Multi-component relaxation in clinically isolated syndrome: Lesion myelination may predict multiple sclerosis conversion

Causal relationship between imaging-derived phenotypes and neurodegenerative diseases: a Mendelian randomization study

Neurodegenerative diseases are incurable conditions that lead to gradual and progressive deterioration of brain function in patients. With the aging population, the prevalence of these diseases is expected to increase, posing a significant economic burden on society. Imaging techniques play a crucial role in the diagnosis and monitoring of neurodegenerative diseases. This study utilized a two-sample Mendelian randomization (MR) analysis to assess the causal relationship between different imaging-derived phenotypes (IDP) in the brain and neurodegenerative diseases. Multiple MR methods were employed to minimize bias and obtain reliable estimates of the potential causal relationship between the variable exposures of interest and the outcomes. The study found potential causal relationships between different IDPs and Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and frontotemporal dementia (FTD). Specifically, the study identified potential causal relationships between 2 different types of IDPs and AD, 8 different types of IDPs and PD, 11 different types of imaging-derived phenotypes and ALS, 1 type of IDP and MS, and 1 type of IDP and FTD. This study provides new insights for the prevention, diagnosis, and treatment of neurodegenerative diseases, offering important clues for understanding the pathogenesis of these diseases and developing relevant intervention strategies.

Detection of diffusely abnormal white matter in multiple sclerosis on multiparametric brain MRI using semi-supervised deep learning

In addition to focal lesions, diffusely abnormal white matter (DAWM) is seen on brain MRI of multiple sclerosis (MS) patients and may represent early or distinct disease processes. The role of MRI-observed DAWM is understudied due to a lack of automated assessment methods. Supervised deep learning (DL) methods are highly capable in this domain, but require large sets of labeled data. To overcome this challenge, a DL-based network (DAWM-Net) was trained using semi-supervised learning on a limited set of labeled data for segmentation of DAWM, focal lesions, and normal-appearing brain tissues on multiparametric MRI. DAWM-Net segmentation performance was compared to a previous intensity thresholding-based method on an independent test set from expert consensus (N = 25). Segmentation overlap by Dice Similarity Coefficient (DSC) and Spearman correlation of DAWM volumes were assessed. DAWM-Net showed DSC > 0.93 for normal-appearing brain tissues and DSC > 0.81 for focal lesions. For DAWM-Net, the DAWM DSC was 0.49 ± 0.12 with a moderate volume correlation (ρ = 0.52, p < 0.01). The previous method showed lower DAWM DSC of 0.26 ± 0.08 and lacked a significant volume correlation (ρ = 0.23, p = 0.27). These results demonstrate the feasibility of DL-based DAWM auto-segmentation with semi-supervised learning. This tool may facilitate future investigation of the role of DAWM in MS.

Exploring in vivo lesion myelination dynamics: Longitudinal Myelin Water Imaging in early Multiple Sclerosis

BACKGROUND

Multiple Sclerosis (MS) lesions are pathologically heterogeneous and the temporal behavior in terms of growth and myelination status of individual lesions is highly variable, especially in the early phase of the disease. Thus, monitoring the development of individual lesion myelination by using quantitative magnetic resonance myelin water imaging (MWI) could be valuable to capture the variability of disease pathology and get an individual insight into the subclinical disease activity.

OBJECTIVE

The goal of this work was (1) to observe the variation and longitudinal change of in vivo lesion myelination by means of MWI and its parameter Myelin Water Fraction (MWF), and, (2) to identify individual lesion myelination patterns in early MS.

METHODS

In this study n = 12 patients obtained conventional MRI and quantitative MWI derived from multi-component driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) within four weeks after presenting a clinically isolated syndrome and remained within the study if clinically definitive MS was diagnosed within the 12 months study period. Four MRI sessions were acquired at baseline, 3, 6, and 12 months. The short-term and long-term variability of MWF maps was evaluated by scan-rescan measures and the coefficient of variation was determined in four healthy controls. Tracking of individual lesions was performed using the Automatic Follow-up of Individual Lesions (AFIL) algorithm. Lesion volume and MWF were evaluated for every individual lesion in all patients. Median lesion MWF change was used to define lesion categories as decreasing, varying, increasing and invariant for MWF variation.

