SIENA-XL for improving the assessment of gray and white matter volume changes on brain MRI

Subventricular Zone Microstructure in Pediatric-Onset Multiple Sclerosis

OBJECTIVE

The aim of this study was to explore the microstructural dynamics of the subventricular zone (SVZ) with aging and their associations with clinical disability and brain structural damage in pediatric-onset multiple sclerosis (MS) patients.

METHODS

One-hundred and forty-one pediatric-onset MS patients (67 pediatric and 74 adults with pediatric-onset) and 233 healthy controls (HC) underwent neurological and 3.0 T MRI assessment. Fractional anisotropy (FA) and mean diffusivity (MD) were extracted from the SVZ and the thalamus (as control region).

RESULTS

In HC, SVZ FA was higher until age 40 then declined, whereas MD was lower until age 35 before rising (false discovery rate p value [pFDR] ≤ 0.008). Thalamic FA was higher until age 30 and then declined, whereas MD was higher until age 50 (pFDR ≤ 0.007). Pediatric MS patients showed significantly higher SVZ FA than pediatric HC (pFDR < 0.001), while adult patients showed no differences compared to adult HC (pFDR ≤ 0.724). Adult patients had lower thalamic FA and higher MD (pFDR < 0.001). Adults had lower SVZ FA and MD, but higher thalamic MD compared to pediatric patients (pFDR < 0.001). In pediatric MS, higher SVZ FA and MD were associated with higher white matter (WM) lesion volume (LV) and choroid plexus volume and lower brain and thalamic volumes (pFDR ≤ 0.047). In adult patients, higher SVZ MD associated with higher WM LV, lower brain volumes, and lower z-SDMT (pFDR≤0.019). Thalamic microstructural abnormalities were associated with more severe disability and brain damage in both groups (pFDR ≤ 0.018).

INTERPRETATION

Our findings suggest that microstructural changes in the SVZ occur early in pediatric MS and are associated with brain structural damage but not with clinical impairment. ANN NEUROL 2025;97:979-992.

Cervical spinal cord gray matter damage predicts disability worsening in multiple sclerosis: a longitudinal study

OBJECTIVE

Cervical spinal cord (cSC) gray matter (GM) damage is associated with current disability in multiple sclerosis (MS), but its prognostic value remains unexplored. We aimed to investigate whether cSC GM damage may predict disability worsening in MS.

MATERIALS AND METHODS

Seventy-nine MS patients and 49 healthy controls (HC) underwent 3 T brain and cSC MRI at baseline and two neurological evaluations after median follow-up of 1.3 years. Total and GM cSC lesions were identified on axial T2-weighted sequences, whereas global and GM cSC cross-sectional areas (CSAs) at C3-C4 level were quantified on phase-sensitive inversion recovery sequences. Brain lesional and volumetric measures were also assessed. At follow-up, disability worsening was defined as deterioration on ≥ 1/3 components of the Expanded Disability Status Scale (EDSS)-plus score (EDSS worsening or ≥ 20% change in timed 25-foot walk [T25FWT] or 9-hole peg test [9-HPT]).

RESULTS

At follow-up, 40/79 (50.6%) patients showed EDSS-plus worsening, with 13/79 (16.4%) worsening at EDSS score, 13/79 (16.4%) at 9-HPT, and 29/79 (36.7%) at T25FWT. Progressive phenotype (odds ratio [OR] = 8.65) predicted EDSS worsening (p = 0.001, C-index = 0.79). Progressive phenotype (OR = 5.56), lower cortical volume (OR = 0.41), and higher cSC GM T2-hyperintense lesion volume (OR = 2.28) (p ≤ 0.035, C-index = 0.88) predicted 9-HPT worsening. Longer disease duration (OR = 1.64), progressive phenotype (OR = 4.74), and lower cSC GM CSA (OR = 0.51) predicted T25FWT worsening (p ≤ 0.050, C-index = 0.77). Male sex (OR = 6.12), older age (OR = 1.71), progressive phenotype (OR = 7.40), and lower cSC GM CSA (OR = 0.47) predicted EDSS-plus worsening (p ≤ 0.055, C-index = 0.83).

CONCLUSIONS

cSC GM damage emerged as a relevant MRI predictor of disability worsening in MS, highlighting its prognostic relevance.

