Low-field MRI for use in neurological diseases.

PURPOSE OF REVIEW: To review recent clinical uses of low-field magnetic resonance imaging (MRI) to guide incorporation into neurological practice. RECENT FINDINGS: Use of low-field MRI has been demonstrated in applications including tumours, vascular pathologies, multiple sclerosis, brain injury, and paediatrics. Safety, workflow, and image quality have also been evaluated. SUMMARY: Low-field MRI has the potential to increase access to critical brain imaging for patients who otherwise may not obtain imaging in a timely manner. This includes areas such as the intensive care unit and emergency room, where patients could be imaged at the point of care rather than be transported to the MRI scanner. Such systems are often more affordable than conventional systems, allowing them to be more easily deployed in resource constrained settings. A variety of systems are available on the market or in a research setting and are currently being used to determine clinical uses for these devices. The utility of such devices must be fully evaluated in clinical scenarios before adoption into standard practice can be achieved. This review summarizes recent clinical uses of low-field MR as well as safety, workflows, and image quality to aid practitioners in assessing this new technology.

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study.

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.

Applicability of multiple quantitative magnetic resonance methods in genetic brain white matter disorders.

BACKGROUND AND PURPOSE: Magnetic resonance imaging (MRI) measures of tissue microstructure are important for monitoring brain white matter (WM) disorders like leukodystrophies and multiple sclerosis. They should be sensitive to underlying pathological changes. Three whole-brain isotropic quantitative methods were applied and compared within a cohort of controls and leukodystrophy patients: two novel myelin water imaging (MWI) techniques (multi-compartment relaxometry diffusion-informed MWI: MCR-DIMWI, and multi-echo T2 relaxation imaging with compressed sensing: METRICS) and neurite orientation dispersion and density imaging (NODDI). METHODS: For 9 patients with different leukodystrophies (age range 0.4-62.4 years) and 15 control subjects (2.3-61.3 years), T1-weighted MRI, fluid-attenuated inversion recovery, multi-echo gradient echo with variable flip angles, METRICS, and multi-shell diffusion-weighted imaging were acquired on 3 Tesla. MCR-DIMWI, METRICS, NODDI, and quality control measures were extracted to evaluate differences between patients and controls in WM and deep gray matter (GM) regions of interest (ROIs). Pearson correlations, effect size calculations, and multi-level analyses were performed. RESULTS: MCR-DIMWI and METRICS-derived myelin water fractions (MWFs) were lower and relaxation times were higher in patients than in controls. Effect sizes of MWF values and relaxation times were large for both techniques. Differences between patients and controls were more pronounced in WM ROIs than in deep GM. MCR-DIMWI-MWFs were more homogeneous within ROIs and more bilaterally symmetrical than METRICS-MWFs. The neurite density index was more sensitive in detecting differences between patients and controls than fractional anisotropy. Most measures obtained from MCR-DIMWI, METRICS, NODDI, and diffusion tensor imaging correlated strongly with each other. CONCLUSION: This proof-of-concept study shows that MCR-DIMWI, METRICS, and NODDI are sensitive techniques to detect changes in tissue microstructure in WM disorders.

The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity.

Quantitative magnetic resonance imaging (MRI) techniques are powerful tools for the study of human tissue, but, in practice, their utility has been limited by lengthy acquisition times. Here, we introduce the Constrained, Adaptive, Low-dimensional, Intrinsically Precise Reconstruction (CALIPR) framework in the context of myelin water imaging (MWI); a quantitative MRI technique generally regarded as the most rigorous approach for noninvasive, in vivo measurement of myelin content. The CALIPR framework exploits data redundancy to recover high-quality images from a small fraction of an imaging dataset, which allowed MWI to be acquired with a previously unattainable sequence (fully sampled acquisition 2 hours:57 min:20 s) in 7 min:26 s (4.2% of the dataset, acceleration factor 23.9). CALIPR quantitative metrics had excellent precision (myelin water fraction mean coefficient of variation 3.2% for the brain and 3.0% for the spinal cord) and markedly increased sensitivity to demyelinating disease pathology compared to a current, widely used technique. The CALIPR framework facilitates drastically improved MWI and could be similarly transformative for other quantitative MRI applications.

Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis.

Introduction: Machine learning (ML) has great potential for using health data to predict clinical outcomes in individual patients. Missing data are a common challenge in training ML algorithms, such as when subjects withdraw from a clinical study, leaving some samples with missing outcome labels. In this study, we have compared three ML models to determine whether accounting for label uncertainty can improve a model's predictions. Methods: We used a dataset from a completed phase-III clinical trial that evaluated the efficacy of minocycline for delaying the conversion from clinically isolated syndrome to multiple sclerosis (MS), using the McDonald 2005 diagnostic criteria. There were a total of 142 participants, and at the 2-year follow-up 81 had converted to MS, 29 remained stable, and 32 had uncertain outcomes. In a stratified 7-fold cross-validation, we trained three random forest (RF) ML models using MRI volumetric features and clinical variables to predict the conversion outcome, which represented new disease activity within 2 years of a first clinical demyelinating event. One RF was trained using subjects with the uncertain labels excluded (RFexclude), another RF was trained using the entire dataset but with assumed labels for the uncertain group (RFnaive), and a third, a probabilistic RF (PRF, a type of RF that can model label uncertainty) was trained on the entire dataset, with probabilistic labels assigned to the uncertain group. Results: Probabilistic random forest outperformed both the RF models with the highest AUC (0.76, compared to 0.69 for RFexclude and 0.71 for RFnaive) and F1-score (86.6% compared to 82.6% for RFexclude and 76.8% for RFnaive). Conclusion: Machine learning algorithms capable of modeling label uncertainty can improve predictive performance in datasets in which a substantial number of subjects have unknown outcomes.

Ocrelizumab-treated patients with relapsing multiple sclerosis show volume loss rates similar to healthy aging.

BACKGROUND: Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system characterized by two major and interconnected hallmarks: inflammation and progressive neurodegeneration. OBJECTIVE: The aim of this work was to compare neurodegenerative processes, in the form of global and regional brain volume loss rates, in healthy controls (HCs) and in patients with relapsing MS (RMS) treated with ocrelizumab, which suppresses acute inflammation. METHODS: Whole brain, white matter, cortical gray matter, thalamic, and cerebellar volume loss rates were assessed in 44 HCs that were part of a substudy in the OPERA II randomized controlled trial (NCT01412333) and 59 patients with RMS enrolled in the same substudy as well as age- and sex-matched patients in OPERA I (NCT01247324) and II. Volume loss rates were computed using random coefficients models over a period of 2 years. RESULTS: Ocrelizumab-treated patients showed global and regional brain volume loss rates that were approaching that of HCs. CONCLUSION: These findings are consistent with an important role of inflammation on overall tissue loss and the role of ocrelizumab in reducing this phenomenon.

Neural network algorithms predict new diffusion MRI data for multi-compartmental analysis of brain microstructure in a clinical setting.

High angular resolution diffusion imaging (HARDI) is a promising method for advanced analysis of brain microstructure. However, comprehensive HARDI analysis requires multiple acquisitions of diffusion images (multi-shell HARDI), which is time consuming and often impractical in clinical settings. This study aimed to establish neural network models that can predict new diffusion datasets from clinically feasible brain diffusion MRI for multi-shell HARDI. The development included 2 algorithms: multi-layer perceptron (MLP) and convolutional neural network (CNN). Both followed a voxel-based approach for model training (70%), validation (15%), and testing (15%). The investigations involved 2 multi-shell HARDI datasets: 1) 11 healthy subjects from the Human Connectome Project (HCP); and 2) 10 local subjects with multiple sclerosis (MS). To assess outcomes, we conducted neurite orientation dispersion and density imaging using both predicted and original data and compared their orientation dispersion index (ODI) and neurite density index (NDI) in different brain tissues with 2 measures: peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Results showed that both models achieved robust predictions, which provided competitive ODI and NDI, especially in brain white matter. The CNN outperformed MLP with the HCP data on both PSNR (p < 0.001) and SSIM (p < 0.01). With the MS data, the models performed similarly. Overall, the optimized neural networks can help generate non-acquired brain diffusion MRI, which will make advanced HARDI analysis possible in clinical practice following further validation. Enabling detailed characterization of brain microstructure will allow enhanced understanding of brain function in both health and disease.

