Background

In early multiple sclerosis, a clearer understanding of normal-brain tissue microstructural and metabolic abnormalities will provide valuable insights into its pathophysiology. Here, we studied the brain of patients with their first demyelinating episode using neurite orientation dispersion and density imaging (NODDI), for information about neuro-axonal density and spatial distribution, and ²³Na MRI, for total sodium concentration reflecting neuro-axonal metabolic dysfunction and loss.

Objectives

To detect, using a multi-parametric quantitative MRI approach, clinically relevant alterations in the brain of early patients not captured by conventional MRI.

Methods

We enrolled 42 patients with clinically isolated syndrome or multiple sclerosis within 3 months from the onset and 16 healthy controls. We assessed physical and cognitive scales. On a 3T scanner, we acquired brain and spinal cord structural scans, and brain NODDI. Thirty-two patients and 13 healthy controls also underwent brain ²³Na MRI. In the brain normal-appearing white matter, white matter lesions, and grey matter, we measured, from NODDI, the neurite density index (NDI), a marker of neuro-axonal density, and the orientation dispersion index (ODI), reflecting the fanning and crossing of neurites, and, from ²³Na MRI, the TSC. We used linear regression models, adjusted for brain parenchymal fraction and lesion load, and Spearman correlation tests. For robust regression estimates, we used a p≤0.01.

Results

Patients showed higher ODI in normal-appearing white matter, including the corpus callosum, where they also showed lower NDI and higher TSC, compared with controls. In grey matter, compared with controls, patients had lower ODI in frontal, parietal and temporal cortex; lower NDI in parietal, temporal and occipital cortex; and higher TSC in limbic and frontal cortex. Brain volumes did not differ between patients and controls. In patients, higher ODI in corpus callosum was associated with worse performance on timed walk test (p=0.009, B=0.01, 99% Confidence Interval=0.0001-0.02), independent of brain and lesion volumes. Higher TSC in left frontal middle gyrus was associated with higher disability on Expanded Disability Status Scale (r_s=0.5, p=0.005).

Conclusions

We found increased axonal dispersion in normal-appearing white matter, particularly corpus callosum, where we found also reduced axonal density and total sodium accumulation suggesting that this structure can be early affected by neurodegeneration. The association between increased axonal dispersion in the corpus callosum and worse walking performance implies that morphological and metabolic alterations in this structure may contribute to disability in multiple sclerosis. Brain volumes were neither altered nor related to disability in patients, so these two advanced MRI techniques can be more sensitive at detecting clinically relevant pathology in very early multiple sclerosis.

Background

In early multiple sclerosis, a clearer understanding of normal-brain tissue microstructural and metabolic abnormalities will provide valuable insights into its pathophysiology. Here, we studied the brain of patients with their first demyelinating episode using neurite orientation dispersion and density imaging (NODDI), for information about neuro-axonal density and spatial distribution, and ²³Na MRI, for total sodium concentration reflecting neuro-axonal metabolic dysfunction and loss.

Objectives

To detect, using a multi-parametric quantitative MRI approach, clinically relevant alterations in the brain of early patients not captured by conventional MRI.

Methods

We enrolled 42 patients with clinically isolated syndrome or multiple sclerosis within 3 months from the onset and 16 healthy controls. We assessed physical and cognitive scales. On a 3T scanner, we acquired brain and spinal cord structural scans, and brain NODDI. Thirty-two patients and 13 healthy controls also underwent brain ²³Na MRI. In the brain normal-appearing white matter, white matter lesions, and grey matter, we measured, from NODDI, the neurite density index (NDI), a marker of neuro-axonal density, and the orientation dispersion index (ODI), reflecting the fanning and crossing of neurites, and, from ²³Na MRI, the TSC. We used linear regression models, adjusted for brain parenchymal fraction and lesion load, and Spearman correlation tests. For robust regression estimates, we used a p≤0.01.

Results

Patients showed higher ODI in normal-appearing white matter, including the corpus callosum, where they also showed lower NDI and higher TSC, compared with controls. In grey matter, compared with controls, patients had lower ODI in frontal, parietal and temporal cortex; lower NDI in parietal, temporal and occipital cortex; and higher TSC in limbic and frontal cortex. Brain volumes did not differ between patients and controls. In patients, higher ODI in corpus callosum was associated with worse performance on timed walk test (p=0.009, B=0.01, 99% Confidence Interval=0.0001-0.02), independent of brain and lesion volumes. Higher TSC in left frontal middle gyrus was associated with higher disability on Expanded Disability Status Scale (r_s=0.5, p=0.005).

