Evidence map›Paper›PMID 39320477›Full record

ArticleEuropean radiology2025

Quantitative MRI distinguishes different leukodystrophies and correlates with clinical measures.

Menno D Stellingwerff, Murtadha L Al-Saady, Kwok-Shing Chan, Adam Dvorak, José P Marques, Shannon Kolind, Daphne H Schoenmakers, Romy van Voorst, Stefan D Roosendaal, Frederik Barkhof and 4 more

Abstract read
In one paragraph

Article in European radiology, 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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Menno D StellingwerffAmsterdam Leukodystrophy Center, Department of Child Neurology, Emma Children's Hospital, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Cellular & Molecular Mechanisms, Vrije Universiteit, Amsterdam, The Netherlands.
Murtadha L Al-SaadyAmsterdam Leukodystrophy Center, Department of Child Neurology, Emma Children's Hospital, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Cellular & Molecular Mechanisms, Vrije Universiteit, Amsterdam, The Netherlands.
Kwok-Shing ChanDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
Adam DvorakDepartment of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.
José P MarquesDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
Shannon KolindDepartment of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.
Daphne H SchoenmakersAmsterdam Leukodystrophy Center, Department of Child Neurology, Emma Children's Hospital, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Cellular & Molecular Mechanisms, Vrije Universiteit, Amsterdam, The Netherlands.
Romy van VoorstAmsterdam Leukodystrophy Center, Department of Child Neurology, Emma Children's Hospital, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Cellular & Molecular Mechanisms, Vrije Universiteit, Amsterdam, The Netherlands.
Stefan D RoosendaalDepartment of Radiology and Nuclear Medicine, Amsterdam University Medical Centers and Amsterdam Neuroscience, Amsterdam, The Netherlands.
Frederik BarkhofDepartment of Radiology and Nuclear Medicine, Amsterdam University Medical Centers and Amsterdam Neuroscience, Amsterdam, The Netherlands.
Nicole I WolfAmsterdam Leukodystrophy Center, Department of Child Neurology, Emma Children's Hospital, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Cellular & Molecular Mechanisms, Vrije Universiteit, Amsterdam, The Netherlands.
Johannes BerkhofDepartment of Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit, Amsterdam, The Netherlands.
Petra J W Pouwels *Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers and Amsterdam Neuroscience, Amsterdam, The Netherlands.
Marjo S van der Knaap *Amsterdam Leukodystrophy Center, Department of Child Neurology, Emma Children's Hospital, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Cellular & Molecular Mechanisms, Vrije Universiteit, Amsterdam, The Netherlands. ms.vanderknaap@amsterdamumc.nl.ORCID http://orcid.org/0000-0001-8912-0954

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe leukodystrophy "vanishing white matter" (VWM) and "metachromatic leukodystrophy" (MLD) affect the brain's white matter, but have very different underlying pathology. We aim to determine whether quantitative MRI reflects known neuropathological differences and correlates with clinical scores in these leukodystrophies.

methodsVWM and MLD patients and controls were prospectively included between 2020 and 2023. Clinical scores were recorded. MRI at 3 T included multi-compartment relaxometry diffusion-informed myelin water imaging (MCR-DIMWI) and multi-echo T2-relaxation imaging with compressed sensing (METRICS) to determine myelin water fractions (MWF). Multi-shell diffusion-weighted data were used for diffusion tensor imaging measures and neurite orientation dispersion and density imaging (NODDI) analysis, which estimates neurite density index, orientation dispersion index, and free water fraction. As quantitative MRI measures are age-dependent, ratios between actual and age-expected MRI measures were calculated. We performed the multilevel analysis with subsequent post-hoc and correlation tests to assess differences between groups and clinico-radiological correlations.

resultsSixteen control (age range: 2.3-61.3 years, 8 male), 37 VWM (2.4-56.5 years, 20 male), and 14 MLD (2.2-41.7 years, 6 male) subjects were included. Neurite density index and MWF were lower in patients than in controls (p < 0.001). Free water fraction was highest in VWM (p = 0.01), but similar to controls in MLD (p = 0.99). Changes in diffusion tensor imaging measures relative to controls were generally more pronounced in VWM than in MLD. In both patient groups, MCR-DIMWI MWF correlated strongest with clinical measures.

conclusionQuantitative MRI correlates to clinical measures and yields differential profiles in VWM and MLD, in line with differences in neuropathology. KEY POINTS: Question Can quantitative MRI reflect known neuropathological differences and correlate with clinical scores for these leukodystrophies? Finding Quantitative MRI measures, e.g., MWF, neurite density index, and free water fraction differ between leukodystrophies and controls, in correspondence to known histological differences. Clinical relevance MRI techniques producing quantitative, biologically-specific, measures regarding the health of myelin and axons deliver more comprehensive information regarding pathological changes in leukodystrophies than current approaches, and are thus viable tools for monitoring patients and providing clinical trial outcome measures.

Indexed as

Leukodystrophy, MetachromaticMagnetic Resonance ImagingAdolescentAdultBrainChildChild, PreschoolDiagnosis, DifferentialDiffusion Tensor ImagingFemaleHumansMaleMiddle AgedProspective StudiesWhite MatterYoung AdultBrainMagnetic resonance imagingMetachromatic leukodystrophyPediatrics and adultsVanishing white matter

Identifiers

PMID39320477
PMCPMC11914348

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