Imaging biomarkers in multiple sclerosis

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.

A cross-sectional study of MRI features and the gut microbiome in pediatric-onset multiple sclerosis

OBJECTIVE

To identify gut microbiome features associated with MRI lesion burden in persons with pediatric-onset multiple sclerosis (symptom onset <18 years).

METHODS

A cross-sectional study involving the Canadian Paediatric Demyelinating Disease Network study participants. Gut microbiome features (alpha diversity, phylum- and genus-level taxa) were derived using 16S rRNA sequencing from stool samples. T1- and T2-weighted lesion volumes were measured on brain MRI obtained within 6 months of stool sample procurement. Associations between the gut microbiota and MRI metrics (cube-root-transformed) were assessed using standard and Lasso regression models.

RESULTS

Thirty-four participants were included; mean ages at symptom onset and MRI were 15.1 and 19.0 years, respectively, and 79% were female. The T1- and T2-weighted lesion volumes were not significantly associated with alpha diversity (age and sex-adjusted p > 0.08). At the phylum level, high Tenericutes (relative abundance) was associated with higher T1 and T2 volumes (β coefficient = 0.25, 0.37) and high Firmicutes, Patescibacteria or Actinobacteria with lower lesion volumes (β coefficient = -0.30 to -0.07). At the genus level, high Ruminiclostridium, whereas low Coprococcus 3 and low Erysipelatoclostridium were associated with higher lesion volumes.

INTERPRETATION

Our study characterized the gut microbiota features associated with MRI lesion burden in pediatric-onset MS, shedding light onto possible pathophysiological mechanisms.

Cortical Thin Patch Fraction Reflects Disease Burden in MS: The Mosaic Approach

BACKGROUND AND PURPOSE

GM pathology plays an essential role in MS disability progression, emphasizing the importance of neuroradiologic biomarkers to capture the heterogeneity of cortical disease burden. This study aimed to assess the validity of a patch-wise, individual interpretation of cortical thickness data to identify GM pathology, the "mosaic approach," which was previously suggested as a biomarker for assessing and localizing atrophy.

MATERIALS AND METHODS

We investigated the mosaic approach in a cohort of 501 patients with MS with respect to 89 internal and 651 external controls. The resulting metric of the mosaic approach is the so-called thin patch fraction, which is an estimate of overall cortical disease burden per patient. We evaluated the mosaic approach with respect to the following: 1) discrimination between patients with MS and controls, 2) classification between different MS phenotypes, and 3) association with established biomarkers reflecting MS disease burden, using general linear modeling.

RESULTS

The thin patch fraction varied significantly between patients with MS and healthy controls and discriminated among MS phenotypes. Furthermore, the thin patch fraction was associated with disease burden, including the Expanded Disability Status Scale, cognitive and fatigue scores, and lesion volume.

CONCLUSIONS

This study demonstrates the validity of the mosaic approach as a neuroradiologic biomarker in MS. The output of the mosaic approach, namely the thin patch fraction, is a candidate biomarker for assessing and localizing cortical GM pathology. The mosaic approach can furthermore enhance the development of a personalized cortical MS biomarker, given that the thin patch fraction provides a feature on which artificial intelligence methods can be trained. Most important, we showed the validity of the mosaic approach when referencing data with respect to external control MR imaging repositories.

[Consensus recommendations on regional interdisciplinary standardization of MRI diagnostics for multiple sclerosis in the metropolitan area of Essen]

Magnetic resonance imaging (MRI) is of exceptional importance in the diagnostics and monitoring of multiple sclerosis (MS); however, a close interdisciplinary cooperation between neurologists in private practice, (neuro)radiological practices, hospitals or specialized MS centers is only rarely established. In particular, there is a lack of standardized MRI protocols for image acquisition as well as established quality parameters, which guarantee the comparability of MRI records; however, this is a fundamental prerequisite for an effective application of MRI in the treatment of MS patients, e.g., for making the diagnosis or treatment monitoring. To address these challenges a group of neurologists and (neuro)radiologists developed a consensus proposal for standardization of image acquisition, interpretation and transmission of results and for improvement in interdisciplinary cooperation. This pilot project in the metropolitan area of Essen used a modified Delphi process and was based on the most up to date scientific knowledge. The recommendation takes the medical, economic, temporal and practical aspects of MRI in MS into consideration. The model of interdisciplinary cooperation between radiologists and neurologists with the aim of a regional standardization of MRI could serve as an example for other regions of Germany in order to optimize MRI for MS.

Choroid Plexus Volume Change-A Candidate for a New Radiological Marker of MS Progression

(1) Background: Multiple sclerosis (MS) is an auto-immune, chronic, neuroinflammatory, demyelinating disease that affects mainly young patients. This progressive inflammatory process causes the chronic loss of brain tissue and results in a deterioration in quality of life. To monitor neuroinflammatory process activity and predict the further development of disease, it is necessary to find a suitable biomarker that could easily be used. In this research, we verify the usability of choroid plexus (CP) volume, a new MS biomarker, in the monitoring of the progression of multiple sclerosis disease. (2) Methods: A single-center, prospective study with three groups of patients was conducted based on the following groups: MS patients who received experimental cellular therapy (Treg), treatment-naïve MS patients and healthy controls. (3) Results: This study concludes that there is a correlation between the CPV/TIV (choroid plexus/total intracranial volume) ratio and the progress of multiple sclerosis disease-patients with MS (MS + Treg) had larger volumes of choroid plexuses. CPV/TIV ratios in MS groups were constantly and significantly growing. In the Treg group, patients with relapses had larger plexuses in comparison to the group with no relapses of MS. A similar correlation was observed for the GD+ group (patients with postcontrast enhancing plaques) compared against the non-GD group (patients without postcontrast enhancing plaques). (4) Conclusion: Choroid plexus volume, due to its immunological function, correlates with the inflammatory process in the central nervous system. We consider it to become a valuable radiological biomarker of MS activity.

Multiple sclerosis patients treated with cladribine tablets: expert opinion on practical management after year 4

Multiple sclerosis (MS) is a chronic, progressive neurological disease involving neuroinflammation, neurodegeneration, and demyelination. Cladribine tablets are approved for immune reconstitution therapy in patients with highly active relapsing-remitting MS based on favorable efficacy and tolerability results from the CLARITY study that have been confirmed in long-term extension studies. The approved 4-year dosing regimen foresees a cumulative dose of 3.5 mg/kg administered in two cycles administered 1 year apart, followed by 2 years of observation. Evidence on managing patients beyond year 4 is scarce; therefore, a group of 10 neurologists has assessed the available evidence and formulated an expert opinion on management of the growing population of patients now completing the approved 4-year regimen. We propose five patient categories based on response to treatment during the first 4-year regimen, and corresponding management pathways that envision close monitoring with clinical visits, magnetic resonance imaging (MRI) and/or biomarkers. At the first sign of clinical or radiological disease activity, patients should receive a highly effective disease-modifying therapy, comprising either a full cladribine regimen as described in regulatory documents (cumulative dose 7.0 mg/kg) or a comparably effective treatment. Re-treatment decisions should be based on the intensity and timing of onset of disease activity, clinical and radiological assessments, as well as patient eligibility for treatment and treatment preference.

