MR myelin imaging in multiple sclerosis: A scoping review

The inability of disease-modifying therapies to stop the progression of multiple sclerosis (MS), has led to the development of a new therapeutic strategy focussing on myelin repair. While conventional MRI lacks sensitivity for quantifying myelin damage, advanced MRI techniques are proving effective. The development of targeted therapeutics requires histological validation of myelin imaging results, alongside the crucial task of establishing correlations between myelin imaging results and clinical assessments, so that the effectiveness of therapeutic interventions can be evaluated. The aims of this scoping review were to identify myelin imaging methods - some of which have been histologically validated, and to determine how these approaches correlate with clinical assessments of people with MS (pwMS), thus allowing for effective therapeutic evaluation. A search of two databases was undertaken for publications relating to studies on adults MS using either MRI/MR-histology of the MS brain in the range 1990-to-2022. The myelin imaging methods specified were relaxometry, magnetization transfer, and quantitative susceptibility. Relaxometry was used most frequently, with myelin water fraction (MWF) being the primary metric. Studies conducted on tissue from various regions of the brain showed that MWF was significantly lower in pwMS than in healthy controls. Magnetization transfer ratio indicated that the macromolecular content of lesions was lower than that of normal-appearing tissue. Higher magnetic susceptibility of lesions were indicative of myelin breakdown and iron accumulation. Several myelin imaging metrics were correlated with disability, disease severity and duration. Many studies showed a good correlation between myelin measured histologically and by MR myelin imaging techniques.

Distinct characteristics and severity of brain magnetic resonance imaging lesions in women and men with multiple sclerosis assessed using verified texture analysis measures

Background and goal

In vivo characterization of brain lesion types in multiple sclerosis (MS) has been an ongoing challenge. Based on verified texture analysis measures from clinical magnetic resonance imaging (MRI), this study aimed to develop a method to identify two extremes of brain MS lesions that were approximately severely demyelinated (sDEM) and highly remyelinated (hREM), and compare them in terms of common clinical variables.

Method

Texture analysis used an optimized gray-level co-occurrence matrix (GLCM) method based on FLAIR MRI from 200 relapsing-remitting MS participants. Two top-performing metrics were calculated: texture contrast and dissimilarity. Lesion identification applied a percentile approach according to texture values calculated: ≤ 25 percentile for hREM and ≥75 percentile for sDEM.

Results

The sDEM had a greater total normalized volume yet smaller average size, and worse MRI texture than hREM. In lesion distribution mapping, the two lesion types appeared to overlap largely in location and were present the most in the corpus callosum and periventricular regions. Further, in sDEM, the normalized volume was greater and in hREM, the average size was smaller in men than women. There were no other significant results in clinical variable-associated analyses.

Conclusion

Percentile statistics of competitive MRI texture measures may be a promising method for probing select types of brain MS lesion pathology. Associated findings can provide another useful dimension for improved measurement and monitoring of disease activity in MS. The different characteristics of sDEM and hREM between men and women likely adds new information to the literature, deserving further confirmation.

Prolonged, Low-Level Exposure to the Marine Toxin, Domoic Acid, and Measures of Neurotoxicity in Nonhuman Primates

BACKGROUND

The excitotoxic molecule, domoic acid (DA), is a marine algal toxin known to induce overt hippocampal neurotoxicity. Recent experimental and epidemiological studies suggest adverse neurological effects at exposure levels near the current regulatory limit (20 ppm, ∼0.075-0.1mg/kg). At these levels, cognitive effects occur in the absence of acute symptoms or evidence of neuronal death.

OBJECTIVES

This study aimed to identify adverse effects on the nervous system from prolonged, dietary DA exposure in adult, female Macaca fascicularis monkeys.

METHODS

Monkeys were orally exposed to 0, 0.075, and 0.15mg/kg per day for an average of 14 months. Clinical blood counts, chemistry, and cytokine levels were analyzed in the blood. In-life magnetic resonance (MR) imaging assessed volumetric and tractography differences in and between the hippocampus and thalamus. Histology of neurons and glia in the fornix, fimbria, internal capsule, thalamus, and hippocampus was evaluated. Hippocampal RNA sequencing was used to identify differentially expressed genes. Enrichment of gene networks for neuronal health, excitotoxicity, inflammation/glia, and myelin were assessed with Gene Set Enrichment Analysis.

