A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Resting-state networks of the neonate brain identified using independent component analysis
Tekijät: Rajasilta O, Tuulari JJ, Björnsdotter M, Scheinin NM, Lehtola SJ, Saunavaara J, Häkkinen S, Merisaari H, Parkkola R, Lähdesmäki T, Karlsson L, Karlsson H
Kustantaja: WILEY
Julkaisuvuosi: 2020
Journal: Developmental Neurobiology
Tietokannassa oleva lehden nimi: DEVELOPMENTAL NEUROBIOLOGY
Lehden akronyymi: DEV NEUROBIOL
Vuosikerta: 80
Numero: 3-4
Sivujen määrä: 15
ISSN: 1932-8451
eISSN: 1932-846X
DOI: https://doi.org/10.1002/dneu.22742
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/47223570
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