High-resolution multi-shot diffusion-weighted MRI combining markerless prospective motion correction and locally low-rank constrained reconstruction

PURPOSE

Subject head motion is a major challenge in DWI, leading to image blurring, signal losses, and biases in the estimated diffusion parameters. Here, we investigate a combined application of prospective motion correction and spatial-angular locally low-rank constrained reconstruction to obtain robust, multi-shot, high-resolution diffusion-weighted MRI under substantial motion.

METHODS

Single-shot EPI with retrospective motion correction can mitigate motion artifacts and resolve any mismatching of gradient encoding orientations; however, it is limited by low spatial resolution and image distortions. Multi-shot acquisition strategies could achieve higher resolution and image fidelity but increase the vulnerability to motion artifacts and phase variations related to cardiac pulsations from shot to shot. We use prospective motion correction with optical markerless motion tracking to remove artifacts and reduce image blurring due to bulk motion, combined with locally low-rank regularization to correct for remaining artifacts due to shot-to-shot phase variations.

RESULTS

The approach was evaluated on healthy adult volunteers at 3 Tesla under different motion patterns. In multi-shot DWI, image blurring due to motion with 20 mm translations and 30° rotations was successfully removed by prospective motion correction, and aliasing artifacts caused by shot-to-shot phase variations were addressed by locally low-rank regularization. The ability of prospective motion correction to preserve the orientational information in DTI without requiring a reorientation of the b-matrix is highlighted.

CONCLUSION

The described technique is proved to hold valuable potential for mapping brain diffusivity and connectivity at high resolution for studies in subjects/cohorts where motion is common, including neonates, pediatrics, and patients with neurological disorders.

Automated Cervical Spinal Cord Segmentation in Real-World MRI of Multiple Sclerosis Patients by Optimized Hybrid Residual Attention-Aware Convolutional Neural Networks

Magnetic resonance (MR) imaging is the most sensitive clinical tool in the diagnosis and monitoring of multiple sclerosis (MS) alterations. Spinal cord evaluation has gained interest in this clinical scenario in recent years, but, unlike the brain, there is a more limited choice of algorithms to assist spinal cord segmentation. Our goal was to investigate and develop an automatic MR cervical cord segmentation method, enabling automated and seamless spinal cord atrophy assessment and setting the stage for the development of an aggregated algorithm for the extraction of lesion-related imaging biomarkers. The algorithm was developed using a real-world MR imaging dataset of 121 MS patients (96 cases used as a training dataset and 25 cases as a validation dataset). Transversal, 3D T1-weighted gradient echo MR images (TE/TR/FA = 1.7-2.7 ms/5.6-8.2 ms/12°) were acquired in a 3 T system (Signa HD, GEHC) as standard of care in our clinical practice. Experienced radiologists supervised the manual labelling, which was considered the ground-truth. The 2D convolutional neural network consisted of a hybrid residual attention-aware segmentation method trained to delineate the cervical spinal cord. The training was conducted using a focal loss function, based on the Tversky index to address label imbalance, and an automatic optimal learning rate finder. Our automated model provided an accurate segmentation, achieving a validation DICE coefficient of 0.904 ± 0.101 compared with the manual delineation. An automatic method for cervical spinal cord segmentation on T1-weighted MR images was successfully implemented. It will have direct implications serving as the first step for accelerating the process for MS staging and follow-up through imaging biomarkers.

Neurometabolic changes in multiple sclerosis: Fingolimod versus beta interferon or glatiramer acetate therapy

BACKGROUND AND PURPOSE

Fingolimod has been shown to be more effective in reducing relapse rate and disability than injectable therapies in clinical trials. An increase in N-acetylaspartate (NAA) as measured by MR spectroscopy is correlated with maintaining axonal metabolic functions. This study compared the neurometabolic and volumetric changes in relapsing-remitting multiple sclerosis (RRMS) patients on fingolimod or injectable therapies with healthy controls (HCs).

METHODS

Ninety-eight RRMS (52 on fingolimod, 46 on injectable therapies (27 on glatiramer acetate and 19 on interferon) were age and sex-matched to 51 HCs. RRMS patients underwent cognitive, fatigue, and mental health assessments, as well as an Expanded disability status scale (EDSS). MRI/S was acquired from the hippocampus, posterior cingulate gyrus (PCG), and prefrontal cortex (PFC). Volumetric and neurometabolic measures were compared across cohorts using a univariate general linear model and correlated with clinical severity and neuropsychological scores.

RESULTS

Clinical parameters, MR-volumetric, and neurometabolic profiles showed no differences between treatment groups (p > .05). Compared to HCs, both RRMS cohorts showed volume changes in white matter (-13%), gray matter (-16%), and cerebral spinal fluid (CSF) (+17-23%), as well as reduced NAA (-17%, p = .001, hippocampus), (-7%, p = .001, PCG), and (-9%, p = .001, PFC). MRI/S metrics in three regions were moderately correlated with cognition and fatigue functions.

CONCLUSION

While both treatment arms showed overall similar volumetric and neurometabolic profiles, longitudinal studies are warranted to clarify neurometabolic changes and associations with treatment efficacy.

Fully automated detection of paramagnetic rims in multiple sclerosis lesions on 3T susceptibility-based MR imaging

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Paramagnetic rim lesions are an important subtype of multiple sclerosis lesion.

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Automated methods can accelerate the assessment of paramagnetic rim lesions.

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APRL automatically identifies and accurately classifies paramagnetic rim lesions.

Background and Purpose

The presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis lesion. Increased prevalence of these paramagnetic rim lesions is associated with a more severe disease course in MS, but manual identification is time-consuming. We present APRL, a method to automatically detect paramagnetic rim lesions on 3T T2*-phase images.

Methods

T1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 20 subjects with MS. The images were then processed with automated lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 951 lesions were identified, 113 (12%) of which contained a paramagnetic rim. We divided our data into a training set (16 patients, 753 lesions) and a testing set (4 patients, 198 lesions), fit a random forest classification model on the training set, and assessed our ability to classify paramagnetic rim lesions on the test set.

Results

The number of paramagnetic rim lesions per subject identified via our automated lesion labelling method was highly correlated with the gold standard count per subject, r = 0.86 (95% CI [0.68, 0.94]). The classification algorithm using radiomic features classified lesions with an area under the curve of 0.82 (95% CI [0.74, 0.92]).

Conclusion

This study develops a fully automated technique, APRL, for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.

FLAIR2 post-processing: improving MS lesion detection in standard MS imaging protocols

BACKGROUND

Technical improvements in magnetic resonance imaging (MRI) acquisition, such as higher field strength and optimized sequences, lead to better multiple sclerosis (MS) lesion detection and characterization. Multiplication of 3D-FLAIR with 3D-T2 sequences (FLAIR²) results in isovoxel images with increased contrast-to-noise ratio, increased white-gray-matter contrast, and improved MS lesion visualization without increasing MRI acquisition time. The current study aims to assess the potential of 3D-FLAIR² in detecting cortical/leucocortical (LC), juxtacortical (JC), and white matter (WM) lesions.

