Stability of longitudinal DTI metrics in MS with treatment of injectables, fingolimod and dimethyl fumarate

Improvement of the thalamocortical white matter network in people with stable treated relapsing-remitting multiple sclerosis over time

Advanced imaging techniques (tractography) enable the mapping of white matter (WM) pathways and the understanding of brain connectivity patterns. We combined tractography with a network-based approach to examine WM microstructure on a network level in people with relapsing-remitting multiple sclerosis (pw-RRMS) and healthy controls (HCs) over 2 years. Seventy-six pw-RRMS matched with 43 HCs underwent clinical assessments and 3T MRI scans at baseline (BL) and 2-year follow-up (2-YFU). Probabilistic tractography was performed, accounting for the effect of lesions, producing connectomes of 25 million streamlines. Network differences in fibre density across pw-RRMS and HCs at BL and 2-YFU were quantified using network-based statistics (NBS). Longitudinal network differences in fibre density were quantified using NBS in pw-RRMS, and were tested for correlations with disability, cognition and fatigue scores. Widespread network reductions in fibre density were found in pw-RRMS compared with HCs at BL in cortical regions, with more reductions detected at 2-YFU. Pw-RRMS had reduced fibre density at BL in the thalamocortical network compared to 2-YFU. This effect appeared after correction for age, was robust across different thresholds, and did not correlate with lesion volume or disease duration. Pw-RRMS demonstrated a robust and long-distance improvement in the thalamocortical WM network, regardless of age, disease burden, duration or therapy, suggesting a potential locus of neuroplasticity in MS. This network's role over the disease's lifespan and its potential implications in prognosis and treatment warrants further investigation.

Diffusion tensor imaging changes of the cortico-thalamic-striatal tracts correlate with fatigue and disability in people with relapsing-remitting MS

PURPOSE

To investigate how the microstructural neural integrity of cortico-thalamic-striatal (CTS) tracts correlate with fatigue and disability over time. The primary outcome was diffusion tensor imaging (DTI) metrics change over time, and the secondary outcome was correlations with fatigue and disability in people with RRMS (pw-RRMS).

METHODS

76 clinically stable pw-RRMS and 43 matched healthy controls (HCs). The pw-RRMS cohort consisted of three different treatment subgroups. All participants underwent disability, cognitive, fatigue and mental health assessments. Structural and diffusion scans were performed at baseline (BL) and 2-year follow-up (2-YFU) for all participants. Fractional anisotropy (FA), mean, radial and axial diffusivities (MD, RD, AD) of normal-appearing white matter (NAWM) and white matter lesion (WML) in nine tracts-of-interests (TOIs) were estimated using our MRtrix3 in-house pipeline.

RESULTS

We found significant BL and 2-YFU differences in most diffusion metrics in TOIs in pw-RRMS compared to HCs (pFDR ≤ 0.001; false-detection-rate (FDR)-corrected). There was a significant decrease in WML diffusivities and an increase in FA over the follow-up period in most TOIs (pFDR ≤ 0.001). Additionally, there were no differences in DTI parameters across treatment groups. AD and MD were positively correlated with fatigue scores (r ≤ 0.33, p ≤ 0.01) in NAWM-TOIs, while disability (EDSS) was negatively correlated with FA in most NAWM-TOIs (|r|≤0.31, p ≤ 0.01) at both time points. Disability scores correlated with all diffusivity parameters (r ≤ 0.29, p ≤ 0.01) in most WML-TOIs at both time points.

CONCLUSION

Statistically significant changes in diffusion metrics in WML might be indicative of integrity improvement over two years in CTS tracts in clinically stable pw-RRMS. This finding represents structural changes within lesioned tracts. Measuring diffusivity in pw-RRMS affected tracts might be a relevant measure for future remyelination clinical trials.

