Evidence map›Paper›PMID 29676536›Full record

ArticleHuman brain mapping2018

Core networks and their reconfiguration patterns across cognitive loads.

Nianming Zuo, Zhengyi Yang, Yong Liu, Jin Li, Tianzi Jiang

Abstract read
In one paragraph

Article in Human brain mapping, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  15. Abnormal Large-Scale Neuronal Network in High Myopia.Frontiers in human neuroscience · 2022
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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

5 authors.

Nianming ZuoBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.ORCID 0000-0002-6726-4575
Zhengyi YangBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Yong LiuBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.ORCID 0000-0002-1862-3121
Jin LiBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Tianzi JiangBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.ORCID 0000-0001-9531-291X

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
NIMH NIH HHS U54 MH091657
6 · The paper itself

Abstract

Different cognitively demanding tasks recruit globally distributed but functionally specific networks. However, the configuration of core networks and their reconfiguration patterns across cognitive loads remain unclear, as does whether these patterns are indicators for the performance of cognitive tasks. In this study, we analyzed functional magnetic resonance imaging data of a large cohort of 448 subjects, acquired with the brain at resting state and executing N-back working memory (WM) tasks. We discriminated core networks by functional interaction strength and connection flexibility. Results demonstrated that the frontoparietal network (FPN) and default mode network (DMN) were core networks, but each exhibited different patterns across cognitive loads. The FPN and DMN both showed strengthened internal connections at the low demand state (0-back) compared with the resting state (control level); whereas, from the low (0-back) to high demand state (2-back), some connections to the FPN weakened and were rewired to the DMN (whose connections all remained strong). Of note, more intensive reconfiguration of both the whole brain and core networks (but no other networks) across load levels indicated relatively poor cognitive performance. Collectively these findings indicate that the FPN and DMN have distinct roles and reconfiguration patterns across cognitively demanding loads. This study advances our understanding of the core networks and their reconfiguration patterns across cognitive loads and provides a new feature to evaluate and predict cognitive capability (e.g., WM performance) based on brain networks.

Indexed as

ConnectomeMagnetic Resonance ImagingCognitionDatasets as TopicFemaleHumansMaleMemory, Short-TermNeuropsychological TestsRestbrain networkfunctional connectivityfunctional MRInetwork reconfigurationworking memory

Identifiers

PMID29676536
PMCPMC6866471

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.