OBJECTIVE

To compare lesion detection of 3D-FLAIR² with state-of-the-art 3D-T2-FLAIR and 3D-T2-weighted images.

METHODS

We retrospectively analyzed MRI scans of thirteen MS patients, showing previously noted high cortical lesion load. Scans were acquired using a 3 T MRI scanner. WM, JC, and LC lesions were manually labeled and manually counted after randomization of 3D-T2, 3D-FLAIR, and 3D-FLAIR² scans using the ITK-SNAP tool.

RESULTS

LC lesion visibility was significantly improved by 3D-FLAIR² in comparison to 3D-FLAIR (4 vs 1; p = 0.018) and 3D-T2 (4 vs 1; p = 0.007). Comparing LC lesion detection in 3D-FLAIR² vs. 3D-FLAIR, 3D-FLAIR² detected on average 3.2 more cortical lesions (95% CI - 9.1 to 2.8). Comparing against 3D-T2, 3D-FLAIR² detected on average 3.7 more LC lesions (95% CI 3.3-10.7).

CONCLUSIONS

3D-FLAIR² is an easily applicable time-sparing MR post-processing method to improve cortical lesion detection. Larger sampled studies are warranted to validate the sensitivity and specificity of 3D-FLAIR².

Linking immune-mediated damage to neurodegeneration in multiple sclerosis: could network-based MRI help?

Inflammatory demyelination characterizes the initial stages of multiple sclerosis, while progressive axonal and neuronal loss are coexisting and significantly contribute to the long-term physical and cognitive impairment. There is an unmet need for a conceptual shift from a dualistic view of multiple sclerosis pathology, involving either inflammatory demyelination or neurodegeneration, to integrative dynamic models of brain reorganization, where, glia-neuron interactions, synaptic alterations and grey matter pathology are longitudinally envisaged at the whole-brain level. Functional and structural MRI can delineate network hallmarks for relapses, remissions or disease progression, which can be linked to the pathophysiology behind inflammatory attacks, repair and neurodegeneration. Here, we aim to unify recent findings of grey matter circuits dynamics in multiple sclerosis within the framework of molecular and pathophysiological hallmarks combined with disease-related network reorganization, while highlighting advances from animal models (in vivo and ex vivo) and human clinical data (imaging and histological). We propose that MRI-based brain networks characterization is essential for better delineating ongoing pathology and elaboration of particular mechanisms that may serve for accurate modelling and prediction of disease courses throughout disease stages.

Cognitive Decline in Multiple Sclerosis Is Related to the Progression of Retinal Atrophy and Presence of Oligoclonal Bands: A 5-Year Follow-Up Study

Background: Brain atrophy, which is associated with cognitive impairment and retinal nerve fiber layer (RNFL) atrophy, is the main biomarker of neurodegeneration in multiple sclerosis (MS). However, data on the relationship between inflammatory markers, such as oligoclonal bands (OCBs) in the cerebrospinal fluid (CSF), and cognition, RNFL atrophy, and brain atrophy are scarce. The aim of this study was to assess the influence of RNFL thickness, brain atrophy markers, intrathecal OCBs, and the immunoglobulin G (IgG) index on cognitive decline over a 5-year period in patients with MS. Methods: This prospective, single-center, observational cohort study included 49 patients with relapsing MS followed up over 5 years. At baseline, the patients underwent brain magnetic resonance imaging (MRI). Cognitive evaluation was performed using the Brief International Cognitive Assessment for MS (BICAMS), and RNFL thickness was assessed using optical coherence tomography (OCT). OCBs and IgG levels in the CSF were evaluated at baseline. The BICAMS, OCT, and MRI findings were re-evaluated after 5 years. Results: A significant reduction in information processing speed, visual learning, temporal RNFL thickness, the Huckman index, and third ventricle mean diameter was found in all 49 patients with relapsing MS over the observation period (p < 0.05). Of the patients, 63.3% had positive OCBs and 59.2% had elevated IgG indices. The atrophy of the temporal segment and papillomacular bundle and the presence of OCBs were significantly related to a decline in information processing speed in these patients (p < 0.05). However, brain atrophy markers were not found to be significant on the general linear models. Conclusions: RNFL atrophy and the presence of OCBs were related to cognitive decline in patients with MS over a 5-year follow-up period, thereby suggesting their utility as potential biomarkers of cognitive decline in MS.

Heterogeneity of multiple sclerosis lesions in fast diffusional kurtosis imaging

Background

Mean kurtosis (MK), one of the parameters derived from diffusion kurtosis imaging (DKI), has shown increased sensitivity to tissue microstructure damage in several neurological disorders.

Methods

Thirty-seven patients with relapsing-remitting MS and eleven healthy controls (HC) received brain imaging on a 3T MR scanner, including a fast DKI sequence. MK and mean diffusivity (MD) were measured in the white matter of HC, normal-appearing white matter (NAWM) of MS patients, contrast-enhancing lesions (CE-L), FLAIR lesions (FLAIR-L) and black holes (BH).

Results

Overall 1529 lesions were analyzed, including 30 CE-L, 832 FLAIR-L and 667 BH. Highest MK values were obtained in the white matter of HC (0.814 ± 0.129), followed by NAWM (0.724 ± 0.137), CE-L (0.619 ± 0.096), FLAIR-L (0.565 ± 0.123) and BH (0.549 ± 0.12). Lowest MD values were obtained in the white matter of HC (0.747 ± 0.068 10⁻³mm²/sec), followed by NAWM (0.808 ± 0.163 10⁻³mm²/sec), CE-L (0.853 ± 0.211 10⁻³mm²/sec), BH (0.957 ± 0.304 10⁻³mm²/sec) and FLAIR-L (0.976 ± 0.35 10⁻³mm²/sec). While MK differed significantly between CE-L and non-enhancing lesions, MD did not.

Conclusion

MK adds predictive value to differentiate between MS lesions and might provide further information about diffuse white matter injury and lesion microstructure.

Emerging technologies to study glial cells

Development, physiological functions, and pathologies of the brain depend on tight interactions between neurons and different types of glial cells, such as astrocytes, microglia, oligodendrocytes, and oligodendrocyte precursor cells. Assessing the relative contribution of different glial cell types is required for the full understanding of brain function and dysfunction. Over the recent years, several technological breakthroughs were achieved, allowing "glio-scientists" to address new challenging biological questions. These technical developments make it possible to study the roles of specific cell types with medium or high-content workflows and perform fine analysis of their mutual interactions in a preserved environment. This review illustrates the potency of several cutting-edge experimental approaches (advanced cell cultures, induced pluripotent stem cell (iPSC)-derived human glial cells, viral vectors, in situ glia imaging, opto- and chemogenetic approaches, and high-content molecular analysis) to unravel the role of glial cells in specific brain functions or diseases. It also illustrates the translation of some techniques to the clinics, to monitor glial cells in patients, through specific brain imaging methods. The advantages, pitfalls, and future developments are discussed for each technique, and selected examples are provided to illustrate how specific "gliobiological" questions can now be tackled.

