A new window in multiple sclerosis pathology: non-conventional quantitative magnetic resonance imaging outcomes

Real-world analysis of brain atrophy in multiple sclerosis patients with an artificial intelligence based software tool

BACKGROUND

Atrophy of white and grey matter volumes occurs early in the brains of people with multiple sclerosis (pwMS) and has great clinical relevance. In clinical trials, brain atrophy can be quantified by magnetic resonance imaging (MRI) with automated software tools.

METHODS

In this study, we analyze volumes of various brain regions with the software "md brain" based on routine MRI scans of 53 pwMS in a real-world setting. We compare brain volumes of pwMS with an EDSS ≥ 3.5 and a disease duration ≥ 10 years to the brain volumes of pwMS with an EDSS < 3.5 and a disease duration < 10 years as well as with or without immunotherapy.

RESULTS

pwMS with an EDSS ≥ 3.5 and a disease duration ≥ 10 years had significantly lower volumes of the total brain, the grey matter and of the frontal, temporal, parietal and occipital lobe regions as compared to pwMS with an EDSS < 3.5 and a disease duration < 10 years. Regional brain volumes were significantly lower in pwMS without immunotherapy.

CONCLUSIONS

The study showed that higher EDSS, longer disease duration and absence of immunotherapy was associated with lower volumes in a number of brain regions. Further real-world studies may include larger patient cohorts in longitudinal analyses.

T1 mapping from routine 3D T1-weighted inversion recovery sequences in clinical practice: comparison against reference inversion recovery fast field echo T1 scans and feasibility in multiple sclerosis

BACKGROUND AND PURPOSE

Quantitative T1 mapping can be an essential tool for assessing tissue injury in multiple sclerosis (MS). We introduce T1-REQUIRE, a method that converts a single high-resolution anatomical 3D T1-weighted Turbo Field Echo (3DT1TFE) scan into a parametric T1 map that could be used for quantitative assessment of tissue damage. We present the accuracy and feasibility of this method in MS.

METHODS

14 subjects with relapsing-remitting MS and 10 healthy subjects were examined. T1 maps were generated from 3DT1TFE images using T1-REQUIRE, which estimates T1 values using MR signal equations and internal tissue reference T1 values. Estimated T1 of lesions, white, and gray matter regions were compared with reference Inversion-Recovery Fast Field Echo T1 values and analyzed via correlation and Bland-Altman (BA) statistics.

RESULTS

159 T1-weighted (T1W) hypointense MS lesions and 288 gray matter regions were examined. T1 values for MS lesions showed a Pearson's correlation of r = 0.81 (p < 0.000), R2 = 0.65, and Bias = 4.18%. BA statistics showed a mean difference of -53.95 ms and limits of agreement (LOA) of -344.20 and 236.30 ms. Non-lesional normal-appearing white matter had a correlation coefficient of r = 0.82 (p < 0.000), R2 = 0.67, Bias = 8.78%, mean difference of 73.87 ms, and LOA of -55.67 and 203.41 ms.

CONCLUSIONS

We demonstrate the feasibility of retroactively derived high-resolution T1 maps from routinely acquired anatomical images, which could be used to quantify tissue pathology in MS. The results of this study will set the stage for testing this method in larger clinical studies for examining MS disease activity and progression.

Modeling and simulation for prediction of multiple sclerosis progression

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

Performance Comparison of Compressed Sensing Algorithms for Accelerating T1ρ Mapping of Human Brain

BACKGROUND

3D-T_1ρ mapping is useful to quantify various neurologic disorders, but data are currently time-consuming to acquire.

PURPOSE

To compare the performance of five compressed sensing (CS) algorithms-spatiotemporal finite differences (STFD), exponential dictionary (EXP), 3D-wavelet transform (WAV), low-rank (LOW) and low-rank plus sparse model with spatial finite differences (L + S SFD)-for 3D-T_1ρ mapping of the human brain with acceleration factors (AFs) of 2, 5, and 10.

STUDY TYPE

Retrospective.

SUBJECTS

Eight healthy volunteers underwent T_1ρ imaging of the whole brain.

FIELD STRENGTH/SEQUENCE

The sequence was fully sampled 3D Cartesian ultrafast gradient echo sequence with a customized T_1ρ preparation module on a clinical 3T scanner.

ASSESSMENT

The fully sampled data was undersampled by factors of 2, 5, and 10 and reconstructed with the five CS algorithms. Image reconstruction quality was evaluated and compared to the SENSE reconstruction of the fully sampled data (reference) and T_1ρ estimation errors were assessed as a function of AF.

