Article in Neurology(R) neuroimmunology & neuroinflammation, 2025. 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.
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
43 authors.
Xinjie ChenDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0002-3683-6489
Po-Jui LuDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0001-6031-6865
Mario Ocampo-PinedaDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0003-2239-7355
Alessandro CagolDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0002-7703-0553
Sabine Anna SchaedelinDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0002-1150-0962
Esther RuberteDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0002-0195-4602
Matthias WeigelDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0002-4910-1414
Federico SpagnoloDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0001-9606-0400
Pascal BenkertClinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Switzerland.ORCID 0000-0001-6525-8174
Johanna Maria LiebDivision of Diagnostic and Interventional Neuroradiology, Clinic for Radiology and Nuclear Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0002-9407-6473
David LeppertDepartment of Neurology, University Hospital Basel, Switzerland.ORCID 0000-0001-6172-801X
Özgür YaldizliDepartment of Neurology, University Hospital Basel, Switzerland.ORCID 0000-0002-0800-4845
Johanna OechteringDepartment of Neurology, University Hospital Basel, Switzerland.ORCID 0000-0001-5359-7961
Marcus D'SouzaDepartment of Neurology, University Hospital Basel, Switzerland.ORCID 0000-0001-5175-9541
Bettina Fischer-BarnicolDepartment of Neurology, University Hospital Basel, Switzerland.ORCID 0009-0000-9369-7238
Tobias J DerfussDepartment of Neurology, University Hospital Basel, Switzerland.ORCID 0000-0001-8656-4250
Clara EkerdtDonders 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.ORCID 0000-0002-0983-7929
Kwok-Shing ChanAthinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA.ORCID 0000-0001-8427-169X
Marcel ZwiersDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.ORCID 0000-0001-5483-2935
Andrew ChanDepartment of Neurology, Inselspital, Bern University Hospital and University of Bern, Switzerland.ORCID 0000-0003-3434-7283
Robert HoepnerDepartment of Neurology, Inselspital, Bern University Hospital and University of Bern, Switzerland.ORCID 0000-0002-0115-7021
Franca WagnerDepartment of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland.ORCID 0000-0001-7502-4814
Caroline PotDepartment of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Switzerland.ORCID 0000-0002-1146-3129
Renaud A Du PasquierDepartment of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Switzerland.
Sebastian FinkenerDepartment of Neurology, Cantonal Hospital Aarau, Switzerland.ORCID 0000-0001-6449-0756
Michael DiepersDepartment of Neuroradiology, Cantonal Hospital Aarau, Switzerland.ORCID 0000-0002-1363-5289
Claire BridelDepartment of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, Switzerland.ORCID 0000-0001-7267-7676
Patrice H LaliveDepartment of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, Switzerland.ORCID 0000-0003-0607-5778
Marjolaine UginetDepartment of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, Switzerland.ORCID 0000-0001-5997-7500
Patrick RothDepartment of Neurology, University Hospital of Zurich, University of Zurich, Switzerland.ORCID 0000-0003-3897-214X
Jochen VehoffDepartment of Neurology, Cantonal Hospital St. Gallen, Switzerland.ORCID 0000-0001-9531-8262
Stefanie MuellerDepartment of Neurology, University Teaching and Research Hospital, St. Gallen, Switzerland; and.ORCID 0009-0007-0914-9214
Olaf Chan-Hi KimClinic for Radiology, Cantonal Hospital St. Gallen, Switzerland.
Ludwig KapposDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0003-4175-5509
Lester Melie-GarciaDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0001-5602-8916
José P MarquesDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.ORCID 0000-0001-8157-8864
Cristina GranzieraDepartment of Biomedical Engineering, Translational Imaging in Neurology (ThINk) Basel, Faculty of Medicine, University Hospital Basel and University of Basel, Switzerland.ORCID 0000-0002-4917-8761
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
BACKGROUND AND
objectivesIn multiple sclerosis (MS), neurodegeneration results from the interplay between disease-specific pathology and normal aging. Conventional MRI captures morphologic changes in neurodegeneration, while quantitative MRI (qMRI) provides biophysical measures of microstructural alterations. Combining these modalities may reveal how aging and pathology interact and contribute to disability progression in people with MS.
methodsWe analyzed cross-sectional and longitudinal morphometry data from 1,353 patients with MS and 3,462 healthy controls (HCs). In addition, cross-sectional qMRI data, available for 378 HCs and 169 patients with MS, were analyzed separately. Morphometric measures and quantitative metrics were used to estimate brain-predicted age differences (brain-PADs) with machine learning. We assessed the added value of quantitative metrics over a model based exclusively on morphometric measures in brain age prediction. We also investigated the associations of brain-PADs derived from conventional and qMRI-based predictive models with clinical disability, serum inflammatory biomarkers of neuroaxonal and astrocytic injury, and lesion burden.
resultsModels combining morphometry and qMRI data achieved the best performance (mean absolute error: 5.73), outperforming those based on qMRI (6.62) or morphometry alone (8.00). Cross-sectional and longitudinal morphometry-based brain-PAD correlated with clinical disability, serum neurofilament light chain, and serum glial fibrillary acidic protein levels (all DISCUSSION: Integrating qMRI improves brain age predictions. Brain-PAD serves as an imaging biomarker to quantify MS-associated aging and track disability and neuroinflammation progression.
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.
Unraveling Microstructural and Macrostructural Brain Age Dynamics in Multiple Sclerosis. · full record | OpenQuestion