Reduced White Matter Fiber Density in Patients with Multiple Sclerosis.

Introduction: Improved understanding of multiple sclerosis (MS) symptomatology, disease mechanisms, and clinical effectiveness can be achieved by investigating microstructural damage. The aim was to gain deeper insights into changes in white matter (WM) tracts in MS patients. Methods: Diffusion magnetic resonance imaging-based tractography was utilized to segment WM tracts into regions of interest for further quantitative analysis. However, tractography is susceptible to false-positive findings, reducing its specificity and clinical feasibility. To address these limitations, the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) technique was used. COMMIT was used to derive measures of intracellular compartment (IC) and isotropic compartments from multishell diffusion data of 40 healthy controls (HCs) and 40 MS patients. Results: The analysis revealed a widespread pattern of significantly decreased IC values in MS patients compared with HCs across 61,581 voxels (pFWE < 0.05, threshold-free cluster enhancement [TFCE] corrected). Similar WM structures studied using the fractional anisotropy (FA) value also showed a reduction in FA among MS patients compared with HCs across 57,304 voxels (pFWE < 0.05, TFCE corrected). Out of the 61,581 voxels exhibiting lower IC, a substantial overlap of 47,251 voxels (76.72%) also demonstrated lower FA in MS patients compared with HCs. Discussion: The data suggested that lower IC values contributed to the explanation of FA reductions. In addition, IC showed promising potential for evaluating microstructural abnormalities in WM in MS, potentially being more sensitive than the frequently used FA value.

Symmetry differences of structural connectivity in multiple sclerosis and healthy state.

Focal and diffuse cerebral damages occur in Multiple Sclerosis (MS) that promotes profound shifts in local and global structural connectivity parameters, mainly derived from diffusion tensor imaging. Most of the reconstruction analyses have applied conventional tracking algorithms largely based on the controversial streamline count. For a more credible explanation of the diffusion MRI signal, we used convex optimization modeling for the microstructure-informed tractography2 (COMMIT2) framework. All multi-shell diffusion data from 40 healthy controls (HCs) and 40 relapsing-remitting MS (RRMS) patients were transformed into COMMIT2-weighted matrices based on the Schefer-200 parcels atlas (7 networks) and 14 bilateral subcortical regions. The success of the classification process between MS and healthy state was efficiently predicted by the left DMN-related structures and visual network-associated pathways. Additionally, the lesion volume and age of onset were remarkably correlated with the components of the left DMN. Using complementary approaches such as global metrics revealed differences in WM microstructural integrity between MS and HCs (efficiency, strength). Our findings demonstrated that the cutting-edge diffusion MRI biomarkers could hold the potential for interpreting brain abnormalities in a more distinctive way.

Study of structural network connectivity using DTI tractography in insomnia disorder.

Most of tractography studies on insomnia disorder (ID) have reported decreased structural connectivity between cortical and subcortical structures. Tractography based on standard diffusion tensor imaging (DTI) can generate high number of false-positive streamlines connections between gray matter regions. In the present study, we employed the convex optimization modeling for microstructure informed tractography-2 (COMMIT2) to improve the accuracy of the reconstructed whole-brain connectome and filter implausible brain connections in 28 patients with ID and compared with 27 healthy controls. Then, we used NBS-predict (a prediction-based extension to the network-based statistic method) in the COMMIT2-weighted connectome. Our results revealed decreased structural connectivity between subregions of the left somatomotor, ventral attention, frontoparietal, dorsal attention and default mode networks in the insomnia group. Moreover, there is a negative correlation between sleep efficiency and structural connectivity within the left frontoparietal, visual, default mode network, limbic, dorsal attention, right dorsal attention as well as right default mode networks. By comparing with standard connectivity analysis, we showed that by removing of false-positive streamlines connections after COMMIT2 filtering, abnormal structural connectivity was reduced in patients with ID compared to controls. Our results demonstrate the importance of improving the accuracy of tractography for understanding structural connectivity networks in ID.