Diffusion tensor imaging metrics associated with future disability in multiple sclerosis

White Matter Tract Degeneration in Multiple Sclerosis Patients With Progression Independent of Relapse Activity

BACKGROUND AND OBJECTIVES

Progression independent of relapse activity (PIRA) is associated with worse outcomes in people with multiple sclerosis (pwMS). Although previous research has linked PIRA to accelerated brain and spinal cord atrophy and compartmentalized chronic inflammation, the role of white matter (WM) tract degeneration remains unclear. This study aimed to explore the relationship between PIRA and the integrity of major WM tracts using diffusion tensor imaging (DTI).

METHODS

A cohort of 258 pwMS was stratified based on the presence or absence of PIRA over a 4-year follow-up period. At the end of follow-up, DTI metrics were compared between groups using propensity score-weighted linear regression models to account for potential confounders.

RESULTS

PwMS with ≥1 PIRA event (n = 39) exhibited significant reductions in fractional anisotropy and increases in radial, axial, and mean diffusivity within the corpus callosum and motor tracts (false discovery rate-adjusted p ≤ 0.04) compared with those without PIRA, indicating more pronounced WM damage.

DISCUSSION

Our findings highlight an association between PIRA and microstructural damage in key WM tracts. The observed DTI changes likely reflect processes such as Wallerian degeneration and contribute to the growing evidence linking PIRA to neurodegeneration.

A cross-sectional multicentre study of multishell diffusion MRI in multiple sclerosis

BACKGROUND AND OBJECTIVES

White matter (WM) microstructural properties from advanced multishell diffusion MRI (dMRI) have been linked to clinical disability in multiple sclerosis (MS). This multicentre study used multishell dMRI to compute WM metrics and test for differences between people with MS (pwMS) and healthy controls (HCs).

METHODS

We included multishell dMRI data from 251 pwMS or clinically isolated syndrome (CIS) (mean age 40.7 years, 72.4 % women, 88.8 % relapsing remitting MS) at six MAGNIMS centres and 543 HCs. Eleven scalar metric maps were estimated from multishell dMRI sequences, based on diffusion tensor imaging (DTI) and restriction spectrum imaging (RSI). The maps were analysed using tract-based spatial statistics (TBSS). The diffusion output was submitted to paired sampled t-tests to test for case-control differences and linear regression models to test for associations with Expanded Disability Status Scale (EDSS) scores, while accounting for confounders. In a sub-sample from Oslo, we tested for correlations between EDSS and dMRI metrics within WM lesions.

RESULTS

Significant group differences were found in nine out of eleven dMRI metrics. Linear regression models revealed significant correlations between EDSS and fractional anisotropy (FA) fast (β=-4.54, p = 0.01) and apparent diffusion coefficient (ADC) fast (β=10.92, p = 8.7 × 10-3).

CONCLUSIONS

Diffusion MRI based on clinically feasible multishell sequences uncovers WM group differences between pwMS and HCs, but only a selection of the advanced multishell parameters were sensitive to disability, and no statistically significant correlations with disability remained after Bonferroni correction.

Association between diffusion tensor imaging measurements and cognitive performances in people with multiple sclerosis: A systematic review and meta-analysis

BACKGROUND

Alterations in structural connectivity of brain networks have been linked to complex cognitive functions in people with multiple sclerosis (PwMS). However, a definitive consensus on the optimal diffusion tensor imaging (DTI) markers as indicators of cognitive performance remains incomplete and inconclusive. This systematic review and meta-analysis aimed to explore the evidence on the correlation between DTI metrics and cognitive functions in PwMS.

METHODS

A comprehensive literature search was conducted across PubMed/MEDLINE, Embase, Scopus, and the Web of Science up to March 2024 to identify studies reporting the correlation between DTI metrics and cognitive functions. Cognitive function was assessed using the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT), and Brief Visuospatial Memory Test-Revised (BVMT-R). The pooled correlation coefficients were estimated using R software version 4.4.0 with the random effect model.

RESULTS

Out of 1952 studies, 38 studies on 2055 PwMS fulfilled the inclusion criteria. The meta-analysis indicated that the SDMT exhibited the greatest correlation with corpus callosum fractional anisotropy (FA) (r = 0.54, 95 % CI: 0.4 to 0.66, p-value < 0.001, I2 = 34.1 %, p-heterogeneity = 0.19) and mean diffusivity (MD) (r = -0.48, 95 % CI: 0.61 to -0.33, p-value < 0.001, I2 = 0 %, p-heterogeneity = 0.77), white matter FA (r = 0.39, 95 % CI: 0.24 to 0.52, p-value < 0.001, I2 = 0 %, p-heterogeneity = 0.1), and fornix FA (r = 0.35, 95 % CI: 0.12 to 0.54, p-value = 0.003, I2 = 50.7 %, p-heterogeneity = 0.18) and MD (r = -0.35, 95 % CI: 0.49 to -0.19, p-value < 0.001, I2 = 0 %, p-heterogeneity = 0.5).

