Laura Gaetano

White matter lesion filling improves the accuracy of cortical thickness measurements in multiple sclerosis patients: a longitudinal study

BackgroundPrevious studies have demonstrated that white matter (WM) lesions bias automated brain tissue classifications and cerebral volume measurements. However, filling WM lesions using the intensity of neighbouring normal-appearing WM has been shown to increase the accuracy of automated volume measurements in the brain. In the present study, we investigate the influence of WM lesions on cortical thickness (CTh) measures and assessed the impact of lesion filling on both cross-sectional/longitudinal and global/regional measurements of CTh in multiple sclerosis (MS) patients.MethodsFifty MS patients were studied at baseline as well as after three and six years of follow-up. CTh was estimated using a fully automated pipeline (CIVET) on T1-weighted magnetic resonance images data acquired at 1.5 Tesla without (original) and with WM lesion filling (filled). WM lesions were semi-automatically segmented and then filled with the mean intensity of the neighbouring voxels. For both original and filled T1 images we investigated and compared the main CIVET’s steps: tissue classification, surfaces generation and CTh measurement.ResultsOn the original T1 images, the majority of WM lesion volume (72%) was wrongly classified as gray matter (GM). After lesion filling the accuracy of WM lesions classification improved significantly (p < 0.001, 94% of WM lesion volume correctly classified) as well as the WM surface generation (p < 0.0001). The mean CTh computed on the original T1 images, overall time points, was significantly thinner (p < 0.001) compared the CTh estimated on the filled T1 images. The vertex-wise longitudinal analysis performed on the filled T1 images showed an increased number of vertices in the fronto-temporal region with a significantly decrease of CTh over time compared the analysis performed on the original images.ConclusionThese results indicate that WM lesions bias the CTh estimation both cross-sectionally as well as longitudinally. The lesion filling approach significantly improved the accuracy of the regional CTh estimation and has an impact also on the global estimation of CTh.

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 Freesurfers 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.

Teriflunomide slows BVL in relapsing MS

Objective: To assess, using structural image evaluation using normalization of atrophy (SIENA), the effect of teriflunomide, a once-daily oral immunomodulator, on brain volume loss (BVL) in patients with relapsing forms of MS enrolled in the phase 3 TEMSO study. Methods: TEMSO MR scans were analyzed (study personnel masked to treatment allocation) using SIENA to assess brain volume changes between baseline and years 1 and 2 in patients treated with placebo or teriflunomide. Treatment group comparisons were made via rank analysis of covariance. Results: Data from 969 patient MRI visits were included in this analysis: 808 patients had baseline and year 1 MRI; 709 patients had baseline and year 2 MRI. Median percentage BVL from baseline to year 1 and year 2 for placebo was 0.61% and 1.29%, respectively, and for teriflunomide 14 mg, 0.39% and 0.90%, respectively. BVL was lower for teriflunomide 14 mg vs placebo at year 1 (36.9% relative reduction, p = 0.0001) and year 2 (30.6% relative reduction, p = 0.0001). Teriflunomide 7 mg was also associated with significant reduction in BVL vs placebo over the 2-year study. The significant effects of teriflunomide 14 mg on BVL were observed in both patients with and without on-study disability worsening. Conclusions: The significant reduction of BVL vs placebo over 2 years achieved with teriflunomide is consistent with its effects on delaying disability worsening and suggests a neuroprotective potential. Classification of evidence: Class II evidence shows that teriflunomide treatment significantly reduces BVL over 2 years vs placebo. ClinicalTrials.gov identifier: NCT00134563.

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.

Striatal functional connectivity changes following specific balance training in elderly people: MRI results of a randomized controlled pilot study

BACKGROUND Practice-induced effects of specific balance training on brain structure and activity in elderly people are largely unknown. AIM In the present study, we investigated morphological and functional brain changes following slacking training (balancing over nylon ribbons) in a group of elderly people. METHODS Twenty-eight healthy volunteers were recruited and randomly assigned to the intervention (mean age: 62.3±5.4years) or control group (mean age: 61.8±5.3years). The intervention group completed six-weeks of slackline training. Brain morphological changes were investigated using voxel-based morphometry and functional connectivity changes were computed via independent component analysis and seed-based analyses. All analyses were applied to the whole sample and to a subgroup of participants who improved in slackline performance. RESULTS The repeated measures analysis of variance showed a significant interaction effect between groups and sessions. Specifically, the Tukey post-hoc analysis revealed a significantly improved slackline standing performance after training for the left leg stance time (pre: 4.5±3.6s vs. 26.0±30.0s, p<0.038) as well as for tandem stance time (pre: 1.4±0.6s vs. post: 4.5±4.0s, p=0.003) in the intervention group. No significant changes in balance performance were observed in the control group. The MRI analysis did not reveal morphological or functional connectivity differences before or after the training between the intervention and control groups (whole sample). However, subsequent analysis in subjects with improved slackline performance showed a decrease of connectivity between the striatum and other brain areas during the training period. CONCLUSION These preliminary results suggest that improved balance performance with slackline training goes along with an increased efficiency of the striatal network.

