Evidence map›Paper›PMID 42400972›Full record

ArticleDevelopmental cognitive neuroscience2026

Neonatal brain-age models in full- and preterm infants.

Howard Chiu, Adam C Richie-Halford, Molly F Lazarus, Ariel Rokem, Rocio Velasco Poblaciones, Virginia A Marchman, Katherine E Travis, Melissa L Scala, Heidi M Feldman, Jason D Yeatman

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

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

5 · Who and what money

Authors and funding

10 authors.

Howard ChiuGraduate School of Education, Stanford University, Stanford, CA, USA. Electronic address: howardchiu@stanford.edu.
Adam C Richie-HalfordGraduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Molly F LazarusDivision of Developmental Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Burke-Cornell Medical Research Institute, Department of Pediatrics, Weill Medical College, Cornell University, New York, NY, USA.
Ariel RokemDepartment of Psychology, University of Washington, Seattle, WA, USA; eScience Institute, University of Washington, Seattle, WA, USA.
Rocio Velasco PoblacionesDivision of Developmental Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Virginia A MarchmanDivision of Developmental Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Psychology, Stanford University, Stanford, CA, USA.
Katherine E TravisDivision of Developmental Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Burke-Cornell Medical Research Institute, Department of Pediatrics, Weill Medical College, Cornell University, New York, NY, USA.
Melissa L ScalaDepartment of Pediatrics, Division of Neonatology, Stanford University, Stanford, CA, USA.
Heidi M FeldmanDivision of Developmental Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Jason D YeatmanGraduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Psychology, Stanford University, Stanford, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prematurity affects brain development and increases risk for neurodevelopmental impairments. Yet reliable biomarkers for at-risk infants remain limited. We developed brain-age prediction models using diffusion magnetic resonance imaging-derived white matter features from two datasets: (1) the developing Human Connectome Project (dHCP; 368 healthy infants) and (2) a clinical sample collected at the Lucile Packard Children's Hospital (LPCH; 162 high-risk preterm infants). The goals of this study were (1a) to construct a white matter neonatal brain-age model including full-term and preterm neonates from a large research data set, (1b) to evaluate the accuracy of a separate brain-age model using the same architecture but trained and evaluated via cross-validation on a restricted subset of very preterm infants (<32 weeks gestational age) from the same data set to improve comparability with our clinical cohort; (2) to determine if a similar model can predict brain-age based on clinical MRI scans from high-risk neonates born preterm; and (3) to evaluate whether this preterm white matter-based brain-age model provides information about the infant's health beyond conventional clinical and demographic measures. White matter features demonstrated strong predictive performance in the dHCP dataset (within 3.9 days) and the LPCH clinical dataset (within 6.6 days). However, brain-age metrics (i.e., brain-age gap) showed no significant associations with health complications measured by a composite score of common prematurity complications. While tractometry-derived brain-age models accurately characterize brain maturation in the neonatal brain, their sensitivity to clinical complications in preterm infants appears limited. Global white matter maturation measures derived from clinical grade data may be insufficiently sensitive to capture the cumulative burden of prematurity-related morbidities, suggesting need for multimodal or longitudinal biomarkers.

Indexed as

BrainInfant, PrematureWhite MatterConnectomeDiffusion Magnetic Resonance ImagingFemaleGestational AgeHumansInfantInfant, NewbornMaleNeurodevelopmentBrain-ageBrain developmentDiffusion imagingNeonatalPrematurityTractometryWhite matter

Identifiers

PMID42400972
PMCPMC13355775

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.