Brain age prediction and deviations from normative trajectories in the neonatal connectome

Huili Sun (), Saloni Mehta, Milana Khaitova, Bin Cheng, Xuejun Hao, Marisa Spann and Dustin Scheinost
Additional contact information
Huili Sun: Yale University
Saloni Mehta: Yale School of Medicine
Milana Khaitova: Yale School of Medicine
Bin Cheng: Columbia University
Xuejun Hao: New York State Psychiatric Institute
Marisa Spann: New York State Psychiatric Institute
Dustin Scheinost: Yale University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain’s maturity relative to normative data. The difference between the individual’s predicted and chronological age—or brain age gap (BAG)—represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We use resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predict PMA for term and preterm infants. Predicted ages from each modality are correlated. At the network level, nearly all canonical brain networks—even putatively later developing ones—generate accurate PMA prediction. Additionally, BAGs are associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-54657-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54657-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-54657-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().