Ibudilast reduces slowly enlarging lesions in progressive multiple sclerosis.

BACKGROUND: Ibudilast has shown beneficial effects on several imaging outcomes in progressive multiple sclerosis (MS). Slowly enlarging lesions are a proposed imaging biomarker of compartmentalized inflammation within chronic active lesions. OBJECTIVE: To assess the treatment effect of ibudilast on slowly enlarging lesion volumes over 96 weeks from a phase II clinical trial of ibudilast (Secondary and Primary Progressive Ibudilast NeuroNEXT Trial in Multiple Sclerosis [SPRINT-MS]). METHODS: In total, 255 participants with progressive MS from 28 sites were randomized to oral ibudilast or placebo. Participants with at least four analyzable magnetic resonance imaging (MRI) were included. Slowly enlarging lesions were quantified using Jacobian determinant maps. A linear model was used to assess the effect of ibudilast. Magnetization transfer ratio within slowly enlarging lesions was assessed to determine the effect of ibudilast on tissue integrity. RESULTS: In total, 195 participants were included in this analysis. Ibudilast significantly decreased slowly enlarging lesion volume (23%, p = 0.003). Ibudilast also reduced magnetization transfer ratio change in slowly enlarging lesions: 0.22%/year, p = 0.04. CONCLUSION: Ibudilast showed a significant effect on baseline volume of lesions that were slowly enlarging and magnetization transfer ratio in slowly enlarging lesions. The results support the use of slowly enlarging lesions for assessment of compartmentalized inflammation represented by chronic active lesions and provide further support for the neuroprotective effects of ibudilast in progressive MS.

Natalizumab reduces loss of gray matter and thalamic volume in patients with relapsing-remitting multiple sclerosis: A post hoc analysis from the randomized, placebo-controlled AFFIRM trial.

BACKGROUND: Loss of brain gray matter fractional volume predicts multiple sclerosis (MS) progression and is associated with worsening physical and cognitive symptoms. Within deep gray matter, thalamic damage is evident in early stages of MS and correlates with physical and cognitive impairment. Natalizumab is a highly effective treatment that reduces disease progression and the number of inflammatory lesions in patients with relapsing-remitting MS (RRMS). OBJECTIVE: To evaluate the effect of natalizumab on gray matter and thalamic atrophy. METHODS: A combination of deep learning-based image segmentation and data augmentation was applied to MRI data from the AFFIRM trial. RESULTS: This post hoc analysis identified a reduction of 64.3% (p = 0.0044) and 64.3% (p = 0.0030) in mean percentage gray matter volume loss from baseline at treatment years 1 and 2, respectively, in patients treated with natalizumab versus placebo. The reduction in thalamic fraction volume loss from baseline with natalizumab versus placebo was 57.0% at year 2 (p < 0.0001) and 41.2% at year 1 (p = 0.0147). Similar findings resulted from analyses of absolute gray matter and thalamic fraction volume loss. CONCLUSION: These analyses represent the first placebo-controlled evidence supporting a role for natalizumab treatment in mitigating gray matter and thalamic fraction atrophy among patients with RRMS. CLINICALTRIALS.GOV IDENTIFIER: NCT00027300URL: https://clinicaltrials.gov/ct2/show/NCT00027300.

Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration.

PURPOSE: Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross-sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions. METHODS: A DL model combining UNet and DenseNet was trained and tested using manually segmented thighs from 16 patients (32 legs). Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and average symmetric surface distance (ASSD). A UNet model was trained for comparison. These segmentations were used to obtain CSA and FF quantification. Reproducibility of CSA and FF quantification was tested with scan and rescan of six healthy subjects. RESULTS: The proposed UNet and DenseNet had high agreement with manual segmentation (DSC >0.97, ASSD < 0.24) and improved performance compared with UNet. For hamstrings of the operated knee, the automated pipeline had largest absolute difference of 6.01% for CSA and 0.47% for FF as compared to manual segmentation. In reproducibility analysis, the average difference (absolute) in CSA quantification between scan and rescan was better for the automatic method as compared with manual segmentation (2.27% vs. 3.34%), whereas the average difference (absolute) in FF quantification were similar. CONCLUSIONS: The proposed method exhibits excellent accuracy and reproducibility in CSA and FF quantification compared with manual segmentation and can be used in large-scale patient studies.

