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Front Behav Neurosci. 2016 Feb 25;10:27. doi: 10.3389/fnbeh.2016.00027. eCollection 2016.

Resting-State Coupling between Core Regions within the Central-Executive and Salience Networks Contributes to Working Memory Performance.

Author information

1
Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
2
Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences Beijing, China.
3
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
4
Key Laboratory of Cognition and Personality (Ministry of Education), School of Psychology, Southwest University Chongqing, China.
5
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK.
6
Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China; Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of SciencesBeijing, China; Queensland Brain Institute, University of QueenslandBrisbane, QLD, Australia.

Abstract

Previous studies investigated the distinct roles played by different cognitive regions and suggested that the patterns of connectivity of these regions are associated with working memory (WM). However, the specific causal mechanism through which the neuronal circuits that involve these brain regions contribute to WM is still unclear. Here, in a large sample of healthy young adults, we first identified the core WM regions by linking WM accuracy to resting-state functional connectivity with the bilateral dorsolateral prefrontal cortex (dLPFC; a principal region in the central-executive network, CEN). Then a spectral dynamic causal modeling (spDCM) analysis was performed to quantify the effective connectivity between these regions. Finally, the effective connectivity was correlated with WM accuracy to characterize the relationship between these connections and WM performance. We found that the functional connections between the bilateral dLPFC and the dorsal anterior cingulate cortex (dACC) and between the right dLPFC and the left orbital fronto-insular cortex (FIC) were correlated with WM accuracy. Furthermore, the effective connectivity from the dACC to the bilateral dLPFC and from the right dLPFC to the left FIC could predict individual differences in WM. Because the dACC and FIC are core regions of the salience network (SN), we inferred that the inter- and causal-connectivity between core regions within the CEN and SN is functionally relevant for WM performance. In summary, the current study identified the dLPFC-related resting-state effective connectivity underlying WM and suggests that individual differences in cognitive ability could be characterized by resting-state effective connectivity.

KEYWORDS:

dorsolateral prefrontal cortex; effective connectivity; functional connectivity; resting state fMRI; spectral dynamic causal modeling; working memory

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