Longitudinal assessment of neurocognitive function in people with relapsing multiple sclerosis initiating alemtuzumab in routine clinical practice: LEM-COG study results.

BACKGROUND: Alemtuzumab is effective in reducing relapse rate and disability, but limited data exist on its effect on cognitive function in relapsing multiple sclerosis (RMS). The present study assessed neurocognitive function and safety associated with alemtuzumab treatment in RMS. METHODS: This longitudinal, single-arm, prospective study included people with RMS (aged 25-55 years) who were treated with alemtuzumab in clinical practice in the United States of America and Canada. The first participant was enrolled in December 2016. The primary endpoint was the change from baseline to post-baseline (month [M] 12/24) in MS-COGnitive (MS-COG) composite score. Secondary endpoints included Paced Auditory Serial Addition Test (PASAT), Symbol Digit Modalities Test (SDMT), Brief Visuospatial Memory Test-Revised (BVMT-R), Selective Reminding Test (SRT), Controlled Oral Word Association Test (COWAT), and Automated Neuropsychological Assessment Metrics (ANAM) scores. Depression and fatigue were assessed using Hamilton Rating Scale-Depression (HAM-D) and Fatigue Severity Scale (FSS)/Modified Fatigue Impact Scale (MFIS), respectively. Magnetic resonance imaging (MRI) parameters were assessed when available. Safety was assessed throughout the study. Descriptive statistics were used for the pre-specified statistical analyses. Since the study was terminated early (November 2019) because of operational and resource difficulties, post hoc analyses for statistical inference were performed among participants who had a baseline value and at least one complete post-baseline assessment for cognitive parameters, fatigue, or depression. RESULTS: Of the 112 participants enrolled, 39 were considered as the primary analysis population at M12. At M12, a mean change of 0.25 (95% confidence interval [CI]: 0.04, 0.45; p = 0.0049; effect size [ES]: 0.39) was observed in the MS-COG composite score. Improvements were observed in processing speed (based on PASAT and SDMT; p < 0.0001; ES: 0.62), as well as in individual PASAT, SDMT and COWAT scores. An improvement was also noted in HAM-D (p = 0.0054; ES: -0.44), but not in fatigue scores. Among MRI parameters, decreases in burden of disease volume (BDV; ES: -0.12), new gadolinium-enhancing lesions (ES: -0.41) and newly active lesions (ES: -0.07) were observed at M12. About 92% of participants showed stable or improved cognitive status at M12. There were no new safety signals reported in the study. The most common adverse events (≥10% of participants) were headache, fatigue, nausea, insomnia, urinary tract infection, pain in extremity, chest discomfort, anxiety, dizziness, arthralgia, flushing, and rash. Hypothyroidism (3.7%) was the most frequent adverse event of special interest. CONCLUSION: The findings from this study suggest that alemtuzumab has a positive impact on cognitive function with significant improvements in processing speed and depression in people with RMS over a period of 12 months. The safety profile of alemtuzumab was consistent with previous studies.

Employment status, productivity loss, and associated factors among people with multiple sclerosis.