Effect of ibudilast on thalamic magnetization transfer ratio and volume in progressive multiple sclerosis.

BACKGROUND: Thalamic volume (TV) is a sensitive biomarker of disease burden of injury in multiple sclerosis (MS) and appears to reflect overall lesion loads. Ibudilast showed significant treatment effect on brain atrophy and magnetization transfer ratio (MTR) of normal-appearing brain tissue but not in new/enlarging T2 lesion in the SPRINT-MS randomized clinical trial. OBJECTIVE: To evaluate the effect of ibudilast on thalamic tissue integrity and volume in the SPRINT-MS. METHODS: A total of 255 participants with progressive MS were randomized to oral ibudilast or placebo, and thalamic MTR and normalized TV over 96 weeks were quantified. Mixed-effect modeling assessed treatment effects on the thalamic MTR and TV, separately. Similarly, the measures were compared between the participants with confirmed disability progression (CDP). RESULTS: Ibudilast's treatment effect was observed compared to placebo for thalamic MTR (p = 0.03) but not for TV (p = 0.68) while TV correlated with T2 lesion volume (p < 0.001). CDP associated with thalamic MTR (p = 0.04) but not with TV (p = 0.7). CONCLUSION: Ibudilast showed an effect on thalamic MTR, which was associated with CDP, suggesting a clinically relevant effect on thalamic tissue integrity. However, the treatment effect was not observed in TV, suggesting that thalamic atrophy is more closely associated with global inflammatory activity than local tissue integrity. CLINICALTRIALS.GOV: NCT01982942.

The impact of cigarette smoking on cognitive processing speed and brain atrophy in multiple sclerosis.

BACKGROUND: Smoking is associated with an increased risk of multiple sclerosis (MS) and disability worsening. The relationship between smoking, cognitive processing speed, and brain atrophy remains uncertain. OBJECTIVE: To quantify the impact of smoking on processing speed and brain volume in MS and to explore the longitudinal relationship between smoking and changes in processing speed. METHODS: A retrospective study of MS patients who completed the processing speed test (PST) between September 2015 and March 2020. Demographics, disease characteristics, smoking history, and quantitative magnetic resonance imaging (MRI) were collected. Cross-sectional associations between smoking, PST performance, whole-brain fraction (WBF), gray matter fraction (GMF), and thalamic fraction (TF) were assessed using multivariable linear regression. The longitudinal relationship between smoking and PST performance was assessed by linear mixed modeling. RESULTS: The analysis included 5536 subjects of whom 1314 had quantitative MRI within 90 days of PST assessment. Current smokers had lower PST scores than never smokers at baseline, and this difference persisted over time. Smoking was associated with reduced GMF but not with WBF or TF. CONCLUSION: Smoking has an adverse relationship with cognition and GMF. Although causality is not demonstrated, these observations support the importance of smoking cessation counseling in MS management.

Effect of GBCA Use on Detection and Diagnostic Performance of the Central Vein Sign: Evaluation Using a 3-T FLAIR* Sequence in Patients With Suspected Multiple Sclerosis.

