SynthesisBiological psychiatry2023
Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.
Synthesis in Biological psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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Who cites it
11 citing papers in PubMed.
- Language network functional connectivity in infancy predicts developmental language trajectories.Developmental cognitive neuroscience · 2026Article
- Wired from the womb: a narrative review of fetal and neonatal connectomics via fMRI and emerging neurotechnologies.Pediatric research · 2026Review
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Aperiodic parameters of the fMRI power spectrum associate with preterm birth and neonatal age.Communications biology · 2026Article
- Utilizing functional neuroimaging to study early language development.Developmental cognitive neuroscience · 2025Review
- Theta activity as a marker of cognitive development in infancy: A longitudinal study across the first two years of life.Developmental cognitive neuroscience · 2025Article
- Child-Parent Interaction Quality Shows Opposite Relationships with Language Comprehension Skill and Autism Symptomatology.Journal of autism and developmental disorders · 2025Article
- Language network functional connectivity in infancy predicts developmental language trajectories.bioRxiv : the preprint server for biology · 2025Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Brain age prediction and deviations from normative trajectories in the neonatal connectome.Nature communications · 2024Article
- Brain-phenotype predictions can survive across diverse real-world data.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.
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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.