Evidence map›Paper›PMID 34758381›Full record

ArticleNeuroImage2021

Association between brain structural network efficiency at term-equivalent age and early development of cerebral palsy in very preterm infants.

Julia E Kline, Weihong Yuan, Karen Harpster, Mekibib Altaye, Nehal A Parikh

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03345069 (Early Prediction of Cognitive and Motor Deficits Using Advanced MRI in Very Preterm Infants), which is not on this map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
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.

NCT03345069 active not recruitingnot on this map

Early Prediction of Cognitive and Motor Deficits Using Advanced MRI in Very Preterm Infants

TypeobservationalSponsorChildren's Hospital Medical Center, CincinnatiRan2016 to 2027Enrolled393ConditionsPremature Infant
3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 12 citations in OpenAlex.

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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

5 authors at 2 institutions in 1 country.

Julia E KlinePerinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229, United States.
Weihong YuanPediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Division of Occupational Therapy and Physical Therapy, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
Karen HarpsterCincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Rehabilitation, Exercise, and Nutrition Sciences, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH, United States.
Mekibib AltayeDepartment of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
Nehal A ParikhPerinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229, United States; Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States. Electronic address: Nehal.Parikh@cchmc.org.
University of Cincinnati Medical Center · USCincinnati Children's Hospital Medical Center · US

Funding

A New Model to Identify Preterm Neonates at High-Risk for Cognitive Impairments and School ReadinessR01NS094200 · NINDS · CINCINNATI CHILDRENS HOSP MED CTR · PI NEHAL A. PARIKH · 2016 to 2026
$6.7M
Early Prediction of Cerebral Palsy in Premature Infants using Advanced MRI BiomarkersR01NS096037 · NINDS · CINCINNATI CHILDRENS HOSP MED CTR · PI PARIKH, NEHAL A. · 2016 to 2020
$2.2M
MRI and Deep Learning for Early Prediction of Neurodevelopmental Deficits in Very Preterm InfantsR01EB029944 · NIBIB · CINCINNATI CHILDRENS HOSP MED CTR · PI HE, LILI · 2020 to 2023
$2.1M
Early Prediction of Cognitive Deficits in Very Preterm Infants using Machine Learning and Brain ConnectomeR21HD094085 · NICHD · CINCINNATI CHILDRENS HOSP MED CTR · PI HE, LILI · 2018 to 2019
$437k
NIBIB NIH HHS R01 EB029944NICHD NIH HHS R21 HD094085NINDS NIH HHS R01 NS094200NINDS NIH HHS R01 NS096037
6 · The paper itself

Abstract

Very preterm infants (born at less than 32 weeks gestational age) are at high risk for serious motor impairments, including cerebral palsy (CP). The brain network changes that antecede the early development of CP in infants are not well characterized, and a better understanding may suggest new strategies for risk-stratification at term, which could lead to earlier access to therapies. Graph theoretical methods applied to diffusion MRI-derived brain connectomes may help quantify the organization and information transfer capacity of the preterm brain with greater nuance than overt structural or regional microstructural changes. Our aim was to shed light on the pathophysiology of early CP development, before the occurrence of early intervention therapies and other environmental confounders, to help identify the best early biomarkers of CP risk in VPT infants. In a cohort of 395 very preterm infants, we extracted cortical morphometrics and brain volumes from structural MRI and also applied graph theoretical methods to diffusion MRI connectomes, both acquired at term-equivalent age. Metrics from graph network analysis, especially global efficiency, strength values of the major sensorimotor tracts, and local efficiency of the motor nodes and novel non-motor regions were strongly inversely related to early CP diagnosis. These measures remained significantly associated with CP after correction for common risk factors of motor development, suggesting that metrics of brain network efficiency at term may be sensitive biomarkers for early CP detection. We demonstrate for the first time that in VPT infants, early CP diagnosis is anteceded by decreased brain network segregation in numerous nodes, including motor regions commonly-associated with CP and also novel regions that may partially explain the high rate of cognitive impairments concomitant with CP diagnosis. These advanced MRI biomarkers may help identify the highest risk infants by term-equivalent age, facilitating earlier interventions that are informed by early pathophysiological changes.

Indexed as

Infant, Extremely PrematureBrain MappingCerebral PalsyConnectomeDiffusion Tensor ImagingFemaleGestational AgeHumansInfant, NewbornMagnetic Resonance ImagingMaleNeonatal ScreeningNeural PathwaysRisk FactorsBiomarkers Abbreviations: very preterm (VPT)bronchopulmonary dysplasia (BPD), fractional anisotropy (FA)Cerebral palsycerebral palsy (CP)developing human connectome project (dHCP)Diffusion MRIGeneral Movements Assessment (GMA)gestational age (GA)global efficiency (Eg(lob))Hammersmith Infant Neurological Examination (HINE)Infantlocal efficiency (E(loc)), clustering coefficient (CC)Magnetic resonance imagingPrematureretinopathy of prematurity (ROP)White matter

Identifiers

PMID34758381
PMCPMC9264481
OpenAlexW3213754740

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.