BACKGROUND. The central vein sign (CVS) is a proposed MRI biomarker of multiple sclerosis (MS). The impact of gadolinium-based contrast agent (GBCA) administration on CVS evaluation remains poorly investigated. OBJECTIVE. The purpose of this study was to assess the effect of GBCA use on CVS detection and on the diagnostic performance of the CVS for MS using a 3-T FLAIR* sequence. METHODS. This study was a secondary analysis of data from the pilot study for the prospective multicenter Central Vein Sign: A Diagnostic Biomarker in Multiple Sclerosis (CAVS-MS), which recruited adults with suspected MS from April 2018 to February 2020. Participants underwent 3-T brain MRI including FLAIR and precontrast and post-contrast echo-planar imaging T2*-weighted acquisitions. Postprocessing was used to generate combined FLAIR and T2*-weighted images (hereafter, FLAIR*). MS diagnoses were established using the 2017 McDonald criteria. Thirty participants (23 women, seven men; mean age, 45 years) were randomly selected from the CAVS-MS pilot study cohort. White matter lesions (WMLs) were marked using FLAIR* images. A single observer, blinded to clinical data and GBCA use, reviewed marked WMLs on FLAIR* images for the presence of the CVS. RESULTS. Thirteen of 30 participants had MS. Across participants, on precontrast FLAIR* imaging, 218 CVS-positive and 517 CVS-negative WMLs were identified; on post-contrast FLAIR* imaging, 269 CVS-positive and 459 CVS-negative WMLs were identified. The fraction of WMLs that were CVS-positive on precontrast and postcontrast images was 48% and 58% in participants with MS and 7% and 10% in participants without MS, respectively. The median patient-level CVS-positivity rate on precontrast and postcontrast images was 43% and 67% for participants with MS and 4% and 8% for participants without MS, respectively. In a binomial model adjusting for MS diagnoses, GBCA use was associated with an increased likelihood of at least one CVS-positive WML (odds ratio, 1.6; p < .001). At a 40% CVS-positivity threshold, the sensitivity of the CVS for MS increased from 62% on precontrast images to 92% on postcontrast images (p = .046). Specificity was not significantly different between precontrast (88%) and postcontrast (82%) images (p = .32). CONCLUSION. GBCA use increased CVS detection on FLAIR* images, thereby increasing the sensitivity of the CVS for MS diagnoses. CLINICAL IMPACT. The postcontrast FLAIR* sequence should be considered for CVS evaluation in future investigational trials and clinical practice.

Sensitivity of T1/T2-weighted ratio in detection of cortical demyelination is similar to magnetization transfer ratio using post-mortem MRI.

BACKGROUND: Detecting cortical demyelination using magnetic resonance imaging (MRI) in multiple sclerosis (MS) remains a challenge. Magnetization transfer ratio (MTR), T1-weighted/T2-weighted ratio (T1T2R), and T2-weighted (T2w) signal are sensitive to cortical demyelination, but their accuracy is unknown. OBJECTIVES: To quantify the sensitivity, specificity, and accuracy of postmortem T1T2R, MTR, and T2w in detecting cortical demyelination. METHODS: In situ postmortem MRIs from 9 patients were used to measure T1T2R, MTR, and T2w along the midline of cortical gray matter and classified as normal or abnormal. MRIs were co-registered and compared to hemispheric myelin staining. The sensitivity, specificity, and accuracy of T1T2R, MTR, and T2w in detecting cortical demyelination were measured. RESULTS: The mean age (standard deviation) at death was 64.7 (+/-13.7) years with a disease duration of 23.8 (+/-10.5) years. The sensitivity was 78% for MTR, 75% for T1T2R, and 63% for T2w. The specificity was 46% (T2w), 13% (T1T2R), and 29% (MTR). The accuracy was 71% (T2w), 39% (MTR), and 42% (T1T2R). There were no significant differences between different MRI measures in cortical demyelination or intracortical/subpial lesion detection. CONCLUSIONS: Although somewhat sensitive, the modest specificity of conventional MRI modalities for cortical demyelination indicates that they are influenced by cortical changes other than demyelination. Improved acquisition and post-processing are needed to reliably measure cortical lesion load.

Phase 2 Trial of Ibudilast in Progressive Multiple Sclerosis.