The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach

Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML to PROs and CAOs of people with relapsing-remitting (RR) and secondary progressive (SP) form of multiple sclerosis (MS), to promptly identifying information useful to predict disease progression. For our analysis, a dataset of 3398 evaluations from 810 persons with MS (PwMS) was adopted. Three steps were provided: course classification; extraction of the most relevant predictors at the next time point; prediction if the patient will experience the transition from RR to SP at the next time point. The Current Course Assignment (CCA) step correctly assigned the current MS course with an accuracy of about 86.0%. The MS course at the next time point can be predicted using the predictors selected in CCA. PROs/CAOs Evolution Prediction (PEP) followed by Future Course Assignment (FCA) was able to foresee the course at the next time point with an accuracy of 82.6%. Our results suggest that PROs and CAOs could help the clinician decision-making in their practice.

Role of developmental venous anomalies in etiopathogenesis of demyelinating diseases

OBJECTIVE

Multiple sclerosis (MS) is a demyelinating autoimmune disease of the central nervous system. T2W-hyperintense demyelinating lesions are detected in cranial magnetic resonance imaging (MRI). Developmental venous anomalies (DVAs) have frequently been detected in enhanced cranial MRI images, and are generally accepted as normal variants of venous development. The aim of the present study was to investigate whether there was an association between demyelinating diseases and venous anomalies.

METHODS

One hundred five patients who were diagnosed as having MS in accordance with the McDonald diagnostic criteria, and 105 patients who were diagnosed as having vascular headache who had no lesions similar to MS were included in the present retrospective study.

RESULTS

DVAs were detected in 31 of the study group and in 14 patients in the control group. A statistically significant higher rate of DVAs and abnormal signal increase in the neighboring tissue was detected in the study group (p = 0.004) (p = 0.006). The DVA was superficially localized in the RRMS, It was deeply located in RIS.

CONCLUSION

Recent studies have emphasized the association of the central vein and the lesion severity of MS with the detection of the central collecting vein in MS lesions. In our study, DVAs, which are generally regarded as innocent developmental anomalies, and neighboring signal increase were found significantly higher in the MS group compared with the control group. The role of DVAs in the etiology of demyelinating lesions must be clarified through comprehensive future studies that use more advanced techniques.

Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data

Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients. To this end, we acquired magnetic resonance imaging data from relapsing-remitting multiple sclerosis patients and healthy subjects. Fractional anisotropy maps, structural and functional connectivity were extracted from the scans. Each of them were used as separate input features to construct support vector machine classifiers. A fourth input feature was created by combining structural and functional connectivity. Patients were divided in two groups according to their degree of disability and, together with the control group, three group pairs were formed for comparison. Twelve separate classifiers were built from the combination of these four input features and three group pairs. The classifiers were able to distinguish between patients and healthy subjects, reaching accuracy levels as high as 89% ± 2%. In contrast, the performance was noticeably lower when comparing the two groups of patients with different levels of disability, reaching levels below 63% ± 5%. The brain regions that contributed the most to the classification were the right occipital, left frontal orbital, medial frontal cortices and lingual gyrus. The developed classifiers based on MRI data were able to distinguish multiple sclerosis patients and healthy subjects reliably. Moreover, the resulting classification models identified brain regions, and functional and structural connections relevant for better understanding of the disease.

Clinical Course of Multiple Sclerosis

The 1996 originally established multiple sclerosis (MS) subtypes, based solely on clinical impression and consensus, were revised in 2013 to review potential imaging and biological correlates and to reflect recently identified clinical aspects of MS. As a result, potential new disease phenotypes, radiologically isolated syndrome, and clinically isolated syndrome were considered along with the addition of two new descriptor subtypes: activity and progression applied to relapsing remitting and progressive MS phenotypes. In this way, the description of an individual patient's disease course is refined and provides temporal information about the ongoing disease process. There is still a lack of imaging and biological markers that would distinguish MS phenotypes and prognosticate the disease course on an individual patient's level, creating a pressing need for large collaborative research efforts in this field.

Spinal Cord MRI in Multiple Sclerosis

Spinal cord (SC) MRI in multiple sclerosis (MS) has significant usefulness in clinical and investigational settings. Conventional MRI of the SC is used in clinical practice, because it has both diagnostic and prognostic value. A number of advanced, quantitative SC MRI measures that assess the structural and functional integrity of the SC have been evaluated in investigational settings. These techniques have collectively demonstrated usefulness in providing insight into microstructural and functional changes relevant to disability in MS. With further development, these techniques may be useful in clinical trial settings as biomarkers of neurodegeneration and protection, and in day-to-day clinical practice.

Effect of Region of Interest Size on the Repeatability of Quantitative Brain Imaging Biomarkers

In the repeatability analysis, when the measurement is the mean value of a parametric map within a region of interest (ROI), the ROI size becomes important as by increasing the size, the measurement will have a smaller variance. This is important in decision-making in prospective clinical studies of brain when the ROI size is variable, e.g., in monitoring the effect of treatment on lesions by quantitative MRI, and in particular when the ROI is small, e.g., in the case of brain lesions in multiple sclerosis. Thus, methods to estimate repeatability measures for arbitrary sizes of ROI are desired. We propose a statistical model of the values of parametric map within the ROI and a method to approximate the model parameters, based on which we estimate a number of repeatability measures including repeatability coefficient, coefficient of variation, and intraclass correlation coefficient for an ROI with an arbitrary size. We also show how this gives an insight into related problems such as spatial smoothing in voxel-wise analysis. Experiments are conducted on simulated data as well as on scan-rescan brain MRI of healthy subjects. The main application of this study is the adjustment of the decision threshold based on the lesion size in treatment monitoring.

Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches

A structural or functional pattern of neuroplasticity that could systematically discriminate between people with impaired and preserved motor performance could help us to understand the brain networks contributing to preservation or compensation of behavior in multiple sclerosis (MS). This study aimed to (1) investigate whether a machine learning-based technique could accurately classify MS participants into groups defined by upper extremity function (i.e. motor function preserved (MP) vs. motor function impaired (MI)) based on their regional grey matter measures (GMM, cortical thickness and deep grey matter volume) and inter-regional functional connection (FC), (2) investigate which features (GMM, FC, or GMM + FC) could classify groups more accurately, and (3) identify the multivariate patterns of GMM and FCs that are most discriminative between MP and MI participants, and between each of these groups and the healthy controls (HCs). With 26 MP, 25 MI, and 21 HCs (age and sex matched) underwent T1-weighted and resting-state functional MRI at 3 T, we applied support vector machine (SVM) based classification to learn discriminant functions indicating regions in which GMM or between which FCs were most discriminative between groups. This study demonstrates that there exist structural and FC patterns sufficient for correct classification of upper limb motor ability of people with MS. The classifier with GMM + FC features yielded the highest accuracy of 85.61 % (p < 0.001) to distinguish between the MS groups using leave-one-out cross-validation. It suggests that a machine-learning approach combining structural and functional features is useful for identifying the specific neural substrates that are necessary and sufficient to preserve motor function among people with MS.

