Evidence map›Paper›PMID 41756306›Full record

ArticleImaging neuroscience (Cambridge, Mass.)

Cross-site quantitative MRI harmonization: The impact on age modeling in health and disease.

Xinjie Chen, Mario Ocampo-Pineda, Po-Jui Lu, Clara Ekerdt, Matthias Weigel, Michelle G Jansen, Alessandro Cagol, Kwok-Shing Chan, Sabine Schädelin, Marcel Zwiers and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Xinjie ChenTranslational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-3683-6489
Mario Ocampo-PinedaTranslational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.
Po-Jui LuTranslational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.
Clara EkerdtDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
Matthias WeigelTranslational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.
Michelle G JansenDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
Alessandro CagolTranslational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.
Kwok-Shing ChanAthinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.
Sabine SchädelinTranslational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.
Marcel ZwiersDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
Joukje M OostermanDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
David G NorrisDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
Johanna M M BayerDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
Andre F MarquandDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
Willeke M MenksDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
Jens KuhleDepartment of Neurology, University Hospital Basel, Basel, Switzerland.
Ludwig KapposDepartment of Neurology, University Hospital Basel, Basel, Switzerland.
Lester Melie-GarciaTranslational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.
Cristina GranzieraTranslational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.
José P MarquesDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.ORCID https://orcid.org/0000-0001-8157-8864

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cross-site quantitative MRI (qMRI) studies are hindered by site-related variability, particularly when integrating cohorts with varying acquisition protocols and biological variables, such as age. Such batch effects can obscure accurate biological signals, thus affecting normative age modeling and clinical interpretations of pathology. This study aimed to (i) compare the harmonization performance of multiple strategies across multi-site cohorts with matched or unmatched qMRI protocols; (ii) quantify the impact of harmonization on normative age modeling across cohorts with comparable or partially overlapping age ranges; and (iii) examine whether clinically relevant deviations are preserved. Quantitative MRI data from three healthy control (HC) cohorts (n = 530) and one cohort of people with multiple sclerosis (pwMS) (n = 98) were included. Batch effects in the raw data were assessed using intra-class correlation coefficients (ICC) and analysis of variance (ANOVA), before and after adjustment for age and sex. Data harmonization was performed separately using Empirical Bayes-based generalized additive models (GAM) and Hierarchical Bayesian Regression (HBR) with B-spline fitting. Regional age-related effects across cortical grey matter (cGM), superficial white matter (sWM), and white matter (WM) bundles were modeled using second-degree polynomial regression, with the turning point defined as the peak age when the fitted curve reaches its maximum or minimum, and further used to assess potential shifts in age-related models between raw and harmonized datasets. For clinical validation, group-level differences in qMRI metrics between pwMS and controls were evaluated using Cohen's d. Regional Z-scores derived from the HBR framework were then used to assess associations with clinical disability, as measured by the Expanded Disability Status Scale (EDSS). Both harmonization methods reduced site-related variance, achieving post-harmonization ICC (<0.001) and η

Indexed as

brain agingComBathierarchical Bayesian regressionmultiple sclerosisnormative modeling

Identifiers

PMID41756306
PMCPMC12934009

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.