Evaluation of cervical spinal cord atrophy using a modified SIENA approach

Spinal cord (SC) atrophy obtained from structural magnetic resonance imaging has gained relevance as an indicator of neurodegeneration in various neurological disorders. The common method to assess SC atrophy is by comparing numerical differences of the cross-sectional spinal cord area (CSA) between time points. However, this indirect approach leads to considerable variability in the obtained results. Studies showed that this limitation can be overcome by using a registration-based technique. The present study introduces the Structural Image Evaluation using Normalization of Atrophy on the Spinal Cord (SIENA-SC), which is an adapted version of the original SIENA method, designed to directly calculate the percentage of SC volume change over time from clinical brain MRI acquired with an extended field of view to cover the superior part of the cervical SC. In this work, we compared SIENA-SC with the Generalized Boundary Shift Integral (GBSI) and the CSA change. On a scan-rescan dataset, SIENA-SC was shown to have the lowest measurement error than the other two methods. When comparing a group of 190 Healthy Controls with a group of 65 Multiple Sclerosis patients, SIENA-SC provided significantly higher yearly rates of atrophy in patients than in controls and a lower sample size when measured for treatment effect sizes of 50%, 30% and 10%. Our findings indicate that SIENA-SC is a robust, reproducible, and sensitive approach for assessing longitudinal changes in spinal cord volume, providing neuroscientists with an accessible and automated tool able to reduce the need for manual intervention and minimize variability in measurements.

Magnetic Resonance Imaging Evidence Supporting the Efficacy of Cladribine Tablets in the Treatment of Relapsing-Remitting Multiple Sclerosis

Numerous therapies are currently available to modify the disease course of multiple sclerosis (MS). Magnetic resonance imaging (MRI) plays a pivotal role in assessing treatment response by providing insights into disease activity and clinical progression. Integrating MRI findings with clinical and laboratory data enables a comprehensive assessment of the disease course. Among available MS treatments, cladribine is emerging as a promising option due to its role as a selective immune reconstitution therapy, with a notable impact on B cells and a lesser effect on T cells. This work emphasizes the assessment of MRI's contribution to MS treatment, particularly focusing on the influence of cladribine tablets on imaging outcomes, encompassing data from pivotal and real-world studies. The evidence highlights that cladribine, compared with placebo, not only exhibits a reduction in inflammatory imaging markers, such as T1-Gd+, T2 and combined unique active (CUA) lesions, but also mitigates the effect on brain volume loss, particularly within grey matter. Importantly, cladribine reveals early action by reducing CUA lesions within the first months of treatment, regardless of a patient's initial conditions. The selective mechanism of action, and sustained efficacy beyond year 2, combined with its early onset of action, collectively position cladribine tablets as a pivotal component in the therapeutic paradigm for MS. Overall, MRI, along with clinical measures, has played a substantial role in showcasing the effectiveness of cladribine in addressing both the inflammatory and neurodegenerative aspects of MS.

Methods for Brain Atrophy MR Quantification in Multiple Sclerosis: Application to the Multicenter INNI Dataset

BACKGROUND

Current therapeutic strategies in multiple sclerosis (MS) target neurodegeneration. However, the integration of atrophy measures into the clinical scenario is still an unmet need.

PURPOSE

To compare methods for whole-brain and gray matter (GM) atrophy measurements using the Italian Neuroimaging Network Initiative (INNI) dataset.

STUDY TYPE

Retrospective (data available from INNI).

POPULATION

A total of 466 patients with relapsing-remitting MS (mean age = 37.3 ± 10 years, 323 women) and 279 healthy controls (HC; mean age = 38.2 ± 13 years, 164 women).

FIELD STRENGTH/SEQUENCE

A 3.0-T, T1-weighted (spin echo and gradient echo without gadolinium injection) and T2-weighted spin echo scans at baseline and after 1 year (170 MS, 48 HC).

ASSESSMENT

Structural Image Evaluation using Normalization of Atrophy (SIENA-X/XL; version 5.0.9), Statistical Parametric Mapping (SPM-v12); and Jim-v8 (Xinapse Systems, Colchester, UK) software were applied to all subjects.

