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Resting‐state networks of the neonate brain identified using independent component analysis
Author(s) -
Rajasilta Olli,
Tuulari Jetro J.,
Björnsdotter Malin,
Scheinin Noora M.,
Lehtola Satu J.,
Saunavaara Jani,
Häkkinen Suvi,
Merisaari Harri,
Parkkola Riitta,
Lähdesmäki Tuire,
Karlsson Linnea,
Karlsson Hasse
Publication year - 2020
Publication title -
developmental neurobiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.716
H-Index - 129
eISSN - 1932-846X
pISSN - 1932-8451
DOI - 10.1002/dneu.22742
Subject(s) - default mode network , resting state fmri , task positive network , independent component analysis , neuroscience , functional magnetic resonance imaging , biology , brain mapping , psychology , artificial intelligence , computer science
Resting‐state functional magnetic resonance imaging (rs‐fMRI) has been successfully used to probe the intrinsic functional organization of the brain and to study brain development. Here, we implemented a combination of individual and group independent component analysis (ICA) of FSL on a 6‐min resting‐state data set acquired from 21 naturally sleeping term‐born (age 26 ± 6.7 d), healthy neonates to investigate the emerging functional resting‐state networks (RSNs). In line with the previous literature, we found evidence of sensorimotor, auditory/language, visual, cerebellar, thalmic, parietal, prefrontal, anterior cingulate as well as dorsal and ventral aspects of the default‐mode‐network. Additionally, we identified RSNs in frontal, parietal, and temporal regions that have not been previously described in this age group and correspond to the canonical RSNs established in adults. Importantly, we found that careful ICA‐based denoising of fMRI data increased the number of networks identified with group‐ICA, whereas the degree of spatial smoothing did not change the number of identified networks. Our results show that the infant brain has an established set of RSNs soon after birth.