Multi-modal neuroimaging signatures predict cognitive decline in multiple sclerosis: A 5-year longitudinal study

BACKGROUND

Cognitive impairment is a hallmark of multiple sclerosis (MS) but is usually an under-recorded symptom of disease progression. Identifying the predictive signatures of cognitive decline in people with MS (pwMS) over time is important to ensure effective preventative treatment strategies. Structural and functional brain characteristics as measured by various magnetic resonance (MR) methods have been correlated with variation in cognitive function in MS, but typically these studies are limited to a single MR modality and/or are cross-sectional designs. Here we assess the predictive value of multiple different MR modalities in relation to cognitive decline in pwMS over 5 years.

METHODS

A cohort of 43 pwMS was assessed at baseline and 5 years follow-up. Baseline (input) data consisted of 70 multi-modal MRI measures for different brain regions including magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and standard volumetrics. Age, sex, disease duration and treatment were included as clinical inputs. Cognitive function was assessed using the Audio Recorded Cognitive Screen (ARCS) and the Symbol Digit Modalities Test (SDMT). Prediction modelling was performed using the machine learning package - GLMnet, where a penalised regression was applied to identify multi-modal signatures with the most predictive value (and the least error) for each outcome.

RESULTS

The multi-modal approach to neuroimaging was able to accurately predict cognitive decline in pwMS. The best performing model for change in total ARCS (tARCS) included 16 features from across the various MR modalities and explained 54 % of the variation in change over time (R2=0.54, 95 % CI=0.48-0.51). The features included nine MRS, four volumetric and two DTI parameters. The model also selected disease duration, but not treatment, as a predictive feature. By comparison, the best model for SDMT included several of the same above features and explained 39 % of the change over time (R2=0.39, 95 % CI=0.48-0.51). Conventional volumetric measures were about half as good at predicting change in tARCS score compared to the best multi-modal model (R2=0.26 95 % CI:0.22-0.29). The clinical interpretation of the best predictive model for change in tARCS showed that cognitive decline could be predicted with >90 % accuracy in this cohort (AUC=0.92, SE=0.86 - 0.94).

CONCLUSION

Multi-modal MRI signatures can predict cognitive decline in a cohort of pwMS over 5 years with high accuracy. Future studies will benefit from the inclusion of even more MR modalities e.g., functional MRI, quantitative susceptibility mapping, magnetisation transfer imaging, as well as other potential predictors e.g., genetic and environmental factors. With further validation, this signature could be used in future trials with high-risk patients to personalise the management of cognitive decline in pwMS, even in the absence of relapses.

Functional and structural brain MRI changes associated with cognitive worsening in multiple sclerosis: a 3-year longitudinal study

BACKGROUND

Heterogeneous processes may contribute to cognitive impairment in multiple sclerosis (MS).

OBJECTIVE

To apply a longitudinal multiparametric MRI approach to identify mechanisms associated with cognitive worsening in MS patients.

METHODS

3 T brain functional and structural MRI scans were acquired at baseline and after a median follow-up of 3.4 years in 35 MS patients and 22 healthy controls (HC). Associations between cognitive worsening (reliable change index score < - 1.25 at the Rao's battery) and longitudinal changes in regional T2-hyperintense white matter (WM) lesions, diffusion tensor microstructural WM damage, gray matter (GM) atrophy and resting state (RS) functional connectivity (FC) were explored.

RESULTS

At follow-up, HC showed no clusters of significant microstructural WM damage progression, GM atrophy or changes in RS FC. At follow-up, 10 MS patients (29%) showed cognitive worsening. Compared to cognitively stable, cognitively worsened MS patients showed more severe GM atrophy of the right anterior cingulate cortex and bilateral supplementary motor area (p < 0.001). Cognitively worsened vs cognitively stable MS patients showed also decreased RS FC in the right hippocampus of the right working memory network and in the right insula of the default mode network. Increased RS FC in the left insula of the executive control network was found in the opposite comparison (p < 0.001). No significant regional accumulation of focal WM lesions nor microstructural WM abnormalities occurred in both patients' groups.

CONCLUSIONS

GM atrophy progression in cognitively relevant brain regions combined with functional impoverishment in networks involved in cognitive functions may represent the substrates underlying cognitive worsening in MS.