Advances in multiple sclerosis and its variants: conventional and newer imaging techniques

Lesion follows function: video-oculography compared with MRI to diagnose internuclear ophthalmoplegia in patients with multiple sclerosis

BACKGROUND

Video-oculography (VOG) is used to quantify functional deficits in internuclear ophthalmoplegia (INO), whereas MRI can detect the corresponding structural lesions in the medial longitudinal fasciculus (MLF). This study investigates the diagnostic agreement of MRI compared to VOG measurements.

METHODS

We prospectively compared structural MRI findings and functional VOG measures of 63 MS patients to assess their diagnostic agreement for INO.

RESULTS

MRI detected 12 true-positive and 92 true-negative MLF lesions for INO compared to VOG (12 true-positive and 38 true-negative patients) but identified one-third of the MLF lesions on the wrong side. MRI ratings were specific (92.0%) to detect MLF lesions but not sensitive (46.2%) for diagnosing INO (86.4% and 63.2% by patient). Accordingly, MRI has a high positive likelihood ratio of 5.77 but a modest negative likelihood ratio of 0.59 for the probability of INO (4.63 and 0.43) with an accuracy of 82.5% (79.4%).

CONCLUSION

MRI assessments are highly specific but not sensitive for detecting INO compared to VOG. While MRI identifies MLF lesions in INO, VOG quantifies the deficit. As a simple, quick, and non-invasive test for diagnosing and tracking functional INO deficits, it will hopefully find its place in the diagnostic and therapeutic pathways of MS.

Skeletonized mean diffusivity and neuropsychological performance in relapsing-remitting multiple sclerosis

BACKGROUND

Peak width of Skeletonized Mean Diffusivity (PSMD), as a novel marker of white matter (WM) microstructure damage, is associated with cognitive decline in several WM pathologies (i.e., small vessel disorders). We hypothesized that markers combining alterations in whole WM could be associated with cognitive dysfunction in relapsing-remitting multiple sclerosis (RRMS) patients.

METHODS

We used PSMD based on tract-based spatial statistics (TBSS) of diffusion tensor imaging (DTI) magnetic resonance (MR) scans. We investigated RRMS patients (n = 73) undergoing interferon beta (IFN-β) therapy. In this cross-sectional study, we investigated the association between neuropsychological data and clinical and MRI variables: PSMD, WM hypointensities, and normalized brain volume (NBV).

RESULTS

In our cohort, 37 (50.7%) patients were recognized as cognitively impaired (CI) and 36 (49.3%) patients were cognitively normal (CN). In regression analysis, PSMD was a statistically significant contributor in the California Verbal Learning Test (CVLT) list A (p = 0.04) and semantic fluency (p = 0.036). PSMD (p < 0.001, r²  = 0.35), NBV (p = 0.002, r²  = 2.6) and WM hypointensities (p < 0.001, r²  = 0.40) were major contributors to upper extremity disability (9HPT) in the CN subgroup. A significant contributor in the majority of neuropsychological measures was education attainment.

CONCLUSION

We investigated PSMD as a new parameter of WM microstructure damage that is a contributor in complex cognitive tasks, CVLT performance, and semantic fluency. PSMD was a statistically significant contributor to upper extremity disability (9HPT) together with WM hypointensities and NBV. Education attainment proved to be relevant in the majority of cognitive domains. Further studies are needed to estimate PSMD relevance as a marker of CI in MS.

