A multi-shell multi-tissue diffusion study of brain connectivity in early multiple sclerosis

Abstract

Background: 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.

Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis

“…Moreover, in a heterogeneous MS population (RR, SP, PP), decreased global efficiency across the brain correlated with worse SDMT ( 73 ). Based on NODDI data, CIS patients showed that higher whole-brain modularity coefficient was associated with worse IPS as measured by SDMT ( 80 ). Of note, the standardized regression coefficient describing such relationship was greater when the modularity coefficient was obtained with NODDI data than with conventional DTI, indicating a better sensitivity of NODDI for MS ( 80 ).…”

Beyond Lesion-Load: Tractometry-Based Metrics for Characterizing White Matter Lesions within Fibre Pathways

“…Indeed, applying tractography to MS data can be problematic due to premature termination of streamlines within lesions [16,17]. However, recent advances in local modeling and tractography such as multi-tissue multi-shell constrained spherical deconvolution (MSMT-CSD, [18]) and anatomically-based tractography [19,20] allow the propagation of streamlines through lesions more reliably [21][22][23][24][25][26][27]. Furthermore, although tractography still faces significant challenges in the field in general [28][29][30], recent machine learning based approaches have shown promise in reproducible tract segmentation across subjects [31].…”

Spatial patterns of brain lesions assessed through covariance estimations of lesional voxels in multiple Sclerosis: The SPACE-MS technique

“…We speculate that more widespread distributions of lesions may reflect greater dissemination of demyelinating changes in the brain tissue. Also, greater dispersion might imply a greater number of WM tracts affected, therefore having detrimental consequences for the efficiency of the whole-brain network, which has been associated with worse disability outcomes on a number of occasions ( Charalambous et al, 2019 , Solana et al, 2018 , Tur et al, 2020 ). Of note, the presence of juxtacortical/cortical lesions may also contribute to a higher dispersion of brain lesions.…”