Quantification of multiple-sclerosis-related brain atrophy in two heterogeneous MRI datasets using mixed-effects modeling

Brain atrophy measurement over a MRI scanner change in multiple sclerosis

BACKGROUND

A change in MRI hardware impacts brain volume measurements. The aim of this study was to use MRI data from multiple sclerosis (MS) patients and healthy control subjects (HCs) to statistically model how to adjust brain atrophy measures in MS patients after a major scanner upgrade.

METHODS

We scanned 20 MS patients and 26 HCs before and three months after a major scanner upgrade (1.5 T Siemens Healthineers Magnetom Avanto to 3 T Siemens Healthineers Skyra Fit). The patient group also underwent standardized serial MRIs before and after the scanner change. Percentage whole brain volume changes (PBVC) measured by Structural Image Evaluation using Normalization of Atrophy (SIENA) in the HCs was used to estimate a corrective term based on a linear model. The factor was internally validated in HCs, and then applied to the MS group.

RESULTS

Mean PBVC during the scanner change was higher in MS than HCs (-4.1 ± 0.8 % versus -3.4 ± 0.6 %). A fixed corrective term of 3.4 (95% confidence interval: 3.13-3.67)% was estimated based on the observed average changes in HCs. Age and gender did not have a significant influence on this corrective term. After adjustment, a linear mixed effects model showed that the brain atrophy measures in MS during the scanner upgrade were not anymore associated with the scanner type (old vs new scanner; p = 0.29).

CONCLUSION

A scanner change affects brain atrophy measures in longitudinal cohorts. The inclusion of a corrective term based on changes observed in HCs helps to adjust for the known and unknown factors associated with a scanner upgrade on a group level.

Automated Differentiation of Atypical Parkinsonian Syndromes Using Brain Iron Patterns in Susceptibility Weighted Imaging

In recent studies, iron overload has been reported in atypical parkinsonian syndromes. The topographic patterns of iron distribution in deep brain nuclei vary by each subtype of parkinsonian syndrome, which is affected by underlying disease pathologies. In this study, we developed a novel framework that automatically analyzes the disease-specific patterns of iron accumulation using susceptibility weighted imaging (SWI). We constructed various machine learning models that can classify diseases using radiomic features extracted from SWI, representing distinctive iron distribution patterns for each disorder. Since radiomic features are sensitive to the region of interest, we used a combination of T1-weighted MRI and SWI to improve the segmentation of deep brain nuclei. Radiomics was applied to SWI from 34 patients with a parkinsonian variant of multiple system atrophy, 21 patients with cerebellar variant multiple system atrophy, 17 patients with progressive supranuclear palsy, and 56 patients with Parkinson’s disease. The machine learning classifiers that learn the radiomic features extracted from iron-reflected segmentation results produced an average area under receiver operating characteristic curve (AUC) of 0.8607 on the training data and 0.8489 on the testing data, which is superior to the conventional classifier with segmentation using only T1-weighted images. Our radiomic model based on the hybrid images is a promising tool for automatically differentiating atypical parkinsonian syndromes.

Comparison of image quality in brain MRI with and without MR compatible incubator and predictive value of brain MRI at expected delivery date in preterm babies

Objectives MR compatible incubators (MRcI) offer the examination of preterm and critically ill infants in controlled environment. The aim of the study was to compare objective and subjective image quality as well as diagnostic value of MRI brain examinations with and without using the MRcI. Thus, predictive value of brain MRI at expected delivery date in general was investigated. Methods This retrospective study included MRI brain examinations conducted at patients' corrected age ≤6 months and presence of four standard sequences (PD TSE transversal, T2 TSE transversal, T2 TSE sagittal and T1 SE transversal). Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) was calculated. Subjective image quality was estimated using a 5-point Likert scale. Findings of MRI were compared with those of previous transfontanellar ultrasound because of additional diagnostic information. Severe brain abnormality scaled by score of Kidokoro was related to results of Munich Functional Developmental Diagnostics (MFDD) within first year. Results One hundred MRI brain examinations (76 with MRcI, 24 without MRcI) were performed in 79 patients. Using the MRcI SNR and CNR were significantly higher in PD- and in T2-weighted sequences (p<0.05). TSE PD transversal demonstrated a higher risk of non-diagnostic quality using MRcI (OR 5.23; 95%-CI 1.86-14.72). MRcI revealed additional diagnostic information (OR 5.69; 95%-CI 1.15-28.24). Severe brain abnormality was associated with walking deficits (r=0.570; p=0.021). Conclusions The MRcI increased objective image quality and revealed additional diagnostic information to transfontanellar ultrasound. Nevertheless, prediction of infants' future development remains limited.

Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study

BACKGROUND AND PURPOSE

Brain MRI-derived lesions and atrophy are related to multiple sclerosis (MS) disability. In the Serially Unified Multicenter MS Investigation (SUMMIT), from Brigham and Women's Hospital (BWH) and University of California, San Francisco (UCSF), we assessed whether MRI methodologic heterogeneity may limit the ability to pool multisite data sets to assess 5-year clinical-MRI associations.

METHODS

Patients with relapsing-remitting (RR) MS (n = 100 from each site) underwent baseline brain MRI and baseline and 5-year clinical evaluations. Patients were matched on sex (74 women each), age, disease duration, and Expanded Disability Status Scale (EDSS) score. MRI was performed with differences between sites in both acquisition (field strength, voxel size, pulse sequences), and postprocessing pipeline to assess brain parenchymal fraction (BPF) and T2 lesion volume (T2LV).

RESULTS

The UCSF cohort showed higher correlation than the BWH cohort between T2LV and disease duration. UCSF showed a higher inverse correlation between BPF and age than BWH. UCSF showed a higher inverse correlation than BWH between BPF and 5-year EDSS score. Both cohorts showed inverse correlations between BPF and T2LV, with no between-site difference. The pooled but not individual cohort data showed a link between a lower baseline BPF and the subsequent 5-year worsening in disability in addition to other stronger relationships in the data.

CONCLUSIONS

MRI acquisition and processing differences may result in some degree of heterogeneity in assessing brain lesion and atrophy measures in patients with MS. Pooling of data across sites is beneficial to correct for potential biases in individual data sets.

DeepHarmony: A deep learning approach to contrast harmonization across scanner changes

Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data.

Fingolimod effect on gray matter, thalamus, and white matter in patients with multiple sclerosis

Objective

To study the effect of fingolimod on deep gray matter (dGM), thalamus, cortical GM (cGM), white matter (WM), and ventricular volume (VV) in patients with relapsing-remitting multiple sclerosis (RRMS).

Methods

Data were pooled from 2 phase III studies. A total of 2,064 of 2,355 (88%) contributed to the analysis: fingolimod 0.5 mg n = 783, fingolimod 1.25 mg n = 799, or placebo n = 773. Percentage change from baseline in dGM and thalamic volumes was evaluated with FMRIB’s Integrated Registration & Segmentation Tool; WM, cGM, and VV were evaluated with structural image evaluation using normalization of atrophy cross-sectional version (SIENAX) at months 12 and 24.

Results

At baseline, compound brain volume (brain volume in the z block [BVz] = cGM + dGM + WM) correlated with SIENAX-normalized brain volume (r = 0.938, p < 0.001); percentage change from baseline in BVz over 2 years correlated with structural image evaluation using normalization of atrophy percentage brain volume change (r = 0.713, p < 0.001). For placebo, volume reductions were most pronounced in cGM, and relative changes from baseline were strongest in dGM. Over 24 months, there were significant reductions with fingolimod vs placebo for dGM (0.5 mg −14.5%, p = 0.017; 1.25 mg −26.6%, p < 0.01) and thalamus (0.5 mg −26.1%, p = 0.006; 1.25 mg −49.7%, p < 0.001). Reduction of cGM volume loss was not significant. Significantly less WM loss and VV enlargement were seen with fingolimod vs placebo (all p < 0.001). A high T2 lesion volume at baseline predicted on-study cGM, dGM, and thalamic volume loss (p < 0.0001) but not WM loss. Patients taking placebo with high dGM (hazard ratio [HR] 0.54, p = 0.0323) or thalamic (HR 0.58, p = 0.0663) volume at baseline were less likely to show future disability worsening.