STATISTICAL TESTS

In MS and HC, we evaluated the intraclass correlation coefficient (ICC) among FSL-SIENA(XL), SPM-v12, and Jim-v8 for cross-sectional whole-brain and GM tissue volumes and their longitudinal changes, the effect size according to the Cohen's d at baseline and the sample size requirement for whole-brain and GM atrophy progression at different power levels (lowest = 0.7, 0.05 alpha level). False discovery rate (Benjamini-Hochberg procedure) correction was applied. A P value <0.05 was considered statistically significant.

RESULTS

SPM-v12 and Jim-v8 showed significant agreement for cross-sectional whole-brain (ICC = 0.93 for HC and ICC = 0.84 for MS) and GM volumes (ICC = 0.66 for HC and ICC = 0.90) and longitudinal assessment of GM atrophy (ICC = 0.35 for HC and ICC = 0.59 for MS), while no significant agreement was found in the comparisons between whole-brain and GM volumes for SIENA-X/XL and both SPM-v12 (P = 0.19 and P = 0.29, respectively) and Jim-v8 (P = 0.21 and P = 0.32, respectively). SPM-v12 and Jim-v8 showed the highest effect size for cross-sectional GM atrophy (Cohen's d = -0.63 and -0.61). Jim-v8 and SIENA(XL) showed the smallest sample size requirements for whole-brain (58) and GM atrophy (152), at 0.7 power level.

DATA CONCLUSION

The findings obtained in this study should be considered when selecting the appropriate brain atrophy pipeline for MS studies.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 1.

Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice

MRI plays a central role in the diagnosis of multiple sclerosis (MS) and in the monitoring of disease course and treatment response. Advanced MRI techniques have shed light on MS biology and facilitated the search for neuroimaging markers that may be applicable in clinical practice. MRI has led to improvements in the accuracy of MS diagnosis and a deeper understanding of disease progression. This has also resulted in a plethora of potential MRI markers, the importance and validity of which remain to be proven. Here, five recent emerging perspectives arising from the use of MRI in MS, from pathophysiology to clinical application, will be discussed. These are the feasibility of noninvasive MRI-based approaches to measure glymphatic function and its impairment; T1-weighted to T2-weighted intensity ratio to quantify myelin content; classification of MS phenotypes based on their MRI features rather than on their clinical features; clinical relevance of gray matter atrophy versus white matter atrophy; and time-varying versus static resting-state functional connectivity in evaluating brain functional organization. These topics are critically discussed, which may guide future applications in the field.

The spatio-temporal relationship between concurrent lesion and brain atrophy changes in early multiple sclerosis: A post-hoc analysis of the REFLEXION study

BACKGROUND

White matter (WM) lesions and brain atrophy are present early in multiple sclerosis (MS). However, their spatio-temporal relationship remains unclear.

METHODS

Yearly magnetic resonance images were analysed in 387 patients with a first clinical demyelinating event (FCDE) from the 5-year REFLEXION study. Patients received early (from baseline; N = 258; ET) or delayed treatment (from month-24; N = 129; DT) with subcutaneous interferon beta-1a. FSL-SIENA/VIENA were used to provide yearly percentage volume change of brain (PBVC) and ventricles (PVVC). Yearly total lesion volume change (TLVC) was determined by a semi-automated method. Using linear mixed models and voxel-wise analyses, we firstly investigated the overall relationship between TLVC and PBVC and between TLVC and PVVC in the same follow-up period. Analyses were then separately performed for: the untreated period of DT patients (first two years), the first year of treatment (year 1 for ET and year 3 for DT), and a period where patients had received at least 1 year of treatment (stable treatment; ET: years 2, 3, 4, and 5; DT: years 4 and 5).

RESULTS

Whole brain: across the whole study period, lower TLVC was related to faster atrophy (PBVC: B = 0.046, SE = 0.013, p < 0.001; PVVC: B = -0.466, SE = 0.118, p < 0.001). Within the untreated period of DT patients, lower TLVC was related to faster atrophy (PBVC: B = 0.072, SE = 0.029, p = 0.013; PVVC: B = -0.917, SE = 0.306, p = 0.003). A similar relationship was found within the first year of treatment of ET patients (PBVC: B = 0.081, SE = 0.027, p = 0.003; PVVC: B = -1.08, SE = 0.284, p < 0.001), consistent with resolving oedema and pseudo-atrophy. Voxel-wise: overall, higher TLVC was related to faster ventricular enlargement. Lower TLVC was related to faster widespread atrophy in year 1 in both ET (first year of treatment) and DT (untreated) patients. In the second untreated year of DT patients and within the stable treatment period of ET patients (year 4), faster periventricular and occipital lobe atrophy was associated with higher TLVC.