BACKGROUND: There are limited treatments for progressive multiple sclerosis. Ibudilast inhibits several cyclic nucleotide phosphodiesterases, macrophage migration inhibitory factor, and toll-like receptor 4 and can cross the blood-brain barrier, with potential salutary effects in progressive multiple sclerosis. METHODS: We enrolled patients with primary or secondary progressive multiple sclerosis in a phase 2 randomized trial of oral ibudilast (≤100 mg daily) or placebo for 96 weeks. The primary efficacy end point was the rate of brain atrophy, as measured by the brain parenchymal fraction (brain size relative to the volume of the outer surface contour of the brain). Major secondary end points included the change in the pyramidal tracts on diffusion tensor imaging, the magnetization transfer ratio in normal-appearing brain tissue, the thickness of the retinal nerve-fiber layer, and cortical atrophy, all measures of tissue damage in multiple sclerosis. RESULTS: Of 255 patients who underwent randomization, 129 were assigned to ibudilast and 126 to placebo. A total of 53% of the patients in the ibudilast group and 52% of those in the placebo group had primary progressive disease; the others had secondary progressive disease. The rate of change in the brain parenchymal fraction was -0.0010 per year with ibudilast and -0.0019 per year with placebo (difference, 0.0009; 95% confidence interval, 0.00004 to 0.0017; P=0.04), which represents approximately 2.5 ml less brain-tissue loss with ibudilast over a period of 96 weeks. Adverse events with ibudilast included gastrointestinal symptoms, headache, and depression. CONCLUSIONS: In a phase 2 trial involving patients with progressive multiple sclerosis, ibudilast was associated with slower progression of brain atrophy than placebo but was associated with higher rates of gastrointestinal side effects, headache, and depression. (Funded by the National Institute of Neurological Disorders and Stroke and others; NN102/SPRINT-MS ClinicalTrials.gov number, NCT01982942 .).

Intrinsic and Extrinsic Mechanisms of Thalamic Pathology in Multiple Sclerosis.

OBJECTIVE: Thalamic atrophy is among the earliest brain changes detected in patients with multiple sclerosis (MS) and the degree of thalamic atrophy is a strong predictor of disability progression. The causes of thalamic atrophy are not fully understood. Here, we investigate the contributions of thalamic demyelinated lesions, thalamic neuronal loss, and cerebral white matter (WM) lesions to thalamic volume. METHODS: We used postmortem in situ magnetic resonance imaging (MRI) scans of 95 subjects with MS to correlate thalamic lesion volumes with global MRI metrics. We histologically characterized thalamic demyelination patterns and compared neuronal loss and neuritic pathology in the thalami with the extremes of volume. RESULTS: Grossly apparent thalamic discolorations in cm-thick brain slices were T2/fluid-attenuated inversion recovery (FLAIR) hyperintense, T1-hypointense, and appeared as perivascular demyelinated lesions with dystrophic neurons/axons. Subependymal demyelinated lesions with axonal loss and microglial/macrophage activation were also observed. The 12 subjects with the least thalamic volume had a 17.6% reduction of median neuronal density in the dorsomedial/ventrolateral and pulvinar nuclei compared with the 14 subjects with the greatest thalamic volume (p = 0.03). After correcting for age, disease duration, sex, and T2 lesion volume, the total (p = 0.20), ovoid (p = 0.31), or subependymal (p = 0.44) MRI thalamic lesion volumes correlated with thalamic volume. Thalamic volume correlated with cerebral T2 lesion volume (Spearman's rho = -0.65, p < 0.001; p < 0.0001 after correcting for age, disease duration, and sex). INTERPRETATION: Our findings suggest the degeneration of efferent/afferent thalamic projections and/or a neurodegenerative process as greater contributors to thalamic atrophy than thalamic demyelinating lesions. ANN NEUROL 2020 ANN NEUROL 2020;88:81-92.

Juxtacortical susceptibility changes in progressive multifocal leukoencephalopathy at the gray-white matter junction correlates with iron-enriched macrophages.

