Evidence map›Paper›PMID 33270664›Full record

ArticlePloS one2020

Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition.

Howard Muchen Hsu, Zai-Fu Yao, Kai Hwang, Shulan Hsieh

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

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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

4 authors.

Howard Muchen HsuDepartment of Psychology, National Cheng Kung University, Tainan, Taiwan.
Zai-Fu YaoDepartment of Psychology, Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlands.
Kai HwangDepartment of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa, United States of America.
Shulan HsiehDepartment of Psychology, National Cheng Kung University, Tainan, Taiwan.ORCID 0000-0001-6247-244X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.

Indexed as

AdultAgedAged, 80 and overAttentionBrainBrain MappingFemaleHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedMotor ActivityNerve NetYoung Adult

Identifiers

PMID33270664
PMCPMC7714245

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.