Evidence map›Paper›PMID 33037972›Full record

ArticleBrain imaging and behavior2021

Intrinsic non-hub connectivity predicts human inter-temporal decision-making.

Qiang Wang, Yuxuan Zhu, Yajie Wang, Chuansheng Chen, Qinghua He, Gui Xue

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Article in Brain imaging and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

6 authors.

Qiang WangKey Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China.ORCID http://orcid.org/0000-0002-1081-6690
Yuxuan ZhuFaculty of Psychology, Tianjin Normal University, Tianjin, 300387, China.
Yajie WangFaculty of Psychology, Tianjin Normal University, Tianjin, 300387, China.
Chuansheng ChenDepartment of Psychological Science, University of California, Irvine, CA, 92697-7085, USA.
Qinghua HeFaculty of Psychology, Southwest University, Chongqing, 400715, China. heqinghua@swu.edu.cn.
Gui XueNational Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China. gxue@bnu.edu.cn.

Funding

National Natural Science Foundation of China 31972906
6 · The paper itself

Abstract

Inter-temporal decision-making is ubiquitous in daily life and has been considered as a critical characteristic associated with an individual's success. Such decisions require us to tradeoff between short-term and long-term benefits. Prior studies have indicated that inter-temporal decision involves various brain regions that tend to occupy the central hubs. However, it is unclear whether the functional connectivities among hub as well as non-hub regions can predict discounting behaviors. Here, we combined with graph-theoretical algorithm and multivariate pattern analysis to explore whether voxel-wise functional connectivity strength in the whole brain could predict discounting rates (indexed as logk, based on the adaptive delay-discounting task) in a relatively large sample (n = 429) of young adults. Results revealed that short- and long-distance as well as all-range non-hub functional connectivity strength in the limbic system (i.e., medial orbitofrontal cortex and parahippocampus) were inversely associated with discounting rates. Furthermore, these results were robust and did not appear to be due to potential confounding factors. Above weight-based degree metric is commonly indicative of the communication pattern of local and global parallel information processing, and it therefore provides novel insights into the neural mechanisms underlying inter-temporal decision-making from the perspective of human brain topological organizations.

Indexed as

Brain MappingMagnetic Resonance ImagingBrainCerebral CortexFrontal LobeHumansNeural PathwaysYoung AdultFunctional connectivity strengthHub regionInter-temporal decision-makingMultivariate pattern analysis

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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.