Evidence map›Paper›PMID 39382787›Full record

ArticleBrain imaging and behavior2024

Deficient salience and default mode functional integration in high worry-proneness subject: a connectome-wide association study.

Youxue Zhang, Mingjun Duan, Hui He

Abstract read
In one paragraph

Article in Brain imaging and behavior, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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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

3 authors.

Youxue ZhangSchool of Education and Psychology, Chengdu Normal University, Chengdu, 611130, China.
Mingjun DuanThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China.
Hui HeThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China. hehui_hhwdwd@163.com.

Funding

National Natural Science Foundation of China 62003076, 81801775Project of Natural Science Foundation of Sichuan Province 2023NSFSC1489, 2023NSFSC1186
6 · The paper itself

Abstract

Worry has been conceptualized as a relatively uncontrollable chain of thought that increases the risk of mental problems, such as anxiety disorders. Here, we examined the link between individual variation in the functional connectome and worry proneness, which remains unclear. A total of 32 high worry-proneness (HWP) subjects and 25 low worry-proneness (LWP) subjects were recruited. We conducted multivariate distance-based matrix regression to identify phenotypic relationships in high-dimensional brain resting-state functional connectivity data from HWP subjects. Multiple hub regions, including key brain nodes of the salience network (SN) and default mode network (DMN), were identified in HWP subjects. Follow-up analyses revealed that a high worry-proneness score was dominated by functional connectivity between the SN and the DMN. Moreover, HWP subjects showed hypoconnectivity between the cerebellum and the SN and DMN compared with LWP subjects. This cross-sectional study could not fully measure the causal relationships between changes in functional networks and worry proneness in healthy subjects. Functional changes in the cerebellum-cortical region might affect the modulation of external stimuli processing. Together, our results provide new insight into the role of key networks, including the SN, DMN and cerebellum, in understanding the potential mechanism underlying the high worry dimension in healthy subjects.

Indexed as

AnxietyBrainConnectomeDefault Mode NetworkMagnetic Resonance ImagingNeural PathwaysAdultBrain MappingCerebellumCross-Sectional StudiesFemaleHumansMaleNerve NetRestYoung AdultBrain–behaviour relationshipDefault model networkFunctional connectivitySalience networkWorry proneness

Identifiers

PMID39382787
PMCPMC11680634

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LicenceCC BY-NC-ND
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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.