OBJECTIVE: Describe magnetic resonance imaging (MRI) susceptibility changes in progressive multifocal leukoencephalopathy (PML) and identify neuropathological correlates. METHODS: PML cases and matched controls with primary central nervous system lymphoma (PCNSL) were retrospectively identified. MRI brain at 3 T and 7 T were reviewed. MRI-pathology correlations in fixed brain autopsy tissue were conducted in three subjects with confirmed PML. RESULTS: With PML (n = 26 total, n = 5 multiple sclerosis natalizumab-associated), juxtacortical changes on susceptibility-weighted imaging (SWI) or gradient echo (GRE) sequences were noted in 3/3 cases on 7 T MRI and 14/22 cases (63.6%) on 1.5 T or 8/22 (36.4%) 3 T MRI. Similar findings were only noted in 3/25 (12.0%) of PCNSL patients (odds ratio (OR) 12.83, 95% confidence interval (CI), 2.9-56.7, p < 0.001) on 1.5 or 3 T MRI. On susceptibility sequences available prior to diagnosis of PML, 7 (87.5%) had changes present on average 2.7 ± 1.8 months (mean ± SD) prior to diagnosis. Postmortem 7 T MRI showed SWI changes corresponded to areas of increased iron density along the gray-white matter (GM-WM) junction predominantly in macrophages. CONCLUSION: Susceptibility changes in PML along the GM-WM junction can precede noticeable fluid-attenuated inversion recovery (FLAIR) changes and correlates with iron accumulation in macrophages.

Importance of incorporating quantitative imaging biomarker technical performance characteristics when estimating treatment effects.

BACKGROUND/AIMS: Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. METHODS: Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. RESULTS: Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect. CONCLUSION: Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

Brain volume loss in individuals over time: Source of variance and limits of detectability.

BACKGROUND: Brain volume loss measured from magnetic resonance imaging (MRI) is a marker of neurodegeneration and predictor of disability progression in MS, and is commonly used to assess drug efficacy at the group level in clinical trials. Whether measures of brain volume loss could be useful to help guide management of individual patients depends on the relative magnitude of the changes over a given interval to physiological and technical sources of variability. GOAL: To understand the relative contributions of neurodegeneration vs. physiological and technical sources of variability to measurements of brain volume loss in individuals. MATERIAL AND METHODS: Multiple T1-weighted 3D MPRAGE images were acquired from a healthy volunteer and MS patient over varying time intervals: 7 times on the first day (before breakfast at 7:30AM and then every 2 â€‹h for 12 â€‹h), each day for the next 6 working days, and 6 times over the remainder of the year, on 2 â€‹Siemens MRI scanners: 1.5T Sonata (S1) and 3.0T TIM Trio (S2). Scan-reposition-rescan data were acquired on S2 for daily, monthly and 1-year visits. Percent brain volume change (PBVC) was measured from baseline to each follow-up scan using FSL/SIENA. We estimated the effect of physiologic fluctuations on brain volume using linear regression of the PBVC values over hourly and daily intervals. The magnitude of the physiological effect was estimated by comparing the root-mean-square error (RMSE) of the regression of all the data points relative to the regression line, for the hourly scans vs the daily scans. Variance due to technical sources was assessed as the RMSE of the regression over time using the intracranial volume as a reference. RESULTS: The RMSE of PBVC over 12 â€‹h, for both scanners combined, ("Hours", 0.15%), was similar to the day-to-day variation over 1 week ("Days", 0.14%), and both were smaller than the RMS error over the year (0.21%). All of these variations, however, were smaller than the scan-reposition-rescan RMSE (0.32%). The variability of PBVC for the individual scanners followed the same trend. The standard error of the mean (SEM) for PBVC was 0.26 for S1, and 0.22 for S2. From these values, we computed the minimum detectable change (MDC) to be 0.7% on S1 and 0.6% on S2. The location of the brain along the z-axis of the magnet inversely correlated with brain volume change for hourly and daily brain volume fluctuations (p â€‹< â€‹0.01). CONCLUSION: Consistent diurnal brain volume fluctuations attributable to physiological shifts were not detectable in this small study. Technical sources of variation dominate measured changes in brain volume in individuals until the volume loss exceeds around 0.6-0.7%. Reliable interpretation of measured brain volume changes as pathological (greater than normal aging) in individuals over 1 year requires changes in excess of about 1.1% (depending on the scanner). Reliable brain atrophy detection in an individual may be feasible if the rate of brain volume loss is large, or if the measurement interval is sufficiently long.