BACKGROUND: Multiple Sclerosis (MS) affects people in their most productive years of life. Consequently, MS can substantially affect employment and work-related outcomes. OBJECTIVES: This study characterizes productivity loss and employment status of people with multiple sclerosis (pwMS) and investigates associated factors. METHODS: We used baseline data collected as part of the Canadian Prospective Cohort Study to Understand Progression in Multiple Sclerosis (CanProCo). Using the Valuation of Lost Productivity questionnaire, we measured MS-related paid work productivity loss for those employed, productivity losses incurred by those unemployed (i.e. lost employment time), and unpaid work productivity losses for all. A set of sociodemographic, disease, and performance-related factors were investigated using a two-part regression model for productivity loss and a multinomial logistic model for employment status. RESULTS: From the cohort of 888 pwMS enrolled at baseline (mostly showing mild to moderate disability), 75% were employed, and of those unemployed, 69% attributed their unemployment to health-related issues. Total productivity loss over a 3-month period averaged 64 and 395 hours for those employed and unemployed, respectively. Some factors that affected productivity loss and employment status included use of disease-modifying therapies, fatigue, and performance indicators such as cognitive processing speed. CONCLUSION: Productivity loss experienced by employed and unemployed pwMS is substantial. Targeting the identified modifiable factors is likely to improve work productivity and permanence of MS patients in the workforce.

Myelin heterogeneity for assessing normal appearing white matter myelin damage in multiple sclerosis.

BACKGROUND AND PURPOSE: Conventional MRI measures of multiple sclerosis (MS) disease severity, such as lesion volume and brain atrophy, do not provide information about microstructural tissue changes, which may be driving physical and cognitive progression. Myelin damage in normal-appearing white matter (NAWM) is likely an important contributor to MS disability. Myelin water fraction (MWF) provides quantitative measurements of myelin. Mean MWF reflects average myelin content, while MWF standard deviation (SD) describes variation in myelin within regions. The myelin heterogeneity index (MHI = SD/mean MWF) is a composite metric of myelin content and myelin variability. We investigated how mean MWF, SD, and MHI compare in differentiating MS from controls and their associations with physical and cognitive disability. METHODS: Myelin water imaging data were acquired from 91 MS participants and 31 healthy controls (HC). Segmented whole-brain NAWM and corpus callosum (CC) NAWM, mean MWF, SD, and MHI were compared between groups. Associations of mean MWF, SD, and MHI with Expanded Disability Status Scale and Symbol Digit Modalities Test were assessed. RESULTS: NAWM and CC MHI had the highest area under the curve: .78 (95% confidence interval [CI]: .69, .86) and .84 (95% CI: .76, .91), respectively, distinguishing MS from HC. CONCLUSIONS: Mean MWF, SD, and MHI provide complementary information when assessing regional and global NAWM abnormalities in MS and associations with clinical outcome measures. Examining all three metrics (mean MWF, SD, and MHI) enables a more detailed interpretation of results, depending on whether regions of interest include areas that are more heterogeneous, earlier in the demyelination process, or uniformly injured.

A pilot study comparing myelin measurements from myelin water imaging and 11C-PIB PET in multiple sclerosis.

MRI-based myelin water fraction (MWF) and PET-based Pittsburgh compound B (PiB) imaging both have potential to measure myelin in multiple sclerosis (MS). We characterised the differences in MWF and PiB binding in MS lesions relative to normal-appearing white matter and assessed the correlation between MWF and PiB binding in 11 MS participants and 3 healthy controls within 14 white matter regions of interest. Both PiB binding and MWF were reduced in MS lesions relative to NAWM, and a modest within subject correlation between MWF and PiB binding was found. This pilot study shows that MWF and PET-PiB provide different information about myelin loss in MS.

Myelin water imaging in relapsing multiple sclerosis treated with ocrelizumab and interferon beta-1a.

