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Lower resting brain entropy is associated with stronger task activation and deactivation.

Authors :
Lin, Liandong
Chang, Da
Song, Donghui
Li, Yiran
Wang, Ze
Source :
NeuroImage. Apr2022, Vol. 249, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

• Correlations between rest brain entropy and task activation were assessed. • In activated brain regions, lower rest entropy correlates with stronger activation. • In deactivated area, lower rest entropy correlates with stronger deactivation. • Rest brain entropy predicts the magnitude of brain activation or deactivation. • Rest entropy explains the individual differences of activation/deactivations. Brain entropy (BEN) calculated from resting state fMRI has been the subject of increasing research interest in recent years. Previous studies have shown the correlations between rest BEN and neurocognition and task performance, but how this relates to task-evoked brain activations and deactivations remains unknown. The purpose of this study is to address this open question using large data (n = 862). Voxel wise correlations were calculated between rest BEN and task activations/deactivations of five different tasks. For most of the assessed tasks, lower rest BEN was found to be associated with stronger activations (negative correlations) and stronger deactivations (positive correlations) only in brain regions activated or deactivated by the tasks. Higher workload evoked spatially more extended negative correlations between rest BEN and task activations. These results not only confirm that resting brain activity can predict brain activity during task performance but also for the first time show that resting brain activity may facilitate both task activations and deactivations. In addition, the results provide a clue to understanding the individual differences of task performance and brain activations. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10538119
Volume :
249
Database :
Academic Search Index
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
155191255
Full Text :
https://doi.org/10.1016/j.neuroimage.2022.118875