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Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network
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Title
Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network
Issued Date
2024-11
Citation
Park, Kyeongwon. (2024-11). Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network. NeuroImage, 301. doi: 10.1016/j.neuroimage.2024.120884
Type
Article
Author Keywords
Resting-state fMRITask fMRISingular value decompositionEssential modeDefault mode networkGenuine time oscillation
Keywords
FUNCTIONAL CONNECTIVITYCOMPONENT ANALYSISPOWER SPECTRAEPI
ISSN
1053-8119
Abstract
Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz. © 2024
URI
http://hdl.handle.net/20.500.11750/57382
DOI
10.1016/j.neuroimage.2024.120884
Publisher
Elsevier
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