MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

Stefano, Nicola De; Battaglini, Marco; Pareto, Deborah; Cortese, Rosa; Zhang, Jian; Oesingmann, Niels; Prados, Ferrán; Rocca, Maria; Valsasina, Paola; Vrenken, Hugo; Wheeler-Kingshott, C; Filippi, Massimo; Barkhof, Frederik

doi:10.1016/j.nicl.2022.102972

Cited by 11 publications

(11 citation statements)

References 139 publications

(162 reference statements)

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“…Harmonization techniques are used to handle non-biological variance introduced by differences in MRI scanners and acquisition protocols [ 25 ]. To remove these unwanted effects, we applied the ComBat harmonization [ 53 , 54 ] in its R implementation (freely available at https://github.com/Jfortin1/ComBatHarmonization ) to all datasets.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, the heterogeneity of scanners and acquisition protocols can potentially affect the consistency of MRI-derived features and, therefore, undermine statistical testing and/or classification performances [ 20 , 21 ]. For this reason, harmonizing the MRI data is generally considered important [ 22 ]–[ 24 ] and specific recommendations for MS multicenter studies have been recently updated [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

et al. 2022

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The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

et al. 2022

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“…This serves as a reminder that all the three components of NEDA are highly subjective and there is no real consensus regarding the definitions of the different components of NEDA ( 11 , 27 ). Apart from between-rater variability ( 28 ), MRI findings may vary across centers due to differing MRI machines and protocols ( 29 ). Relapses are subjective, both for the pwMS and the clinician, and dependent on pwMS -clinician contact ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

Rebaseline no evidence of disease activity (NEDA-3) as a predictor of long-term disease course in a Norwegian multiple sclerosis population

et al. 2022

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IntroductionNo evidence of disease activity with three components (NEDA-3) is achieved if the person with MS (pwMS) has no new MRI lesions, no new relapses and no change in Expanded disability status scale (EDSS) over 1 year. Whether NEDA-3 is a good tool in measuring disease activity is up for discussion, but it is superior to the individual parameters separately and user-friendly. There is disagreement on whether NEDA-3 is a good predictor of long-term disability.MethodsThis is a retrospective cohort study using real-world data with limited selection bias from the complete MS population at two hospitals in the southeast of Norway. We included pwMS diagnosed between 2006 and 2017 who had enough information to determine time to failure of NEDA-3 after diagnosis.ResultsOf 536 pwMS, only 38% achieved NEDA 1 year after diagnosis. PwMS achieving NEDA were more likely to be started on a high efficacy drug as the initial drug, but there were no demographic differences. Mean time to NEDA failure was 3.3 (95% CI 2.9–3.7) years. Starting a high efficiacy therapy was associated with an increased risk of sustaining NEDA as compared to those receiving moderate efficacy therapy. PwMS who achieved NEDA at year one had a mean time to EDSS 6 of 33.8 (95% CI 30.9–36.8) years vs. 30.8 (95% CI 25.0–36.6) years in pwMS who did not achieve NEDA, p < 0.001. When rebaselining NEDA 1 year after diagnosis, 52.2% achieved NEDA in the 1st year after rebaseline, mean time to NEDA failure was 3.4 (95% CI 3.0–3.7) years and mean time to EDSS 6 was 44.5 (95% CI 40.4–48.5) years in pwMS achieving NEDA vs. 29.6 (95% CI 24.2–35.0) years in pwMS not achieving NEDA, p < 0.001. After rebaseline, pwMS with a high efficacy therapy as the initial drug had a mean time from diagnosis to NEDA fail of 4.8 years (95% CI 3.9–5.8) vs. 3.1 years (95% CI 2.7–3.5) in pwMS started on a moderate efficacy therapy, p < 0.001. In pwMS with NEDA failure at year one, 70% failed one, 28% failed two and 2% failed three components. New MRI lesions were the most common cause of NEDA failure (63%), followed by new relapses (50%) and EDSS change (25%).ConclusionNEDA-3 from rebaseline after 1 year, once treatment is stabilized, can predict the long-term disease course in MS. Starting a high efficacy DMT is associated with longer time to NEDA failure than moderate therapies. Finally, most pwMS only fail one component and new MRI lesions are the most likely cause of NEDA failure.

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“…Comparisons of global and local network descriptors between people with MS and HVs were performed with Student’s t tests, and the statistical significance was set at p < 0.05. Since the FA-weighted adjacency matrices could suffer from intersite variability because of the heterogeneity of both acquisition protocols, we harmonized the data using the ComBat model ( Fortin et al, 2017 ; De Stefano et al, 2022 ). All analyses were performed using Python software (version 3.8.8) and the SciPy package (version 1.8.0).…”

Section: Methodsmentioning

confidence: 99%

Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns

Casas-Roma

Martínez‐Heras

Solé-Ribalta

et al. 2022

Network Neuroscience

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In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or grey matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural and functional brain connectivity networks. The aim of this study was to combine the morphological, structural and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analysing multiple types of relational data from the same objects simultaneously using graph-mining techniques. The main contribution of this research is the design, development and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with grey matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with MS, and results show that several brain regions with a synchronised connectivity deterioration could be identified.

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MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

Cited by 11 publications

References 139 publications

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

Rebaseline no evidence of disease activity (NEDA-3) as a predictor of long-term disease course in a Norwegian multiple sclerosis population

Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns

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