BACKGROUND: Myelin water imaging is a magnetic resonance imaging (MRI) technique that quantifies myelin damage and repair in multiple sclerosis (MS) via the myelin water fraction (MWF). OBJECTIVE: In this substudy of a phase 3 therapeutic trial, OPERA II, MWF was assessed in relapsing MS participants assigned to interferon beta-1a (IFNb-1a) or ocrelizumab (OCR) during a two-year double-blind period (DBP) followed by a two-year open label extension (OLE) with ocrelizumab treatment. METHODS: MWF in normal appearing white matter (NAWM), including both whole brain NAWM and 5 white matter structures, and chronic lesions, was assessed in 29 OCR and 26 IFNb-1a treated participants at weeks 0, 24, 48 and 96 (DBP), and weeks 144 and 192 (OLE), and in white matter for 23 healthy control participants at weeks 0, 48 and 96. RESULTS: Linear mixed-effects models of data from baseline to week 96 showed a difference in the change in MWF over time favouring ocrelizumab in all NAWM regions. At week 192, lesion MWF was lower for participants originally randomised to IFNb-1a compared to those originally randomised to OCR. Controls showed no change in MWF over 96 weeks in any region. CONCLUSION: Ocrelizumab appears to protect against demyelination in MS NAWM and chronic lesions and may allow for a more permissive micro environment for remyelination to occur in focal and diffusely damaged tissue.

Update on myelin imaging in neurological syndromes.

PURPOSE OF REVIEW: Myelin water imaging (MWI) is generally regarded as the most rigorous approach for noninvasive, in-vivo measurement of myelin content, which has been histopathologically validated. As such, it has been increasingly applied to neurological diseases with white matter involvement, especially those affecting myelin. This review provides an overview of the most recent research applying MWI in neurological syndromes. RECENT FINDINGS: Myelin water imaging has been applied in neurological syndromes including multiple sclerosis, Alzheimer's disease, Huntington's disease, traumatic brain injury, Parkinson's disease, cerebral small vessel disease, leukodystrophies and HIV. These syndromes generally showed alterations observable with MWI, with decreased myelin content tending to correlate with lower cognitive scores and worse clinical presentation. MWI has also been correlated with genetic variation in the APOE and PLP1 genes, demonstrating genetic factors related to myelin health. SUMMARY: MWI can detect and quantify changes not observable with conventional imaging, thereby providing insight into the pathophysiology and disease mechanisms of a diverse range of neurological syndromes.

Ocrelizumab reduces thalamic volume loss in patients with RMS and PPMS.

BACKGROUND: In multiple sclerosis (MS), thalamic integrity is affected directly by demyelination and neuronal loss, and indirectly by gray/white matter lesions outside the thalamus, altering thalamic neuronal projections. OBJECTIVE: To assess the efficacy of ocrelizumab compared with interferon beta-1a (IFNß1a)/placebo on thalamic volume loss and the effect of switching to ocrelizumab on volume change in the Phase III trials in relapsing MS (RMS, OPERA I/II; NCT01247324/NCT01412333) and in primary progressive MS (PPMS, ORATORIO; NCT01194570). METHODS: Thalamic volume change was computed using paired Jacobian integration and analyzed using an adjusted mixed-effects repeated measurement model. RESULTS: Over the double-blind period, ocrelizumab treatment significantly reduced thalamic volume loss with the largest effect size (Cohen's d: RMS: 0.561 at week 96; PPMS: 0.427 at week 120) compared with whole brain, cortical gray matter, and white matter volume loss. At the end of up to 7 years of follow-up, patients initially randomized to ocrelizumab still showed less thalamic volume loss than those switching from IFNß1a (p < 0.001) or placebo (p < 0.001). CONCLUSION: Ocrelizumab effectively reduced thalamic volume loss compared with IFNß1a/placebo. Early treatment effects on thalamic tissue preservation persisted over time. Thalamic volume loss could be a potential sensitive marker of persisting tissue damage.

Serum neurofilament light chain correlates with myelin and axonal magnetic resonance imaging markers in multiple sclerosis.

