ArticleNeuroImage2019
White matter connectomes at birth accurately predict cognitive abilities at age 2.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review.Pediatric research · 2023Pooled it
- A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants.Scientific reports · 2020Trial
- Hierarchical maturation of structural brain connectomes from birth to childhood.Nature communications · 2026Article
- Machine learning models for neurocognitive outcome prediction in preterm born infants.Pediatric research · 2025Article
- Application of machine learning in early childhood development research: a scoping review.BMJ open · 2025Article
- The overgrowth of structure-function coupling in premature brain during infancy.Developmental cognitive neuroscience · 2025Article
- Structural connectome gradients and their relationship to IQ in childhood.Frontiers in human neuroscience · 2025Article
- Machine-learning based prediction of future outcome using multimodal MRI during early childhood.Seminars in fetal & neonatal medicine · 2024Review
- Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning.Human brain mapping · 2024Article
- Article
- Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected.Scientific reports · 2024Article
- Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected.bioRxiv : the preprint server for biology · 2024Article
- Network controllability of structural connectomes in the neonatal brain.Nature communications · 2023Article
- MRI-based brain age prediction model for children under 3 years old using deep residual network.Brain structure & function · 2023Article
- The challenges and prospects of brain-based prediction of behaviour.Nature human behaviour · 2023Review
- Predicting neurodevelopmental outcomes from neonatal cortical microstructure: A conceptual replication study.Neuroimage. Reports · 2023Article
- ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans.IEEE transactions on medical imaging · 2023Article
- Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Cognitive ability is an important predictor of mental health outcomes that is influenced by neurodevelopment. Evidence suggests that the foundational wiring of the human brain is in place by birth, and that the white matter (WM) connectome supports developing brain function. It is unknown, however, how the WM connectome at birth supports emergent cognition. In this study, a deep learning model was trained using cross-validation to classify full-term infants (n = 75) as scoring above or below the median at age 2 using WM connectomes generated from diffusion weighted magnetic resonance images at birth. Results from this model were used to predict individual cognitive scores. We additionally identified WM connections important for classification. The model was also evaluated in a separate set of preterm infants (n = 37) scanned at term-age equivalent. Findings revealed that WM connectomes at birth predicted 2-year cognitive score group with high accuracy in both full-term (89.5%) and preterm (83.8%) infants. Scores predicted by the model were strongly correlated with actual scores (r = 0.98 for full-term and r = 0.96 for preterm). Connections within the frontal lobe, and between the frontal lobe and other brain areas were found to be important for classification. This work suggests that WM connectomes at birth can accurately predict a child's 2-year cognitive group and individual score in full-term and preterm infants. The WM connectome at birth appears to be a useful neuroimaging biomarker of subsequent cognitive development that deserves further study.
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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.