The Relationship between Gray Matter Quantitative MRI and Disability in Secondary Progressive Multiple Sclerosis

MRI features and disability in multiple sclerosis: A systematic review and meta-analysis

BACKGROUND

In this systematic review and meta-analysis, we aimed to investigate the correlation between disability in patients with Multiple sclerosis (MS) measured by the Expanded Disability Status Scale (EDSS) and brain Magnetic Resonance Imaging (MRI) features to provide reliable results on which characteristics in the MRI can predict disability and prognosis of the disease.

METHODS

A systematic literature search was performed using three databases including PubMed, Scopus, and Web of Science. The selected peer-reviewed studies must report a correlation between EDSS scores and MRI features. The correlation coefficients of included studies were converted to the Fisher's z scale, and the results were pooled.

RESULTS

Overall, 105 studies A total of 16,613 patients with MS entered our study. We found no significant correlation between total brain volume and EDSS assessment (95 % CI: -0.37 to 0.08; z-score: -0.15). We examined the potential correlation between the volume of T1 and T2 lesions and the level of disability. A positive significant correlation was found (95 % CI: 0.19 to 0.43; z-score: 0.31), (95 % CI: 0.17 to 0.33; z-score: 0.25). We observed a significant correlation between white matter volume and EDSS score in patients with MS (95 % CI: -0.37 to -0.03; z-score: -0.21). Moreover, there was a significant negative correlation between gray matter volume and disability (95 % CI: -0.025 to -0.07; z-score: -0.16).

CONCLUSION

In conclusion, this systematic review and meta-analysis revealed that disability in patients with MS is linked to extensive changes in different brain regions, encompassing gray and white matter, as well as T1 and T2 weighted MRI lesions.

Characterization of white matter lesions in multiple sclerosis using proton density and T1-relaxation measures

PURPOSE

Although lesion dissemination in time is a defining characteristic of multiple sclerosis (MS), there is a limited understanding of lesion heterogeneity. Currently, conventional sequences such as fluid attenuated inversion recovery (FLAIR) and T1-weighted (T1W) data are used to assess MS lesions qualitatively. Estimating water content could provide a measure of local tissue rarefaction, or reduced tissue density, resulting from chronic inflammation. Our goal was to utilize the proton spin density (PD), derived from a rapid, multi-contrast STAGE (strategically acquired gradient echo) protocol to characterize white matter (WM) lesions seen on T2W, FLAIR and T1W data.

MATERIALS AND METHODS

Twenty (20) subjects with relapsing-remitting MS were scanned at 3 T using T1W, T2-weighted, FLAIR and strategically acquired gradient echo (STAGE) sequences. PD and T1 maps were derived from the STAGE data. Disease severity scores, including Extended Disability Status Scale (EDSS) and Multiple Sclerosis Functional Composite (MSFC), were correlated with total, high PD and high T1 lesion volumes. A probability map of high PD regions and all lesions across all subjects was generated. Five perilesional normal appearing WM (NAWM) bands surrounding the lesions were generated to compare the median PD and T1 values in each band with the lesional values and the global WM.

RESULTS

T1W intensity was negatively correlated with PD as expected (R = -0.87, p < 0.01, R2 = 0.756) and the FLAIR signal was suppressed for high PD volumes within the lesions, roughly for PD ≥ 0.85. The threshold for high PD and T1 regions was set to 0.909 and 1953.6 ms, respectively. High PD regions showed a high probability of occurrence near the boundary of the lateral ventricles. EDSS score and nine-hole peg test (dominant and non-dominant hand) were significantly correlated with the total lesion volume and the volumes of high PD and T1 regions (p < 0.05). There was a significant difference in PD/T1 values between the high PD/T1 regions within the lesions and the remaining lesional tissue (p < 0.001). In addition, the PD values of the first NAWM perilesional band directly adjacent to the lesional boundary displayed a significant difference (p < 0.05) compared to the global WM.

CONCLUSION

Lesions with high PD and T1s had the highest probability of occurrence at the boundary of the lateral ventricles and likely represent chronic lesions with significant local tissue rarefaction. Moreover, the perilesional NAWM exhibited subtly increasing PD and T1 values from the NAWM up to the lesion boundary. Unlike on the T1 maps, the perilesional band adjacent to the lesion boundary possessed a significantly higher PD value than the global WM PD values. This shows that PD maps were sensitive to the subtle changes in NAWM surrounding the lesions.