BACKGROUND: Neurofilaments are cytoskeletal proteins that are detectable in the blood after neuroaxonal injury. Multiple sclerosis (MS) disease progression, greater lesion volume, and brain atrophy are associated with higher levels of serum neurofilament light chain (NfL), but few studies have examined the relationship between NfL and advanced magnetic resonance imaging (MRI) measures related to myelin and axons. We assessed the relationship between serum NfL and brain MRI measures in a diverse group of MS participants. METHODS AND MATERIALS: 103 participants (20 clinically isolated syndrome, 33 relapsing-remitting, 30 secondary progressive, 20 primary progressive) underwent 3T MRI to obtain myelin water fraction (MWF), geometric mean T2 (GMT2), water content, T1; high angular resolution diffusion imaging (HARDI)-derived axial diffusivity (AD), radial diffusivity (RD), fractional anisotropy (FA); diffusion basis spectrum imaging (DBSI)-derived AD, RD, FA; restricted, hindered, water and fiber fractions; and volume measurements of normalized brain, lesion, thalamic, deep gray matter (GM), and cortical thickness. Multiple linear regressions assessed the strength of association between serum NfL (dependent variable) and each MRI measure in whole brain (WB), normal appearing white matter (NAWM) and T2 lesions (independent variables), while controlling for age, expanded disability status scale, and disease duration. RESULTS: Serum NfL levels were significantly associated with metrics of axonal damage (FA: R2WB-HARDI = 0.29, R2NAWM-HARDI = 0.31, R2NAWM-DBSI = 0.30, R2Lesion-DBSI = 0.31; AD: R2WB-HARDI=0.31), myelin damage (MWF: R2WB = 0.29, R2NAWM = 0.30, RD: R2WB-HARDI = 0.32, R2NAWM-HARDI = 0.34, R2Lesion-DBSI = 0.30), edema and inflammation (T1: R2Lesion = 0.32; GMT2: R2WB = 0.31, R2Lesion = 0.31), and cellularity (restricted fraction R2WB = 0.30, R2NAWM = 0.32) across the entire MS cohort. Higher serum NfL levels were associated with significantly higher T2 lesion volume (R2 = 0.35), lower brain structure volumes (thalamus R2 = 0.31; deep GM R2 = 0.33; normalized brain R2 = 0.31), and smaller cortical thickness R2 = 0.31). CONCLUSION: The association between NfL and myelin MRI markers suggest that elevated serum NfL is a useful biomarker that reflects not only acute axonal damage, but also damage to myelin and inflammation, likely due to the known synergistic myelin-axon coupling relationship.

Productivity loss among people with early multiple sclerosis: A Canadian study.

OBJECTIVES: To analyze work productivity loss and costs, including absenteeism (time missed from work), presenteeism (reduced productivity while working), and unpaid work loss, among a sample of employed people with multiple sclerosis (pwMS) in Canada, as well as its association with clinical, sociodemographic, and work-related factors. METHODS: We used cross-sectional data collected as part of the Canadian Prospective Cohort Study to Understand Progression in MS (CanProCo) and information from the Valuation of Lost Productivity questionnaire. RESULTS: Among 512 pwMS who were employed, 97% showed no or mild disability and 55% experienced productivity loss due to MS in the prior 3 months. Total productivity time loss over a 3-month period averaged 60 hours (SD = 107; 23 from presenteeism, 19 from absenteeism, and 18 from unpaid work), leading to a mean cost of lost productivity of CAD$2480 (SD = 4282) per patient, with an hourly paid productivity loss greater than the wage loss. Fatigue retained significant associations with all productivity loss outcomes. CONCLUSION: Unpaid work loss and productivity losses exceeding those of the employee alone (due to teamwork and associated factors) are key additional contributors of the high economic burden of MS. Workplace accommodations and treatments targeted at fatigue could lessen the economic impact of MS.

Cervical Spinal Cord Atrophy can be Accurately Quantified Using Head Images.

