Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.

Autor: Scheinost D; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Child Study Center, Yale School of Medicine, New Haven, Connecticut; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut. Electronic address: dustin.scheinost@yale.edu., Pollatou A; Department of Psychiatry, Columbia University Irving Medical Center, New York, New York., Dufford AJ; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut., Jiang R; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut., Farruggia MC; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut., Rosenblatt M; Department of Biomedical Engineering, Yale University, New Haven, Connecticut., Peterson H; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut., Rodriguez RX; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut., Dadashkarimi J; Department of Computer Science, Yale University, New Haven, Connecticut., Liang Q; Department of Biomedical Engineering, Yale University, New Haven, Connecticut., Dai W; Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut., Foster ML; Department of Biomedical Engineering, Yale University, New Haven, Connecticut., Camp CC; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut., Tejavibulya L; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut., Adkinson BD; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut., Sun H; Department of Biomedical Engineering, Yale University, New Haven, Connecticut., Ye J; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut., Cheng Q; Departments of Neuroscience and Psychology, Smith College, Northampton, Massachusetts., Spann MN; Department of Psychiatry, Columbia University Irving Medical Center, New York, New York., Rolison M; Child Study Center, Yale School of Medicine, New Haven, Connecticut., Noble S; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut., Westwater ML; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut.
Jazyk: angličtina
Zdroj: Biological psychiatry [Biol Psychiatry] 2023 May 15; Vol. 93 (10), pp. 893-904. Date of Electronic Publication: 2022 Oct 29.
DOI: 10.1016/j.biopsych.2022.10.014
Abstrakt: 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.
(Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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