Conclusions

Fingolimod significantly reduced dGM volume loss (including thalamus) vs placebo in patients with RRMS. Reducing dGM and thalamic volume loss might improve long-term outcome.

Thalamic atrophy in multiple sclerosis: A magnetic resonance imaging marker of neurodegeneration throughout disease

OBJECTIVE

Thalamic volume is a candidate magnetic resonance imaging (MRI)-based marker associated with neurodegeneration to hasten development of neuroprotective treatments. Our objective is to describe the longitudinal evolution of thalamic atrophy in MS and normal aging, and to estimate sample sizes for study design.

METHODS

Six hundred one subjects (2,632 MRI scans) were analyzed. Five hundred twenty subjects with relapse-onset MS (clinically isolated syndrome, n = 90; relapsing-remitting MS, n = 392; secondary progressive MS, n = 38) underwent annual standardized 3T MRI scans for an average of 4.1 years, including a 1mm³ 3-dimensional T1-weighted sequence (3DT1; 2,485 MRI scans). Eighty-one healthy controls (HC) were scanned longitudinally on the same scanner using the same protocol (147 MRI scans). 3DT1s were processed using FreeSurfer's longitudinal pipeline after lesion inpainting. Rates of normalized thalamic volume loss in MS and HC were compared in linear mixed effects models. Simulation-based sample size calculations were performed incorporating the rate of atrophy in HC.

RESULTS

Thalamic volume declined significantly faster in MS subjects compared to HC, with an estimated decline of -0.71% per year (95% confidence interval [CI] = -0.77% to -0.64%) in MS subjects and -0.28% per year (95% CI = -0.58% to 0.02%) in HC (p for difference = 0.007). The rate of decline was consistent throughout the MS disease duration and across MS clinical subtypes. Eighty or 100 subjects per arm (α = 0.1 or 0.05, respectively) would be needed to detect the maximal effect size with 80% power in a 24-month study.

INTERPRETATION

Thalamic atrophy occurs early and consistently throughout MS. Preliminary sample size calculations appear feasible, adding to its appeal as an MRI marker associated with neurodegeneration. Ann Neurol 2018;83:223-234.

Automated segmentation of cerebral deep gray matter from MRI scans: effect of field strength on sensitivity and reliability

Background

The cerebral subcortical deep gray matter nuclei (DGM) are a common, early, and clinically-relevant site of atrophy in multiple sclerosis (MS). Robust and reliable DGM segmentation could prove useful to evaluate putative neuroprotective MS therapies. The objective of the study was to compare the sensitivity and reliability of DGM volumes obtained from 1.5T vs. 3T MRI.

Methods

Fourteen patients with MS [age (mean, range) 50.2 (32.0–60.8) years, disease duration 18.4 (8.2–35.5) years, Expanded Disability Status Scale score 3.1 (0–6), median 3.0] and 15 normal controls (NC) underwent brain 3D T1-weighted paired scan-rescans at 1.5T and 3T. DGM (caudate, thalamus, globus pallidus, and putamen) segmentation was obtained by the fully automated FSL-FIRST pipeline. Both raw and normalized volumes were derived.

Results

DGM volumes were generally higher at 3T vs. 1.5T in both groups. For raw volumes, 3T showed slightly better sensitivity (thalamus: p = 0.02; caudate: p = 0.10; putamen: p = 0.02; globus pallidus: p = 0.0004; total DGM: p = 0.01) than 1.5T (thalamus: p = 0.05; caudate: p = 0.09; putamen: p = 0.03; globus pallidus: p = 0.0006; total DGM: p = 0.02) for detecting DGM atrophy in MS vs. NC. For normalized volumes, 3T but not 1.5T detected atrophy in the globus pallidus in the MS group. Across all subjects, scan-rescan reliability was generally very high for both platforms, showing slightly higher reliability for some DGM volumes at 3T. Raw volumes showed higher reliability than normalized volumes. Raw DGM volume showed higher reliability than the individual structures.