CONCLUSIONS

WM lesion changes and atrophy occurred simultaneously in early MS. Spatio-temporal correspondence of these two processes involved mostly the periventricular area. Within the first year of the study, in both treatment groups, faster atrophy was linked to lower lesion volume changes, consistent with higher shrinking and disappearing lesion activity. This might reflect the pseudo-atrophy phenomenon that is probably related to the therapy driven (only in ET patients, as they received treatment from baseline) and "natural" (both ET and DT patients entered the study after a FCDE) resolution of oedema. In an untreated period and later on during stable treatment, (real) atrophy was related to higher lesion volume changes, consistent with increased new and enlarging lesion activity.

Reduction in grey matter atrophy in patients with relapsing multiple sclerosis following treatment with cladribine tablets

BACKGROUND AND PURPOSE

Measures of atrophy in the whole brain can be used to reliably assess treatment effect in clinical trials of patients with multiple sclerosis (MS). Trials assessing the effect of treatment on grey matter (GM) and white matter (WM) atrophy are very informative, but hindered by technical limitations. This study aimed to measure GM and WM volume changes, using a robust longitudinal method, in patients with relapsing MS randomized to cladribine tablets 3.5 mg/kg or placebo in the CLARITY study.

METHODS

We analysed T1-weighted magnetic resonance sequences using SIENA-XL, from 0 to 6 months (cladribine, n = 267; placebo, n = 265) and 6 to 24 months (cladribine, n = 184; placebo, n = 186). Mean percentage GM and WM volume changes (PGMVC and PWMVC) were compared using a mixed-effect model.

RESULTS

More GM and WM volume loss was found in patients taking cladribine versus those taking placebo in the first 6 months of treatment (PGMVC: cladribine: -0.53 vs. placebo: -0.25 [p = 0.045]; PWMVC: cladribine: -0.49 vs. placebo: -0.34 [p = 0.137]), probably due to pseudoatrophy. However, over the period 6 to 24 months, GM volume loss was significantly lower in patients on cladribine than in those on placebo (PGMVC: cladribine: -0.90 vs. placebo: -1.27 [p = 0.026]). In this period, volume changes in WM were similar in the two treatment arms (p = 0.52).

CONCLUSIONS

After a short period of pseudoatrophy, treatment with cladribine 3.5 mg/kg significantly reduced GM atrophy in comparison with placebo. This supports the relevance of GM damage in MS and may have important implications for physical and cognitive disability progression.

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS.

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

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Sharing data from cooperative studies is essential to develop new biomarkers in MS.

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Differences in MRI acquisition, analysis, storage represent a substantial constraint.

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We review the state of the art and developments in the harmonization of MRI.

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We provide recommendations to harmonize large MRI datasets in the MS field.

There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.

Analysis of Clinical Traits Associated With Cardiovascular Health, Genomic Profiles, and Neuroimaging Markers of Brain Health in Adults Without Stroke or Dementia

IMPORTANCE

The American Heart Association (AHA) Life's Simple 7 (LS7) score captures 7 biological and lifestyle factors associated with promoting cardiovascular health.

OBJECTIVES

To test whether healthier LS7 profiles are associated with significant brain health benefits in persons without stroke or dementia, and to evaluate whether genomic information can recapitulate the observed LS7.

DESIGN, SETTING, AND PARTICIPANTS

This genetic association study was a nested neuroimaging study within the UK Biobank, a large population-based cohort study in the United Kingdom. Between March 2006 and October 2010, the UK Biobank enrolled 502 480 community-dwelling persons aged 40 to 69 years at recruitment. This study focused on a subset of 35 914 participants without stroke or dementia who completed research brain magnetic resonance imaging (MRI) and had available genome-wide data. All analyses were conducted between March 2021 and March 2022.

EXPOSURES

The LS7 (blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, smoking, exercise, diet, and body mass index) profiles were ascertained clinically and genomically. Independent genetic variants known to influence each of the traits included in the LS7 were assessed. The total LS7 score ranges from 0 (worst) to 14 (best) and was categorized as poor (≤4), average (>4 to 9) and optimal (>9).

