Neuroimaging assessment of facility-bound severely-affected MS reveals the critical role of cortical gray matter pathology: results from the CASA-MS case-controlled study.

BACKGROUND: A subgroup of people with multiple sclerosis (pwMS) will develop severe disability. The pathophysiology underlying severe MS is unknown. The comprehensive assessment of severely affected MS (CASA-MS) was a case-controlled study that compared severely disabled in skilled nursing (SD/SN) (EDSS ≥ 7.0) to less-disabled (EDSS 3.0-6.5) community dwelling (CD) progressive pwMS, matched on age-, sex- and disease-duration (DDM). OBJECTIVES: To identify neuroimaging and molecular biomarker characteristics that distinguish SD/SN from DDM-CD progressive pwMS. METHODS: This study was carried at SN facility and at a tertiary MS center. The study collected clinical, molecular (serum neurofilament light chain, sNfL and glial acidic fibrillary protein, sGFAP) and MRI quantitative lesion-, brain volume-, and tissue integrity-derived measures. Statistical analyses were controlled for multiple comparisons. RESULTS: 42 SD/SN and 42 DDM-CD were enrolled. SD/SN pwMS showed significantly lower cortical volume (CV) (p < 0.001, d = 1.375) and thalamic volume (p < 0.001, d = 0.972) compared to DDM-CD pwMS. In a logistic stepwise regression model, the SD/SN pwMS were best differentiated from the DDM-CD pwMS by lower CV (p < 0.001) as the only significant predictor, with the accuracy of 82.3%. No significant differences between the two groups were observed for medulla oblongata volume, a proxy for spinal cord atrophy and white matter lesion burden, while there was a statistical trend for numerically higher sGFAP in SD/SN pwMS. CONCLUSIONS: The CASA-MS study showed significantly more gray matter atrophy in severe compared to less-severe progressive MS.

Assessment of T2 lesion-based disease activity volume outcomes in predicting disease progression in multiple sclerosis over 10 years.

BACKGROUND: New/enlarging T2 lesion count and T2-lesion volume (LV) are used as conventional secondary endpoints in clinical trials of patients with multiple sclerosis (PwMS). However, those outcomes may have several limitations, such as inability to account for heterogeneity of lesion formation/enlargement frequency and their dynamic volumetric behavior. Measurement of volume rather than count of new/enlarging lesions may be more representative outcome of dynamic changes over time. OBJECTIVES: To investigate whether new/enlarging T2-LV is more predictive of confirmed disability progression (CDP), compared to total T2-LV or new/enlarging T2 lesion count over long-term follow-up. METHODS: We studied 176 early relapsing-remitting PwMS who were followed with annual MRI examinations over 10 years. T2-LV, new/enlarging T2-LV, and new/enlarging lesion count were determined. Cumulative count/volumes were obtained. 10-year CDP was confirmed after 48-weeks. ANCOVA analysis detected MRI outcome differences in stable (n = 76) and CDP (n = 100) groups at different time points, after correction for multiple comparisons. RESULTS: PwMS with CDP had greater cumulative new/enlarging T2-LV at 4 years (p = 0.049), and enlarging T2-LV at 4- (p = 0.039) and 6-year follow-up (p = 0.032), compared to stable patients. PwMS with CDP did not differ from stable ones in new/enlarging T2 lesion count or total T2-LV at any of the study timepoints. PwMS with Expanded Disability Status Scale change >2.0 had significantly greater enlarging T2 lesion count (p = 0.01) and enlarging T2-LV (p = 0.038) over the 10-year follow-up. CONCLUSION: Enlargement of T2 lesions is more strongly associated with long-term disability progression compared to other conventional T2 lesion-based outcomes.

MRI-based thalamic volumetry in multiple sclerosis using FSL-FIRST: Systematic assessment of common error modes.

