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Infant functional networks are modulated by state of consciousness and circadian rhythm
Author(s) -
Rachel J. Smith,
Ehsan Alipourjeddi,
Cristal Garner,
Amy L. Maser,
Daniel W. Shrey,
Beth A. Lopour
Publication year - 2021
Publication title -
network neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.128
H-Index - 18
ISSN - 2472-1751
DOI - 10.1162/netn_a_00194
Subject(s) - wakefulness , circadian rhythm , neuroscience , rhythm , psychology , functional connectivity , dynamic functional connectivity , electroencephalography , consciousness , sleep (system call) , audiology , medicine , computer science , operating system
Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

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