Conclusions

These results suggest somewhat higher sensitivity and reliability of DGM volumes obtained from 3T vs. 1.5T MRI. Further studies should assess the role of this 3T pipeline in tracking potential MS neurotherapeutic effects.

Longitudinal MR spectroscopy of neurodegeneration in multiple sclerosis with diffusion of the intra-axonal constituent N-acetylaspartate

Multiple sclerosis (MS) is a pathologically complex CNS disease: inflammation, demyelination, and neuroaxonal degeneration occur concurrently and may depend on one another. Current therapies are aimed at the immune-mediated, inflammatory destruction of myelin, whereas axonal degeneration is ongoing and not specifically targeted. Diffusion-weighted magnetic resonance spectroscopy can measure the diffusivity of metabolites in vivo, such as the axonal/neuronal constituent N-acetylaspartate, allowing compartment-specific assessment of disease-related changes. Previously, we found significantly lower N-acetylaspartate diffusivity in people with MS compared to healthy controls (Wood et al., 2012) suggesting that this technique can measure axonal degeneration and could be useful in developing neuroprotective agents. In this longitudinal study, we found that N-acetylaspartate diffusivity decreased by 8.3% (p < 0.05) over 6 months in participants who were experiencing clinical or MRI evidence of inflammatory activity (n = 13), whereas there was no significant change in N-acetylaspartate diffusivity in the context of clinical and radiological stability (n = 6). As N-acetylaspartate diffusivity measurements are thought to more specifically reflect the intra-axonal space, these data suggest that N-acetylaspartate diffusivity can report on axonal health on the background of multiple pathological processes in MS, both cross-sectionally and longitudinally.

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.

An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM)

Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one of the most popular software tools for automated subcortical segmentation based on T₁-weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST's default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of >2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method.

Diffusion Tensor Imaging-Defined Sulcal Enlargement Is Related to Cognitive Impairment in Multiple Sclerosis

BACKGROUND AND PURPOSE

Cerebrospinal fluid (CSF) in the brain can be compartmentalized into two main divisions: ventricular CSF and subarachnoid space (sulcal CSF). Changes in CSF volumetry are seen in many neurological conditions including multiple sclerosis (MS) and found to correlate with clinical outcomes. We aimed to test the relation between the volumetry of sulcal and ventricular CSF and cognitive impairment (CI) based on the minimal assessment of cognitive function in MS (MACFIMS) in patients with MS.

MATERIAL AND METHODS

Forty-six patients with MS underwent the MACFIMS battery and classified as nonimpaired (MSNI) (n = 10) and cognitively impaired (MSCI) (n = 30) and borderline (MSBD) MS patients (n = 6). Volumes of sulcal and ventricular CSF along with global gray and white matter volumes and cortical thickness were obtained by diffusion tensor imaging (DTI) and T1-weighted (T1w)-based segmentation. These measures were statistically analyzed for associations with CI after adjusting for the age, education in years, lesion load, and disease duration.

RESULTS

Sulcal CSF showed the strongest correlation with CI (r = .51, P = .001) in our cohort, whereas ventricular CSF (P = .28, P = .19) along with cortical thickness and gray matter volume failed to show a significant correlation. Group analyses unadjusted for multiple comparisons showed significant difference in volumes of sulcal CSF and ventricular CSF between MSNI and MSCI groups (P < .05).

CONCLUSION

Sulcal CSF correlates with CI in patients with MS, possibly explained by cortical atrophy. DTI/T1w-based sulcal CSF segmentation method might be used as an indirect and simple neuroimaging marker to monitor CI in MS patients.

A Personalized Approach in Progressive Multiple Sclerosis: The Current Status of Disease Modifying Therapies (DMTs) and Future Perspectives

Using the term of progressive multiple sclerosis (PMS), we considered a combined population of persons with secondary progressive MS (SPMS) and primary progressive MS (PPMS). These forms of MS cannot be challenged with efficacy by the licensed therapy. In the last years, several measures of risk estimation were developed for predicting clinical course in MS, but none is specific for the PMS forms. Personalized medicine is a therapeutic approach, based on identifying what might be the best therapy for an individual patient, taking into account the risk profile. We need to achieve more accurate estimates of useful predictors in PMS, including unconventional and qualitative markers which are not yet currently available or practicable routine diagnostics. The evaluation of an individual patient is based on the profile of disease activity.Within the neurology field, PMS is one of the fastest-moving going into the future.