RESULTS

Clinical blood counts, chemistry, and cytokine levels were not altered with DA exposure in nonhuman primates. Transcriptome analysis of the hippocampus yielded 748 differentially expressed genes (fold change≥1.5; p≤0.05), reflecting differences in a broad molecular profile of intermediate early genes (e.g., FOS, EGR) and genes related to myelin networks in DA animals. Between exposed and control animals, MR imaging showed comparable connectivity of the hippocampus and thalamus and histology showed no evidence of hypomyelination. Histological examination of the thalamus showed a larger microglia soma size and an extension of cell processes, but suggestions of a GFAP+astrocyte response showed no indication of astrocyte hypertrophy.

DISCUSSION

In the absence of overt hippocampal excitotoxicity, chronic exposure of Macaca fascicularis monkeys to environmentally relevant levels of DA suggested a subtle shift in the molecular profile of the hippocampus and the microglia phenotype in the thalamus that was possibly reflective of an adaptive response due to prolonged DA exposure. https://doi.org/10.1289/EHP10923.

Texture analysis in brain T2 and diffusion MRI differentiates histology-verified grey and white matter pathology types in multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a co mplex disease of the central nervous system involving several types of brain pathology that are difficult to characterize using conventional imaging methods.

NEW METHOD

We originated novel texture analysis and machine learning approaches for classifying MS pathology subtypes as compared with 2 common advanced MRI measures: magnetization transfer ratio (MTR) and fractional anisotropy (FA). Texture analysis used an optimized grey level co-occurrence matrix method with histology-informed 7T T2-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) from 15 MS and 12 control brain specimens. DTI analysis took an innovative approach that assessed the texture across diffusion directions upsampled from 30 to 90. Tissue types included de- and re-myelinated lesions and normal-appearing areas in both grey and white matter, and diffusely abnormal white matter. Data analyses were stepwise, including: (1) group-wise classification using random forest algorithms based on all or individual imaging parameters; (2) parameter importance ranking; and (3) pairwise analysis using top-ranked features.

RESULTS

Texture analysis performed better than MTR and FA, with T2 texture performed the best. T2 texture measures ranked the highest in classifying most grey and white matter tissue types, including de- versus re-myelinated lesions and among grey matter lesion subtypes (accuracy=0.86-0.59; kappa=0.60-0.41). Diffusion texture best differentiated normal appearing and control white matter.

COMPARISON WITH EXISTING METHODS

There is no established method in imaging for differentiating MS pathology subtypes. In combined texture analysis and machine learning studies, there is also no direct evidence comparing conventional with advanced MRI measures for assessing MS pathology. Further, this study is unique in conducting innovative texture analysis with DTI following data-augmentation using robust methods.

CONCLUSIONS

T2 and diffusion MRI texture analysis integrated with machine learning may be valuable approaches for characterizing MS pathology.

Fluid-attenuated inversion recovery magnetic resonance imaging textural features as sensitive markers of white matter damage in midlife adults

White matter hyperintensities are common radiological findings in ageing and a typical manifestation of cerebral small vessel disease. White matter hyperintensity burden is evaluated by quantifying their volume; however, subtle changes in the white matter may not be captured by white matter hyperintensity volumetry. In this cross-sectional study, we investigated whether magnetic resonance imaging texture of both white matter hyperintensities and normal appearing white matter was associated with reaction time, white matter hyperintensity volume and dementia risk in a midlife cognitively normal population. Data from 183 cognitively healthy midlife adults from the PREVENT-Dementia study (mean age 51.9 ± 5.4; 70% females) were analysed. White matter hyperintensities were segmented from 3 Tesla fluid-attenuated inversion recovery scans using a semi-automated approach. The fluid-attenuated inversion recovery images were bias field corrected and textural features (intensity mean and standard deviation, contrast, energy, entropy, homogeneity) were calculated in white matter hyperintensities and normal appearing white matter based on generated textural maps. Textural features were analysed for associations with white matter hyperintensity volume, reaction time and the Cardiovascular Risk Factors, Aging and Dementia risk score using linear regression models adjusting for age and sex. The extent of normal appearing white matter surrounding white matter hyperintensities demonstrating similar textural associations to white matter hyperintensities was further investigated by defining layers surrounding white matter hyperintensities at increments of 0.86 mm thickness. Lower mean intensity within white matter hyperintensities was a significant predictor of longer reaction time (t = −3.77, P < 0.01). White matter hyperintensity volume was predicted by textural features within white matter hyperintensities and normal appearing white matter, albeit in opposite directions. A white matter area extending 2.5 – 3.5 mm further from the white matter hyperintensities demonstrated similar associations. White matter hyperintensity volume was not related to reaction time, although interaction analysis revealed that participants with high white matter hyperintensity burden and less homogeneous white matter hyperintensity texture demonstrated slower reaction time. Higher Cardiovascular Risk Factors, Aging, and Dementia score was associated with a heterogeneous normal appearing white matter intensity pattern. Overall, greater homogeneity within white matter hyperintensities and a more heterogeneous normal appearing white matter intensity profile were connected to a higher white matter hyperintensity burden, while heterogeneous intensity was related to prolonged reaction time (white matter hyperintensities of larger volume) and dementia risk (normal appearing white matter). Our results suggest that the quantified textural measures extracted from widely used clinical scans, might capture underlying microstructural damage and might be more sensitive to early pathological changes compared to white matter hyperintensity volumetry.

