Introduction

Multiple sclerosis (MS) is the most common inflammatory demyelinating disease of the central nervous system, characterized by a series of pathological processes mainly caused by focal lesions in gray and white matter (WM) in the entire brain, including inflammation, demyelination, axonal loss, and remyelination.
Although there are some discrepancies between the findings of previous studies, it has been suggested that diffusion tensor imaging (DTI) can be used to detect alterations in DTI scalars as a biomarker of WM microstructural damages in patients with RRMS. Among these techniques for DTI analyses which have merits and demerits respectively, AFQ is a novel algorithm that can automated perform the reconstruction and segmentation of WM fiber tracts at anatomically multiple equivalent locations along their trajectories.
To our knowledge, there are few applications of AFQ for patients with RRMS, which results in a more detailed cross-sectional and longitudinal characterization of whole-brain WM fiber tracts and its correlation with cognitive performance still requires further investigation.

Methods

This retrospective study was approved by the Institutional Review Board of the First Affiliated Hospital of Chongqing Medical University, Chongqing, China, and written informed consent was obtained from each participant before MRI scans.
All MR scans were performed on a 3.0 T MR scanner (Magnetom Skyra, Siemens Healthcare GmbH, Erlangen, Germany) using a 32-channel head coil. The MRI protocol included a 3-dimensional (3D) T1-weighted magnetization prepared rapid gradient echo (MPRAGE) sequence, a 3D fluid-attenuated inversion recovery (FLAIR) sequence and a diffusion kurtosis imaging (DKI) sequence.
For the DTI data, it was preprocessed by using FMRIB Software Library (FSL) software (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). The pre-processed DTI and T1MPRAGE images were fed into the open-source MATLAB version of AFQ software (https://github.com/jyeatman/AFQ) to identify and quantify 100 equally spaced nodes of 20 specific WM fiber tracts for each participant.
All statistical analyses were performed by using the SPSS software (version 25.0; SPSS, Chicago, IL, United States).

Results

At baseline, compared with HC, RRMS showed a widespread FA decrease and MD increase among tracts. While in the pointwise comparison, within the more detailed abnormalities localized to specific positions. At follow up, compared with baseline, although there was no statistical difference on FA and MD values in entire manner, FA reduced in the posterior portion of the right superior longitudinal fasciculus (R_SLF) and MD elevated in the anterior and posterior portion of the right arcuate fasciculus (R_AF) in the pointwise analysis. Furthermore, the altered metrics were widely correlated with cognitive performance in RRMS.

Discussion

In line with previous studies, at baseline comparison of entire WM fiber tracts, our finding revealed significant DTI metrics alterations (FA in 11/20 and MD in 18/20) in WM fiber tracts, which may indicate a widespread microstructural damage in RRMS. The MD metrics were seemed to be more sensitive to WM damage than FA, consistent with the previous studies. As to the tracts (bilateral CST, CC, IFOF, ILF; L_UF and CF) altered significantly both in FA and MD, it may result from the loss of axonal integrity and demyelination or edema in fiber tracts.
At follow-up, although there were no significant mean DTI metrics changes in patients with RRMS, there was a FA reduction in the posterior portion of R_SLF and a MD elevation in the anterior and posterior portion of R_AF in the pointwise manner. This also further illustrates the high sensitivity of the AFQ algorithm.
To further understand the underlying mechanisms, the correlation analyses were performed between WM microstructure damages and cognitive performance with age, gender, and education level as covariates. After FDR correction, our results showed that the altered DTI metrics were widely correlated with cognitive performance in RRMS. It was consistent with our hypothesis above about the potential contribution on cognitive impairment by altered DTI metrics in the WM fiber tracts. Moreover, a wider range of results were shown compared to previous studies, which demonstrated the advantages of the AFQ algorithm and revealed more pathophysiological mechanisms associated with cognitive impairment.

Conclusion

In conclusion, the application of AFQ showed that RRMS patients had a widespread WM microstructure alteration at baseline and some certain regions alterations at a short-term follow-up, which will enhance our understanding of WM microstructural abnormalities in RRMS patients. And the correlation between altered DTI metrics and cognitive performance in RRMS can be used as imaging markers for early identification of RRMS cognitive impairment.

Acknowledgements

We thank all the subjects who participated in this study.

References

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