STATISTICAL TESTS

Normalized root mean squared errors (nRMSE) and median normalized absolute deviation (MNAD) errors were calculated to compare image reconstruction errors and T_1ρ estimation errors, respectively. Linear regression plots, Bland-Altman plots, and Pearson correlation coefficients (CC) are shown.

RESULTS

For image reconstruction quality, at AF = 2, EXP transforms had the lowest mRMSE (1.56%). At higher AF values, STFD performed better, with the smallest errors (3.16% at AF = 5, 4.32% at AF = 10). For whole-brain quantitative T_1ρ mapping, at AF = 2, EXP performed best (MNAD error = 1.62%). At higher AF values (AF = 5, 10), the STFD technique had the least errors (2.96% at AF = 5, 4.24% at AF = 10) and the smallest variance from the reference T_1ρ estimates.

DATA CONCLUSION

This study demonstrates the use of different CS algorithms that may be useful in reducing the scan time required to perform volumetric T_1ρ mapping of the brain.

LEVEL OF EVIDENCE

2.

TECHNICAL EFFICACY STAGE

1.

Preservation of neuronal function as measured by clinical and MRI endpoints in relapsing-remitting multiple sclerosis: how effective are current treatment strategies?

INTRODUCTION

Approved medications for relapsing-remitting multiple sclerosis have shown to be effective in terms of their anti-inflammatory potential. However, it is also crucial to evaluate what long-term effects a patient can expect from current MS drugs in terms of preventing neurodegeneration. Here we aim to provide an overview of the current treatment strategies in MS with a specific focus on potential neuroprotective effects. Areas covered: Randomized, double-blind and placebo or referral-drug controlled phase 2a/b and phase 3 trials were examined; non-blinded phase 4 studies (extension studies) were included to provide long-term data, if not otherwise available. Endpoints considered were expanded disability status scale, various neuropsychological tests, percent brain volume change and T1-hypointense lesions as well as multiple sclerosis functional composite, confirmed disease progression, and no evidence of disease activity. Expert commentary: Overall, neuroprotective functions of classical MS therapeutics are not sufficiently investigated, but available data show limited effects. Thus, further research and development in neuroprotection are warranted. When counselling patients, potential long-term beneficial effects should be presented more conservatively.

CROP - The Clinico-Radiologico-Ophthalmological Paradox in Multiple Sclerosis: Are Patterns of Retinal and MRI Changes Heterogeneous and Thus Not Predictable?

BACKGROUND

To date, no direct scientific evidence has been found linking tissue changes in multiple sclerosis (MS) patients, such as demyelination, axonal destruction or gliosis, with either steady progression and/or stepwise accumulation of focal CNS lesions. Tissue changes such as reduction of the retinal nerve fiber layer (RNFL) and the total macular volume (TMV), or brain- and spinal cord atrophy indicates an irreversible stage of tissue destruction. Whether these changes are found in all MS patients, and if there is a correlation with clinical disease state, remains controversial. The objective of our study was to determine, whether there was any correlation between the RNFL or TMV of patients with MS, and: (1) the lesion load along the visual pathways, (2) the ratios and absolute concentrations of metabolites in the normal-appearing white matter (NAWM), (3) standard brain atrophy indices, (4) disease activity or (5) disease duration.

METHODS

28 MS patients (RRMS, n = 23; secondary progressive MS (SPMS), n = 5) with moderately-high disease activity or long disease course were included in the study. We utilised: (1) magnetic resonance imaging (MRI) and (2) -spectroscopy (MRS), both operating at 3 Tesla, and (3) high-resolution spectral domain-OCT with locked reference images and eye tracking mode) to undertake the study.

RESULTS

There was no consistency in the pattern of CNS metabolites, brain atrophy indices and the RNFL/TMV between individuals, which ranged from normal to markedly-reduced levels. Furthermore, there was no strict correlation between CNS metabolites, lesions along the visual pathways, atrophy indices, RNFL, TMV, disease duration or disability.

CONCLUSIONS

Based on the findings of this study, we recommend that the concept of 'clinico-radiologico paradox' in multiple sclerosis be extended to CROP-'clinico-radiologico-ophthalmological paradox'. Furthermore, OCT data of MS patients should be interpreted with caution.

Accelerated whole-brain multi-parameter mapping using blind compressed sensing

PURPOSE

To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain T1ρ and T2 mapping.

METHODS

BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing, the dictionary and the sparse coefficients are jointly estimated from undersampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors (R).

RESULTS

From 2D retrospective undersampling experiments, the mean square errors in T1ρ and T2 maps were observed to be within 0.1% up to R = 10. BCS was observed to be more robust to patient-specific motion as compared to other compressed sensing schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions.