BACKGROUND: Spinal cord atrophy provides a clinically relevant metric for monitoring MS. However, the spinal cord is imaged far less frequently than brain due to artefacts and acquisition time, whereas MRI of the brain is routinely performed. OBJECTIVE: To validate spinal cord cross-sectional area measurements from routine 3DT1 whole-brain MRI versus those from dedicated cord MRI in healthy controls and people with MS. METHODS: We calculated cross-sectional area at C1 and C2/3 using T2*-weighted spinal cord images and 3DT1 brain images, for 28 healthy controls and 73 people with MS. Correlations for both groups were assessed between: (1) C1 and C2/3 using cord images; (2) C1 from brain and C1 from cord; and (3) C1 from brain and C2/3 from cord. RESULTS AND CONCLUSION: C1 and C2/3 from cord were strongly correlated in controls (r = 0.94, p<0.0001) and MS (r = 0.85, p<0.0001). There was strong agreement between C1 from brain and C2/3 from cord in controls (r = 0.84, p<0.0001) and MS (r = 0.81, p<0.0001). This supports the use of C1 cross-sectional area calculated from brain imaging as a surrogate for the traditional C2/3 cross-sectional area measure for spinal cord atrophy.

Cortical morphology predicts placebo response in multiple sclerosis.

Despite significant insights into the neural mechanisms of acute placebo responses, less is known about longer-term placebo responses, such as those seen in clinical trials, or their interactions with brain disease. We examined brain correlates of placebo responses in a randomized trial of a then controversial and now disproved endovascular treatment for multiple sclerosis. Patients received either balloon or sham extracranial venoplasty and were followed for 48 weeks. Venoplasty had no therapeutic effect, but a subset of both venoplasty- and sham-treated patients reported a transient improvement in health-related quality of life, suggesting a placebo response. Placebo responders did not differ from non-responders in total MRI T2 lesion load, count or location, nor were there differences in normalized brain volume, regional grey or white matter volume or cortical thickness (CT). However, responders had higher lesion activity. Graph theoretical analysis of CT covariance showed that non-responders had a more small-world-like CT architecture. In non-responders, lesion load was inversely associated with CT in somatosensory, motor and association areas, precuneus, and insula, primarily in the right hemisphere. In responders, lesion load was unrelated to CT. The neuropathological process in MS may produce in some a cortical configuration less capable of generating sustained placebo responses.

Characterization of multiple sclerosis neuroinflammation and neurodegeneration with relaxation and diffusion basis spectrum imaging.

BACKGROUND: Advanced magnetic resonance imaging (MRI) methods can provide more specific information about various microstructural tissue changes in multiple sclerosis (MS) brain. Quantitative measurement of T1 and T2 relaxation, and diffusion basis spectrum imaging (DBSI) yield metrics related to the pathology of neuroinflammation and neurodegeneration that occurs across the spectrum of MS. OBJECTIVE: To use relaxation and DBSI MRI metrics to describe measures of neuroinflammation, myelin and axons in different MS subtypes. METHODS: 103 participants (20 clinically isolated syndrome (CIS), 33 relapsing-remitting MS (RRMS), 30 secondary progressive MS and 20 primary progressive MS) underwent quantitative T1, T2, DBSI and conventional 3T MRI. Whole brain, normal-appearing white matter, lesion and corpus callosum MRI metrics were compared across MS subtypes. RESULTS: A gradation of MRI metric values was seen from CIS to RRMS to progressive MS. RRMS demonstrated large oedema-related differences, while progressive MS had the most extensive abnormalities in myelin and axonal measures. CONCLUSION: Relaxation and DBSI-derived MRI measures show differences between MS subtypes related to the severity and composition of underlying tissue damage. RRMS showed oedema, demyelination and axonal loss compared with CIS. Progressive MS had even more evidence of increased oedema, demyelination and axonal loss compared with CIS and RRMS.

Diffusely abnormal white matter in multiple sclerosis.

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