Application of a novel T1 retrospective quantification using internal references (T1-REQUIRE) algorithm to derive quantitative T1 relaxation maps of the brain

Most MRI sequences used clinically are qualitative or weighted. While such images provide useful information for clinicians to diagnose and monitor disease progression, they lack the ability to quantify tissue damage for more objective assessment. In this study, an algorithm referred to as the T1-REQUIRE is presented as a proof-of-concept which uses nonlinear transformations to retrospectively estimate T1 relaxation times in the brain using T1-weighted MRIs, the appropriate signal equation, and internal, healthy tissues as references. T1-REQUIRE was applied to two T1-weighted MR sequences, a spin-echo and a MPRAGE, and validated with a reference standard T1 mapping algorithm in vivo. In addition, a multiscanner study was run using MPRAGE images to determine the effectiveness of T1-REQUIRE in conforming the data from different scanners into a more uniform way of analyzing T1-relaxation maps. The T1-REQUIRE algorithm shows good agreement with the reference standard (Lin's concordance correlation coefficients of 0.884 for the spin-echo and 0.838 for the MPRAGE) and with each other (Lin's concordance correlation coefficient of 0.887). The interscanner studies showed improved alignment of cumulative distribution functions after T1-REQUIRE was performed. T1-REQUIRE was validated with a reference standard and shown to be an effective estimate of T1 over a clinically relevant range of T1 values. In addition, T1-REQUIRE showed excellent data conformity across different scanners, providing evidence that T1-REQUIRE could be a useful addition to big data pipelines.

T1 Relaxation Times in the Cortex and Thalamus Are Associated With Working Memory and Information Processing Speed in Patients With Multiple Sclerosis

Background: Cortical and thalamic pathologies have been associated with cognitive impairment in patients with multiple sclerosis (MS). Objective: We aimed to quantify cortical and thalamic damage in patients with MS using a high-resolution T1 mapping technique and to evaluate the association of these changes with clinical and cognitive impairment. Methods: The study group consisted of 49 patients with mainly relapsing-remitting MS and 17 age-matched healthy controls who received 3T MRIs including a T1 mapping sequence (MP2RAGE). Mean T1 relaxation times (T1-RT) in the cortex and thalami were compared between patients with MS and healthy controls. Additionally, correlation analysis was performed to assess the relationship between MRI parameters and clinical and cognitive disability. Results: Patients with MS had significantly decreased normalized brain, gray matter, and white matter volumes, as well as increased T1-RT in the normal-appearing white matter, compared to healthy controls (p < 0.001). Partial correlation analysis with age, sex, and disease duration as covariates revealed correlations for T1-RT in the cortex (r = -0.33, p < 0.05), and thalami (right thalamus: r = -0.37, left thalamus: r = -0.50, both p < 0.05) with working memory and information processing speed, as measured by the Symbol-Digit Modalities Test. Conclusion: T1-RT in the cortex and thalamus correlate with information processing speed in patients with MS.

Whole brain 3D MR fingerprinting in multiple sclerosis: a pilot study

Background

MR fingerprinting (MRF) is a novel imaging method proposed for the diagnosis of Multiple Sclerosis (MS). This study aims to determine if MR Fingerprinting (MRF) relaxometry can differentiate frontal normal appearing white matter (F-NAWM) and splenium in patients diagnosed with MS as compared to controls and to characterize the relaxometry of demyelinating plaques relative to the time of diagnosis.

Methods

Three-dimensional (3D) MRF data were acquired on a 3.0T MRI system resulting in isotropic voxels (1 × 1 × 1 mm³) and a total acquisition time of 4 min 38 s. Data were collected on 18 subjects paired with 18 controls. Regions of interest were drawn over MRF-derived T₁ relaxometry maps encompassing selected MS lesions, F-NAWM and splenium. T₁ and T₂ relaxometry features from those segmented areas were used to classify MS lesions from F-NAWM and splenium with T-distributed stochastic neighbor embedding algorithms. Partial least squares discriminant analysis was performed to discriminate NAWM and Splenium in MS compared with controls.