BACKGROUND AND PURPOSE: FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL-FIRST) is a widely used and well-validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple sclerosis (MS). However, FSL-FIRST's algorithm is based on shape models derived from non-MS groups. As such, the present study sought to systematically assess common thalamic segmentation errors made by FSL-FIRST on MRIs from people with multiple sclerosis (PwMS). METHODS: FSL-FIRST was applied to generate thalamic segmentation masks for 890 MR images in PwMS. Images and masks were reviewed systematically to classify and quantify errors, as well as associated anatomical variations and MRI abnormalities. For cases with overt errors (n = 362), thalamic masks were corrected and quantitative volumetric differences were calculated. RESULTS: In the entire quantitative volumetric group, the mean volumetric error of FSL-FIRST was 2.74% (0.360 ml): among only corrected cases, the mean volumetric error was 6.79% (0.894 ml). The average percent volumetric error associated with seven error types, two anatomical variants, and motions artifacts are reported. Additional analyses showed that the presence of motion artifacts or anatomical variations significantly increased the probability of error (χ2  = 18.14, p < .01 and χ2  = 64.89, p < .001, respectively). Finally, thalamus volume error was negatively associated with degree of atrophy, such that smaller thalami were systematically overestimated (r = -.28, p < .001). CONCLUSIONS: In PwMS, FSL-FIRST thalamic segmentation miscalculates thalamic volumetry in a predictable fashion, and may be biased to overestimate highly atrophic thalami. As such, it is recommended that segmentations be reviewed and corrected manually when appropriate for specific studies.

Visual deficits and cognitive assessment of multiple sclerosis: confounder, correlate, or both?

BACKGROUND: The relationship between visual impairment and cognitive performance in multiple sclerosis (MS) remains poorly understood. OBJECTIVE: To determine associations between visual acuity and optical coherence tomography (OCT) measures with cognitive performance of MS patients and healthy controls (HCs). METHODS: 141 MS patients (with and without MS optic neuritis; MSON) and 50 HCs underwent neuropsychological, visual, and OCT testing. California Verbal Learning Test (CVLT-II), Brief Visuospatial Memory Test (BVMT-R), and Symbol Digit Modalities Test (SDMT) were used. Patients with test performance below - 1.5 standard deviations of the mean HCs scores were labeled as cognitive impairment. Visual ability was assessed with 100%, 2.5%, and 1.25% low-contrast letter acuity (LCLA) charts. OCT-derived peripapillary retinal nerve fiber layer (pRNFL) thickness, macular volume (MV), macular ganglion cell inner plexiform (mGCIP) thickness (as a sum of GC and IP layers), and macular inner nuclear layer (mINL) were computed. RESULTS: 100% and 2.5% LCLA associated with SDMT in MS and HCs (p < 0.001; and p < 0.012, respectively). In MSON patients, visually demanding tests were explained by pRNFL and macular volume for SDMT (ß = 0.172, p = 0.039 and ß = 0.27, p = 0.001) and MV for BVMT-R (ß = 0.21, p = 0.012). In non-MSON, only mINL was predictor of CVLT-II. pRNFL and MV predicted cognitive impairment with an accuracy of 72.2% (Negelkerke R2 = 0.234). These findings were driven by associations within the progressive MS subgroup. HC's SDMT performance was explained by mGCIP (ß = 0.316, p = 0.001). CONCLUSIONS: Both LCLA and OCT-based measures (pRNFL and macular volume) were associated with MS cognitive performance. OCT-based measures were also significant predictors of cognitive status in MS patients. mGCIP associated with cognitive performance in HCs.

Assessment of mesoscopic properties of deep gray matter iron through a model-based simultaneous analysis of magnetic susceptibility and R2* - A pilot study in patients with multiple sclerosis and normal controls.