MAIN OUTCOMES AND MEASURES

The outcomes of interest were 2 neuroimaging markers of brain health: white matter hyperintensity (WMH) volume and brain volume (BV).

RESULTS

The final analytical sample included 35 914 participants (mean [SD] age 64.1 [7.6] years; 18 830 [52.4%] women). For WMH, compared with persons with poor observed LS7 profiles, those with average profiles had 16% (β = -0.18; SE, 0.03; P < .001) lower mean volume and those with optimal profiles had 39% (β = -0.39; SE, 0.03; P < .001) lower mean volume. Similar results were obtained using the genomic LS7 for WMH (average LS7 profile: β = -0.06; SE, 0.014; P < .001; optimal LS7 profile: β = -0.08; SE, 0.018; P < .001). For BV, compared with persons with poor observed LS7 profiles, those with average LS7 profiles had 0.55% (β = 0.09; SE, 0.02; P < .001) higher volume, and those with optimal LS7 profiles had 1.9% (β = 0.14; SE, 0.02; P < .001) higher volume. The genomic LS7 profiles were not associated with BV.

CONCLUSIONS AND RELEVANCE

These findings suggest that healthier LS7 profiles were associated with better profiles of 2 neuroimaging markers of brain health in persons without stroke or dementia, indicating that cardiovascular health optimization was associated with improved brain health in asymptomatic persons. Genomic information appropriately recapitulated 1 of these associations, confirming the feasibility of modeling the LS7 genomically and pointing to an important role of genetic predisposition in the observed association among cardiometabolic and lifestyle factors and brain health.

Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors

Brain atrophy quantification plays a fundamental role in neuroinformatics since it permits studying brain development and neurological disorders. However, the lack of a ground truth prevents testing the accuracy of longitudinal atrophy quantification methods. We propose a deep learning framework to generate longitudinal datasets by deforming T1-w brain magnetic resonance imaging scans as requested through segmentation maps. Our proposal incorporates a cascaded multi-path U-Net optimised with a multi-objective loss which allows its paths to generate different brain regions accurately. We provided our model with baseline scans and real follow-up segmentation maps from two longitudinal datasets, ADNI and OASIS, and observed that our framework could produce synthetic follow-up scans that matched the real ones (Total scans= 584; Median absolute error: 0.03 ± 0.02; Structural similarity index: 0.98 ± 0.02; Dice similarity coefficient: 0.95 ± 0.02; Percentage of brain volume change: 0.24 ± 0.16; Jacobian integration: 1.13 ± 0.05). Compared to two relevant works generating brain lesions using U-Nets and conditional generative adversarial networks (CGAN), our proposal outperformed them significantly in most cases (p < 0.01), except in the delineation of brain edges where the CGAN took the lead (Jacobian integration: Ours - 1.13 ± 0.05 vs CGAN - 1.00 ± 0.02; p < 0.01). We examined whether changes induced with our framework were detected by FAST, SPM, SIENA, SIENAX, and the Jacobian integration method. We observed that induced and detected changes were highly correlated (Adj. R² > 0.86). Our preliminary results on harmonised datasets showed the potential of our framework to be applied to various data collections without further adjustment.

Mild gray matter atrophy in patients with long-standing multiple sclerosis and favorable clinical course

The mechanisms responsible for the favorable clinical course in multiple sclerosis (MS) remain unclear. In this longitudinal study, we assessed whether magnetic resonance imaging (MRI)-based changes in focal and diffuse brain damage are associated with a long-term favorable MS diseases course. We found that global brain and gray matter (GM) atrophy changes were milder in MS patients with long-standing disease (⩾30 years from onset) and favorable (no/minimal disability) clinical course than in sex-age-matched disable MS patients, independently of lesions accumulation. Data showed that different trajectories of volume changes, as reflected by mild GM atrophy, may characterize patients with long-term favorable evolution.

Dynamics of pseudo-atrophy in RRMS reveals predominant gray matter compartmentalization

Objective

To assess the dynamics of “pseudo‐atrophy,” the accelerated brain volume loss observed after initiation of anti‐inflammatory therapies, in patients with multiple sclerosis (MS).