Automated cartilage and meniscus segmentation of knee MRI with conditional generative adversarial networks.

PURPOSE: Fully automatic tissue segmentation is an essential step to translate quantitative MRI techniques to clinical setting. The goal of this study was to develop a novel approach based on the generative adversarial networks for fully automatic segmentation of knee cartilage and meniscus. THEORY AND METHODS: Defining proper loss function for semantic segmentation to enforce the learning of multiscale spatial constraints in an end-to-end training process is an open problem. In this work, we have used the conditional generative adversarial networks to improve segmentation performance of convolutional neural network, such as UNet alone by overcoming the problems caused by pixel-wise mapping based objective functions, and to capture cartilage features during the training of the network. Furthermore, the Dice coefficient and cross entropy losses were incorporated to the loss functions to improve the model performance. The model was trained and tested on 176, 3D DESS (double-echo steady-state) knee images from the Osteoarthritis Initiative data set. RESULTS: The proposed model provided excellent segmentation performance for cartilages with Dice coefficients ranging from 0.84 in patellar cartilage to 0.91 in lateral tibial cartilage, with an average Dice coefficient of 0.88. For meniscus segmentation, the model achieves 0.89 Dice coefficient for lateral meniscus and 0.87 Dice coefficient for medial meniscus. The results are superior to previously published automatic cartilage and meniscus segmentation methods based on deep learning models such as convolutional neural network. CONCLUSION: The proposed UNet-conditional generative adversarial networks based model demonstrated a fully automated segmentation method with high accuracy for knee cartilage and meniscus.

Estimating and accounting for the effect of MRI scanner changes on longitudinal whole-brain volume change measurements.

OBJECTIVE: Longitudinal MRI studies are often subjected to mid-study scanner changes, which may alter image characteristics such as contrast, signal-to-noise ratio, contrast-to-noise ratio, intensity non-uniformity and geometric distortion. Measuring brain volume loss under these conditions can render the results potentially unreliable across the timepoint of the change. Estimating and accounting for this effect may improve the reliability of estimates of brain atrophy rates. METHODS: We analyzed 237 subjects who were scanned at 1.5 T for the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and were subject to intra-vendor or inter-vendor scanner changes during follow-up (up to 8 years). Sixty-three subjects scanned on GE Signa HDx and HDxt platforms were also subject to a T1-weighted sequence change from Magnetization Prepared Rapid Gradient Echo (MP-RAGE) to Fast Spoiled Gradient Echo with IR Preparation (IR-FSPGR), as part of the transition from ADNI-1 to ADNI-2/GO. Two-timepoint percentage brain volume changes (PBVCs) between the baseline "screening" and the follow-up scans were calculated using SIENA. A linear mixed-effects model with subject-specific random slopes and intercepts was applied to estimate the fixed effects of scanner hardware changes on the PBVC measures. The same model also included a term to estimate the fixed effects of the T1-weighted sequence change. RESULTS: Different hardware upgrade or change combinations led to different offsets in the PBVC (SE; p): Philips Intera to Siemens Avanto, -1.81% (0.30; p < 0.0001); GE Genesis Signa to Philips Intera, 0.99% (0.47, p = 0.042); GE Signa Excite to Signa HDx, 0.33% (0.095, p = 0.0005); GE Signa Excite to Signa HDxt, -0.023% (0.23, p = 0.92); GE Signa Excite to Signa HDx to Signa HDxt, 0.25% (0.095, p = 0.010) and 0.27% (0.16, p = 0.098), respectively; GE Signa HDx to Signa HDxt, -0.24% (0.25, p = 0.34); Siemens Symphony to Symphony TIM, -0.39% (0.16; p = 0.019). The sequence change from MP-RAGE to IR-SPGR was associated with an average -1.63% (0.12; p < 0.0001) change. CONCLUSION: Inter-vendor scanner changes generally led to greater effects on PBVC measurements than did intra-vendor scanner upgrades. The effect of T1-weighted sequence change was comparable to that of the inter-vendor scanner changes. Inclusion of the corrective fixed-effects terms for the scanner hardware and T1-weighted sequence changes yielded better model goodness-of-fits, and thus, potentially more reliable estimates of whole-brain atrophy rates.

