The potential of multi-shell diffusion imaging to produce accurate brain connectivity metrics able to unravel key pathophysiological processes in multiple sclerosis (MS) has scarcely been investigated.
Objective:
To test, in patients with a clinically isolated syndrome (CIS), whether multi-shell imaging-derived connectivity metrics can differentiate patients from controls, correlate with clinical measures, and perform better than metrics obtained with conventional single-shell protocols.
Methods:
Nineteen patients within 3 months from the CIS and 12 healthy controls underwent anatomical and 53-direction multi-shell diffusion-weighted 3T images. Patients were cognitively assessed. Voxel-wise fibre orientation distribution functions were estimated and used to obtain network metrics. These were also calculated using a conventional single-shell diffusion protocol. Through linear regression, we obtained effect sizes and standardised regression coefficients.
Results:
Patients had lower mean nodal strength (p = 0.003) and greater network modularity than controls (p = 0.045). Greater modularity was associated with worse cognitive performance in patients, even after accounting for lesion load (p = 0.002). Multi-shell-derived metrics outperformed single-shell-derived ones.
Conclusion:
Connectivity-based nodal strength and network modularity are abnormal in the CIS. Furthermore, the increased network modularity observed in patients, indicating microstructural damage, is clinically relevant. Connectivity analyses based on multi-shell imaging can detect potentially relevant network changes in early MS.
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