Subhash Tummala

Serum lipid antibodies are associated with cerebral tissue damage in multiple sclerosis

Objective: To determine whether peripheral immune responses as measured by serum antigen arrays are linked to cerebral MRI measures of disease severity in multiple sclerosis (MS). Methods: In this cross-sectional study, serum samples were obtained from patients with relapsing-remitting MS (n = 21) and assayed using antigen arrays that contained 420 antigens including CNS-related autoantigens, lipids, and heat shock proteins. Normalized compartment-specific global brain volumes were obtained from 3-tesla MRI as surrogates of atrophy, including gray matter fraction (GMF), white matter fraction (WMF), and total brain parenchymal fraction (BPF). Total brain T2 hyperintense lesion volume (T2LV) was quantified from fluid-attenuated inversion recovery images. Results: We found serum antibody patterns uniquely correlated with BPF, GMF, WMF, and T2LV. Furthermore, we identified immune signatures linked to MRI markers of neurodegeneration (BPF, GMF, WMF) that differentiated those linked to T2LV. Each MRI measure was correlated with a specific set of antibodies. Strikingly, immunoglobulin G (IgG) antibodies to lipids were linked to brain MRI measures. Based on the association between IgG antibody reactivity and each unique MRI measure, we developed a lipid index. This comprised the reactivity directed against all of the lipids associated with each specific MRI measure. We validated these findings in an additional independent set of patients with MS (n = 14) and detected a similar trend for the correlations between BPF, GMF, and T2LV vs their respective lipid indexes. Conclusions: We propose serum antibody repertoires that are associated with MRI measures of cerebral MS involvement. Such antibodies may serve as biomarkers for monitoring disease pathology and progression.

Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis

The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. They assessed intersite variability in scan data, by imaging a volunteer with relapsing-remitting MS with a scan-rescan at each site. In multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses. BACKGROUND AND PURPOSE: MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. To assess intersite variability in scan data, we imaged a volunteer with relapsing-remitting MS with a scan-rescan at each site. MATERIALS AND METHODS: All imaging was acquired on Siemens scanners (4 Skyra, 2 Tim Trio, and 1 Verio). Expert segmentations were manually obtained for T1-hypointense and T2 (FLAIR) hyperintense lesions. Several automated lesion-detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied. Statistical analyses were conducted to assess variability across sites, as well as systematic biases in the volumetric measurements that were site-related. RESULTS: Systematic biases due to site differences in expert-traced lesion measurements were significant (P < .01 for both T1 and T2 lesion volumes), with site explaining >90% of the variation (range, 13.0–16.4 mL in T1 and 15.9–20.1 mL in T2) in lesion volumes. Site also explained >80% of the variation in most automated volumetric measurements. Output measures clustered according to scanner models, with similar results from the Skyra versus the other 2 units. CONCLUSIONS: Even in multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses.

Association Between Serum MicroRNAs and Magnetic Resonance Imaging Measures of Multiple Sclerosis Severity