Sensitivity of multi-shell NODDI to multiple sclerosis white matter changes: a pilot study

Diffusion tensor imaging (DTI) is sensitive to white matter (WM) damage in multiple sclerosis (MS), not only in focal lesions but also in the normal-appearing WM (NAWM). However, DTI indices can also be affected by natural spatial variation in WM, as seen in crossing and dispersing white matter fibers. Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion-weighted imaging technique that provides distinct indices of fiber density and dispersion. We performed NODDI of lesion tissue and NAWM in five MS patients and five controls, comparing the technique with traditional DTI. Both DTI and NODDI identified tissue damage in NAWM and in lesions. NODDI was able to detect additional changes and it provided better contrast in MS-NAWM microstructure, because it distinguished orientation dispersion and fiber density better than DTI. We showed that NODDI is viable in MS patients and that it offers, compared with DTI parameters, improved sensitivity and possibly greater specificity to microstructure features such as neurite orientation.

The clinical perspective: How to personalise treatment in MS and how may biomarkers including imaging contribute to this?

BACKGROUND

Multiple sclerosis (MS) is a highly heterogeneous disease, both in its course and in its response to treatments. Effective biomarkers may help predict disability progression and monitor patients' treatment responses.

OBJECTIVE

The aim of this review was to focus on how biomarkers may contribute to treatment individualisation in MS patients.

METHODS

This review reflects the content of presentations, polling results and discussions on the clinical perspective of MS during the first and second Pan-European MS Multi-stakeholder Colloquia in Brussels in May 2014 and 2015.

RESULTS

In clinical practice, magnetic resonance imaging (MRI) measures play a significant role in the diagnosis and follow-up of MS patients. Together with clinical markers, the rate of MRI-visible lesion accrual once a patient has started treatment may also help to predict subsequent treatment responsiveness. In addition, several molecular (immunological, genetic) biomarkers have been established that may play a role in predictive models of MS relapses and progression. To reach personalised treatment decisions, estimates of disability progression and likely treatment response should be carefully considered alongside the risk of serious adverse events, together with the patient's treatment expectations.

CONCLUSION

Although biomarkers may be very useful for individualised decision making in MS, many are still research tools and need to be validated before implementation in clinical practice.

The use of multiparametric quantitative magnetic resonance imaging for evaluating visually assigned lesion groups in patients with multiple sclerosis

In multiple sclerosis (MS), inflammatory lesions present a broad spectrum of histopathologic processes. For a better discrimination, lesions are visually defined into different lesion groups according to their appearance on conventional magnetic resonance imaging (MRI). The aim of this study was to investigate the properties of different MS lesion groups using multiparametric quantitative MRI. 35 patients diagnosed with relapsing-remitting MS received 3 Tesla MRI including magnetization-prepared 2 rapid acquisition gradient echo, diffusion tensor imaging and magnetization transfer imaging. Lesion segmentation was performed for T2 lesions, black holes and contrast-enhancing lesions. A subtraction mask was created including only T2 lesions that did not correspond to a black hole or contrast-enhancing lesion. T1 relaxation time (T1-RT), magnetization transfer ratio (MTR), mean diffusivity (MD) and fractional anisotropy (FA) were determined for every lesion and in normal-appearing white matter. Only MD differed significantly between all lesion groups and NAWM (p < 0.05), while FA differed between all lesion groups but not NAWM. T1-RT and MTR were not useful imaging biomarkers to distinguish between lesion groups. A lack of sensitivity and specificity and unproportional alterations of quantitative MRI measures, due to heterogenous histopathologic processes within lesions, may be a possible explanation for missing discrimination. Thus, not only interpretation of visually defined MS lesion but also interpretation of quantitative MRI measures remains challenging and should be conducted carefully.

Biomarkers in the evolution of multiple sclerosis

Nonimaging biomarkers can be applied in differential diagnosis, evaluation of disease progression and therapy monitoring of multiple sclerosis (MS). Presence of oligoclonal IgG bands in cerebrospinal fluid is a diagnostic element and a negative predictor of MS evolution. AQP4 antibodies are pathogenic and diagnostic for neuromyelitis optica spectrum disorder. Antibodies to myelin oligodendrocyte glycoprotein develop in about 50% of predominantly pediatric patients with acute disseminated encephalomyelitis, but their possible role in pathogenesis is unknown. Currently, there are no individualized biomarkers suitable to track disease progression. Neutralizing antibodies against IFN-β, natalizumab and daclizumab arise with variable frequency and reduce treatment efficacy. The anti-John Cunningham virus antibody index has potential as a biomarker for risk of progressive multifocal leukoencephalopathy.

The Role of T1-Weighted Derived Measures of Neurodegeneration for Assessing Disability Progression in Multiple Sclerosis

Introduction

Multiple sclerosis (MS) is characterised by the accumulation of permanent neurological disability secondary to irreversible tissue loss (neurodegeneration) in the brain and spinal cord. MRI measures derived from T1-weighted image analysis (i.e., black holes and atrophy) are correlated with pathological measures of irreversible tissue loss. Quantifying the degree of neurodegeneration in vivo using MRI may offer a surrogate marker with which to predict disability progression and the effect of treatment. This review evaluates the literature examining the association between MRI measures of neurodegeneration derived from T1-weighted images and disability in MS patients.

Methods

A systematic PubMed search was conducted in January 2017 to identify MRI studies in MS patients investigating the relationship between “black holes” and/or atrophy in the brain and spinal cord, and disability. Results were limited to human studies published in English in the previous 10 years.

Results

A large number of studies have evaluated the association between the previous MRI measures and disability. These vary considerably in terms of study design, duration of follow-up, size, and phenotype of the patient population. Most, although not all, have shown that there is a significant correlation between disability and black holes in the brain, as well as atrophy of the whole brain and grey matter. The results for brain white matter atrophy are less consistently positive, whereas studies evaluating spinal cord atrophy consistently showed a significant correlation with disability. Newer ways of measuring atrophy, thanks to the development of segmentation and voxel-wise methods, have allowed us to assess the involvement of strategic regions of the CNS (e.g., thalamus) and to map the regional distribution of damage. This has resulted in better correlations between MRI measures and disability and in the identification of the critical role played by some CNS structures for MS clinical manifestations.

Conclusion

The evaluation of MRI measures of atrophy as predictive markers of disability in MS is a highly active area of research. At present, measurement of atrophy remains within the realm of clinical studies, but its utility in clinical practice has been recognized and barriers to its implementation are starting to be addressed.

