ArticleHuman brain mapping2017
Can we predict subject-specific dynamic cortical thickness maps during infancy from birth?
Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Early Postnatal Infection With Human Cytomegalovirus Has Long-Term Consequences on Brain Structure of Former Preterm Born Children.Brain and behavior · 2025Article
- Flexible Individualized Developmental Prediction of Infant Cortical Surface Maps via Intensive Triplet Autoencoder.IEEE transactions on medical imaging · 2025Article
- Longitudinally Consistent Individualized Prediction of Infant Cortical Morphological Development.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Multi-task Joint Prediction of Infant Cortical Morphological and Cognitive Development.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet Network.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Spherical Deformable U-Net: Application to Cortical Surface Parcellation and Development Prediction.IEEE transactions on medical imaging · 2021Article
- Infant and Child MRI: A Review of Scanning Procedures.Frontiers in neuroscience · 2021Review
- Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning.Frontiers in neuroscience · 2021Article
- Neuroanatomical Differences Among Sexual Offenders: A Targeted Review with Limitations and Implications for Future Directions.Violence and gender · 2020Review
- Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks.IEEE transactions on medical imaging · 2019Article
- Construction of 4D infant cortical surface atlases with sharp folding patterns via spherical patch-based group-wise sparse representation.Human brain mapping · 2019Article
- Spherical U-Net on Cortical Surfaces: Methods and Applications.Information processing in medical imaging : proceedings of the ... conference · 2019Article
- Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning.IEEE transactions on medical imaging · 2019Article
- Computational neuroanatomy of baby brains: A review.NeuroImage · 2019Review
- A computational method for longitudinal mapping of orientation-specific expansion of cortical surface in infants.Medical image analysis · 2018Article
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Authors and funding
7 authors.
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Abstract
Understanding the early dynamic development of the human cerebral cortex remains a challenging problem. Cortical thickness, as one of the most important morphological attributes of the cerebral cortex, is a sensitive indicator for both normal neurodevelopment and neuropsychiatric disorders, but its early postnatal development remains largely unexplored. In this study, we investigate a key question in neurodevelopmental science: can we predict the future dynamic development of cortical thickness map in an individual infant based on its available MRI data at birth? If this is possible, we might be able to better model and understand the early brain development and also early detect abnormal brain development during infancy. To this end, we develop a novel learning-based method, called Dynamically-Assembled Regression Forest (DARF), to predict the development of the cortical thickness map during the first postnatal year, based on neonatal MRI features. We applied our method to 15 healthy infants and predicted their cortical thickness maps at 3, 6, 9, and 12 months of age, with respectively mean absolute errors of 0.209 mm, 0.332 mm, 0.340 mm, and 0.321 mm. Moreover, we found that the prediction precision is region-specific, with high precision in the unimodal cortex and relatively low precision in the high-order association cortex, which may be associated with their differential developmental patterns. Additional experiments also suggest that using more early time points for prediction can further significantly improve the prediction accuracy. Hum Brain Mapp 38:2865-2874, 2017. © 2017 Wiley Periodicals, Inc.
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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 OpenQuestion graph.