Results

Mean out-of-fold machine learning prediction accuracy for discriminant results between MS patients and controls for F-NAWM was 65 % (p = 0.21) and approached 90 % (p < 0.01) for the splenium. There was significant positive correlation between time since diagnosis and MS lesions mean T2 (p = 0.015), minimum T1 (p = 0.03) and negative correlation with splenium uniformity (p = 0.04). Perfect discrimination (AUC = 1) was achieved between selected features from MS lesions and F-NAWM.

Conclusions

3D-MRF has the ability to differentiate between MS and controls based on relaxometry properties from the F-NAWM and splenium. Whole brain coverage allows the assessment of quantitative properties within lesions that provide chronological assessment of the time from MS diagnosis.

A Comparison of Quantitative R1 and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex

Brain development and aging are complex processes that unfold in multiple brain regions simultaneously. Recently, models of brain age prediction have aroused great interest, as these models can potentially help to understand neurological diseases and elucidate basic neurobiological mechanisms. We test whether quantitative magnetic resonance imaging can contribute to such age prediction models. Using R1, the longitudinal rate of relaxation, we explore lifespan dynamics in cortical gray matter. We compare R1 with cortical thickness, a well-established biomarker of brain development and aging. Using 160 healthy individuals (6-81 years old), we found that R1 and cortical thickness predicted age similarly, but the regions contributing to the prediction differed. Next, we characterized R1 development and aging dynamics. Compared with anterior regions, in posterior regions we found an earlier R1 peak but a steeper postpeak decline. We replicate these findings: firstly, we tested a subset (N = 10) of the original dataset for whom we had additional scans at a lower resolution; and second, we verified the results on an independent dataset (N = 34). Finally, we compared the age prediction models on a subset of 10 patients with multiple sclerosis. The patients are predicted older than their chronological age using R1 but not with cortical thickness.

Voxel-based analysis of gray matter relaxation rates shows different correlation patterns for cognitive impairment and physical disability in relapsing-remitting multiple sclerosis

BACKGROUND

Regional analyses of markers of microstructural gray matter (GM) changes, including relaxation rates, have shown inconsistent correlations with physical and cognitive impairment in MS.

OBJECTIVE

To assess voxelwise the correlation of the R1 and R2 relaxation rates with the physical and cognitive impairment in MS.

METHODS

GM R1 and R2 relaxation rate maps were obtained in 241 relapsing-remitting MS patients by relaxometric segmentation of MRI studies. Correlations with the Expanded Disability Status Scale (EDSS) and the percentage of impaired cognitive test (Brief Repeatable Battery and Stroop Test, available in 186 patients) were assessed voxelwise, including voxel GM content as nuisance covariate to remove the effect of atrophy on the correlations.

RESULTS

Extensive clusters of inverse correlation between EDSS and R2 were detected throughout the brain, while inverse correlations with R1 were mostly limited to perirolandic and supramarginal cortices. Cognitive impairment correlated negatively with R1, and to a lesser extent with R2, in the middle frontal, mesial temporal, midcingulate and medial parieto-occipital cortices.

CONCLUSION

In relapsing-remitting MS patients, GM microstructural changes correlate diffusely with physical disability, independent of atrophy, with a preferential role of the sensorimotor cortices. Neuronal damage in the limbic system and dorsolateral prefrontal cortices correlates with cognitive dysfunction.

Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study

Effective connectivity (EC) is able to explore causal effects between brain areas and can depict mechanisms that underlie repair and adaptation in chronic brain diseases. Thus, the application of EC techniques in multiple sclerosis (MS) has the potential to determine directionality of neuronal interactions and may provide an imaging biomarker for disease progression. Here, serial longitudinal structural and resting-state fMRI was performed at 12-week intervals over one year in twelve MS patients. Twelve healthy subjects served as controls (HC). Two approaches for EC quantification were used: Causal Bayesian Network (CBN) and Time-resolved Partial Directed Coherence (TPDC). The EC strength was correlated with the Expanded Disability Status Scale (EDSS) and Fatigue Scale for Motor and Cognitive functions (FSMC). Our findings demonstrated a longitudinal increase in EC between specific brain regions, detected in both the CBN and TPDC analysis in MS patients. In particular, EC from the deep grey matter, frontal, prefrontal and temporal regions showed a continuous increase over the study period. No longitudinal changes in EC were attested in HC during the study. Furthermore, we observed an association between clinical performance and EC strength. In particular, the EC increase in fronto-cerebellar connections showed an inverse correlation with the EDSS and FSMC. Our data depict continuous functional reorganization between specific brain regions indicated by increasing EC over time in MS, which is not detectable in HC. In particular, fronto-cerebellar connections, which were closely related to clinical performance, may provide a marker of brain plasticity and functional reserve in MS.