Exploring mania-associated white matter injury by comparison with multiple sclerosis: a diffusion tensor imaging study

Bipolar disorder (BD), especially in its active phases, has shown some neuroimaging and immunological similarities with multiple sclerosis (MS). The objective of this study was to compare white matter (WM) alterations in BD patients in manic phase (M-BD) and MS patients at early stage of disease and with low lesion burden. We compared diffusion tensor imaging (DTI)-derived fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD) in a priori selected WM regions (i.e., corpus callosum and cingulum) betwixt 23 M-BD, 23 MS patients and 46 healthy controls. Both M-BD and MS showed WM changes in the corpus callosum, which, however, showed a greater impairment in MS patients. However, considering the different sub-regions of corpus callosum separately (i.e., genu, body, splenium), M-BD and MS presented an opposite pattern in spatial distribution of WM microstructure alterations, with a greater impairment in the anterior region in M-BD and in the posterior region in MS. Common features as well as divergent patterns in DTI changes are detected in M-BD and early MS, prompting a deeper investigation of analogies and differences in WM and immunological alterations of these disorders.

Disruption of neurite morphology parallels MS progression

Objectives

To apply advanced diffusion MRI methods to the study of normal-appearing brain tissue in MS and examine their correlation with measures of clinical disability.

Methods

A multi-compartment model of diffusion MRI called neurite orientation dispersion and density imaging (NODDI) was used to study 20 patients with relapsing-remitting MS (RRMS), 15 with secondary progressive MS (SPMS), and 20 healthy controls. Maps of NODDI were analyzed voxel-wise to assess the presence of abnormalities within the normal-appearing brain tissue and the association with disease severity. Standard diffusion tensor imaging (DTI) parameters were also computed for comparing the 2 techniques.

Results

Patients with MS showed reduced neurite density index (NDI) and increased orientation dispersion index (ODI) compared with controls in several brain areas (p < 0.05), with patients with SPMS having more widespread abnormalities. DTI indices were also sensitive to some changes. In addition, patients with SPMS showed reduced ODI in the thalamus and caudate nucleus. These abnormalities were associated with scores of disease severity (p < 0.05). The association with the MS functional composite score was higher in patients with SPMS compared with patients with RRMS.

Conclusions

NODDI and DTI findings are largely overlapping. Nevertheless, NODDI helps interpret previous findings of increased anisotropy in the thalamus of patients with MS and are consistent with the degeneration of selective axon populations.

Clinical applications of diffusion weighted imaging in neuroradiology

Abstract

Diffusion-weighted imaging (DWI) has revolutionised stroke imaging since its introduction in the mid-1980s, and it has also become a pillar of current neuroimaging. Diffusion abnormalities represent alterations in the random movement of water molecules in tissues, revealing their microarchitecture, and occur in many neurological conditions. DWI provides useful information, increasing the sensitivity of MRI as a diagnostic tool, narrowing the differential diagnosis, providing prognostic information, aiding in treatment planning and evaluating response to treatment. Recently, there have been several technical improvements in DWI, leading to reduced acquisition time and artefacts and enabling the development of diffusion tensor imaging (DTI) as a tool for assessing white matter. We aim to review the main clinical uses of DWI, focusing on the physiological mechanisms that lead to diffusion abnormalities. Common pitfalls will also be addressed.

Teaching Points

• DWI includes EPI, TSE, RESOLVE or EPI combined with reduced volume excitation.

• DWI is the most sensitive sequence in stroke diagnosis and provides information about prognosis.

• DWI helps in the detection of intramural haematomas (arterial dissection).

• In diffusion imaging, ADC is inversely proportional to tumour cellularity.

• DWI and DTI derived parameters can be used as biomarkers in different pathologies.

Disability in progressive MS is associated with T2 lesion changes

BACKGROUND

Progressive multiple sclerosis (MS) is characterised by diffuse changes on brain magnetic resonance imaging (MRI), which complicates the use of MRI as a diagnostic and prognostic marker. The relationship between MRI measures (conventional and non-conventional) and clinical disability in progressive MS therefore warrants further investigation.

OBJECTIVE

To investigate the relationship between clinical disability and MRI measures in patients with progressive MS.