CONCLUSION

BCS considerably reduces scan time for multiparameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current compressed sensing and principal component analysis-based techniques.

Clinical relevance of brain volume measures in multiple sclerosis

Multiple sclerosis (MS) is a chronic disease with an inflammatory and neurodegenerative pathology. Axonal loss and neurodegeneration occurs early in the disease course and may lead to irreversible neurological impairment. Changes in brain volume, observed from the earliest stage of MS and proceeding throughout the disease course, may be an accurate measure of neurodegeneration and tissue damage. There are a number of magnetic resonance imaging-based methods for determining global or regional brain volume, including cross-sectional (e.g. brain parenchymal fraction) and longitudinal techniques (e.g. SIENA [Structural Image Evaluation using Normalization of Atrophy]). Although these methods are sensitive and reproducible, caution must be exercised when interpreting brain volume data, as numerous factors (e.g. pseudoatrophy) may have a confounding effect on measurements, especially in a disease with complex pathological substrates such as MS. Brain volume loss has been correlated with disability progression and cognitive impairment in MS, with the loss of grey matter volume more closely correlated with clinical measures than loss of white matter volume. Preventing brain volume loss may therefore have important clinical implications affecting treatment decisions, with several clinical trials now demonstrating an effect of disease-modifying treatments (DMTs) on reducing brain volume loss. In clinical practice, it may therefore be important to consider the potential impact of a therapy on reducing the rate of brain volume loss. This article reviews the measurement of brain volume in clinical trials and practice, the effect of DMTs on brain volume change across trials and the clinical relevance of brain volume loss in MS.

Application of quantitative MRI for brain tissue segmentation at 1.5 T and 3.0 T field strengths

BACKGROUND

Brain tissue segmentation of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) are important in neuroradiological applications. Quantitative Mri (qMRI) allows segmentation based on physical tissue properties, and the dependencies on MR scanner settings are removed. Brain tissue groups into clusters in the three dimensional space formed by the qMRI parameters R1, R2 and PD, and partial volume voxels are intermediate in this space. The qMRI parameters, however, depend on the main magnetic field strength. Therefore, longitudinal studies can be seriously limited by system upgrades. The aim of this work was to apply one recently described brain tissue segmentation method, based on qMRI, at both 1.5 T and 3.0 T field strengths, and to investigate similarities and differences.

METHODS

In vivo qMRI measurements were performed on 10 healthy subjects using both 1.5 T and 3.0 T MR scanners. The brain tissue segmentation method was applied for both 1.5 T and 3.0 T and volumes of WM, GM, CSF and brain parenchymal fraction (BPF) were calculated on both field strengths. Repeatability was calculated for each scanner and a General Linear Model was used to examine the effect of field strength. Voxel-wise t-tests were also performed to evaluate regional differences.

RESULTS

Statistically significant differences were found between 1.5 T and 3.0 T for WM, GM, CSF and BPF (p<0.001). Analyses of main effects showed that WM was underestimated, while GM and CSF were overestimated on 1.5 T compared to 3.0 T. The mean differences between 1.5 T and 3.0 T were -66 mL WM, 40 mL GM, 29 mL CSF and -1.99% BPF. Voxel-wise t-tests revealed regional differences of WM and GM in deep brain structures, cerebellum and brain stem.

CONCLUSIONS

Most of the brain was identically classified at the two field strengths, although some regional differences were observed.

Neurodegeneration in multiple sclerosis: novel treatment strategies

In recent years it has become clear that the neuronal compartment already plays an important role early in the pathology of multiple sclerosis (MS). Neuronal injury in the course of chronic neuroinflammation is a key factor in determining long-term disability in patients. Viewing MS as both inflammatory and neurodegenerative has major implications for therapy, with CNS protection and repair needed in addition to controlling inflammation. Here, the authors' review recently elucidated molecular insights into inflammatory neuronal/axonal pathology in MS and discuss the resulting options regarding neuroprotective and regenerative treatment strategies.

