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Toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder

Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal–neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging d...

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Autores principales: Kim, Jung-Hoon, De Asis-Cruz, Josepheen, Krishnamurthy, Dhineshvikram, Limperopoulos, Catherine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241511/
https://www.ncbi.nlm.nih.gov/pubmed/37184067
http://dx.doi.org/10.7554/eLife.80878
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author Kim, Jung-Hoon
De Asis-Cruz, Josepheen
Krishnamurthy, Dhineshvikram
Limperopoulos, Catherine
author_facet Kim, Jung-Hoon
De Asis-Cruz, Josepheen
Krishnamurthy, Dhineshvikram
Limperopoulos, Catherine
author_sort Kim, Jung-Hoon
collection PubMed
description Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal–neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the life span. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal–neonatal brain development, including nonlinear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a large sample (>500) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called variational autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting-state data in healthy adults. Here, we demonstrated that nonlinear brain features, that is, latent variables, derived with the VAE pretrained on rsfMRI of human adults, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction in the neonate cohort compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, nonlinear fetal–neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life.
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spelling pubmed-102415112023-06-06 Toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder Kim, Jung-Hoon De Asis-Cruz, Josepheen Krishnamurthy, Dhineshvikram Limperopoulos, Catherine eLife Neuroscience Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal–neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the life span. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal–neonatal brain development, including nonlinear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a large sample (>500) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called variational autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting-state data in healthy adults. Here, we demonstrated that nonlinear brain features, that is, latent variables, derived with the VAE pretrained on rsfMRI of human adults, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction in the neonate cohort compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, nonlinear fetal–neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life. eLife Sciences Publications, Ltd 2023-05-15 /pmc/articles/PMC10241511/ /pubmed/37184067 http://dx.doi.org/10.7554/eLife.80878 Text en © 2023, Kim et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Kim, Jung-Hoon
De Asis-Cruz, Josepheen
Krishnamurthy, Dhineshvikram
Limperopoulos, Catherine
Toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder
title Toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder
title_full Toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder
title_fullStr Toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder
title_full_unstemmed Toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder
title_short Toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder
title_sort toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241511/
https://www.ncbi.nlm.nih.gov/pubmed/37184067
http://dx.doi.org/10.7554/eLife.80878
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