METHODS

Data from 93 primary and secondary progressive MS patients who had participated in 3 phase 2 clinical trials were included in this cross-sectional study. From 3T MRI baseline scans we calculated total T2 lesion volume and analysed magnetisation transfer ratio (MTR) and the diffusion tensor imaging indices fractional anisotropy (FA) and mean diffusivity (MD) in T2 lesions, normal-appearing white matter (NAWM) and cortical grey matter. Disability was assessed by the Expanded Disability Status Scale (EDSS) and the MS functional composite.

RESULTS

T2 lesion volume was associated with impairment by all clinical measures. MD and MTR in T2 lesions were significantly related to disability, and lower FA values correlated with worse hand function in NAWM. In multivariable analyses, increasing clinical disability was independently correlated with increasing T2 lesion volumes and MTR in T2 lesions.

CONCLUSION

In progressive MS, clinical disability is related to lesion volume and microstructure.

MRI FLAIR lesion segmentation in multiple sclerosis: Does automated segmentation hold up with manual annotation?

Introduction

Magnetic resonance imaging (MRI) has become key in the diagnosis and disease monitoring of patients with multiple sclerosis (MS). Both, T2 lesion load and Gadolinium (Gd) enhancing T1 lesions represent important endpoints in MS clinical trials by serving as a surrogate of clinical disease activity. T2- and fluid-attenuated inversion recovery (FLAIR) lesion quantification - largely due to methodological constraints – is still being performed manually or in a semi-automated fashion, although strong efforts have been made to allow automated quantitative lesion segmentation. In 2012, Schmidt and co-workers published an algorithm to be applied on FLAIR sequences. The aim of this study was to apply the Schmidt algorithm on an independent data set and compare automated segmentation to inter-rater variability of three independent, experienced raters.

Methods

MRI data of 50 patients with RRMS were randomly selected from a larger pool of MS patients attending the MS Clinic at the Brain and Mind Centre, University of Sydney, Australia. MRIs were acquired on a 3.0T GE scanner (Discovery MR750, GE Medical Systems, Milwaukee, WI) using an 8 channel head coil. We determined T2-lesion load (total lesion volume and total lesion number) using three versions of an automated segmentation algorithm (Lesion growth algorithm (LGA) based on SPM8 or SPM12 and lesion prediction algorithm (LPA) based on SPM12) as first described by Schmidt et al. (2012). Additionally, manual segmentation was performed by three independent raters. We calculated inter-rater correlation coefficients (ICC) and dice coefficients (DC) for all possible pairwise comparisons.

Results

We found a strong correlation between manual and automated lesion segmentation based on LGA SPM8, regarding lesion volume (ICC = 0.958 and DC = 0.60) that was not statistically different from the inter-rater correlation (ICC = 0.97 and DC = 0.66). Correlation between the two other algorithms (LGA SPM12 and LPA SPM12) and manual raters was weaker but still adequate (ICC = 0.927 and DC = 0.53 for LGA SPM12 and ICC = 0.949 and DC = 0.57 for LPA SPM12). Variability of both manual and automated segmentation was significantly higher regarding lesion numbers.

Conclusion

Automated lesion volume quantification can be applied reliably on FLAIR data sets using the SPM based algorithm of Schmidt et al. and shows good agreement with manual segmentation.

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Fully automated and manual MS lesion segmentation on FLAIR images were compared.

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Automated FLAIR lesion volume segmentation holds up with manual annotation.

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When using DC and ICC, SPM8 based algorithm performed better than recent updates.

Optical Coherence Tomography and Magnetic Resonance Imaging in Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder

