Separating Group- and Individual-level Brain Signatures in the Newborn Functional Connectome: A Deep Learning Approach.

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Abstract

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 individual uniqueness present in neonatal FC data, while the conventional linear models failed 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 rs-fMRI from 464 neonates. By training the VAE on rs-fMRI from both adults and newborns, we observed 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. Notably, the VAE model also showed significantly higher individual identification accuracy than linear models (=10∼30%). Importantly, the VAE was able to differentiate between connections reflecting age-related changes and 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.Copyright © 2024. Published by Elsevier Inc.