Importance MicroRNAs (miRNAs) are promising multiple sclerosis (MS) biomarkers. Establishing the association between miRNAs and magnetic resonance imaging (MRI) measures of disease severity will help define their significance and potential impact. Objective To correlate circulating miRNAs in the serum of patients with MS to brain and spinal MRI. Design, Setting, and Participants A cross-sectional study comparing serum miRNA samples with MRI metrics was conducted at a tertiary MS referral center. Two independent cohorts (41 and 79 patients) were retrospectively identified from the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Womens Hospital. Expression of miRNA was determined by locked nucleic acid–based quantitative real-time polymerase chain reaction. Spearman correlation coefficients were used to test the association between miRNA and brain lesions (T2 hyperintense lesion volume [T2LV]), the ratio of T1 hypointense lesion volume [T1LV] to T2LV [T1:T2]), brain atrophy (whole brain and gray matter), and cervical spinal cord lesions (T2LV) and atrophy. The study was conducted from December 2013 to April 2016. Main Outcomes and Measures miRNA expression. Results Of the 120 patients included in the study, cohort 1 included 41 participants (7 [17.1%] men), with mean (SD) age of 47.7 (9.5) years; cohort 2 had 79 participants (26 [32.9%] men) with a mean (SD) age of 43.0 (7.5) years. Associations between miRNAs and MRIs were both protective and pathogenic. Regarding miRNA signatures, a topographic specificity differed for the brain vs the spinal cord, and the signature differed between T2LV and atrophy/destructive measures. Four miRNAs showed similar significant protective correlations with T1:T2 in both cohorts, with the highest for hsa.miR.143.3p (cohort 1: Spearman correlation coefficient rs = −0.452, P = .003; cohort 2: rs = −0.225, P = .046); the others included hsa.miR.142.5p (cohort 1: rs = −0.424, P = .006; cohort 2: rs = −0.226, P = .045), hsa.miR.181c.3p (cohort 1: rs = −0.383, P = .01; cohort 2: rs = −0.222, P = .049), and hsa.miR.181c.5p (cohort 1: rs = −0.433, P = .005; cohort 2: rs = −0.231, P = .04). In the 2 cohorts, hsa.miR.486.5p (cohort 1: rs = 0.348, P = .03; cohort 2: rs = 0.254, P = .02) and hsa.miR.92a.3p (cohort 1: rs = 0.392, P = .01; cohort 2: rs = 0.222, P = .049) showed similar significant pathogenic correlations with T1:T2; hsa.miR.375 (cohort 1: rs = −0.345, P = .03; cohort 2: rs = −0.257, P = .022) and hsa.miR.629.5p (cohort 1: rs = −0.350, P = .03; cohort 2: rs = −0.269, P = .02) showed significant pathogenic correlations with brain atrophy. Although we found several miRNAs associated with MRI outcomes, none of these associations remained significant when correcting for multiple comparisons, suggesting that further validation of our findings is needed. Conclusions and Relevance Serum miRNAs may serve as MS biomarkers for monitoring disease progression and act as surrogate markers to identify underlying disease processes.

MRI detection of hypointense brain lesions in patients with multiple sclerosis: T1 spin-echo vs. gradient-echo

OBJECTIVE Compare T1 spin-echo (T1SE) and T1 gradient-echo (T1GE) sequences in detecting hypointense brain lesions in multiple sclerosis (MS). BACKGROUND Chronic hypointense lesions on T1SE MRI scans are a surrogate of severe demyelination and axonal loss in MS. The role of T1GE images in the detection of such lesions has not been clarified. DESIGN/METHODS In 45 patients with MS [Expanded Disability Status Scale (EDSS) score (mean±SD) 3.5±2.0; 37 relapsing-remitting (RR); 8 secondary progressive (SP)], cerebral T1SE, T1GE, and T2-weighted fluid-attenuated inversion-recovery (FLAIR) images were acquired on a 1.5T MRI scanner. Images were re-sampled to axial 5mm slices before directly comparing lesion detectability using Jim (v.7, Xinapse Systems). Statistical methods included Wilcoxon signed rank tests to compare sequences and Spearman correlations to test associations. RESULTS Considering the entire cohort, T1GE detected a higher lesion volume (5.90±6.21 vs. 4.17±4.84ml, p<0.0001) and higher lesion number (27.82±20.66 vs. 25.20±20.43, p<0.05) than T1SE. Lesion volume differences persisted when considering RR and SP patients separately (both p<0.01). A higher lesion number by T1GE was seen only in the RR group (p<0.05). When comparing correlations between lesion volume and overall neurologic disability (EDSS score), T1SE correlated with EDSS (Spearman r=0.29, p<0.05) while T1GE (r=0.23, p=0.13) and FLAIR (r=0.24, p=0.12) did not. CONCLUSION Our data suggest that hypointense lesions on T1SE and T1GE are not interchangeable in patients with MS. Based on these results, we hypothesize that T1GE shows more sensitivity to lesions at the expense of less pathologic specificity for tissue destruction than T1SE.

Gradient nonlinearity effects on upper cervical spinal cord area measurement from 3D T1‐weighted brain MRI acquisitions

To explore (i) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T1‐weighted scans related to gradient nonlinearity (GNL) and subject positioning; (ii) the effect of vendor‐implemented GNL corrections; and (iii) easily applicable methods that can be used to retrospectively correct data.