Intra- and interscanner variability of magnetic resonance imaging based volumetry in multiple sclerosis

Brain volumetric measurements in multiple sclerosis (MS) reflect not only disease-specific processes but also other sources of variability. The latter has to be considered especially in multicenter and longitudinal studies. Here, we compare data generated by three different 3-Tesla magnetic resonance scanners (Philips Achieva; Siemens Verio; GE Signa MR750). We scanned two patients diagnosed with relapsing remitting MS six times per scanner within three weeks (T1w and FLAIR, 3D). We assessed T2-hyperintense lesions by an automated lesion segmentation tool and determined volumes of grey matter (GM), white matter (WM) and whole brain (GM+WM) from the lesion-filled T1-weighted images using voxel-based morphometry (SPM8/VBM8) and SIENAX (FSL). We measured cortical thickness using FreeSurfer from both, lesion-filled and original T1-weighted images. We quantified brain volume changes with SIENA. In both patients, we found significant differences in total lesion volume, global brain tissue volumes and cortical thickness measures between the scanners. Morphometric measures varied remarkably between repeated scans at each scanner, independent of the brain imaging software tool used. We conclude that for cross-sectional multicenter studies, the effect of different scanners has to be taken into account. For longitudinal monocentric studies, the expected effect size should exceed the size of false positive findings observed in this study. Assuming a physiological loss of brain volume of about 0.3% per year in healthy adult subjects (Good et al., 2001), which may double in MS (De Stefano et al., 2010; De Stefano et al., 2015), with current tools reliable estimation of brain atrophy in individual patients is only possible over periods of several years.

Slowly eroding lesions in multiple sclerosis

BACKGROUND

At autopsy, 20%-40% of chronic multiple sclerosis (MS) lesions are labeled "slowly expanding" and feature myelin phagocytosis at the lesion edge. As pathological lesion classification relies on a single, terminal time point, the rate of lesion expansion cannot be directly measured.

OBJECTIVE

To study long-term volume changes in individual MS lesions.

METHODS

Volumes of individual lesions on proton density magnetic resonance imaging (MRI) acquired between 1992 and 2015 were measured in 22 individuals (one lesion per person). After correction for acquisition protocol, a mixed model evaluated lesion volume changes.

RESULTS

The mean (standard deviation) lesion volume at baseline was 142 (82) mL, falling to 74 (51) mL after 16 (3) years. All lesions shrank over time. Change in lesion volume did not correlate with change in supratentorial brain volume ( p = 0.33). In simulations, the results could be explained by a process of slow radial expansion superimposed on substantially more rapid resorption of damaged tissue.

CONCLUSION

We noted sustained radiological contraction of MS lesions, a surprising result given that fresh myelin breakdown products within chronic active lesions are observed relatively frequently at autopsy. Therefore, the primary pathological process in chronic lesions, even those described as "slowly expanding," is likely to be tissue loss.

Power estimation for non-standardized multisite studies

A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions.

Formation of parametric images using mixed-effects models: a feasibility study

Mixed-effects models have been widely used in the analysis of longitudinal data. By presenting the parameters as a combination of fixed effects and random effects, mixed-effects models incorporating both within- and between-subject variations are capable of improving parameter estimation. In this work, we demonstrate the feasibility of using a non-linear mixed-effects (NLME) approach for generating parametric images from medical imaging data of a single study. By assuming that all voxels in the image are independent, we used simulation and animal data to evaluate whether NLME can improve the voxel-wise parameter estimation. For testing purposes, intravoxel incoherent motion (IVIM) diffusion parameters including perfusion fraction, pseudo-diffusion coefficient and true diffusion coefficient were estimated using diffusion-weighted MR images and NLME through fitting the IVIM model. The conventional method of non-linear least squares (NLLS) was used as the standard approach for comparison of the resulted parametric images. In the simulated data, NLME provides more accurate and precise estimates of diffusion parameters compared with NLLS. Similarly, we found that NLME has the ability to improve the signal-to-noise ratio of parametric images obtained from rat brain data. These data have shown that it is feasible to apply NLME in parametric image generation, and the parametric image quality can be accordingly improved with the use of NLME. With the flexibility to be adapted to other models or modalities, NLME may become a useful tool to improve the parametric image quality in the future. Copyright © 2015 John Wiley & Sons, Ltd.