Methods

Monthly magnetic resonance imaging (MRI) data of patients from the IMPROVE clinical study (NCT00441103) comparing relapsing‐remitting MS patients treated with interferon beta‐1a (IFNβ‐1a) for 40 weeks versus those receiving placebo (16 weeks) and then IFNβ‐1a (24 weeks) were used to assess percentage of gray (PGMVC) and white matter (PWMVC) volume changes. Comparisons of PGMVC and PWMVC slopes were performed with a mixed effect linear model. In the IFNβ‐1a‐treated arm, a quadratic term was included in the model to evaluate the plateauing effect over 40 weeks.

Results

Up to week 16, PGMVC was −0.14% per month in the placebo and −0.27% per month in treated patients (P < 0.001). Over the same period, the decrease in PWMVC was −0.067% per month in the placebo and −0.116% per month in treated patients (P = 0.27). Similar changes were found in the group originally randomized to placebo when starting IFNβ‐1a treatment (week 16–40, reliability analysis). In the originally treated group, over 40 weeks, the decrease in PGMVC showed a significant (P < 0.001) quadratic component, indicating a plateauing at week 20.

Interpretation

Findings reported here add new insights into the complex mechanisms of pseudo‐atrophy and its relation to the compartmentalized inflammation occurring in the GM of MS patients. Ongoing and forthcoming clinical trials including MRI‐derived GM volume loss as an outcome measure need to account for potentially significant GM volume changes as part of the initial treatment effect.

Age-dependent cut-offs for pathological deep gray matter and thalamic volume loss using Jacobian integration

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Longitudinal MRI data of 189 healthy controls and 127 MS patients were analyzed.

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Deep gray matter (deep GMVL) thalamic volume loss (ThalaVL) was assessed.

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Magnitude of measurement error was estimated by means of 162 MRI scan-rescans.

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Age-dependent cut-offs for pathological deep GMVL and ThalaVL are provided.

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Result help interpreting deep GMVL and ThalaVL measurements in individual MS patients.

Introduction

Several recent studies indicate that deep gray matter or thalamic volume loss (VL) might be promising surrogate markers of disease activity in multiple sclerosis (MS) patients. To allow applying these markers to individual MS patients in clinical routine, age-dependent cut-offs distinguishing physiological from pathological VL and an estimation of the measurement error, which provides the confidence of the result, are to be defined.

Methods

Longitudinal MRI scans of the following cohorts were analyzed in this study: 189 healthy controls (HC) (mean age 54 years, 22% female), 98 MS patients from Zurich university hospital (mean age 34 years, 62% female), 33 MS patients from Dresden university hospital (mean age 38 years, 60% female), and publicly available reliability data sets consisting of 162 short-term MRI scan-rescan pairs with scan intervals of days or few weeks. Percentage annualized whole brain volume loss (BVL), gray matter (GM) volume loss (GMVL), deep gray matter volume loss (deep GMVL), and thalamic volume loss (ThalaVL) were computed deploying the Jacobian integration (JI) method. BVL was additionally computed using Siena, an established method used in many Phase III drug trials. A linear mixed effect model was used to estimate the measurement error as the standard deviation (SD) of model residuals of all 162 scan-rescan pairs For estimation of age-dependent cut-offs, a quadratic regression function between age and the corresponding annualized VL values of the HC was computed. The 5th percentile was defined as the threshold for pathological VL per year since 95% of HC subjects exhibit a less pronounced VL for a given age. For the MS patients BVL, GMVL, deep GMVL, and ThalaVL were mutually compared and a paired t-test was used to test whether there are systematic differences in VL between these brain regions.

Results

Siena and JI showed a high agreement for BVL measures, with a median absolute difference of 0.1% and a correlation coefficient of r = 0.78. Siena and GMVL showed a similar standard deviation (SD) of the scan-rescan error of 0.28% and 0.29%, respectively. For deep GMVL, ThalaVL the SD of the scan-rescan error was slightly higher (0.43% and 0.5%, respectively). Among the HC the thalamus showed the highest mean VL (−0.16%, −0.39%, and −0.59% at ages 35, 55, and 75, respectively). Corresponding cut-offs for a pathological VL/year were −0.68%, −0.91%, and −1.11%. The MS cohorts did not differ in BVL and GMVL. However, both MS cohorts showed a significantly (p = 0.05) stronger deep GMVL than BVL per year.