[A current view on the MRI diagnosis of multiple sclerosis: an update of 2016 revised MRI criteria]

Magnetic resonance imaging (MRI) is the primary method for confirming the clinical diagnosis of multiple sclerosis (MS). The article presents the current data on using MRI of the brain and spinal cord for diagnosis in suspected MS. Special attention is paid to the MRI criteria of McDonald and MAGNIMS for relapsing-remitting MS (RRMS) and primary-progressive MS (PPMS) in the latest revisions of 2010 and 2016. The information provided can help radiologists and neurologists to optimize the use of MRI in clinical practice for diagnosis of MS.

Cervical cord myelin water imaging shows degenerative changes over one year in multiple sclerosis but not neuromyelitis optica spectrum disorder

Spinal cord pathology is a feature of both neuromyelitis optica spectrum disorder (NMOSD) and relapsing-remitting multiple sclerosis (MS). While subclinical disease activity has been described in MS using quantitative magnetic resonance imaging measures, current evidence suggests that neurodegeneration is absent between relapses in NMOSD, although most evidence comes from brain studies. We aimed to assess cross-sectional differences and longitudinal changes in myelin integrity in relapse-free MS and NMOSD subjects over one year. 15 NMOSD, 15 MS subjects, and 17 healthy controls were scanned at 3 T using a cervical cord mcDESPOT protocol. A subset of 8 NMOSD, 11 MS subjects and 14 controls completed follow-up. Measures of the myelin water fraction (f_M) within lesioned and non-lesioned cord segments were collected. At baseline, f_M in lesioned and non-lesioned segments was significantly reduced in MS (lesioned: p = 0.002; non-lesioned: p = 0.03) and NMOSD (lesioned: p = 0.0007; non-lesioned: p = 0.002) compared to controls. Longitudinally, f_M decreased within non-lesioned cord segments in the MS group (− 7.3%, p = 0.02), but not in NMOSD (+ 5.8%, p = 0.1), while change in lesioned segments f_M did not differ from controls' in either patient group. These results suggest that degenerative changes outside of lesioned areas can be observed over a short time frame in MS, but not NMOSD, and support the use of longitudinal myelin water imaging for the assessment of pathological changes in the cervical cord in demyelinating diseases.

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MS and NMOSD subjects underwent longitudinal cervical cord myelin water imaging.

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Reduced myelin water fraction in MS and NMOSD normal-appearing and lesioned areas

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Decrease in myelin in normal-appearing tissue over 1 year in MS, but not NMOSD

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Further evidence that disease progression is absent between relapses in NMOSD.

Composite Marker of Cognitive Dysfunction and Brain Atrophy is Highly Accurate in Discriminating Between Relapsing-Remitting and Secondary Progressive Multiple Sclerosis

Background

With the advent of numerous new-generation disease-modifying drugs for multiple sclerosis (MS), the discrimination between relapsing-remitting MS (RRMS) and secondary progressive MS (SPMS) has become a problem of high importance. The aim of our study was to find a simple way to accurately discriminate between RRMS and SPMS that is applicable in clinical practice as a composite marker, using the linear measures of magnetic resonance imaging (MRI) and the results of cognitive tests.

Material/Methods

We included 88 MS patients in the study: 43 participants had RRMS and 45 had SPMS. A battery consisting of 11 tests was used to evaluate cognitive function. We used 11 linear MRI measures and 7 indexes to assess brain atrophy.

Results

Four cognitive tests and 3 linear MRI measures were able to distinguish RRMS from SPMS with the AUC >0.8 based on ROC analysis. Multiple logistic regression models were constructed to identify the best set of cognitive and MRI markers. The model, using the Rey Auditory Verbal Learning Test (RAVLT), Digit Symbol Substitution Test (DSST), and Huckman Index, showed the highest predictive ability: AUC=0.921 (p<0.001). We constructed a simple remission-progression index from the same 3 variables, which discriminated well between RRMS and SPMS: AUC=0.920 (p<0.001), maximal Youden Index=0.702, cut-off=1.68, sensitivity=79.1%, and specificity=91.1%.

Conclusions

The composite remission-progression index, using the RAVLT test, DSST test, and MRI Huckman Index, is highly accurate in discriminating between RRMS and SPMS.

Patch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis

Purpose: Magnetic resonance spectroscopic imaging (MRSI) provides complementary information to conventional magnetic resonance imaging. Acquiring high resolution MRSI is time consuming and requires complex reconstruction techniques.

Methods: In this paper, a patch-based super-resolution method is presented to increase the spatial resolution of metabolite maps computed from MRSI. The proposed method uses high resolution anatomical MR images (T1-weighted and Fluid-attenuated inversion recovery) to regularize the super-resolution process. The accuracy of the method is validated against conventional interpolation techniques using a phantom, as well as simulated and in vivo acquired human brain images of multiple sclerosis subjects.

Results: The method preserves tissue contrast and structural information, and matches well with the trend of acquired high resolution MRSI.

Conclusions: These results suggest that the method has potential for clinically relevant neuroimaging applications.

Fluctuations of spontaneous EEG topographies predict disease state in relapsing-remitting multiple sclerosis

Spontaneous fluctuations of neuronal activity in large-scale distributed networks are a hallmark of the resting brain. In relapsing-remitting multiple sclerosis (RRMS) several fMRI studies have suggested altered resting-state connectivity patterns. Topographical EEG analysis reveals much faster temporal fluctuations in the tens of milliseconds time range (termed “microstates”), which showed altered properties in a number of neuropsychiatric conditions.

We investigated whether these microstates were altered in patients with RRMS, and if the microstates' temporal properties reflected a link to the patients' clinical features.

We acquired 256-channel EEG in 53 patients (mean age 37.6 years, 45 females, mean disease duration 9.99 years, Expanded Disability Status Scale ≤ 4, mean 2.2) and 49 healthy controls (mean age 36.4 years, 33 females). We analyzed segments of a total of 5 min of EEG during resting wakefulness and determined for both groups the four predominant microstates using established clustering methods.

We found significant differences in the temporal dynamics of two of the four microstates between healthy controls and patients with RRMS in terms of increased appearance and prolonged duration. Using stepwise multiple linear regression models with 8-fold cross-validation, we found evidence that these electrophysiological measures predicted a patient's total disease duration, annual relapse rate, disability score, as well as depression score, and cognitive fatigue measure.

In RRMS patients, microstate analysis captured altered fluctuations of EEG topographies in the sub-second range. This measure of high temporal resolution provided potentially powerful markers of disease activity and neuropsychiatric co-morbidities in RRMS.

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EEG microstates analyses provide high resolution of temporal dynamics of brain networks.

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Temporal parameters of EEG microstates are altered in Multiple Sclerosis

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Altered microstate parameters predict several clinical characteristics in patients

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We propose an EEG microstate based marker to characterize disease evolution in patients

The regulator’s perspective: How should new therapies and follow-on products for MS be clinically evaluated in the future?

