Changes in mean magnetic resonance imaging (MRI)-derived measurements between patient groups are often used to determine outcomes in therapeutic trials and other longitudinal studies of multiple sclerosis (MS). However, in day-to-day clinical practice the changes within individual patients may also be of interest In this paper, we estimated the measurement error of an automated brain tissue quantification algorithm and determined the thresholds for statistically significant change of MRI-derived T2 lesion volume and brain atrophy in individual patients. Twenty patients with MS were scanned twice within 30 min. Brain tissue volumes were measured using the computer algorithm. Brain atrophy was estimated by calculation of brain parenchymal fraction. The threshold of change between repeated scans that represented statistically significant change beyond measurement error with 95% certainty was 0.65 mL for T2 lesion burden and 0.0056 for brain parenchymal fraction. Changes in lesion burden and brain atrophy below these thresholds can be safely (with 95% certainty) explained by measurement variability alone. These values provide clinical neurologists with a useful reference to interpret MRI-derived measures in individual patients.
Tristan Glatard, Gregory Kiar, Tristan Aumentado-Armstrong, Natacha Beck, Pierre Bellec, Rémi Bernard, Axel Bonnet, Shawn T Brown, Sorina Camarasu-Pop, Frédéric Cervenansky, Samir Das, Rafael Ferreira da Silva, Guillaume Flandin, Pascal Girard, Krzysztof J Gorgolewski, Charles RG Guttmann, Valérie Hayot-Sasson, Pierre-Olivier Quirion, Pierre Rioux, Marc-Étienne Rousseau, and Alan C Evans. 2018. “Boutiques: a flexible framework to integrate command-line applications in computing platforms.” Gigascience, 7, 5.Abstract
We present Boutiques, a system to automatically publish, integrate, and execute command-line applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core tools facilitates the construction, validation, import, execution, and publishing of applications. Boutiques is currently supported by several distinct virtual research platforms, and it has been used to describe dozens of applications in the neuroinformatics domain. We expect Boutiques to improve the quality of application integration in computational platforms, to reduce redundancy of effort, to contribute to computational reproducibility, and to foster Open Science.
BACKGROUND AND PURPOSE: Enlarged perivascular spaces (EPVSs) have been associated with relapses and brain atrophy in multiple sclerosis (MS). We investigated the association of EPVS with clinical and MRI features of disease worsening in a well-characterized cohort of relapsing-remitting MS patients prospectively followed for up to 10 years.
METHODS: Baseline EPVSs were scored on 1.5T MRI in 30 converters to moderate-severe disability, and 30 nonconverters matched for baseline characteristics.
RESULTS: EPVS scores were not significantly different between converters and nonconverters, nor associated with accrual of lesions or brain atrophy.
CONCLUSIONS: Our preliminary findings from a relatively small study sample argue against a potential use of EPVS as early indicator of risk for disease worsening in relapsing-remitting MS patients in a clinical setting. Although the small sample size and clinical 1.5T MRI may have limited our ability to detect a significant effect, we provided estimates of the association of EPVS with clinical and MRI indicators of disease worsening in a well-characterized cohort of MS patients.
BACKGROUND AND PURPOSE: 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.
METHODS: To address nonuniform distribution of noise/contrast (eg, posterior fossa in 3D-FLAIR) of 3T magnetic resonance imaging, the method employs dual sensitivity (different sensitivities for lesion detection in predefined regions). We tested this approach by assigning different sensitivities to supratentorial and infratentorial regions, and validated the segmentation for accuracy against manual delineation, and for precision in scan-rescans.
RESULTS: Intraclass correlation coefficients of .95, .91, and .86 were observed for WML and CSF segmentation accuracy and brain parenchymal fraction (BPF). Dual sensitivity significantly reduced infratentorial false-positive WMLs, affording increases in global sensitivity without decreasing specificity. Scan-rescan yielded coefficients of variation (COVs) of 8% and .4% for WMLs and BPF and COVs of .8%, 1%, and 2% for GM, WM, and CSF volumes. WML volume difference/precision was .49 ± .72 mL over a range of 0-24 mL. Correlation between BPF and age was r = .62 (P = .0004), and effect size for detecting brain atrophy was Cohen's d = 1.26 (standardized mean difference vs. healthy controls).