RESULTS

In total n = 386 T2 lesions were detected with a subset of n = 225 permanent lesions present at all four time-points. Among those, a heterogeneous lesion MWF reduction was found, with the majority of lesions bearing only mild MWF reduction, approximately a third with an intermediate MWF decrease and highest MWF reduction in acute-inflammatory active lesions. A moderate negative correlation was determined between individual lesion volumes and median MWF consistent across all time-points. Permanent lesions featured variable temporal dynamics with the majority of varying MWF (58 %), however decreasing (16 %), increasing (15 %) and invariant (11 %) subgroups could be identified resembling demyelinating activity and post-demyelinating inactivity known from histopathology studies. Inflammatory-active enhancing lesions showed a distinct pattern of MWF reduction followed by partial recovery after 3 months. This was similar in new enhancing lesions and those with a non-enhancing precursor lesion.

CONCLUSION

This work provides in vivo evidence for an individual evolution of early demyelinated MS lesions measured by means of MWF imaging. Our results support the hypothesis, that MS lesions undergo multiple demyelination and remyelination episodes in the early acute phase. The in vivo MRI surrogate of myelin turnover bears capacity as a novel biomarker to select and potentially monitor personalized MS treatment.

Diffusely abnormal white matter in multiple sclerosis

MRI enables detailed in vivo depiction of multiple sclerosis (MS) pathology. Localized areas of MS damage, commonly referred to as lesions, or plaques, have been a focus of clinical and research MRI studies for over four decades. A nonplaque MRI abnormality which is present in at least 25% of MS patients but has received far less attention is diffusely abnormal white matter (DAWM). DAWM has poorly defined boundaries and a signal intensity that is between normal-appearing white matter and classic lesions on proton density and T₂ -weighted images. All clinical phenotypes of MS demonstrate DAWM, including clinically isolated syndrome, where DAWM is associated with higher lesion volume, reduced brain volume, and earlier conversion to MS. Advanced MRI metric abnormalities in DAWM tend to be greater than those in NAWM, but not as severe as focal lesions, with myelin, axons, and water-related changes commonly reported. Histological studies demonstrate a primary lipid abnormality in DAWM, with some axonal damage and lesser involvement of myelin proteins. This review provides an overview of DAWM identification, summarizes in vivo and postmortem observations, and comments on potential pathophysiological mechanisms, which may underlie DAWM in MS. Given the prevalence and potential clinical impact of DAWM, the number of imaging studies focusing on DAWM is insufficient. Characterization of DAWM significance and microstructure would benefit from larger longitudinal and additional quantitative imaging efforts. Revisiting data from previous studies that included proton density and T₂ imaging would enable retrospective DAWM identification and analysis.

T2 Relaxometry Evidence of Microstructural Changes in Diffusely Abnormal White Matter in Relapsing-Remitting Multiple Sclerosis and Clinically Isolated Syndrome: Impact on Visuomotor Performance

BACKGROUND

Although diffusely abnormal white matter (DAWM) is commonly seen in multiple sclerosis (MS), it is rarely considered in clinical/imaging studies.

PURPOSE

To evaluate quantitative markers of microstructural changes in DAWM of patients with clinically isolated syndrome (CIS) and relapsing-remitting MS (RR-MS) in relation to MS lesions and degree of neurocognitive impairment, by using a multi-echo spin echo (MESE) Proton Density PD-to-T2 sequence.

STUDY TYPE

Prospective, cross-sectional.

POPULATION

Thirty-seven RR-MS patients, 33 CIS patients, and 52 healthy controls.

FIELD STRENGTH/SEQUENCE

1.5 T/T1-, T2-weighted, fluid-attenuated inversion recovery, and MESE sequences.

ASSESSMENT

Long T2, short T2, and myelin water fraction (MWF) values were estimated as indices of intra/extracellular water content and myelin content, respectively, in DAWM, posterior periventricular normal appearing white matter (NAWM), and focal MS lesions, classified according to their signal intensity on T1 sequences. Patients were, also, administered a battery of neuropsychological tests.

STATISTICAL TESTS

Comparisons of T2 and MWF values in DAWM, NAWM, and MS lesions were examined, using two-way mixed analyses of variance. Associations of Grooved Pegboard performance with T2 and MWF values in DAWM and NAWM were assessed using Pearson correlation coefficients.