Background:

Although there is still no cure for multiple sclerosis (MS), the introduction of several innovative drugs with modes of action different from that of the existing drug arsenal and the progress in monitoring disease progression by imaging and using biomarkers are currently causing a knowledge surge. This provides opportunities for improving patient disease management. New therapies are also under development and pose challenges to the regulatory bodies regarding the optimal design of clinical trials with more patient-focused clinical endpoints. Moreover, with the upcoming patent expiry of some of the key first-line MS treatments in Europe, regulatory bodies will also face the challenge of recommending marketing authorisation for generic and abridged versions based on appropriate requirements for demonstrating equality/similarity to the innovator’s product.

Objective:

The goal of this article is to improve the understanding of the relevant guidance documents of the European Medicines Agency (EMA) on clinical investigation of medicinal products and to highlight the issues that the agency will need to clarify regarding follow-on products of first-line MS treatments.

Conclusion:

Today, it is clear that close collaboration between patients, healthcare professionals, regulatory bodies and industry is crucial for developing new safe and effective drugs, which satisfy the needs of MS patients.

Abnormal functional connectivity and cortical integrity influence dominant hand motor disability in multiple sclerosis: a multimodal analysis

Functional reorganization and structural damage occur in the brains of people with multiple sclerosis (MS) throughout the disease course. However, the relationship between resting-state functional connectivity (FC) reorganization in the sensorimotor network and motor disability in MS is not well understood. This study used resting-state fMRI, T1-weighted and T2-weighted, and magnetization transfer (MT) imaging to investigate the relationship between abnormal FC in the sensorimotor network and upper limb motor disability in people with MS, as well as the impact of disease-related structural abnormalities within this network. Specifically, the differences in FC of the left hemisphere hand motor region between MS participants with preserved (n = 17) and impaired (n = 26) right hand function, compared with healthy controls (n = 20) was investigated. Differences in brain atrophy and MT ratio measured at the global and regional levels were also investigated between the three groups. Motor preserved MS participants had stronger FC in structurally intact visual information processing regions relative to motor impaired MS participants. Motor impaired MS participants showed weaker FC in the sensorimotor and somatosensory association cortices and more severe structural damage throughout the brain compared with the other groups. Logistic regression analysis showed that regional MTR predicted motor disability beyond the impact of global atrophy whereas regional grey matter volume did not. More importantly, as the first multimodal analysis combining resting-state fMRI, T1-weighted, T2-weighted and MTR images in MS, we demonstrate how a combination of structural and functional changes may contribute to motor impairment or preservation in MS. Hum Brain Mapp 37:4262-4275, 2016. © 2016 Wiley Periodicals, Inc.

T cell cytokine signatures: Biomarkers in pediatric multiple sclerosis

Although multiple sclerosis is predominantly regarded as a disease of young adulthood, up to 5% of MS patients are diagnosed prior to age eighteen. The predominant form of MS is relapsing-remitting characterized by exacerbations of symptoms followed by periods of remission. The majority of disease modifying drugs target T cell proliferation or block migration into the central nervous system. Although these treatments reduce relapses, disease progression still occurs, warranting therapeutic strategies that protect the CNS. Biomarkers to indicate relapses would facilitate a personalized approach for add-on therapies that protect the CNS. A multiplex cytokine bead array was performed to detect T cell associated cytokines in sera from patients 6-20years of age with pediatric onset MS clinically diagnosed in relapse or remission compared to healthy control patients. Of the 25 cytokines evaluated, 17 were increased in patients clinically diagnosed in relapse compared to sera from control patients in contrast to only 9 cytokines in the clinically diagnosed remission group. Furthermore, a linear regression analysis of cytokine levels in the remission population showed 12 cytokines to be statistically elevated as a function of disease duration, with no effect observed in the relapse population. To further explore this concept, we used a multivariable stepwise discriminate analysis and found that the following four cytokines (IL-10, IL-21, IL-23, and IL-27) are not only a significant predictor for MS, but have important predictive value in determining a relapse. Since IL-10 and IL-27 are considered anti-inflammatory and IL-21 and IL-23 are pro-inflammatory, ratios of these cytokines were evaluated using a Duncan's multiple range test. Of the six possible combinations, increased ratios of IL-10:IL-21, IL-10:IL-23, and IL-10:IL-27 were significant suggesting levels of IL-10 to be a driving force in predicting a relapse.

Diffusion tensor imaging study in Duchenne muscular dystrophy

BACKGROUND

Duchenne muscular dystrophy (DMD) is a progressive muscle disorder associated with an intellectual deficit which is non-progressive. The aim of this study was to investigate brain microstructural changes in DMD and to explore the relationship between such changes and cognitive impairment.

METHODS

All participants (12 DMD patients, 14 age-matched healthy boys), intelligence quotients (IQs) [both full (FIQ) and verbal (VIQ)] were evaluated using the Wechsler intelligence scale for children China revised (WISC-CR) edition, and brain gray matter (GM) and white matter (WM) changes were mapped using diffusion tensor imaging (DTI) with fractional anisotropy (FA). The differences between groups were analyzed using the t-test and the association of cognition with neuroimaging parameters was evaluated using Pearson's correlation coefficient.

RESULTS

Compared to the normal controls, the DMD group had lower FIQ (82.0±15.39 vs. 120.21±16.06) and significantly lower splenium of corpus callosum (CC) FA values (P<0.05). Splenium of CC FA was positively correlated with VIQ (r=0.588, P=0.044).

CONCLUSIONS

There were microstructural changes of splenium of CC in DMD patients, which was associated with cognitive impairment.

Incorporating dixon multi-echo fat water separation for novel quantitative magnetization transfer of the human optic nerve in vivo

PURPOSE

The optic nerve (ON) represents the sole pathway between the eyes and brain; consequently, diseases of the ON can have dramatic effects on vision. However, quantitative magnetization transfer (qMT) applications in the ON have been limited to ex vivo studies, in part because of the fatty connective tissue that surrounds the ON, confounding the magnetization transfer (MT) experiment. Therefore, the aim of this study was to implement a multi-echo Dixon fat-water separation approach to remove the fat component from MT images.

METHODS

MT measurements were taken in a single slice of the ON and frontal lobe using a three-echo Dixon readout, and the water and out-of-phase images were applied to a two-pool model in ON tissue and brain white matter to evaluate the effectiveness of using Dixon fat-water separation to remove fatty tissue from MT images.

RESULTS

White matter data showed no significant differences between image types; however, there was a significant increase (p < 0.05) in variation in the out-of-phase images in the ON relative to the water images.

CONCLUSIONS

The results of this study demonstrate that Dixon fat-water separation can be robustly used for accurate MT quantification of anatomies susceptible to partial volume effects resulting from fat. Magn Reson Med 77:707-716, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Limbic pathway lesions in patients with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a well-known demyelinating disease to cause cognitive dysfunction. The limbic system, relevant to memory, can be easily overlooked in conventional magnetic resonance imaging (MRI).

PURPOSE

To investigate the distribution and frequency of demyelinating lesions affecting white matter connections of the limbic system based on localization with diffusion tensor imaging (DTI)-derived fractional anisotropy (FA) color maps compared to three-dimensional T2-weighted (T2W) and FLAIR volumes in MS patients.