Dual-Sensitivity Multiple Sclerosis Lesion and CSF Segmentation for Multichannel 3T Brain MRI: Dual-Sensitivity MS Lesion and CSF Segmentation

A pipeline for fully automated segmentation of 3T brain MRI scans in multiple sclerosis (MS) is presented. This 3T morphometry (3TM) pipeline provides indicators of MS disease progression from multichannel datasets with high‐resolution 3‐dimensional T1‐weighted, T2‐weighted, and fluid‐attenuated inversion‐recovery (FLAIR) contrast. 3TM segments white (WM) and gray matter (GM) and cerebrospinal fluid (CSF) to assess atrophy and provides WM lesion (WML) volume.

A two-year study using cerebral gray matter volume to assess the response to fingolimod therapy in multiple sclerosis

BACKGROUND Cerebral gray matter (GM) atrophy has clinical relevance in multiple sclerosis (MS). Fingolimod has known efficacy on clinical and conventional MRI findings in MS; the effect on GM is unknown. OBJECTIVE To explore fingolimods treatment effect on cerebral GM atrophy over two years in patients with relapsing forms of MS. DESIGN/METHODS Patients starting fingolimod [n=24, age (mean±SD) 41.2±11.6years, Expanded Disability Status Scale (EDSS) score 1.1±1.4; 58% women] were compared to untreated patients [n=29, age 45.7±8.4years, EDSS 1.0±1.2; 93% women]. Baseline, one and two year MRI was applied to an SPM12 pipeline to assess brain parenchymal fraction (BPF) and cortical GM fraction (cGMF). T2 lesion volume (T2LV) and gadolinium-enhancing lesions were assessed. Change was modeled using a mixed effects linear regression with a random intercept and fixed effects for time, group, and the time-by-group interaction. The group slope difference was assessed using the interaction term. RESULTS Over two years, cGMF remained stable in the fingolimod group (p>0.05), but decreased in the untreated group (p<0.001) (group difference p<0.001). BPF change did not differ between groups (all time-points p>0.05). T2LV increased over two years in the untreated group (p<0.001) but not in the fingolimod group (p≥0.44) (group difference p<0.001). CONCLUSION These results suggest a treatment effect of fingolimod on cerebral GM atrophy in the first two years. GM atrophy is more sensitive to such effects than whole brain atrophy. However, due to the non-randomized, retrospective design, heterogeneous between-group characteristics, and small sample size, these results require confirmation in future studies.

The Contribution of Cortical Lesions to a Composite MRI Scale of Disease Severity in Multiple Sclerosis

Objective To test a new version of the Magnetic Resonance Disease Severity Scale (v.3 = MRDSS3) for multiple sclerosis (MS), incorporating cortical gray matter lesions (CLs) from 3T magnetic resonance imaging (MRI). Background MRDSS1 was a cerebral MRI-defined composite scale of MS disease severity combining T2 lesion volume (T2LV), the ratio of T1 to T2LV (T1/T2), and whole brain atrophy [brain parenchymal fraction (BPF)]. MRDSS2 expanded the scale to include cerebral gray matter fraction (GMF) and upper cervical spinal cord area (UCCA). We tested the contribution of CLs to the scale (MRDSS3) in modeling the MRI relationship to clinical status. Methods We studied 51 patients [3 clinically isolated syndrome, 43 relapsing-remitting, 5 progressive forms, age (mean ± SD) 40.7 ± 9.1 years, Expanded Disability Status Scale (EDSS) score 1.6 ± 1.7] and 20 normal controls by high-resolution cerebrospinal MRI. CLs required visibility on both fluid-attenuated inversion-recovery (FLAIR) and modified driven equilibrium Fourier transform sequences. The MACFIMS battery defined cognitively impaired (n = 18) vs. preserved (n = 33) MS subgroups. Results EDSS significantly correlated with only BPF, UCCA, MRDSS2, and MRDSS3 (all p < 0.05). After adjusting for depressive symptoms, the cognitively impaired group had higher severity of MRI metrics than the cognitively preserved group in regard to only BPF, GMF, T1/T2, MRDSS1, and MRDSS2 (all p < 0.05). CL number was not significantly related to EDSS score or cognition status. Conclusion CLs from 3T MRI did not appear to improve the validity of the MRDSS. Further studies employing advanced sequences or higher field strengths may show more utility for the incorporation of CLs into composite scales.