CONCLUSIONS: This pipeline produces probability maps for brain lesions and tissue classes, facilitating expert review/correction and may provide high throughput, efficient characterization of MS in large datasets.
Mobility impairment in older persons is associated with brain white matter hyperintensities (WMH), a common finding in magnetic resonance images and one established imaging biomarker of small vessel disease. The contribution of possible microstructural abnormalities within normal-appearing white matter (NAWM) to mobility, however, remains unclear. We used diffusion tensor imaging (DTI) measures, i.e. fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), to assess microstructural changes within supratentorial NAWM and WMH sub-compartments, and to investigate their association with changes in mobility performance, i.e. Tinetti assessment and the 2.5-meters walk time test. We analyzed baseline (N = 86, age ≥75 years) and 4-year (N = 41) follow-up data. Results from cross-sectional analysis on baseline data showed significant correlation between WMH volume and NAWM-FA (r = -0.33, p = 0.002), NAWM-AD (r = 0.32, p = 0.003) and NAWM-RD (r = 0.39, p = 0.0002). Our longitudinal analysis showed that after 4-years, FA and AD decreased and RD increased within NAWM. In regional tract-based analysis decrease in NAWM-FA and increase in NAWM-RD within the genu of the corpus callosum correlated with slower walk time independent of age, gender and WMH burden. In conclusion, global DTI indices of microstructural integrity indicate that significant changes occur in the supratentorial NAWM over four years. The observed changes likely reflect white matter deterioration resulting from aging as well as accrual of cerebrovascular injury associated with small vessel disease. The observed association between mobility scores and regional measures of NAWM microstructural integrity within the corpus callosum suggests that subtle changes within this structure may contribute to mobility impairment.
BACKGROUND: Reports on the relationships between white matter lesion load (WMLL) and fatigue and anxiety in multiple sclerosis (MS) are inconsistent.
OBJECTIVE: To investigate the association of total and tract-specific WMLL with fatigue and anxiety.
METHODS: Total and regional T2 WMLL was assessed for 19 tracts in 48 MS patients (30 females). ICBM-DTI-81 Atlas-based parcellation was combined with WMLL segmentation of T2-weighted magnetic resonance imaging (MRI). Fatigue, anxiety, and depression were assessed using Fatigue Impact Scale, State Trait Anxiety Inventory, and Beck Depression Inventory, respectively.
RESULTS: Fatigue, anxiety, and depression showed significant inter-correlation. We found no association between fatigue and total or regional WMLLs, whereas anxiety was associated with total and regional WMLLs in nine tracts. After adjusting for total WMLL, age, and depression, only the column and body of the fornix (CBF) remained significantly associated with anxiety. Post hoc analyses showed no CBF lesions on T1-weighted MRI and suggested, but could not confirm, that the septum pellucidum might play a role in the pathogenesis of anxiety.
CONCLUSION: Our results suggest that anxiety in MS patients may have a neuropathological substrate in the septo-fornical area, which requires further validation using larger sample size and ultra-high-field MRI in targeted prospective studies.
BACKGROUND: Subcortical small vessel disease, represented as white matter hyperintensity (WMH) on magnetic resonance images (MRI) is associated with functional decline in older people with hypertension. We evaluated the relationships of clinic and out-of-office blood pressures (BP) with WMH and functional status in older persons.
METHODS: Using cross-sectional data from 199 older study participants enrolled in the INFINITY trial, we analyzed the clinic, 24-hour ambulatory, and home BPs and their relationships with WMH burden and mobility and cognitive outcomes.
RESULTS: Volume of WMH was associated with clinic and 24-hour ambulatory systolic BP but not home systolic BP. The mobility measure, supine-to-sit time, had a significant association with 24-hour systolic BP and pulse pressure but not with diastolic BP or values obtained by home BP. Cognitive measures of processing speed (Trails Making Test Part A and the Stroop Word Test) were significantly associated with 24-hour systolic BP, but not clinic and home BPs.
CONCLUSION: These data demonstrate that ambulatory BP measurements in older people are more strongly associated with WMH and certain measures of functional status compared to home BP measurements. Hence, home BP may not be a useful substitute for ambulatory BP for assessing subcortical small vessel disease and its consequences. Further longitudinal analyses comparing clinic and various types of out-of-office BP measures with small vessel brain disease are needed. Clinicaltrials.gov identifier: NCT01650402.