MATERIAL AND METHODS

One hundred and fifty patients with a known diagnosis of MS were identified for this Health Insurance Portability and Accountability (HIPAA)-compliant retrospective cross-sectional study. DTI-derived FA color maps, co-registered to T2W and FLAIR images, were analyzed for lesions affecting the three white matter tracts of the limbic system including cingulum, fornix, and mammilothalamic tracts by two investigators. The approximate location of the lesions on FLAIR was always confirmed on the co-registered DTI-derived FA color maps.

RESULTS

Of the 150 patients analyzed, 14.6% had cingulum lesions, 2.6% had fornix lesions, and 2.6% had mammilothalamic tract lesions; 21.3% of patients had at least one of the three tracts affected.

CONCLUSION

A relatively high frequency of lesions involving the limbic tracts may explain memory deficits and emotional dysfunction commonly experienced by patients with MS. The combined information from T2W, FLAIR, and DTI-derived FA color map allowed for more accurate localization of lesions affecting the major white matter tracts of the limbic system.

Brain atrophy in multiple sclerosis: therapeutic, cognitive and clinical impact

ABSTRACT Multiple sclerosis (MS) was always considered as a white matter inflammatory disease. Today, there is an important body of evidence that supports the hypothesis that gray matter involvement and the neurodegenerative mechanism are at least partially independent from inflammation. Gray matter atrophy develops faster than white matter atrophy, and predominates in the initial stages of the disease. The neurodegenerative mechanism creates permanent damage and correlates with physical and cognitive disability. In this review we describe the current available evidence regarding brain atrophy and its consequence in MS patients.

Fatigue in Multiple Sclerosis: Assessing Pontine Involvement Using Proton MR Spectroscopic Imaging

Background/Objective

The underlying mechanism of fatigue in multiple sclerosis (MS) remains poorly understood. Our study investigates the involvement of the ascending reticular activating system (ARAS), originating in the pontine brainstem, in MS patients with symptoms of fatigue.

Methods

Female relapsing-remitting MS patients (n = 17) and controls (n = 15) underwent a magnetic resonance spectroscopic imaging protocol at 1.5T. Fatigue was assessed in every subject using the Fatigue Severity Scale (FSS). Using an FSS cut-off of 36, patients were categorized into a low (n = 9, 22 ± 10) or high (n = 10, 52 ± 6) fatigue group. The brain metabolites N-acetylaspartate (NAA) and total creatine (tCr) were measured from sixteen 5x5x10 mm³ spectroscopic imaging voxels in the rostral pons.

Results

MS patients with high fatigue had lower NAA/tCr concentration in the tegmental pons compared to control subjects. By using NAA and Cr values in the cerebellum for comparison, these NAA/tCr changes in the pons were driven by higher tCr concentration, and that these changes were focused in the WM regions.

Discussion/Conclusion

Since there were no changes in NAA concentration, the increase in tCr may be suggestive of gliosis, or an imbalanced equilibrium of the creatine and phosphocreatine ratio in the pons of relapsing-remitting MS patients with fatigue.

Quantifying brain volumes for Multiple Sclerosis patients follow-up in clinical practice - comparison of 1.5 and 3 Tesla magnetic resonance imaging

INTRODUCTION

There is emerging evidence that brain atrophy is a part of the pathophysiology of Multiple Sclerosis (MS) and correlates with several clinical outcomes of the disease, both physical and cognitive. Consequently, brain atrophy is becoming an important parameter in patients' follow-up. Since in clinical practice both 1.5Tesla (T) and 3T magnetic resonance imaging (MRI) systems are used for MS patients follow-up, questions arise regarding compatibility and a possible need for standardization.

METHODS

Therefore, in this study 18 MS patients were scanned on the same day on a 1.5T and a 3T scanner. For each scanner, a 3D T1 and a 3D FLAIR were acquired. As no atrophy is expected within 1 day, these datasets can be used to evaluate the median percentage error of the brain volume measurement for gray matter (GM) volume and parenchymal volume (PV) between 1.5T and 3T scanners. The results are obtained with MSmetrix, which is developed especially for use in the MS clinical care path, and compared to Siena (FSL), a widely used software for research purposes.

RESULTS

The MSmetrix median percentage error of the brain volume measurement between a 1.5T and a 3T scanner is 0.52% for GM and 0.35% for PV. For Siena this error equals 2.99%. When data of the same scanner are compared, the error is in the order of 0.06-0.08% for both MSmetrix and Siena.

CONCLUSIONS

MSmetrix appears robust on both the 1.5T and 3T systems and the measurement error becomes an order of magnitude higher between scanners with different field strength.

Nonconventional MRI and microstructural cerebral changes in multiple sclerosis

MRI has become the most important paraclinical tool for diagnosing and monitoring patients with multiple sclerosis (MS). However, conventional MRI sequences are largely nonspecific in the pathology they reveal, and only provide a limited view of the complex morphological changes associated with MS. Nonconventional MRI techniques, such as magnetization transfer imaging (MTI), diffusion-weighted imaging (DWI) and susceptibility-weighted imaging (SWI) promise to complement existing techniques by revealing more-specific information on microstructural tissue changes. Past years have witnessed dramatic advances in the acquisition and analysis of such imaging data, and numerous studies have used these tools to probe tissue alterations associated with MS. Other MRI-based techniques-such as myelin-water imaging, (23)Na imaging, magnetic resonance elastography and magnetic resonance perfusion imaging-might also shed new light on disease-associated changes. This Review summarizes the rapid technical progress in the use of MRI in patients with MS, with a focus on nonconventional structural MRI. We critically discuss the present utility of nonconventional MRI in MS, and provide an outlook on future applications, including clinical practice. This information should allow appropriate selection of advanced MRI techniques, and facilitate their use in future studies of this disease.

Motor network efficiency and disability in multiple sclerosis

OBJECTIVE

To develop a composite MRI-based measure of motor network integrity, and determine if it explains disability better than conventional MRI measures in patients with multiple sclerosis (MS).

METHODS

Tract density imaging and constrained spherical deconvolution tractography were used to identify motor network connections in 22 controls. Fractional anisotropy (FA), magnetization transfer ratio (MTR), and normalized volume were computed in each tract in 71 people with relapse onset MS. Principal component analysis was used to distill the FA, MTR, and tract volume data into a single metric for each tract, which in turn was used to compute a composite measure of motor network efficiency (composite NE) using graph theory. Associations were investigated between the Expanded Disability Status Scale (EDSS) and the following MRI measures: composite motor NE, NE calculated using FA alone, FA averaged in the combined motor network tracts, brain T2 lesion volume, brain parenchymal fraction, normal-appearing white matter MTR, and cervical cord cross-sectional area.

RESULTS

In univariable analysis, composite motor NE explained 58% of the variation in EDSS in the whole MS group, more than twice that of the other MRI measures investigated. In a multivariable regression model, only composite NE and disease duration were independently associated with EDSS.

CONCLUSIONS

A composite MRI measure of motor NE was able to predict disability substantially better than conventional non-network-based MRI measures.