Separating group- and individual-level brain signatures in the newborn functional connectome: A deep learning approach

• Published in: NeuroImage (Orlando, Fla.) Vol. 299; p. 120806
• Main Authors: Kim, Jung-Hoon, De Asis-Cruz, Josepheen, Limperopoulos, Catherine
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2024; Elsevier Limited; Elsevier
• Subjects: Adult; Age; Age prediction; Babies; Brain - diagnostic imaging; Brain - growth & development; Brain - physiology; Brain mapping; Brain research; Connectome - methods; Datasets; Deep Learning; DNA fingerprinting; Female; Functional magnetic resonance imaging; Humans; Individual uniqueness; Infant, Newborn; Magnetic Resonance Imaging - methods; Male; Neonate; Neonates; Nerve Net - diagnostic imaging; Nerve Net - physiology; Neural networks; Neurodevelopment; Neuroimaging; Newborn babies; Older people; Phenotypes; Resting-state fMRI; Sensorimotor system; Subject identification; Variables; Variational autoencoder; Young Adult; Young adults; Resting-state fMRI; Variational autoencoder; Neonate; Age prediction; Subject identification; Individual uniqueness
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2024.120806

•The VAE shows significant subject identification accuracy with whole-brain FC profiles, surpassing the identification accuracy obtained with linear countermodels.•The VAE is able to reliably separate the neonatal FC profile into connections that track age and connections that reflect individual brain signatures.•Cortical functional network, derived by the VAE, varied in their representation of brain maturation and individual signatures; notably, certain CFNs that failed to capture neurodevelopmental traits, in fact, exhibited individual signatures. Recent studies indicate that differences in cognition among individuals may be partially attributed to unique brain wiring patterns. While functional connectivity (FC)-based fingerprinting has demonstrated high accuracy in identifying adults, early studies on neonates suggest that individualized FC signatures are absent. We posit that individual uniqueness is present in neonatal FC data and that conventional linear models fail to capture the rapid developmental trajectories characteristic of newborn brains. To explore this hypothesis, we employed a deep generative model, known as a variational autoencoder (VAE), leveraging two extensive public datasets: one comprising resting-state functional MRI (rs-fMRI) scans from 100 adults and the other from 464 neonates. VAE models trained on rs-fMRI from both adults and newborns produced superior age prediction performance (with r between predicted- and actual age ∼ 0.7) and individual identification accuracy (∼45 %) compared to models trained solely on adult or neonatal data. The VAE model also showed significantly higher individual identification accuracy than linear models (=10∼30 %). Importantly, the VAE differentiated connections reflecting age-related changes from those indicative of individual uniqueness, a distinction not possible with linear models. Moreover, we derived 20 latent variables, each corresponding to distinct patterns of cortical functional network (CFNs). These CFNs varied in their representation of brain maturation and individual signatures; notably, certain CFNs that failed to capture neurodevelopmental traits, in fact, exhibited individual signatures. CFNs associated with neonatal neurodevelopment predominantly encompassed unimodal regions such as visual and sensorimotor areas, whereas those linked to individual uniqueness spanned multimodal and transmodal brain regions. The VAE's capacity to extract features from rs-fMRI data beyond the capabilities of linear models positions it as a valuable tool for delineating cognitive traits inherent in rs-fMRI and exploring individualized imaging phenotypes.