MRI-derived g-ratio and lesion severity in newly diagnosed multiple sclerosis

Myelin loss is associated with axonal damage in established multiple sclerosis. This relationship is challenging to study in vivo in early disease. Here, we ask whether myelin loss is associated with axonal damage at diagnosis by combining non-invasive neuroimaging and blood biomarkers. We performed quantitative microstructural MRI and single-molecule ELISA plasma neurofilament measurement in 73 patients with newly diagnosed, immunotherapy naïve relapsing-remitting multiple sclerosis. Myelin integrity was evaluated using aggregate g-ratios, derived from magnetization transfer saturation and neurite orientation dispersion and density imaging diffusion data. We found significantly higher g-ratios within cerebral white matter lesions (suggesting myelin loss) compared with normal-appearing white matter (0.61 versus 0.57, difference 0.036, 95% CI: 0.029-0.043, P < 0.001). Lesion volume (Spearman's rho rs= 0.38, P < 0.001) and g-ratio (rs= 0.24, P < 0.05) correlated independently with plasma neurofilament. In patients with substantial lesion load (n = 38), those with higher g-ratio (defined as greater than median) were more likely to have abnormally elevated plasma neurofilament than those with normal g-ratio (defined as less than median) [11/23 (48%) versus 2/15 (13%), P < 0.05]. These data suggest that, even at multiple sclerosis diagnosis, reduced myelin integrity is associated with axonal damage. MRI-derived g-ratio may provide useful additional information regarding lesion severity and help to identify individuals with a high degree of axonal damage at disease onset.

Precise segmentation of non-enhanced computed tomography in patients with ischemic stroke based on multi-scale U-Net deep network model

BACKGROUND AND OBJECTIVE

Acute ischemic stroke requires timely diagnosis and thrombolytic therapy, but it is difficult to locate and quantify the lesion site manually. The purpose of this study was to explore a more rapid and effective method for automatic image segmentation of acute ischemic stroke.

METHODS

The image features of 30 stroke patients were segmented from non-enhanced computed tomography (CT) images using a multi-scale U-Net deep network model. The Dice loss function training model was used to counter the similar imbalance problem in the data. The difference was compared between manual segmentation and automatic segmentation.

RESULTS

The Dice similarity coefficient based on multi-scale convolution U-Net network segmentation was 0.86±0.04, higher than the Dice based on classic U-Net (0.81±0.07, P=0.001). The lesion contour of automatic segmentation based on multi-scale U-Net was very close to manual segmentation. The error of lesion area is 1.28±0.59 mm², and the Pearson correlation coefficient was r=0.986 (P<0.01). The motion time of automatic segmentation is less than 20 ms.

CONCLUSIONS

Multi-scale U-Net deep network model can effectively segment ischemic stroke lesions in non-enhanced CT and meet real-time clinical requirements.

Evaluation of the diffusion MRI white matter tract integrity model using myelin histology and Monte-Carlo simulations

Quantitative evaluation of brain myelination has drawn considerable attention. Conventional diffusion-based magnetic resonance imaging models, including diffusion tensor imaging and diffusion kurtosis imaging (DKI),¹ have been used to infer the microstructure and its changes in neurological diseases. White matter tract integrity (WMTI) was proposed as a biophysical model to relate the DKI-derived metrics to the underlying microstructure. Although the model has been validated on ex vivo animal brains, it was not well evaluated with ex vivo human brains. In this study, histological samples (namely corpus callosum) from postmortem human brains have been investigated based on WMTI analyses on a clinical 3T scanner and comparisons with gold standard myelin staining in proteolipid protein and Luxol fast blue. In addition, Monte Carlo simulations were conducted to link changes from ex vivo to in vivo conditions based on the microscale parameters of water diffusivity and permeability. The results show that WMTI metrics, including axonal water fraction AWF, radial extra-axonal diffusivity D_e^⊥, and intra-axonal diffusivity D_awere needed to characterize myelin content alterations. Thus, WMTI model metrics are shown to be promising candidates as sensitive biomarkers of demyelination.

