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

•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 bra...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 299; S. 120806
Hauptverfasser:	Kim, Jung-Hoon, De Asis-Cruz, Josepheen, Limperopoulos, Catherine
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.10.2024 Elsevier Limited Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
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