Identification of protein networks involved in the disease course of experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis

A more detailed insight into disease mechanisms of multiple sclerosis (MS) is crucial for the development of new and more effective therapies. MS is a chronic inflammatory autoimmune disease of the central nervous system. The aim of this study is to identify novel disease associated proteins involved in the development of inflammatory brain lesions, to help unravel underlying disease processes. Brainstem proteins were obtained from rats with MBP induced acute experimental autoimmune encephalomyelitis (EAE), a well characterized disease model of MS. Samples were collected at different time points: just before onset of symptoms, at the top of the disease and following recovery. To analyze changes in the brainstem proteome during the disease course, a quantitative proteomics study was performed using two-dimensional difference in-gel electrophoresis (2D-DIGE) followed by mass spectrometry. We identified 75 unique proteins in 92 spots with a significant abundance difference between the experimental groups. To find disease-related networks, these regulated proteins were mapped to existing biological networks by Ingenuity Pathway Analysis (IPA). The analysis revealed that 70% of these proteins have been described to take part in neurological disease. Furthermore, some focus networks were created by IPA. These networks suggest an integrated regulation of the identified proteins with the addition of some putative regulators. Post-synaptic density protein 95 (DLG4), a key player in neuronal signalling and calcium-activated potassium channel alpha 1 (KCNMA1), involved in neurotransmitter release, are 2 putative regulators connecting 64% of the identified proteins. Functional blocking of the KCNMA1 in macrophages was able to alter myelin phagocytosis, a disease mechanism highly involved in EAE and MS pathology. Quantitative analysis of differentially expressed brainstem proteins in an animal model of MS is a first step to identify disease-associated proteins and networks that warrant further research to study their actual contribution to disease pathology.

Fast MR parameter mapping using k-t principal component analysis

Quantification of magnetic resonance parameters plays an increasingly important role in clinical applications, such as the detection and classification of neurodegenerative diseases. The major obstacle that remains for its widespread use in clinical routine is the long scanning times. Therefore, strategies that allow for significant decreases in scan time are highly desired. Recently, the k-t principal component analysis method was introduced for dynamic cardiac imaging to accelerate data acquisition. This is done by undersampling k-t space and constraining the reconstruction of the aliased data based on the k-t Broad-use Linear Acquisition Speed-up Technique (BLAST) concept and predetermined temporal basis functions. The objective of this study was to investigate whether the k-t principal component analysis concept can be adapted to parameter quantification, specifically allowing for significant acceleration of an inversion recovery fast imaging with steady state precession (TrueFISP) acquisition. We found that three basis functions and a single training data line in central k-space were sufficient to achieve up to an 8-fold acceleration of the quantification measurement. This allows for an estimation of relaxation times T(1) and T(2) and spin density in one slice with sub-millimeter in-plane resolution, in only 6 s. Our findings demonstrate that the k-t principal component analysis method is a potential candidate to bring the acquisition time for magnetic resonance parameter mapping to a clinically acceptable level.

Anatomical distribution of central nervous system plaques in multiple sclerosis: an Iranian experience

Multiple Sclerosis (MS) begins most commonly in young adults and is characterized by multiple areas of Central Nervous System (CNS) white matter inflammation, demyelination and glial scarring. The most valuable laboratory aid for diagnosing MS is Magnetic Resonance Imaging (MRI). An advanced type of MRI that exploits molecular diffusion can detect acute and active lesions. Early diagnosis and onset of treatment help to hinder disease progression. The aim of this study was to compare the findings of conventional and Diffusion-Weighted (DW) MRI in assessing the cerebral lesions of MS patients. Thirty patients with clinically definite MS (mean age 32.76 +/- 8.79 years) and an age- and sex-matched control group of 30 healthy volunteers (mean age 32.75 +/- 9.23 years) were enrolled in this 12 month descriptive-prospective survey. Both groups were subjected to conventional and DW MRI and were compared in respect of the total number, morphology, location and the mean size of the intra-cerebral MS plaques. The sensitivities and specificities of both imaging methods in detecting these plaques were determined. The conventional method revealed significantly more plaques within the brain (p < 0.05) and showed more ovoid lesions. More lesions were detected by the conventional method in the periventricular area, centrum semiovale and corpus callosum. The minimum plaque size was significantly lower in the conventional method group. The sensitivity of both methods was 100%. The specificities of conventional and DW MRI were 86.6 and 96.6%, respectively, so DW MRI may detect lesions that are not obvious by routine methods.

Transgenic mouse models of multiple sclerosis

Multiple sclerosis (MS) is an inflammatory demyelinating disease affecting the central nervous system (CNS) and a frequent cause of neurological disability in young adults. Multifocal inflammatory lesions in the CNS white matter, demyelination, oligodendrocyte loss, axonal damage, as well as astrogliosis represent the histological hallmarks of the disease. These pathological features of MS can be mimicked, at least in part, using animal models. This review discusses the current concepts of the immune effector mechanisms driving CNS demyelination in murine models. It highlights the fundamental contribution of transgenesis in identifying the mediators and mechanisms involved in the pathophysiology of MS models.