Toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder
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Abstract
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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Non-linear components of networks tend to predict fetal age more accurately than conventional linear models.
“Notably, non-linear components of networks tend to predict fetal age more accurately than conventional linear models [66].”
From Brain structural and functional outcomes in the offspring of women experiencing psychological distress during pregnancy (2024) · cites this paper as [66]
The Results section on cross-center age prediction reports that nonlinear VAE representations outperformed linear representations across fetal–neonatal datasets. But the Results section on gestational and postmenstrual age prediction says that, when fetuses were evaluated separately, a linear cortical-parcel representation predicted fetal age more accurately. The claim omits this important distinction. (LLM, conf=0.88)
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- Authors
- Kim, J.-H., De Asis-Cruz, J., Krishnamurthy, D., & Limperopoulos, C.
- Affiliations
- Children's National (US)
- Developing Brain Institute, Children's National Hospital
- Corresponding author
- Limperopoulos, Catherine
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2023-05-15
- Volume
- 12
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC10241511
- OpenAlex
- W4376613048
- Cited by
- 10
- References
- 70
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 2
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Heart, Lung, and Blood Institute (R01 HL116585-01)
- Canadian Institutes of Health Research (MOP-81116)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- fetal brain development, neonatal brain development, brain connectome, variational autoencoder, latent variables, resting-state fMRI, age prediction, functional brain networks, sensory networks, default mode network, preterm infants
- MeSH terms
- Adult, Brain, Female, Fetus, Humans, Infant, Newborn, Magnetic Resonance Imaging, Pregnancy, Neuroimaging, Connectome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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