Novel composite MRI scale correlates highly with disability in multiple sclerosis patients

Understanding genotype-phenotype relationships or development/validation of biomarkers requires large multicenter cohorts integrated by universal quantification of crucial phenotypical traits, such as central nervous system (CNS) tissue destruction. We hypothesized that mathematical modeling-guided combination of biologically meaningful, semi-quantitative MRI elements characterized by high signal-to-noise ratio will provide such reliable, universal tool for measuring CNS tissue destruction. We retrospectively graded 15 elements in MRI scans performed in 419 untreated subjects with or without neurological diseases, while being blinded to their prospectively acquired clinical scores. We then used 305 subjects for disability-guided mathematical modeling to select and combine MRI elements that had non-redundant contributions to clinical disability, resulting in Combinatorial MRI Scale (COMRIS). We validated our model on the remaining 114 independent subjects. COMRIS requires 5-10 min per scan on average to compute and demonstrates highly significant (p < 0.0001) and validation-consistent Spearman correlation coefficients (0.75, 0.76, and 0.65) for the expanded disability status scale (EDSS), Scripps neurological rating scale (SNRS), and symbol digit modality test (SDMT) measures of neurological disability, respectively. Because COMRIS is not greatly influenced by MRI scanners or protocols and can be computed even in the presence of some motion artifacts, it does not require censoring out patients and it provides comparable results across different cohorts. As such, it represents a broadly available clinical and research tool that can facilitate multicenter research studies and comparative analyses across patient cohorts and research projects.

Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque

OBJECTIVE

An extensive analysis of white matter plaques in a large sample of multiple sclerosis (MS) autopsies provides insights into the dynamic nature of MS pathology.

METHODS

One hundred twenty MS cases (1,220 tissue blocks) were included. Plaque types were classified according to demyelinating activity based on stringent criteria. Early active, late active, smoldering, inactive, and shadow plaques were distinguished. A total of 2,476 MS white matter plaques were identified. Plaque type distribution was analyzed in relation to clinical data.

RESULTS

Active plaques were most often found in early disease, whereas at later stages, smoldering, inactive, and shadow plaques predominated. The presence of early active plaques rapidly declined with disease duration. Plaque type distribution differed significantly by clinical course. The majority of plaques in acute monophasic and relapsing-remitting MS (RRMS) were active. Among secondary progressive MS (SPMS) cases with attacks, all plaque types could be distinguished including active plaques, in contrast to SPMS without attacks, in which inactive plaques predominated. Smoldering plaques were frequently and almost exclusively found in progressive MS. At 47 years of age, an equilibrium was observed between active and inactive plaques, whereas smoldering plaques began to peak. Men displayed a higher proportion of smoldering plaques.

INTERPRETATION

Disease duration, clinical course, age, and gender contribute to the dynamic nature of white matter MS pathology. Active MS plaques predominate in acute and early RRMS and are the likely substrate of clinical attacks. Progressive MS transitions to an accumulation of smoldering plaques characterized by microglial activation and slow expansion of pre-existing plaques. Whether current MS therapeutics impact this pathological driver of disease progression remains uncertain.

Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: Comparison of linear mixed-effect models

Magnetic resonance imaging (MRI) of the brain provides important outcome measures in the longitudinal evaluation of disease activity and progression in MS subjects. Two common measures derived from brain MRI scans are the brain parenchymal fraction (BPF) and T2 hyperintense lesion volume (T2LV), and these measures are routinely assessed longitudinally in clinical trials and observational studies. When measuring each outcome longitudinally, observed changes may be potentially confounded by variability in MRI acquisition parameters between scans. In order to accurately model longitudinal change, the acquisition parameters should thus be considered in statistical models. In this paper, several models for including protocol as well as individual MRI acquisition parameters in linear mixed models were compared using a large dataset of 3453 longitudinal MRI scans from 1341 subjects enrolled in the CLIMB study, and model fit indices were compared across the models. The model that best explained the variance in BPF data was a random intercept and random slope with protocol specific residual variance along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. The model that best explained the variance in T2LV was a random intercept and random slope along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. In light of these findings, future studies pertaining to BPF and T2LV outcomes should carefully account for the protocol factors within longitudinal models to ensure that the disease trajectory of MS subjects can be assessed more accurately.

Whole Brain Volume Measured from 1.5T versus 3T MRI in Healthy Subjects and Patients with Multiple Sclerosis

BACKGROUND

Whole brain atrophy is a putative outcome measure in monitoring relapsing‐remitting multiple sclerosis (RRMS). With the ongoing MRI transformation from 1.5T to 3T, there is an unmet need to calibrate this change. We evaluated brain parenchymal volumes (BPVs) from 1.5T versus 3T in MS and normal controls (NC).

METHODS

We studied MS [n = 26, age (mean, range) 43 (21‐55), 22 (85%) RRMS, Expanded Disability Status Scale (EDSS) 1.98 (0‐6.5), timed 25 foot walk (T25FW) 5.95 (3.2‐33.0 seconds)] and NC [n = 9, age 45 (31‐53)]. Subjects underwent 1.5T (Phillips) and 3T (GE) 3‐dimensional T1‐weighted scans to derive normalized BPV from an automated SIENAX pipeline. Neuropsychological testing was according to consensus panel recommendations.

RESULTS

BPV‐1.5T was higher than BPV‐3T [mean (95% CI) + 45.7 mL (+35.3, +56.1), P < .00001], most likely due to improved tissue‐CSF contrast at 3T. BPV‐3T showed a larger volume decrease and larger effect size in detecting brain atrophy in MS versus NC [−74.5 mL (−126.5, −22.5), P = .006, d = .92] when compared to BPV‐1.5T [−51.3.1 mL (−99.8, −2.8), P = .04, d = .67]. Correlations between BPV‐1.5T and EDSS (r = −.43, P = .027) and BPV‐3T and EDSS (r = −.49, P = .011) and between BPV‐1.5T and T25FW (r = −.46, P = .018) and BPV‐3T and T25FW (r = −.56, P = .003) slightly favored 3T. BPV‐cognition correlations were significant (P < .05) for 6 of 11 subscales to a similar degree at 1.5T (r range = .44‐.58) and 3T (r range = .43‐.53).

CONCLUSIONS

Field strength may impact whole brain volume measurements in patients with MS though the differences are not too divergent between 1.5T and 3T.

Quantification of global cerebral atrophy in multiple sclerosis from 3T MRI using SPM: the role of misclassification errors

Purpose

We tested the validity of a freely available segmentation pipeline to measure compartmental brain volumes from 3T MRI in patients with multiple sclerosis (MS). Our primary focus was methodological to explore the effect of segmentation corrections on the clinical relevance of the output metrics.

Methods

Three-dimensional T1-weighted images were acquired to compare 61 MS patients to 30 age- and gender-matched normal controls (NC). We also tested the within patient MRI relationship to disability (eg, expanded disability status scale [EDSS] score) and cognition. Statistical parametric mapping v. 8 (SPM8)-derived gray matter (GMF), white matter (WMF), and total brain parenchyma fractions (BPF) were derived before and after correcting errors from T1 hypointense MS lesions and/or ineffective deep GM contouring.

Results

MS patients had lower GMF and BPF as compared to NC (P<.05). Cognitively impaired patients had lower BPF than cognitively preserved patients (P<.05). BPF was related to EDSS; BPF and GMF were related to disease duration (all P<.05). Errors caused bias in GMFs and WMFs but had no discernable influence on BPFs or any MRI-clinical associations.

Conclusions

We report the validity of a segmentation pipeline for the detection of MS-related brain atrophy with 3T MRI. Longitudinal studies are warranted to extend these results.