RD in all SCA groups, including SCA7. Decreased FA in the corpus callosum of patients with SCA2 is in agreement with previous studies (Hernandez-Castillo et al., 2015; Mascalchi et al., 2015). In contrast to Kang et al. (Kang et al., 2014), we did not find any change in FA and RD in the corpus callosum, internal capsule and corona radiata in patients with SCA3. These previous DTI studies conducted in SCAs did no make mention of the quality control process used to include or reject data with missing volumes or spikes. It is therefore possible that the noise in their dataset contributed to the statistically significant differences found in many brain regions.

Even though TBSS is highly favored due to its elimination of user drawn regions of interest and reasonable control on misalignments that may arise when different images are registered together, it is still limited as it is highly dependent on FA. But FA cannot differentiate between different fiber populations in a voxel (Alexander et al., 2001; Alexander et al., 2002), hence the need for higher-order models. To resolve the limitations of FA, Rozenfeld et al. performed fiber tracking using the deterministic approach that assumes that fibers have a single orientation within each voxel, and requires prior knowledge of the orientation of fibers before its application (Rozenfeld et al., 2015). However, one cannot be certain of the orientation of the fibers as each voxel contains several populations of axons that may be oriented in different directions. With high uncertainty in estimating the fiber direction and evaluating crossing fibers, the deterministic approach is therefore less robust and less sensitive as compared to the probabilistic approach (Petersen et al., 2016; Schlaier et al., 2017) that overcomes the problem of uncertainty in fiber orientation and accounts for multiple fibers in each voxel. Kang et al. (Kang et al., 2014) and Prakash et al. (Prakash et al., 2009) used the probabilistic approach but with algorithms that have more fiber orientation error rate and low fiber detection rate as compared to the non-negativity CSD approach (Tournier et al., 2007; Wilkins et al., 2015). This is why we chose to implement fiber tract specific analysis, i.e. FBA, in our SCA cohort. FBA has been successfully applied in traumatic brain injury (Wright et al., 2017) and in motor neuron disease (Raffelt et al., 2015; Raffelt et al., 2017). In our study, FBA revealed decreased FD in the pontine crossing, CST, cerebral peduncle, internal capsule and corona radiata in patients with SCA1, SCA2 and SCA3. In patients with SCA7, contrary to the results obtained with FA, FD was decreased in the corpus callosum and cerebral peduncles. Decreased FD could either be a result of atrophy leading to a decrease in the volume, or dense packing of the axons leading to reduced axonal volume. However,

decreased FC suggested a reduced number of axons in these regions in patients with SCA. Furthermore, FDC correlated with cerebellar atrophy in patients with SCA1 and the clinical scores in patients with SCA1 and SCA3. Hence, we can attribute the combined effect of FD and FC to brain atrophy and reduction in the number of fibers in SCAs.

In conclusion, clinical scores such as the SARA and the CCFS are widely used to evaluate ataxia, but their low effect sizes make them less suited for therapeutic trials in very rare disorders like SCAs. Furthermore, they cannot be of use in presymptomatic individuals. Using volumetry methods that limit user interference, and tractography techniques with CSD approach (i.e. FBA) that are sensitive to subtle changes otherwise overlooked by DTI metrics (e.g. FA), we identified biomarkers with very large effect sizes in SCAs, making them suitable for therapeutic trials. Since a single biomarker is likely to fail reflecting the complexity of the neurodegenerative cascades leading to the onset and progression of SCAs, a multimodal biomarkers approach, aiming at the integration and visualization of multivariate datasets, can also be applied, as recently shown in our SCA cohort at baseline (Garali et al., 2017).

Chapter 7

Organisation: