Evidence map›Paper›PMID 42448743›Full record

ArticleScientific reports2026

Radiofrequency electrical conductivity reveals a distinct dimension of early human brain development.

Arnaud Boutillon, Anthony Price, Roy Kurtzbard, Oliver Gale-Grant, Emil Galanides, Andrew Lawrence, Jeffrey Hand, Dafnis Batalle, Jonathan O'Muircheartaigh, Grainne McAlonan and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Arnaud BoutillonImaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. arnaud.boutillon@kcl.ac.uk.
Anthony PriceImaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Roy KurtzbardImaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Oliver Gale-GrantDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
Emil GalanidesDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
Andrew LawrenceDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
Jeffrey HandImaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Dafnis BatalleDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
Jonathan O'MuircheartaighDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
Grainne McAlonanDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
Joseph V HajnalImaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
A David EdwardsEarly Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Tomoki ArichiEarly Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Maria DeprezBiomedical Computing Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Shaihan J MalikImaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Funding

Medical Research Council MR/X502923/1MRC Centre for Neurodevelopmental Disorders [MR/N026063/1MRC senior fellowship MR/YOO9665/1Wellcome/EPSRC Centre for Medical Engineering WT203148/Z/16/ZWellcome Trust
6 · The paper itself

Abstract

Quantitative MRI has characterised early human brain development primarily through measures of water mobility, leaving other biophysical properties of maturing tissue unexplored. Here we show that radiofrequency electrical conductivity, reflecting ionic composition, membrane density and extracellular geometry in addition to water content, can be extracted retrospectively and at scale from the phase of conventional MRI acquisitions, revealing a distinct dimension of early brain tissue maturation and state. Applying electrical property tomography to 888 neonatal MRI sessions (784 subjects, 26-45 weeks post-menstrual age) alongside infant and childhood data, we find that whole-brain conductivity declines steeply during the perinatal period and remains independently associated with age after adjustment for mean diffusivity and ventricular volume, confirming that it represents a distinct developmental signal that is not reducible to tissue water content alone. Preterm birth was associated with elevated conductivity at term-equivalent age, particularly in deep grey matter. In neonates with hypoxic-ischaemic encephalopathy (n = 25), conductivity was elevated while mean diffusivity was reduced within the same tissue compartments, a dissociation indicating that these measures capture mechanistically distinct aspects of the tissue response to injury. These findings establish radiofrequency electrical properties as a new biophysical window on perinatal brain maturation and its perturbation by injury, extending quantitative MRI beyond measures of water mobility.

Indexed as

Developmental neuroimagingElectrical conductivity mappingQuantitative MRI

Identifiers

PMID42448743
PMCPMC13661241

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