Conclusion

It might be methodologically feasible to assess deep GMVL using JI in individual MS patients. However, age and the measurement error need to be taken into account. Furthermore, deep GMVL may be used as a complementary marker to BVL since MS patients exhibit a significantly stronger deep GMVL than BVL.

MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice

Early evaluation of treatment response and prediction of disease evolution are key issues in the management of people with multiple sclerosis (MS). In the past 20 years, MRI has become the most useful paraclinical tool in both situations and is used clinically to assess the inflammatory component of the disease, particularly the presence and evolution of focal lesions - the pathological hallmark of MS. However, diffuse neurodegenerative processes that are at least partly independent of inflammatory mechanisms can develop early in people with MS and are closely related to disability. The effects of these neurodegenerative processes at a macroscopic level can be quantified by estimation of brain and spinal cord atrophy with MRI. MRI measurements of atrophy in MS have also been proposed as a complementary approach to lesion assessment to facilitate the prediction of clinical outcomes and to assess treatment responses. In this Consensus statement, the Magnetic Resonance Imaging in MS (MAGNIMS) study group critically review the application of brain and spinal cord atrophy in clinical practice in the management of MS, considering the role of atrophy measures in prognosis and treatment monitoring and the barriers to clinical use of these measures. On the basis of this review, the group makes consensus statements and recommendations for future research.

Assessing Biological and Methodological Aspects of Brain Volume Loss in Multiple Sclerosis

Importance

Before using brain volume loss (BVL) as a marker of therapeutic response in multiple sclerosis (MS), certain biological and methodological issues must be clarified.

Objectives

To assess the dynamics of BVL as MS progresses and to evaluate the repeatability and exchangeability of BVL estimates with Jacobian Integration (JI) and Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL) (specifically, the Structural Image Evaluation, Using Normalisation, of Atrophy-Cross-Sectional [SIENA-X] tool or FMRIB's Integrated Registration and Segmentation Tool [FIRST]).

Design, Setting, and Participants

A cohort of patients who had either clinically isolated syndrome or MS was enrolled from February 2011 through October 2015. All underwent a series of annual magnetic resonance imaging (MRI) scans. Images from 2 cohorts of healthy volunteers were used to evaluate short-term repeatability of the MRI measurements (n = 34) and annual BVL (n = 20). Data analysis occurred from January to May 2017.

Main Outcomes and Measures

The goodness of fit of different models to the dynamics of BVL throughout the MS disease course was assessed. The short-term test-retest error was used as a measure of JI and FSL repeatability. The correlations (R2) of the changes quantified in the brain using JI and FSL, together with the accuracy of the annual BVL cutoffs to discriminate patients with MS from healthy volunteers, were used to measure compatibility of imaging methods.

Results

A total of 140 patients with clinically isolated syndrome or MS were enrolled, including 95 women (67.9%); the group had a median (interquartile range) age of 40.7 (33.6-48.1) years. Patients underwent 4 MRI scans with a median (interquartile range) interscan period of 364 (351-379) days. The 34 healthy volunteers (of whom 18 [53%] were women; median [IQR] age, 33.5 [26.2-42.5] years) and 20 healthy volunteers (of whom 10 [50%] were women; median [IQR] age, 33.0 [28.7-39.2] years) underwent 2 MRI scans within a median (IQR) of 24.5 (0.0-74.5) days and 384.5 (366.3-407.8) days for the short-term and long-term MRI follow-up, respectively. The BVL rates were higher in the first 5 years after MS onset (R2 = 0.65 for whole-brain volume change and R2 = 0.52 for gray matter volume change) with a direct association with steroids (β = 0.280; P = .02) and an inverse association with age at MS onset, particularly in the first 5 years (β = 0.015; P = .047). The reproducibility of FSL (SIENA) and JI was similar for whole-brain volume loss, while JI gave more precise, less biased estimates for specific brain regions than FSL (SIENA-X and FIRST). The correlation between whole-brain volume loss using JI and FSL was high (R2 = 0.92), but the same correlations were poor for specific brain regions. The area under curve of the whole-brain volume change to discriminate between patients with MS and healthy volunteers was similar, although the thresholds and accuracy index were distinct for JI and FSL.

