Evidence map›Paper›PMID 26941629›Full record

ArticleFrontiers in behavioral neuroscience2016

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

Xiaojing Fang, Yuanchao Zhang, Yuan Zhou, Luqi Cheng, Jin Li, Yulin Wang, Karl J Friston, Tianzi Jiang

Abstract read
In one paragraph

Article in Frontiers in behavioral neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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  19. Real World Problem-Solving.Frontiers in human neuroscience · 2018
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xiaojing FangKey Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
Yuanchao ZhangKey Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
Yuan ZhouKey Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences Beijing, China.
Luqi ChengKey Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
Jin LiNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
Yulin WangKey Laboratory of Cognition and Personality (Ministry of Education), School of Psychology, Southwest University Chongqing, China.
Karl J FristonWellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK.
Tianzi JiangKey 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.

Funding

Wellcome Trust 091593
6 · The paper itself

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.

Indexed as

dorsolateral prefrontal cortexeffective connectivityfunctional connectivityresting state fMRIspectral dynamic causal modelingworking memory

Identifiers

PMID26941629
PMCPMC4766291

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.