T1-/T2-weighted ratio differs in demyelinated cortex in multiple sclerosis.

Detecting cortical demyelination in patients with multiple sclerosis (MS) is difficult. Using magnetic resonance imaging (MRI), ratio maps of T1-weighted (T1w) and T2-weighted (T2w) images may be sensitive to cortical myelin levels. In this MRI-histological study, postmortem in situ scans were acquired from 6 cadavers with MS on a 3T MRI machine. Immunocytochemistry was used to correlate myelin status and cortical T1w/T2w measures. The results showed that the T1w/T2w values significantly differed between demyelinated and myelinated cortex (p < 0.001). The T1w/T2w ratio maps may be a relatively simple, clinically feasible method to assess cortical demyelination. Ann Neurol 2017;82:635-639.

Impact of immunoablation and autologous hematopoietic stem cell transplantation on gray and white matter atrophy in multiple sclerosis.

BACKGROUND: Immunoablation and autologous hematopoietic stem cell transplantation (IA/aHSCT) halts relapses, white matter (WM) lesion formation, and pathological whole-brain (WB) atrophy in multiple sclerosis (MS) patients. Whether the latter was due to effects on gray matter (GM) or WM warranted further exploration. OBJECTIVE: To model GM and WM volume changes after IA/aHSCT to further understand the effects seen on WB atrophy. METHODS: GM and WM volume changes were calculated from serial baseline and follow-up magnetic resonance imaging (MRI) ranging from 1.5 to 10.5 years in 19 MS patients treated with IA/aHSCT. A mixed-effects model with two predictors (total busulfan dose and baseline T1-weighted WM lesion volume "T1LV") characterized the time-courses after IA/aHSCT. RESULTS: Accelerated short-term atrophy of 2.1% and 3.2% occurred in GM and WM, respectively, on average. Both busulfan dose and T1LV were significant predictors of WM atrophy, whereas only busulfan was a significant predictor of GM atrophy. Compared to baseline, a significant reduction in GM atrophy, not WM atrophy, was found. The average rates of long-term GM and WM atrophy were -0.18%/year (standard error (SE): 0.083) and -0.07%/year (SE: 0.14), respectively. CONCLUSION: Chemotherapy-related toxicity affected both GM and WM. WM was further affected by focal T1-weighted lesion-related pathologies. Long-term rates of GM and WM atrophy were comparable to those of normal-aging.

Diurnal fluctuations in brain volume: Statistical analyses of MRI from large populations.

We investigated fluctuations in brain volume throughout the day using statistical modeling of magnetic resonance imaging (MRI) from large populations. We applied fully automated image analysis software to measure the brain parenchymal fraction (BPF), defined as the ratio of the brain parenchymal volume and intracranial volume, thus accounting for variations in head size. The MRI data came from serial scans of multiple sclerosis (MS) patients in clinical trials (n=755, 3269 scans) and from subjects participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n=834, 6114 scans). The percent change in BPF was modeled with a linear mixed effect (LME) model, and the model was applied separately to the MS and ADNI datasets. The LME model for the MS datasets included random subject effects (intercept and slope over time) and fixed effects for the time-of-day, time from the baseline scan, and trial, which accounted for trial-related effects (for example, different inclusion criteria and imaging protocol). The model for ADNI additionally included the demographics (baseline age, sex, subject type [normal, mild cognitive impairment, or Alzheimer's disease], and interaction between subject type and time from baseline). There was a statistically significant effect of time-of-day on the BPF change in MS clinical trial datasets (-0.180 per day, that is, 0.180% of intracranial volume, p=0.019) as well as the ADNI dataset (-0.438 per day, that is, 0.438% of intracranial volume, p<0.0001), showing that the brain volume is greater in the morning. Linearly correcting the BPF values with the time-of-day reduced the required sample size to detect a 25% treatment effect (80% power and 0.05 significance level) on change in brain volume from 2 time-points over a period of 1year by 2.6%. Our results have significant implications for future brain volumetric studies, suggesting that there is a potential acquisition time bias that should be randomized or statistically controlled to account for the day-to-day brain volume fluctuations.