Evaluation of discrete orthogonal versus polar Stockwell Transform for local multi-resolution texture analysis using brain MRI of multiple sclerosis patients

The Stockwell Transform has the potential to perform multi-resolution texture analysis in magnetic resonance imaging (MRI). However, it is computationally intensive and memory demanding. The polar Stockwell Transform (PST) is rotation-invariant and relatively memory efficient, but still computationally demanding. The new Discrete Orthogonal Stockwell Transform (DOST) appears to have addressed both the computation and storage challenges; however, its utility in localized texture analysis remains unclear. Our goal was to investigate the theory and texture analysis ability of the DOST versus PST using both synthetic and MR images, and explore the relative importance of the associated texture features using a simple classification example based on clinical brain MRI of six multiple sclerosis patients. MRI texture analysis focused on FLAIR images, and the classification used a machine learning algorithm, random forest, that differentiated regions of interest (ROIs) into 2 classes: white matter lesions, and the contralateral normal-appearing white matter (control). Our results showed that the PST features had a greater ability in detecting subtle changes in image structure than the DOST and polar-index DOST (PDOST). Quantitatively, based on 187 lesion and 187 control ROIs, both the PST and the rotation-invariant radial PST performed better in the classification than the DOST and PDOST, where the latter were no better than guessing (p = 0.65 and 0.98). Further analysis using a hierarchical random forest showed that combining MRI signal intensity with the PST or DOST predictions increased the classification performance, with the accuracy, sensitivity, and specificity all improved to >85% in the tests. Collectively, the DOST is less competitive than the PST in localized image texture analysis. The PST features may help with texture-based lesion classification in MS based on clinical brain MRI scans following further verification.

Post-Mortem MRI and Histopathology in Neurologic Disease: A Translational Approach

In this review, combined post-mortem brain magnetic resonance imaging (MRI) and histology studies are highlighted, illustrating the relevance of translational approaches to define novel MRI signatures of neuropathological lesions in neuroinflammatory and neurodegenerative disorders. Initial studies combining post-mortem MRI and histology have validated various MRI sequences, assessing their sensitivity and specificity as diagnostic biomarkers in neurologic disease. More recent studies have focused on defining new radiological (bio)markers and implementing them in the clinical (research) setting. By combining neurological and neuroanatomical expertise with radiological development and pathological validation, a cycle emerges that allows for the discovery of novel MRI biomarkers to be implemented in vivo. Examples of this cycle are presented for multiple sclerosis, Alzheimer's disease, Parkinson's disease, and traumatic brain injury. Some applications have been shown to be successful, while others require further validation. In conclusion, there is much to explore with post-mortem MRI and histology studies, which can eventually be of high relevance for clinical practice.

Characterizing Structural Changes With Devolving Remyelination Following Experimental Demyelination Using High Angular Resolution Diffusion MRI and Texture Analysis

BACKGROUND

Changes in myelin integrity are associated with the pathophysiology of many neurological diseases, including multiple sclerosis. However, noninvasive measurement of myelin injury and repair remains challenging. Advanced MRI techniques including diffusion tensor imaging (DTI), neurite orientation dispersion and density index (NODDI), and texture analysis have shown promise in quantifying subtle abnormalities in white matter structure.

PURPOSE

To determine whether and how these advanced imaging methods help understand remyelination changes after demyelination using a mouse model.

STUDY TYPE

Prospective, longitudinal.

ANIMAL MODEL

Demyelination was induced in the thoracic spinal cord of 21 mice using the chemical toxin lysolecithin.

FIELD STRENGTH/SEQUENCES

9.4T ASSESSMENT: Imaging was done at day 7 (demyelination) and days 14 to 35 (ongoing remyelination) postsurgery, followed by histology. Image analysis focused on both lesions and peri-lesional areas where remyelination began. In histology, we quantified the complexity of tissue alignment using angular entropy, in addition to staining area.