Conclusions

We found increased axonal dispersion in normal-appearing white matter, particularly corpus callosum, where we found also reduced axonal density and total sodium accumulation suggesting that this structure can be early affected by neurodegeneration. The association between increased axonal dispersion in the corpus callosum and worse walking performance implies that morphological and metabolic alterations in this structure may contribute to disability in multiple sclerosis. Brain volumes were neither altered nor related to disability in patients, so these two advanced MRI techniques can be more sensitive at detecting clinically relevant pathology in very early multiple sclerosis.

Background

People with MS present disruption of structural brain networks, but the differential characteristics of such changes among MS phenotypes and their clinical impact are not well elucidated.

Objectives

To characterize diffusion-based brain connectivity abnormalities in different MS phenotypes and their relation with disability in a large cohort of patients.

Methods

In this multicenter, retrospective, cross-sectional study, we collected clinical and brain MRI data from 344 patients with MS [median Expanded Disability Status Scale, EDSS 2.0 (range 0-7.0)] and 91 healthy volunteers (HV) from four MAGNIMS centers. Cognition was assessed with the Paced Auditory Serial Addition Test (PASAT) and Symbol Digits Modalities Test (SDMT) in 298 patients. We collected 3D-T1, FLAIR, diffusion-weighted images (DWI) and T2 or field maps acquisitions. FSL and ANTs packages were used to carry out DWI preprocessing and MRtrix software to generate connectivity matrices based on fractional anisotropy values. We computed six network measures (strength, global and local efficiency, clustering coefficient, assortativity and transitivity), and applied the ComBat tool to reduce inter-site variability. We calculated age-adjusted differences in graphs between groups using Mann-Whitney with FDR correction or Kruskal-Wallis with Dunn’s Test when necessary. Associations with clinical features were explored with Spearman’s rank correlation.

Results

Thirty-eight (11%) patients presented a clinically isolated syndrome (CIS), 262 (76%) had relapsing-remitting (RR) and 44 (13%) secondary progressive (SP) MS. CIS patients showed reduced global and local efficiency, clustering coefficient and transitivity compared to HV (corrected p<0.001), whilst RRMS did not differ from CIS patients. Compared with CIS and RRMS, patients with SPMS showed larger changes for the same previous graphs measures (corrected p<0.05), and lower strength than RRMS (corrected p=0.019).

In patients, reduced measures of strength, global and local efficiency, clustering and transitivity correlated with higher EDSS (rho:-0.12–-0.16, corrected p<0.034), lower PASAT (rho:0.26–0.30, corrected p<0.001) and worse SDMT scores (rho:0.28–0.32, corrected p<0.001).

Conclusions

Structural network integrity at the whole brain level is already widely reduced in people with MS from the earliest phases of the disease and becomes more abnormal in SPMS. Network modifications may contribute to the clinical manifestations of the disease.

Background

MRI assessment in MS focuses on the presence of typical white matter (WM) lesions. Neurodegeneration characterised by brain atrophy is recognised in the research field as an important prognostic factor. It is not routinely reported clinically, in part due to difficulty in achieving reproducible measurements. Automated MRI quantification of WM lesions and brain volume could provide important clinical monitoring data. In general, lesion quantification relies on both T1 and FLAIR input images, while tissue volumetry relies on T1. However, T1-weighted scans are not routinely included in the clinical MS protocol, limiting the utility of automated quantification.

Objectives

We address this important translational challenge by assessing the performance of FLAIR-only lesion and brain segmentation, against a conventional approach requiring multi-contrast acquisition. We explore whether FLAIR-only grey matter (GM) segmentation yields more variability in performance compared with two-channel segmentation; whether this is related to field strength; and whether the results meet a level of clinical acceptability demonstrated by the ability to reproduce established biological associations.

Methods

We used a multicentre dataset of subjects with a CIS suggestive of MS scanned at 1.5T and 3T in the same week. WM lesions were manually segmented by two raters, ‘manual 1’ guided by consensus reading of CIS-specific lesions and ‘manual 2’ by any WM hyperintensity. An existing brain segmentation method was adapted for FLAIR-only input. Automated segmentation of WM hyperintensity and brain volumes were performed with conventional (T1/T1+FLAIR) and FLAIR-only methods.

Results

WM lesion volumes were comparable at 3T between ‘manual 2’, T1+FLAIR and FLAIR-only methods. For cortical GM volume, linear regression measures between conventional and FLAIR-only segmentation were high (1.5T: α=1.029, R²=0.997, standard error (SE)= 0.007; 3T: α=1.019, R²=0.998, SE=0.006). Age-associated change in cortical GM volume was a significant covariate in both T1 (p=0.001) and FLAIR-only (p=0.005) methods, confirming the expected relationship between age and GM volume for FLAIR-only segmentations.

Conclusions

FLAIR-only automated frameworks for segmentation of WM lesions and brain volumes were consistent with results obtained through conventional methods and had the ability to demonstrate biological effects in our study population. This could facilitate the integration of automated WM lesion volume and brain atrophy analysis as clinical tools in radiological MS reporting.