RESULTS

T2 and MWF values of DAWM were intermediate between the respective values of NAWM and T1 hypointense focal lesions, while there was no difference between the respective values of DAWM and T1-isointense lesions. T2 values in DAWM were strongly associated with visuomotor performance in CIS patients.

DATA CONCLUSION

Intra/extracellular water and myelin water content of DAWM are similar to those of T1-isointense lesions and predict visuomotor performance in CIS patients.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Simultaneous T2 and T 2 ∗ mapping of multiple sclerosis lesions with radial RARE-EPI

PURPOSE

The characteristic MRI features of multiple sclerosis (MS) lesions make it conceptually appealing to pursue parametric mapping techniques that support simultaneous generation of quantitative maps of 2 or more MR contrast mechanisms. We present a modular rapid acquisition with relaxation enhancement (RARE)-EPI hybrid that facilitates simultaneous T₂ and T 2 ∗ mapping (2in1-RARE-EPI).

METHODS

In 2in1-RARE-EPI the first echoes in the echo train are acquired with a RARE module, later echoes are acquired with an EPI module. To define the fraction of echoes covered by the RARE and EPI module, an error analysis of T₂ and T 2 ∗ was conducted with Monte Carlo simulations. Radial k-space (under)sampling was implemented for acceleration (R = 2). The feasibility of 2in1-RARE-EPI for simultaneous T₂ and T 2 ∗ mapping was examined in a phantom study mimicking T₂ and T 2 ∗ relaxation times of the brain. For validation, 2in1-RARE-EPI was benchmarked versus multi spin-echo (MSE) and multi gradient-echo (MGRE) techniques. The clinical applicability of 2in1-RARE-EPI was demonstrated in healthy subjects and MS patients.

RESULTS

There was a good agreement between T₂ / T 2 ∗ values derived from 2in1-RARE-EPI and T₂ / T 2 ∗ reference values obtained from MSE and MGRE in both phantoms and healthy subjects. In patients, MS lesions in T₂ and T 2 ∗ maps deduced from 2in1-RARE-EPI could be just as clearly delineated as in reference maps calculated from MSE/MGRE.

CONCLUSION

This work demonstrates the feasibility of radially (under)sampled 2in1-RARE-EPI for simultaneous T₂ and T 2 ∗ mapping in MS patients.

Multimodal MRI Response to Fingolimod in Multiple Sclerosis: A Nonrandomized, Single Arm, Observational Study

BACKGROUND AND PURPOSE

Fingolimod has a favorable effect on conventional MRI measures; however, its neuroprotective effect is not clear. We aim to investigate changes of conventional and advanced MRI measures in lesions and normal-appearing white matter (NAWM) over 2 years in fingolimod-treated patients.

METHODS

Fifty relapsing-remitting multiple sclerosis patients and 27 healthy controls were enrolled in the study and underwent baseline, 1-year, and 2-year 3T MRI scans. T2 lesion volume, whole brain volume, cortical gray matter volume, white matter volume, corpus callosum area, percentage brain volume change (PBVC), Expanded Disability Status Scale, gadolinium-enhancing lesions, PBVC, magnetization transfer ratio (MTR), and diffusion tensor imaging metrics (fractional anisotropy [FA] and median diffusivity [MD]) in lesions and NAWM were calculated. Longitudinal changes were examined using one-way repeated measures ANOVA. Bonferroni correction for multiple testing was used when appropriate.

RESULTS

Conventional MRI measures were unchanged in both groups. Lesion MTR increased significantly (P < .001), but NAWM-MTR remained unchanged. Lesion FA improved significantly in year 1 (P = .003) and over the study duration (P = .05). Lesion MD changed significantly from baseline to year 1 (P < .001) and remained stable over 2 years. NAWM-FA was significant from baseline to year 1 (P = .002) and from baseline to year 2 (P < .001). NAWM-MD was significant only from baseline to year 1 (P = .001).

CONCLUSIONS

These findings suggest a possible neuroreparative effect of fingolimod on the MS lesions and NAWM. Larger and longer randomized studies are required to confirm these results.

