Evidence map›Paper›PMID 39489076›Full record

ArticleComputer methods and programs in biomedicine2024

DFC-Igloo: A dynamic functional connectome learning framework for identifying neurodevelopmental biomarkers in very preterm infants.

Junqi Wang, Hailong Li, Kim M Cecil, Mekibib Altaye, Nehal A Parikh, Lili He

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PubMed Full text at PMC Publisher
In one paragraph

Article in Computer methods and programs in biomedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
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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.

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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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

6 authors.

Junqi WangImaging research center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Hailong LiImaging research center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Kim M CecilImaging research center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Mekibib AltayeDepartment of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Nehal A ParikhNeurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Lili HeImaging research center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Department of Computer Science, Biomedical Engineering, Biomedical Informatics, University of Cincinnati, Cincinnati, OH, USA. Electronic address: Lili.He@cchmc.org.

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
NIBIB NIH HHS R01 EB029944NINDS NIH HHS R01 NS094200NINDS NIH HHS R01 NS096037
6 · The paper itself

Abstract

BACKGROUND AND

objectiveVery preterm infants are susceptible to neurodevelopmental impairments, necessitating early detection of prognostic biomarkers for timely intervention. The study aims to explore possible functional biomarkers for very preterm infants at born that relate to their future cognitive and motor development using resting-state fMRI. Prior studies are limited by the sample size and suffer from efficient functional connectome (FC) construction algorithms that can handle the noisy data contained in neonatal time series, leading to equivocal findings. Therefore, we first propose an enhanced functional connectome construction algorithm as a prerequisite step. We then apply the new FC construction algorithm to our large prospective very preterm cohort to explore multi-level neurodevelopmental biomarkers.

methodsThere exists an intrinsic relationship between the structural connectome (SC) and FC, with a notable coupling between the two. This observation implies a putative property of graph signal smoothness on the SC as well. Yet, this property has not been fully exploited for constructing intrinsic dFC. In this study, we proposed an advanced dynamic FC (dFC) learning model, dFC-Igloo, which leveraged SC information to iteratively refine dFC estimations by applying graph signal smoothness to both FC and SC. The model was evaluated on artificial small-world graphs and simulated graph signals.

resultsThe proposed model achieved the best and most robust recovery of the ground truth graph across different noise levels and simulated SC pairs from the simulation. The model was further applied to a cohort of very preterm infants from five Neonatal Intensive Care Units, where an enhanced dFC was obtained for each infant. Based on the improved dFC, we identified neurodevelopmental biomarkers for neonates across connectome-wide, regional, and subnetwork scales.

conclusionThe identified markers correlate with cognitive and motor developmental outcomes, offering insights into early brain development and potential neurodevelopmental challenges.

Indexed as

AlgorithmsBiomarkersConnectomeMagnetic Resonance ImagingBrainFemaleHumansInfant, Extremely PrematureInfant, NewbornInfant, PrematureMachine LearningMaleProspective StudiesBiomarkersBiomarker detectionDynamic graph learningFunctional connectomeVery preterm infants

Identifiers

PMID39489076
PMCPMC11563839

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Registered trials

None linked

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.