Most studies of brain iron relied on the effect of the iron on magnetic resonance (MR) relaxation properties, such as R2∗, and bulk tissue magnetic susceptibility, as measured by quantitative susceptibility mapping (QSM). The present study exploited the dependence of R2∗ and magnetic susceptibility on physical interactions at different length-scales to retrieve information about the tissue microenvironment, rather than the iron concentration. We introduce a method for the simultaneous analysis of brain tissue magnetic susceptibility and R2∗ that aims to isolate those biophysical mechanisms of R2∗ -contrast that are associated with the micro- and mesoscopic distribution of iron, referred to as the Iron Microstructure Coefficient (IMC). The present study hypothesized that changes in the deep gray matter (DGM) magnetic microenvironment associated with aging and pathological mechanisms of multiple sclerosis (MS), such as changes of the distribution and chemical form of the iron, manifest in quantifiable contributions to the IMC. To validate this hypothesis, we analyzed the voxel-based association between R2∗ and magnetic susceptibility in different DGM regions of 26 patients with multiple sclerosis and 33 age- and sex-matched normal controls. Values of the IMC varied significantly between anatomical regions, were reduced in the dentate and increased in the caudate of patients compared to controls, and decreased with normal aging, most strongly in caudate, globus pallidus and putamen.

Clinical relevance of brain atrophy assessment in multiple sclerosis. Implications for its use in a clinical routine.

INTRODUCTION: Brain atrophy measurement in multiple sclerosis (MS) has become an important outcome for determining patients at risk for developing physical and cognitive disability. AREAS COVERED: In this article, we discuss the methodological issues related to using this MRI metric routinely, in a clinical setting. Understanding trajectories of annualized whole brain, gray and white matter, thalamic volume loss, and enlargement of ventricular space in specific MS phenotypes is becoming increasingly important. Evidence is mounting that disease-modifying treatments exert a positive effect on slowing brain atrophy progression in MS. Expert Commentary: While there is a need to translate measurement of brain atrophy to clinical routine at the individual patient level, there are still a number of challenges to be met before this can actually happen, including how to account for biological confounding factors and pseudoatrophy, standardize acquisition and analyses parameters, which can influence the accuracy of the assessments.

Cardiovascular risk factors are associated with increased lesion burden and brain atrophy in multiple sclerosis.

BACKGROUND: Cardiovascular (CV) risk factors have been associated with changes in clinical outcomes in patients with multiple sclerosis (MS). OBJECTIVES: To investigate the frequency of CV risks in patients with MS and their association with MRI outcomes. METHODS: In a prospective study, 326 patients with relapsing-remitting MS and 163 patients with progressive MS, 61 patients with clinically isolated syndrome (CIS) and 175 healthy controls (HCs) were screened for CV risks and scanned on a 3T MRI scanner. Examined CV risks included hypertension, heart disease, smoking, overweight/obesity and type 1 diabetes. MRI measures assessed lesion volumes (LVs) and brain atrophy. Association between individual or multiple CV risks and MRI outcomes was examined adjusting for age, sex, race, disease duration and treatment status. RESULTS: Patients with MS showed increased frequency of smoking (51.7% vs 36.5%, p = 0.001) and hypertension (33.9% vs 24.7%, p=0.035) compared with HCs. In total, 49.9% of patients with MS and 36% of HCs showed ≥ 2 CV risks (p = 0.003), while the frequency of ≥ 3 CV risks was 18.8% in the MS group and 8.6% in the HCs group (p = 0.002). In patients with MS, hypertension and heart disease were associated with decreased grey matter (GM) and cortical volumes (p < 0.05), while overweight/obesity was associated with increased T1-LV (p < 0.39) and smoking with decreased whole brain volume (p = 0.049). Increased lateral ventricle volume was associated with heart disease (p = 0.029) in CIS. CONCLUSIONS: Patients with MS with one or more CV risks showed increased lesion burden and more advanced brain atrophy.

Improved longitudinal gray and white matter atrophy assessment via application of a 4-dimensional hidden Markov random field model.

