ArticleNeuroImage2019
Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.Biological psychiatry · 2023Pooled it
- Application of machine learning in early childhood development research: a scoping review.BMJ open · 2025Article
- Flexible Individualized Developmental Prediction of Infant Cortical Surface Maps via Intensive Triplet Autoencoder.IEEE transactions on medical imaging · 2025Article
- Acceleration of Simultaneous Multislice Magnetic Resonance Fingerprinting With Spatiotemporal Convolutional Neural Network.NMR in biomedicine · 2025Article
- Machine-learning based prediction of future outcome using multimodal MRI during early childhood.Seminars in fetal & neonatal medicine · 2024Review
- 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
- Edge-centric network control on the human brain structural network.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Inferring neurocognition using artificial intelligence on brain MRIs.Frontiers in neuroimaging · 2024Review
- Prediction of Infant Cognitive Development with Cortical Surface-Based Multimodal Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study.IEEE transactions on medical imaging · 2022Article
- Path Signature Neural Network of Cortical Features for Prediction of Infant Cognitive Scores.IEEE transactions on medical imaging · 2022Article
- Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis.IEEE transactions on bio-medical engineering · 2021Article
- Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury.Frontiers in pediatrics · 2021Review
- Infant Cognitive Scores Prediction with Multi-stream Attention-Based Temporal Path Signature Features.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer's Disease Prediction.IEEE transactions on medical imaging · 2020Article
- A Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms.IEEE transactions on bio-medical engineering · 2020Article
- Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning.IEEE transactions on medical imaging · 2019Article
- Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations.Frontiers in neuroscience · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brain development. To precisely chart the early brain developmental trajectories, longitudinal studies with data acquired over a long-enough period of infants' early life is essential. However, in practice, missing data from different time point(s) during the data gathering procedure is often inevitable. This leads to incomplete set of longitudinal data, which poses a major challenge for such studies. In this paper, prediction of multiple future cognitive scores with incomplete longitudinal imaging data is modeled into a multi-task machine learning framework. To efficiently learn this model, we account for selection of informative features (i.e., neuroimaging morphometric measurements for different time points), while preserving the structural information and the interrelation between these multiple cognitive scores. Several experiments are conducted on a carefully acquired in-house dataset, and the results affirm that we can predict the cognitive scores measured at the age of four years old, using the imaging data of earlier time points, as early as 24 months of age, with a reasonable performance (i.e., root mean square error of 0.18).
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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.