STATISTICAL ANALYSIS

Two-way analysis of variance was performed for assessing differences between tissue types and across timepoints, followed by post-hoc analysis to correct for multiple comparisons (P < 0.05).

RESULTS

All diffusion and texture parameters were worse in lesions than the control tissue (P < 0.05) except orientation dispersion index (ODI) and neurite density index (NDI) over late remyelination. Longitudinally, ODI decreased and NDI increased persistently in both lesions and peri-lesion regions (P < 0.05). Fractional anisotropy showed a mild decrease at day 35 after increase, when lesion texture heterogeneity showed a trend to decrease (P > 0.05). Both lesion size and angular entropy decreased over time, and no change in any measure in the control tissue.

DATA CONCLUSION

Diffusion and MRI texture metrics may provide compensatory information on myelin repair and ODI and NDI could be sensitive measures of evolving remyelination, deserving further validation.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1750-1759.

Postmortem magnetic resonance imaging

This chapter provides a brief overview of studies that combine postmortem magnetic resonance imaging (MRI) and histopathology. We touch upon the logistics of setting up a protocol that limits unwanted postmortem delays and explain how combining postmortem MRI and histopathology can elucidate the histologic substrate of signal changes that appear on MRI. This is demonstrated by exemplary studies in multiple sclerosis, and includes various histopathologic techniques and a wide range of conventional and advanced MRI sequences at various field strengths. We cover topics such as how to visualize white-matter pathology and repair with conventional and advanced MRI sequences, describe the history of visualizing pathology of the gray matter (with newly developed MRI and immunohistopathology techniques), and how advanced methods have aided research in other neurologic diseases. We conclude with several suggestions for future development, such as bridging the gap between postmortem and in vivo research and the importance of collecting non-neurological control tissue.

Remyelination therapies: a new direction and challenge in multiple sclerosis

Multiple sclerosis is characterized by inflammatory activity that results in destruction of the myelin sheaths that enwrap axons. The currently available medications for multiple sclerosis are predominantly immune-modulating and do not directly promote repair. White matter regeneration, or remyelination, is a new and exciting potential approach to treating multiple sclerosis, as remyelination repairs the damaged regions of the central nervous system. A wealth of new strategies in animal models that promote remyelination, including the repopulation of oligodendrocytes that produce myelin, has led to several clinical trials to test new reparative therapies. In this Review, we highlight the biology of, and obstacles to, remyelination. We address new strategies to improve remyelination in preclinical models, highlight the therapies that are currently undergoing clinical trials and discuss the challenges of objectively measuring remyelination in trials of repair in multiple sclerosis.

The contribution of oligodendrocytes and oligodendrocyte progenitor cells to central nervous system repair in multiple sclerosis: perspectives for remyelination therapeutic strategies

Oligodencrocytes (OLs) are the main glial cells of the central nervous system involved in myelination of axons. In multiple sclerosis (MS), there is an imbalance between demyelination and remyelination processes, the last one performed by oligodendrocyte progenitor cells (OPCs) and OLs, resulting into a permanent demyelination, axonal damage and neuronal loss. In MS lesions, astrocytes and microglias play an important part in permeabilization of blood-brain barrier and initiation of OPCs proliferation. Migration and differentiation of OPCs are influenced by various factors and the process is finalized by insufficient acummulation of OLs into the MS lesion. In relation to all these processes, the author will discuss the potential targets for remyelination strategies.

Role of Oligodendrocyte Dysfunction in Demyelination, Remyelination and Neurodegeneration in Multiple Sclerosis

Oligodendrocytes (OLs) are the myelinating cells of the central nervous system (CNS) during development and throughout adulthood. They result from a complex and well controlled process of activation, proliferation, migration and differentiation of oligodendrocyte progenitor cells (OPCs) from the germinative niches of the CNS. In multiple sclerosis (MS), the complex pathological process produces dysfunction and apoptosis of OLs leading to demyelination and neurodegeneration. This review attempts to describe the patterns of demyelination in MS, the steps involved in oligodendrogenesis and myelination in healthy CNS, the different pathways leading to OLs and myelin loss in MS, as well as principles involved in restoration of myelin sheaths. Environmental factors and their impact on OLs and pathological mechanisms of MS are also discussed. Finally, we will present evidence about the potential therapeutic targets in re-myelination processes that can be accessed in order to develop regenerative therapies for MS.