Spinal Cord as an Adjunct to Brain Magnetic Resonance Imaging in Defining “No Evidence of Disease Activity” in Multiple Sclerosis

Background Monitoring patients with multiple sclerosis (MS) for “no evidence of disease activity” (NEDA) may help guide disease-modifying therapy (DMT) management decisions. Whereas surveillance brain magnetic resonance imaging (MRI) is common, the role of spinal cord monitoring for NEDA is unknown. Objective To evaluate the role of brain and spinal cord 3T MRI in the 1-year evaluation of NEDA. Methods Of 61 study patients (3 clinically isolated syndrome, 56 relapsing-remitting, 2 secondary progressive), 56 (91.8%) were receiving DMT. The MRI included brain fluid-attenuated inversion recovery and cervical/thoracic T2-weighted fast spin echo images. On MRI, NEDA was defined as the absence of new or enlarging T2 lesions at 1 year. Results Thirty-nine patients (63.9%) achieved NEDA by brain MRI, only one of whom had spinal cord activity. This translates to a false-positive rate for NEDA based on the brain of 2.6% (95% CI, 0.1%–13.5%). Thirty-eight patients (62.3%) had NEDA by brain and spinal cord MRI. Fifty-five patients (90.2%) had NEDA by spinal cord MRI, 17 of whom had brain activity. Of the 22 patients (36.1%) with brain changes, 5 had spinal cord changes. No evidence of disease activity was sustained in 48.3% of patients at 1 year and was the same with the addition of spinal cord MRI. Patients with MRI activity in either the brain or the spinal cord only were more likely to have activity in the brain (P = .0001). Conclusions Spinal cord MRI had a low diagnostic yield as an adjunct to brain MRI at 3T in monitoring patients with MS for NEDA over 1 year. Studies with larger data sets are needed to confirm these findings.

Automatic spinal cord localization, robust to MRI contrasts using global curve optimization

HighlightsAutomatic, fast and robust method to detect the center of the spinal cord on MRI data.Machine learning based method followed by a global curve optimization.Brain and spine regions are automatically separated at the pontomedullary junction.Validation on 804 images, 4 contrasts, 20 centers, large amount of patients.Better results compared to a state‐of‐the‐art technique. Abstract During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improvement of MR sequences adapted to the spinal cord, automatic image processing tools for spinal cord MRI data are not yet as developed as for the brain. There is nonetheless great interest in fully automatic and fast processing methods to be able to propose quantitative analysis pipelines on large datasets without user bias. The first step of most of these analysis pipelines is to detect the spinal cord, which is challenging to achieve automatically across the broad range of MRI contrasts, field of view, resolutions and pathologies. In this paper, a fully automated, robust and fast method for detecting the spinal cord centerline on MRI volumes is introduced. The algorithm uses a global optimization scheme that attempts to strike a balance between a probabilistic localization map of the spinal cord center point and the overall spatial consistency of the spinal cord centerline (i.e. the rostro‐caudal continuity of the spinal cord). Additionally, a new post‐processing feature, which aims to automatically split brain and spine regions is introduced, to be able to detect a consistent spinal cord centerline, independently from the field of view. We present data on the validation of the proposed algorithm, known as “OptiC”, from a large dataset involving 20 centers, 4 contrasts (T2‐weighted n = 287, T1‐weighted n = 120, T2*‐weighted n = 307, diffusion‐weighted n = 90), 501 subjects including 173 patients with a variety of neurologic diseases. Validation involved the gold‐standard centerline coverage, the mean square error between the true and predicted centerlines and the ability to accurately separate brain and spine regions. Overall, OptiC was able to cover 98.77% of the gold‐standard centerline, with a mean square error of 1.02 mm. OptiC achieved superior results compared to a state‐of‐the‐art spinal cord localization technique based on the Hough transform, especially on pathological cases with an averaged mean square error of 1.08 mm vs. 13.16 mm (Wilcoxon signed‐rank test p‐value < .01). Images containing brain regions were identified with a 99% precision, on which brain and spine regions were separated with a distance error of 9.37 mm compared to ground‐truth. Validation results on a challenging dataset suggest that OptiC could reliably be used for subsequent quantitative analyses tasks, opening the door to more robust analysis on pathological cases. Graphical abstract Figure. No Caption available.