Introduction:

MRI is the most common technique to monitor MS patients, but T2 FLAIR intensities only poorly explain some of the clinical symptoms observed 1,2. Advanced MRI imaging techniques, such as diffusion tensor imaging (DTI) could potentially be used to describe some of the deficits by studying water diffusion in disrupted tracts with parameters like Fractional Anisotropy (FA), Mean Diffusivity (MD), Axial Diffusivity (AD) and Radial Diffusivity (RD) 3,4. DTI allows the evaluation of microstructural integrity of myelin sheath of brain white matter 5. An association can then be established between DTI and clinical symptoms6. This study aims to evaluate and compare DTI parameters in relapsing-remitting MS (RRMS) patients with age and sex-matched healthy controls (HCs), and to correlate these DTI metrics values with clinical symptoms and brain volumetric measures showing the differentiation and significant P-values.

Methods:

This observational open-label study involved 37 relapse-onset MS patients aged between 20 to 55 years who had a confirmed diagnosis of RRMS according to the McDonald criteria. Healthy control participants were age and sex-matched to the MS cohort (Table 1). All MRI were undertaken on a 3 Tesla MRI scanner (Prisma, Siemens) located at the Hunter Medical Research Institute, New Lambton Heights, NSW, Australia. A 3D T1 MP-RAGE was used for anatomical reference, and A T2 FLAIR sequence was acquired for assessment of lesion load. Total brain, white matter, grey matter and CSF volumes were calculated by FSL software. The DWI protocol consisted of an echo-planar imaging (EPI) sequence with diffusion weighting b=3000 s/mm2 and 3 diffusion-free (b=0) image volumes. In addition, three b=0 images were acquired with a reversed-phase encoding directions (PA, AP). The DTI pre-processing pipeline 7 was applied to DWI datasets to remove artefacts. DWI datasets were registered to the respective T1 datasets to be able to use segmentations from the T1 image (Figure 1). The DTI tensors were estimated and FA, MD, RD, and AD in total brain white matter (WM) were calculated and analysed.

Results:

The correlation and T-test for the DTI metrics and clinical parameters between the two groups are shown in Table 2. There was a statistically significant increase in MD (3.60%), RD (4.50%) and AD (2.70%) and a significant decrease in FA (-4.30%) for total brain-WM in RRMS patients compared to HCs (p<0.01). FA correlated positively with memory (r=0.406) and total-ARCS (r=0.412) in total brain-WM (Table 3). MD correlated negatively with memory (r= -0.372) and positively with EDSS (r=0.368) in total brain-WM. RD correlated negatively with total-ARCS (r= -0.426) and with memory (r= -0.467) while correlated positively with EDSS (r=0.371). Volumetric segmentation indicated a reduction in total volume of the brain (-5%), GM (-4%) and WM (-4%) with a reciprocal increase in CSF by (26%) in RRMS compared with HCs.

Discussion:

In this study, we found the DTI metrics are sensitive to microstructural changes in the normal appearing WM. The major findings of this study demonstrate that there is marked reduction of FA values in total brain-WM and significantly increased of MD, AD and RD values in RRMS. Our results are in accordance with previous studies suggesting these microstructural changes may reflect various disease processes in MS patients such as neurodegeneration, axonal loss and gliosis 8,9. It showed a significant increase in MD and RD in total brain-WM of RRMS compared to HCs which are more specific for demyelination and axonal injury. Although we found a reduction in FA values in total brain-WM which is non-specific for demyelination, it can be assumed that the increase in MD and RD, which may represent variable degree of disease activity, could be driven by the low FA values 10,11. Moreover, we found moderate correlation between MD/RD in total brain-WM and EDSS but not with FA which is likely not severely affected in early phase of the disease and has lack of specificity with disease processes 12,13. Cognitive decline is one of the important clinical features of disease progression in MS 14. We found a positive correlation between FA and total ARCS in RRMS compared with HCs. Among other cognitive domains, poor memory and information processing speed are the two common deficits observed in MS patients. We noted a moderate negative correlation between memory and MD/RD in total brain-WM, while FA correlated positively with memory in RRMS. Our results agree with others works highlighting the role of DTI as a viable measure of axonal integrity and cognitive function in MS 15.

Conclusion:

There was a statistically significant increase in most of DTI parameters for RRMS patients compared with healthy controls. FA correlated positively with clinical parameters like EDSS and cognitive assessment. Both MD and RD correlated negatively with cognition parameters and positively with EDSS. Quantitative DTI parameters not only differentiate between RRMS patients and HCs, but are also associated with disability and mental health of RRMS.

Acknowledgements

This study was supported by an independent grant provided by Biogen and Novartis Australia Pty. Ltd.

References

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