Conclusions and Relevance

The proposed BVL threshold of less than 0.4% per year as a marker of therapeutic efficiency should be reconsidered because of the different dynamics of BVL as MS progresses and because of the limited reproducibility and variability of estimates using different imaging methods.

Evaluation of the intra- and inter-method agreement of brain MRI segmentation software packages: A comparison between SPM12 and FreeSurfer v6.0

PURPOSE

The lack of inter-method agreement can produce inconsistent results in neuroimaging studies. We evaluated the intra-method repeatability and the inter-method reproducibility of two widely-used automatic segmentation methods for brain MRI: the FreeSurfer (FS) and the Statistical Parametric Mapping (SPM) software packages.

METHODS

We segmented the gray matter (GM), the white matter (WM) and subcortical structures in test-retest MRI data of healthy volunteers from Kirby-21 and OASIS datasets. We used Pearson's correlation (r), Bland-Altman plot and Dice index to study intra-method repeatability and inter-method reproducibility. In order to test whether different processing methods affect the results of a neuroimaging-based group study, we carried out a statistical comparison between male and female volume measures.

RESULTS

A high correlation was found between test-retest volume measures for both SPM (r in the 0.98-0.99 range) and FS (r in the 0.95-0.99 range). A non-null bias between test-retest FS volumes was detected for GM and WM in the OASIS dataset. The inter-method reproducibility analysis measured volume correlation values in the 0.72-0.98 range and the overlap between the segmented structures assessed by the Dice index was in the 0.76-0.83 range. SPM systematically provided significantly greater GM volumes and lower WM and subcortical volumes with respect to FS. In the male vs. female brain volume comparisons, inconsistencies arose for the OASIS dataset, where the gender-related differences appear subtler with respect to the Kirby dataset.

CONCLUSIONS

The inter-method reproducibility should be evaluated before interpreting the results of neuroimaging studies.

Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies

The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.

Lifespan normative data on rates of brain volume changes

We provide here normative values of yearly percentage brain volume change (PBVC/y) as obtained with Structural Imaging Evaluation, using Normalization, of Atrophy, a widely used open-source software, developing a PBVC/y calculator for assessing the deviation from the expected PBVC/y in patients with neurological disorders. We assessed multicenter (34 centers, 11 acquisition protocols) magnetic resonance imaging data of 720 healthy participants covering the whole adult lifespan (16-90 years). Data of 421 participants with a follow-up > 6 months were used to obtain the normative values for PBVC/y and data of 392 participants with a follow-up <1 month were selected to assess the intrasubject variability of the brain volume measurement. A mixed model evaluated PBVC/y dependence on age, sex, and magnetic resonance imaging parameters (scan vendor and magnetic field strength). PBVC/y was associated with age (p < 0.001), with 60- to 70-year-old participants showing twice more volume decrease than participants aged 30-40 years. PBVC/y was also associated with magnetic field strength, with higher decreases when measured by 1.5T than 3T scanners (p < 0.001). The variability of PBVC/y normative percentiles was narrower as the interscan interval was longer (e.g., 80th normative percentile was 50% smaller for participants with 2-year than with 1-year follow-up). The use of these normative data, eased by the freely available calculator, might help in better discriminating pathological from physiological conditions in the clinical setting.

Future Brain and Spinal Cord Volumetric Imaging in the Clinic for Monitoring Treatment Response in MS

PURPOSE OF REVIEW

Volumetric analysis of brain imaging has emerged as a standard approach used in clinical research, e.g., in the field of multiple sclerosis (MS), but its application in individual disease course monitoring is still hampered by biological and technical limitations. This review summarizes novel developments in volumetric imaging on the road towards clinical application to eventually monitor treatment response in patients with MS.

RECENT FINDINGS

In addition to the assessment of whole-brain volume changes, recent work was focused on the volumetry of specific compartments and substructures of the central nervous system (CNS) in MS. This included volumetric imaging of the deep brain structures and of the spinal cord white and gray matter. Volume changes of the latter indeed independently correlate with clinical outcome measures especially in progressive MS. Ultrahigh field MRI and quantitative MRI added to this trend by providing a better visualization of small compartments on highly resolving MR images as well as microstructural information. New developments in volumetric imaging have the potential to improve sensitivity as well as specificity in detecting and hence monitoring disease-related CNS volume changes in MS.