So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging

Myelin water imaging (MWI) is an MRI imaging biomarker for myelin. This method can generate an in vivo whole-brain myelin water fraction map in approximately 10 minutes. It has been applied in various applications including neurodegenerative disease, neurodevelopmental, and neuroplasticity studies. In this review we start with a brief introduction of myelin biology and discuss the contributions of myelin in conventional MRI contrasts. Then the MRI properties of myelin water and four different MWI methods, which are categorized as T₂ -, T₂ *-, T₁ -, and steady-state-based MWI, are summarized. After that, we cover more practical issues such as availability, interpretation, and validation of these methods. To illustrate the utility of MWI as a clinical research tool, MWI studies for two diseases, multiple sclerosis and neuromyelitis optica, are introduced. Additional topics about imaging myelin in gray matter and non-MWI methods for myelin imaging are also included. Although technical and physiological limitations exist, MWI is a potent surrogate biomarker of myelin that carries valuable and useful information of myelin. Evidence Level: 5 Technical Efficacy: 1 J. MAGN. RESON. IMAGING 2021;53:360-373.

Exploring individual multiple sclerosis lesion volume change over time: Development of an algorithm for the analyses of longitudinal quantitative MRI measures

Background

Magnetic resonance imaging (MRI) is used to follow-up multiple sclerosis (MS) and evaluate disease progression and therapy response via lesion quantification. However, there is a lack of automated post-processing techniques to quantify individual MS lesion change.

Objective

The present study developed a secondary post-processing algorithm for MS lesion segmentation routine to quantify individual changes in volume over time.

Methods

An Automatic Follow-up of Individual Lesions (AFIL) algorithm was developed to process time series of pre-segmented binary lesion masks. The resulting consistently labelled lesion masks allowed for the evaluation of individual lesion volumes. Algorithm performance testing was executed in seven early MS patients with four MRI visits, and MS experienced readers verified the accuracy.

Results

AFIL distinguished 328 individual MS lesions with a 0.9% error rate to track persistent or new lesions based on expert assessment. A total of 121 new lesions evolved within the observed time period. The proportional courses of 69.1% lesions in the persistent lesion population exhibited varying volume, 16.9% exhibited stable volume, 3.4% exhibiting continuously increasing, and 0.5% exhibited continuously decreasing volume.

Conclusion

This algorithm tracked individual lesions to automatically create an individual lesion growth profile of MS patients. This approach may allow for characterization of patients based on their individual lesion progression.

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This algorithm can be used for individual tracking of lesion volumes or can read-out parameter changes of quantitative MR images in lesions.

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This method allows the characterization of patients based on their individual lesion growth profile.

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In early MS most lesions had varying volume, few were stable, and very few increased or decreased continuously.

Predicting conversion from clinically isolated syndrome to multiple sclerosis-An imaging-based machine learning approach

Magnetic resonance imaging (MRI) scans play a pivotal role in the evaluation of patients presenting with a clinically isolated syndrome (CIS), as these may depict brain lesions suggestive of an inflammatory cause. We hypothesized that it is possible to predict the conversion from CIS to multiple sclerosis (MS) based on the baseline MRI scan by studying image features of these lesions.

We analyzed 84 patients diagnosed with CIS from a prospective observational single center cohort. The patients were followed up for at least three years. Conversion to MS was defined according to the 2010 McDonald criteria. Brain lesions were segmented based on 3D FLAIR and 3D T1 images. We generated brain lesion masks by a computer assisted manual segmentation. We also generated a set of automated segmentations using the Lesion Segmentation Toolbox for SPM to assess the influence of different segmentation methods. Shape and brightness features were automatically calculated from the segmented masks and used as input data to train an oblique random forest classifier. Prediction accuracies of the resulting model were validated through a three-fold cross-validation.

Conversion from CIS to MS occurred in 66 of 84 patients (79%). The conversion or non-conversion was predicted correctly in 71 patients based on shape features derived from the computer assisted manual segmentation masks (84.5% accuracy). This predictor was more accurate than predicting conversion using dissemination in space at baseline according to the 2010 McDonald criteria (75% accuracy). While shape features strongly contributed to the accuracy of the predictor, including intensity features did not further improve performance.

As patients who convert to definite MS benefit from early treatment, an early classification model is highly desirable. Our study shows that shape parameters of lesions can contribute to predicting the future course of CIS patients more accurately.

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A random forest tool can help to identify patients who convert from clinical isolated syndrome into multiple sclerosis (MS).

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The classifier is driven by shape features of lesions in the first MR scan.

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The found shape features reflect the typical ovoid growth of MS lesions.