Irreversible disability in multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) is largely attributed to neuronal and axonal degeneration, which, along with inflammation, is one of the major pathological hallmarks of these diseases. Optical coherence tomography (OCT) is a non-invasive imaging tool that has been used in MS, NMOSD, and other diseases to quantify damage to the retina, including the ganglion cells and their axons. The fact that these are the only unmyelinated axons within the central nervous system (CNS) renders the afferent visual pathway an ideal model for studying axonal and neuronal degeneration in neurodegenerative diseases. Structural magnetic resonance imaging (MRI) can be used to obtain anatomical information about the CNS and to quantify evolving pathology in MS and NMOSD, both globally and in specific regions of the visual pathway including the optic nerve, optic radiations and visual cortex. Therefore, correlations between brain or optic nerve abnormalities on MRI, and retinal pathology using OCT, may shed light on how damage to one part of the CNS can affect others. In addition, these imaging techniques can help identify important differences between MS and NMOSD such as disease-specific damage to the visual pathway, trans-synaptic degeneration, or pathological changes independent of the underlying disease process. This review focuses on the current knowledge of the role of the visual pathway using OCT and MRI in patients with MS and NMOSD. Emphasis is placed on studies that employ both MRI and OCT to investigate damage to the visual system in these diseases.

Fully automated open-source lesion mapping of T2-FLAIR images with FSL correlates with clinical disability in MS

BACKGROUND

T2 Lesion Volume (T2LV) has been an important biomarker for multiple sclerosis (MS). Current methods available to quantify lesions from MR images generally require manual adjustments or multiple images with different contrasts. Further, implementations are often not easily or openly accessible.

OBJECTIVE

We created a fully unsupervised, single T2 FLAIR image T2LV quantification package based on the popular open-source imaging toolkit FSL.

METHODS

By scripting various processing tools in FSL, we developed an image processing pipeline that distinguishes normal brain tissue from CSF and lesions. We validated our method by hierarchical multiple regression (HMR) with a preliminary study to see if our T2LVs correlate with clinical disability measures in MS when controlled for other variables.

RESULTS

Pearson correlations between T2LV and Expanded Disability Status Scale (EDSS: r = 0.344, P = 0.013), Six-Minute Walk (6MW: r = -0.513, P = 0.000), Timed 25-Foot Walk (T25FW: r = -0.438, P = .000), and Symbol Digit Modalities Test (SDMT: r = -0.499, P = 0.000) were all significant. Partial correlations controlling for age were significant between T2LV and 6MW (r = -0.433, P = 0.002), T25FW (r = -0.392, P = 0.004), and SDMT (r = -0.450, P = 0.001). In HMR, T2LV explained significant additional variance in 6MW (R(2) change = 0.082, P = 0.020), after controlling for confounding variables such as age, white matter volume (WMV), and gray matter volume (GMV).

CONCLUSION

Our T2LV quantification software produces T2LVs from a single FLAIR image that correlate with physical disability in MS and is freely available as open-source software.

Recent Developments in Diffusion Tensor Imaging of Brain

Magnetic resonance imaging (MRI) has come to be known as a unique radiological imaging modality because of its ability to perform tomographic imaging of body without the use of any harmful ionizing radiation. The radiologists use MRI to gain insight into the anatomy of organs, including the brain, while biomedical researchers explore the modality to gain better understanding of the brain structure and function. However, due to limited resolution and contrast, the conventional MRI fails to show the brain microstructure. Diffusion tensor imaging (DTI) harnesses the power of conventional MRI to deduce the diffusion dynamics of water molecules within the tissue and indirectly create a three-dimensional sketch of the brain anatomy. DTI enables visualization of brain tissue microstructure, which is extremely helpful in understanding various neuropathologies and neurodegenerative disorders. In this review, we briefly discuss the background and operating principles of DTI, followed by current trends in DTI applications for biomedical and clinical investigation of various brain diseases and disorders.

[Contrast agents in MRI-diagnosis of multiple sclerosis]

Magnetic resonance imaging using contrast agents plays an important role in diagnosis and assessment of treatment efficacy in multiple sclerosis. The development of contrast agents on the basis of gadolinium or iron oxide nanoparticles has potential for diagnosis of pathological foci (tumors, amyloid plaques, inflammation and foci of demyelination or necrosis) in nervous system diseases. Newly developing types of diagnostic substances for visualization of pathological foci in multiple sclerosis are presented in this review.