CONCLUSION

DTI measurements, including corpus callosum FA and MD, white matter FA, and fornix FA and MD, represent the indicators of cognitive performance in PwMS. Nonetheless, these findings warrant cautious interpretation due to the restricted kinds of cognitive tests and methodological variability across studies.

Artificial Intelligence and Multiple Sclerosis

In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.

An MRI evaluation of white matter involvement in paradigmatic forms of spastic ataxia: results from the multi-center PROSPAX study

BACKGROUND

Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS) and Spastic Paraplegia Type 7 (SPG7) are paradigmatic spastic ataxias (SPAX) with suggested white matter (WM) involvement. Aim of this work was to thoroughly disentangle the degree of WM involvement in these conditions, evaluating both macrostructure and microstructure via the analysis of diffusion MRI (dMRI) data.

MATERIAL AND METHODS

In this multi-center prospective study, ARSACS and SPG7 patients and Healthy Controls (HC) were enrolled, all undergoing a standardized dMRI protocol and a clinimetrics evaluation including the Scale for the Assessment and Rating of Ataxia (SARA). Differences in terms of WM volume or global microstructural WM metrics were probed, as well as the possible occurrence of a spatially defined microstructural WM involvement via voxel-wise analyses, and its correlation with patients' clinical status.

RESULTS

Data of 37 ARSACS (M/F = 21/16; 33.4 ± 12.4 years), 37 SPG7 (M/F = 24/13; 55.7 ± 10.7 years), and 29 HC (M/F = 13/16; 42.1 ± 17.2 years) were analyzed. While in SPG7, only a mild mean microstructural damage was found compared to HC, ARSACS patients present a severe WM involvement, with a reduced global volume (p < 0.001), an alteration of all microstructural metrics (all with p < 0.001), without a spatially defined pattern of damage but with a prominent involvement of commissural fibers. Finally, in ARSACS, a correlation between microstructural damage and SARA scores was found (p = 0.004).

CONCLUSION

In ARSACS, but not SPG7 patients, we observed a complex and multi-faced involvement of brain WM, with a clinically meaningful widespread loss of axonal and dendritic integrity, secondary demyelination and, overall, a reduction in cellularity and volume.

The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain

Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even - 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and - 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.

Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification

PURPOSE

This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes.

MATERIALS AND METHODS

We utilized the automated fiber quantification (AFQ) method to extract 18 WM fiber tracts from the imaging data of 103 MS patients in total. Unsupervised machine learning techniques were applied to conduct cluster analysis and identify distinct subtypes. Clinical and diffusion tensor imaging (DTI) metrics were compared among the subtypes, and survival analysis was conducted to examine disability progression and cognitive impairment.

RESULTS

The clustering analysis revealed three distinct subtypes with variations in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Significant differences were observed in clinical and DTI metrics among the subtypes. Subtype 3 showed the fastest disability progression and cognitive decline, while Subtype 2 exhibited a slower rate, and Subtype 1 fell in between.

CONCLUSIONS

Subtyping MS based on WM fiber tracts using unsupervised machine learning identified distinct subtypes with significant cognitive and disability differences. WM abnormalities may serve as biomarkers for predicting disease outcomes, enabling personalized treatment strategies and prognostic predictions for MS patients.

Advanced MRI Techniques: Diagnosis and Follow-Up of Multiple Sclerosis

Brain and spinal cord imaging plays a pivotal role in aiding clinicians with the diagnosis and monitoring of multiple sclerosis. Nevertheless, the significance of magnetic resonance imaging in MS extends beyond its clinical utility. Advanced imaging modalities have facilitated the in vivo detection of various components of MS pathogenesis, and, in recent years, MRI biomarkers have been utilized to assess the response of patients with relapsing-remitting MS to the available treatments. Similarly, MRI indicators of neurodegeneration demonstrate potential as primary and secondary endpoints in clinical trials targeting progressive phenotypes. This review aims to provide an overview of the latest advancements in brain and spinal cord neuroimaging in MS.

Implications of fitting a two-compartment model in single-shell diffusion MRI

It is becoming increasingly common for studies to fit single-shell diffusion MRI data to a two-compartment model, which comprises a hindered cellular compartment and a freely diffusing isotropic compartment. These studies consistently find that the fraction of the isotropic compartment (f) is sensitive to white matter (WM) conditions and pathologies, although the actual biological source of changes infhas not been validated. In this work we put aside the biological interpretation offand study the sensitivity implications of fitting single-shell data to a two-compartment model. We identify a nonlinear transformation between the one-compartment model (diffusion tensor imaging, DTI) and a two-compartment model in which the mean diffusivities of both compartments are effectively fixed. While the analytic relationship implies that fitting this two-compartment model does not offer any more information than DTI, it explains why metrics derived from a two-compartment model can exhibit enhanced sensitivity over DTI to certain types of WM processes, such as age-related WM differences. The sensitivity enhancement should not be viewed as a substitute for acquiring multi-shell data. Rather, the results of this study provide insight into the consequences of choosing a two-compartment model when only single-shell data is available.