SIENA and similar techniques have demonstrated the utility of performing "direct" measurements as opposed to post-hoc comparison of cross-sectional data for the measurement of whole brain (WB) atrophy over time. However, gray matter (GM) and white matter (WM) atrophy are now widely recognized as important components of neurological disease progression, and are being actively evaluated as secondary endpoints in clinical trials. Direct measures of GM/WM change with advantages similar to SIENA have been lacking. We created a robust and easily-implemented method for direct longitudinal analysis of GM/WM atrophy, SIENAX multi-time-point (SIENAX-MTP). We built on the basic halfway-registration and mask composition components of SIENA to improve the raw output of FMRIB's FAST tissue segmentation tool. In addition, we created LFAST, a modified version of FAST incorporating a 4th dimension in its hidden Markov random field model in order to directly represent time. The method was validated by scan-rescan, simulation, comparison with SIENA, and two clinical effect size comparisons. All validation approaches demonstrated improved longitudinal precision with the proposed SIENAX-MTP method compared to SIENAX. For GM, simulation showed better correlation with experimental volume changes (r=0.992 vs. 0.941), scan-rescan showed lower standard deviations (3.8% vs. 8.4%), correlation with SIENA was more robust (r=0.70 vs. 0.53), and effect sizes were improved by up to 68%. Statistical power estimates indicated a potential drop of 55% in the number of subjects required to detect the same treatment effect with SIENAX-MTP vs. SIENAX. The proposed direct GM/WM method significantly improves on the standard SIENAX technique by trading a small amount of bias for a large reduction in variance, and may provide more precise data and additional statistical power in longitudinal studies.

Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates.

BACKGROUND: Presented is the method "Detection and Outline Error Estimates" (DOEE) for assessing rater agreement in the delineation of multiple sclerosis (MS) lesions. The DOEE method divides operator or rater assessment into two parts: 1) Detection Error (DE) -- rater agreement in detecting the same regions to mark, and 2) Outline Error (OE) -- agreement of the raters in outlining of the same lesion. METHODS: DE, OE and Similarity Index (SI) values were calculated for two raters tested on a set of 17 fluid-attenuated inversion-recovery (FLAIR) images of patients with MS. DE, OE, and SI values were tested for dependence with mean total area (MTA) of the raters' Region of Interests (ROIs). RESULTS: When correlated with MTA, neither DE (ρ = .056, p=.83) nor the ratio of OE to MTA (ρ = .23, p=.37), referred to as Outline Error Rate (OER), exhibited significant correlation. In contrast, SI is found to be strongly correlated with MTA (ρ = .75, p < .001). Furthermore, DE and OER values can be used to model the variation in SI with MTA. CONCLUSIONS: The DE and OER indices are proposed as a better method than SI for comparing rater agreement of ROIs, which also provide specific information for raters to improve their agreement.

Application of hidden Markov random field approach for quantification of perfusion/diffusion mismatch in acute ischemic stroke.

The perfusion/diffusion 'mismatch model' in acute ischemic stroke provides the potential to more accurately understand the consequences of thrombolytic therapy on an individual patient basis. Few methods exist to quantify mismatch extent (ischemic penumbra) and none have shown a robust ability to predict infarcted tissue outcome. Hidden Markov random field (HMRF) approaches have been used successfully in many other applications. The aim of the study was to develop a method for rapid and reliable identification and quantification of perfusion/diffusion mismatch using an HMRF approach. An HMRF model was used in combination with automated contralateral identification to segment normal tissue from non-infarcted tissue with perfusion abnormality. The infarct was used as a seed point to initialize segmentation, along with the contralateral mirror tissue. The two seeds were then allowed to compete for ownership of all unclassified tissue. In addition, a novel method was presented for quantifying tissue salvageability by weighting the volume with the degree of hypoperfusion, allowing the penumbra voxels to contribute unequal potential damage estimates. Simulated and in vivo datasets were processed and compared with results from a conventional thresholding approach. Both simulated and in vivo experiments demonstrated a dramatic improvement in accuracy with the proposed technique. For the simulated dataset, the mean absolute error decreased from 171.9% with conventional thresholding to 2.9% for the delay-weighted HMRF approach. For the in vivo dataset, the mean absolute error decreased from 564.6% for thresholding to 34.2% for the delay-weighted HMRF approach. The described method represents a significant improvement over thresholding techniques.