Spinal cord volume loss: A marker of disease progression in multiple sclerosis

Objective Cross-sectional studies have shown that spinal cord volume (SCV) loss is related to disease severity in multiple sclerosis (MS). However, long-term data are lacking. Our aim was to evaluate SCV loss as a biomarker of disease progression in comparison to other MRI measurements in a large cohort of patients with relapse-onset MS with 6-year follow-up. Methods The upper cervical SCV, the total brain volume, and the brain T2 lesion volume were measured annually in 231 patients with MS (180 relapsing-remitting [RRMS] and 51 secondary progressive [SPMS]) over 6 years on 3-dimensional, T1-weighted, magnetization-prepared rapid-acquisition gradient echo images. Expanded Disability Status Scale (EDSS) score and relapses were recorded at every follow-up. Results Patients with SPMS had lower baseline SCV (p < 0.01) but no accelerated SCV loss compared to those with RRMS. Clinical relapses were found to predict SCV loss over time (p < 0.05) in RRMS. Furthermore, SCV loss, but not total brain volume and T2 lesion volume, was a strong predictor of EDSS score worsening over time (p < 0.05). The mean annual rate of SCV loss was the strongest MRI predictor for the mean annual EDSS score change of both RRMS and SPMS separately, while correlating stronger in SPMS. Every 1% increase of the annual SCV loss rate was associated with an extra 28% risk increase of disease progression in the following year in both groups. Conclusion SCV loss over time relates to the number of clinical relapses in RRMS, but overall does not differ between RRMS and SPMS. SCV proved to be a strong predictor of physical disability and disease progression, indicating that SCV may be a suitable marker for monitoring disease activity and severity.

Clinical Correlations of Brain Lesion Location in Multiple Sclerosis: Voxel-Based Analysis of a Large Clinical Trial Dataset

There is a limited correlation between white matter (WM) lesion load as determined by magnetic resonance imaging and disability in multiple sclerosis (MS). The reasons for this so-called clinico-radiological paradox are diverse and may, at least partly, relate to the fact that not just the overall lesion burden, but also the exact anatomical location of lesions predict the severity and type of disability. We aimed at studying the relationship between lesion distribution and disability using a voxel-based lesion probability mapping approach in a very large dataset of MS patients. T2-weighted lesion masks of 2348 relapsing-remitting MS patients were spatially normalized to standard stereotaxic space by non-linear registration. Relations between supratentorial WM lesion locations and disability measures were assessed using a non-parametric ANCOVA (Expanded Disability Status Scale [EDSS]; Multiple Sclerosis Functional Composite, and subscores; Modified Fatigue Impact Scale) or multinomial ordinal logistic regression (EDSS functional subscores). Data from 1907 (81%) patients were included in the analysis because of successful registration. The lesion mapping showed similar areas to be associated with the different disability scales: periventricular regions in temporal, frontal, and limbic lobes were predictive, mainly affecting the posterior thalamic radiation, the anterior, posterior, and superior parts of the corona radiata. In summary, significant associations between lesion location and clinical scores were found in periventricular areas. Such lesion clusters appear to be associated with impairment of different physical and cognitive abilities, probably because they affect commissural and long projection fibers, which are relevant WM pathways supporting many different brain functions.

Preferential spinal cord volume loss in primary progressive multiple sclerosis

Background: Little is known on longer term changes of spinal cord volume (SCV) in primary progressive multiple sclerosis (PPMS). Objective: Longitudinal evaluation of SCV loss in PPMS and its correlation to clinical outcomes, compared to relapse-onset multiple sclerosis (MS) subtypes. Methods: A total of 60 MS age-, sex- and disease duration-matched patients (12 PPMS, each 24 relapsing-remitting (RRMS) and secondary progressive MS (SPMS)) were analysed annually over 6 years of follow-up. The upper cervical SCV was measured on 3D T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) images using a semi-automatic software (CORDIAL), along with the total brain volume (TBV), brain T2 lesion volume (T2LV) and Expanded Disability Status Scale (EDSS). Results: PPMS showed faster SCV loss over time than RRMS (p < 0.01) and by trend (p = 0.066) compared with SPMS. In contrast to relapse-onset MS, in PPMS SCV loss progressed independent of TBV and T2LV changes. Moreover, in PPMS, SCV was the only magnetic resonance imaging (MRI) measurement associated with EDSS increase over time (p < 0.01), as opposed to RRMS and SPMS. Conclusion: SCV loss is a strong predictor of clinical outcomes in PPMS and has shown to be faster and independent of brain MRI metrics compared to relapse-onset MS.

Design and construction of an innovative brain phantom prototype for MRI

The purpose of this project was to construct a physical brain phantom for MRI, mimicking structure and T1 relaxation properties of white matter (WM) and gray matter (GM).

MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry

Neuroanatomical pattern classification using support vector machines (SVMs) has shown promising results in classifying Multiple Sclerosis (MS) patients based on individual structural magnetic resonance images (MRI). To determine whether pattern classification using SVMs facilitates predicting conversion to clinically definite multiple sclerosis (CDMS) from clinically isolated syndrome (CIS). We used baseline MRI data from 364 patients with CIS, randomised to interferon beta-1b or placebo. Non-linear SVMs and 10-fold cross-validation were applied to predict converters/non-converters (175/189) at two years follow-up based on clinical and demographic data, lesion-specific quantitative geometric features and grey-matter-to-whole-brain volume ratios. We applied linear SVM analysis and leave-one-out cross-validation to subgroups of converters (n = 25) and non-converters (n = 44) based on cortical grey matter segmentations. Highest prediction accuracies of 70.4% (p = 8e-5) were reached with a combination of lesion-specific geometric (image-based) and demographic/clinical features. Cortical grey matter was informative for the placebo group (acc.: 64.6%, p = 0.002) but not for the interferon group. Classification based on demographic/clinical covariates only resulted in an accuracy of 56% (p = 0.05). Overall, lesion geometry was more informative in the interferon group, EDSS and sex were more important for the placebo cohort. Alongside standard demographic and clinical measures, both lesion geometry and grey matter based information can aid prediction of conversion to CDMS.