Diffusion tensor imaging of the normal-appearing deep gray matter in primary and secondary progressive multiple sclerosis

Background

Despite strongly overlapping patterns of clinical and histopathologic findings in primary and secondary progressive multiple sclerosis, differences concerning motor symptoms, central nervous system inflammation, atrophy, and demyelination that cannot be accounted for by lesion load alone remain to be elucidated.

Purpose

To evaluate the normal-appearing deep gray matter in patients with primary and secondary progressive multiple sclerosis, diffusion tensor imaging was used in this study.

Material and Methods

In 14 multiple sclerosis patients with primary and secondary progressive multiple sclerosis, axial echo-planar single-shot diffusion tensor imaging sequences with 32 diffusion-encoding directions and axial FLAIR sequences were acquired on a 3T system using an eight-channel SENSE head coil. FLAIR hyperintense multiple sclerosis lesions were outlined semi-automatically and normal-appearing deep gray matter was outlined manually (caudate nucleus, globus pallidus, putamen, thalamus, substantia nigra, and red nucleus). Fractional anisotropy and mean diffusivity values within the normal-appearing deep gray matter for the two groups were compared.

Results

Interhemispheric differences in mean diffusivity values (but not in fractional anisotropy), were significantly higher in primary progressive multiple sclerosis than in secondary progressive multiple sclerosis for the substantia nigra ( P = 0.04) and the putamen ( P = 0.021). Volumes, mean diffusivity, or fractional anisotropy of the remaining normal-appearing deep gray matter did not differ significantly.

Conclusion

This study showed a higher interhemispheric difference in the mean diffusivity in the substantia nigra and putamen in patients with primary progressive multiple sclerosis than in those with secondary progressive multiple sclerosis. These changes may represent edema, as well as axonal and myelin loss that can affect the normal-appearing deep gray matter of the two hemispheres differently and may point to differences in the laterality of motor symptoms.

Multivariate prediction of multiple sclerosis using robust quantitative MR-based image metrics

OBJECTIVES

The current work investigates the performance of different multivariate supervised machine learning models to predict the presence or absence of multiple sclerosis (MS) based on features derived from quantitative MRI acquisitions. The performance of these models was evaluated for images which are significantly degraded due to subject motion, a problem which is often observed in clinical routine diagnostics. Finally, the difference between a true multivariate analysis and the corresponding univariate analysis based on single parameters alone was addressed.

MATERIALS AND METHODS

52 MS patients and 45 healthy controls where scanned on a 3T system. The datasets showed variable degrees of motion-associated artefacts. For each dataset, the average of T₁, T₂^*, total and myelin bound water content was determined in white and grey matter. Based on these parameters, different multivariate models were trained and their cross-validated performance to predict the presence of MS was evaluated. Furthermore, the univariate distributions of each quantitative parameter were employed to define optimised cut-offs that differentiate MS patients from healthy controls.

RESULTS

For data not affected by motion, 83.7% of all subjects were correctly classified using a crossvalidated multivariate model. Inclusion of data with significant artefacts reduces the rate of correct classification to 74.5%. T₁ in grey and myelin water content in white matter where the most discriminating variables in the multivariate analysis. In contrast, the total water content in white matter and the ratio of white and grey matter total water content each resulted in 77% correct classifications in a univariate regression analysis.

CONCLUSION

The results demonstrate that even simple quantitative MRI-based measures allow for an automated prediction of the presence/absence of multiple sclerosis with good specificity. Importantly, even highly degraded datasets due to motion-artefacts could be correctly classified, especially when pooling features derived from grey and white matter. Finally, the advantage of a multivariate over a univariate analysis of quantitative MR data was shown.