Differentiating multiple sclerosis from non-specific white matter changes using a convolutional neural network image classification model.

BACKGROUND: The diagnosis of multiple sclerosis (MS) relies heavily on neuroimaging with magnetic resonance imaging (MRI) and exclusion of mimics. This can be a challenging task due to radiological overlap in several disorders and may require ancillary testing or longitudinal follow up. One of the most common radiological MS mimickers is non-specific white matter disease (NSWMD). We aimed to develop and evaluate models leveraging machine learning algorithms to help distinguish MS and NSWMD. METHODS: All adult patients who underwent MRI brain using a demyelinating protocol with available electronic medical records between 2015 and 2019 at Cleveland Clinic affiliated facilities were included. Diagnosis of MS and NSWMD were assessed from clinical documentation. Those with a diagnosis of MS and NSWMD were matched using total T2 lesion volume (T2LV) and used to train models with logistic regression and convolutional neural networks (CNN). Performance metrices were reported for each model. RESULTS: A total of 250 NSWMD MRI scans were identified, and 250 unique MS MRI scans were matched on T2LV. Cross validated logistic regression model was able to use 20 variables (including spinal cord area, regional volumes, and fractions) to predict MS compared to NSWMD with 68.0% accuracy while the CNN model was able to classify MS compared to NSWMD in two independent validation and testing cohorts with 77% and 78% accuracy on average. CONCLUSION: Automated methods can be used to differentiate MS compared to NSWMD. These methods can be used to supplement currently available diagnostic tools for patients being evaluated for MS.

Brain volume increase after discontinuing natalizumab therapy: Evidence for reversible pseudoatrophy.

BACKGROUND: The phenomenon of pseudoatropy after initiation of anti-inflammatory therapy is believed to be reversible, but a rebound in brain volume following cessation of highly-effective therapy has not been reported. OBJECTIVES: To evaluate brain volume change in a treatment interruption study (RESTORE) in which relapsing-remitting multiple sclerosis (RRMS) patients were randomized to switch from natalizumab to placebo, from natalizumab to once-monthly intravenous methylprednisolone (IVMP), or to remain on natalizumab. METHODS: T2 lesion volume (T2LV), baseline normalized brain volumes, and follow-up percent brain volume changes (PBVC) were calculated. Approximate T2 relaxation-time (pT2) was calculated within the brain mask and the T2 lesions to estimate changes in water content. Linear mixed effects models were used to detect differences in T2LV, pT2 in whole brain, pT2 in T2-weighted lesions, and PBVC among the placebo, natalizumab, and IVMP groups. We also estimated contributions of T2LV and pT2 (in whole brain and T2 lesions) to PBVC. RESULTS: T2LV increased in the placebo group (by 0.66 ml/year, p<0.0001) and IVMP (+1.98 ml/year, p = 0.05) groups relative to the natalizumab group. The rates of PBVC were significantly different: -0.239%/year with continued natalizumab and +0.126 %/year after switch to placebo (p = 0.03), while the IVMP group showed brain volume loss (-0.74 %/ year, p = 0.08). pT2 was not statistically different between the groups (p ≥ 0.29) and did not have significant effects on PBVC (p ≥ 0.25). CONCLUSION: The increase in the brain volume in patients witching from natalizumab to placebo is consistent with reversal of so-called pseudoatrophy after starting natalizumab.