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Co-activation patterns across multiple tasks reveal robust anti-correlated functional networks

Authors :
Meiling Li
Louisa Dahmani
Danhong Wang
Jianxun Ren
Sophia Stocklein
Yuanxiang Lin
Guoming Luan
Zhiqiang Zhang
Guangming Lu
Fanziska Galiè
Ying Han
Alvaro Pascual-Leone
Meiyun Wang
Michael D. Fox
Hesheng Liu
Source :
NeuroImage, Vol 227, Iss , Pp 117680- (2021)
Publication Year :
2021
Publisher :
Elsevier, 2021.

Abstract

Whether antagonistic brain states constitute a fundamental principle of human brain organization has been debated over the past decade. Some argue that intrinsically anti-correlated brain networks in resting-state functional connectivity are an artifact of preprocessing. Others argue that anti-correlations are biologically meaningful predictors of how the brain will respond to different stimuli. Here, we investigated the co-activation patterns across the whole brain in various tasks and test whether brain regions demonstrate anti-correlated activity similar to those observed at rest. We examined brain activity in 47 task contrasts from the Human Connectome Project (N = 680) and found robust antagonistic interactions between networks. Regions of the default network exhibited the highest degree of cortex-wide negative connectivity. The negative co-activation patterns across tasks showed good correspondence to that derived from resting-state data processed with global signal regression (GSR). Interestingly, GSR-processed resting-state data was a significantly better predictor of task-induced modulation than data processed without GSR. Finally, in a cohort of 25 patients with depression, we found that task-based anti-correlations between the dorsolateral prefrontal cortex (DLPFC) and subgenual anterior cingulate cortex were associated with clinical efficacy of transcranial magnetic stimulation therapy targeting the DLPFC. Overall, our findings indicate that anti-correlations are a biologically meaningful phenomenon and may reflect an important principle of functional brain organization.

Details

Language :
English
ISSN :
10959572
Volume :
227
Issue :
117680-
Database :
Directory of Open Access Journals
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
edsdoj.b28340e0bafd4c2aae994745a12e3be9
Document Type :
article
Full Text :
https://doi.org/10.1016/j.neuroimage.2020.117680