Evidence map›Paper›PMID 33934449›Full record

ArticleHuman brain mapping2021

Neural representations of the amount and the delay time of reward in intertemporal decision making.

Qiang Wang, Yajie Wang, Pinchun Wang, Maomiao Peng, Manman Zhang, Yuxuan Zhu, Shiyu Wei, Chuansheng Chen, Xiongying Chen, Shan Luo and 1 more

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
3.6field-weighted citation impact, top 7% of its field
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

23 citing papers in PubMed, 43 citations in OpenAlex.

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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

11 authors at 5 institutions in 2 countries.

Qiang WangKey Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.ORCID 0000-0002-1081-6690
Yajie WangFaculty of Psychology, Tianjin Normal University, Tianjin, China.
Pinchun WangFaculty of Education, Tianjin Normal University, Tianjin, China.
Maomiao PengDepartment of Psychology, University of Arizona, Tucson, Arizona, USA.
Manman ZhangKey Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.
Yuxuan ZhuFaculty of Psychology, Tianjin Normal University, Tianjin, China.
Shiyu WeiFaculty of Psychology, Tianjin Normal University, Tianjin, China.
Chuansheng ChenDepartment of Psychological Science, University of California, Irvine, California, USA.
Xiongying ChenThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Shan LuoDepartment of Internal Medicine, Division of Endocrinology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.
Xuejun BaiKey Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.
Tianjin Normal University · CNCapital Medical University · CNUniversity of Arizona · USUniversity of California, Irvine · USUniversity of Southern California · US

Funding

Neural Mechanisms of Overeating Among Children Exposed to Gestational Diabetes Mellitus In UteroK01DK115638 · NIDDK · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LUO, SHAN · 2018 to 2021
$758k
NIDDK NIH HHS K01 DK115638NIH HHS K01DK115638
6 · The paper itself

Abstract

Numerous studies have examined the neural substrates of intertemporal decision-making, but few have systematically investigated separate neural representations of the two attributes of future rewards (i.e., the amount of the reward and the delay time). More importantly, no study has used the novel analytical method of representational connectivity analysis (RCA) to map the two dimensions' functional brain networks at the level of multivariate neural representations. This study independently manipulated the amount and delay time of rewards during an intertemporal decision task. Both univariate and multivariate pattern analyses showed that brain activity in the dorsomedial prefrontal cortex (DMPFC) and lateral frontal pole cortex (LFPC) was modulated by the amount of rewards, whereas brain activity in the DMPFC and dorsolateral prefrontal cortex (DLPFC) was modulated by the length of delay. Moreover, representational similarity analysis (RSA) revealed that even for the regions of the DMPFC that overlapped between the two dimensions, they manifested distinct neural activity patterns. In terms of individual differences, those with large delay discounting rates (k) showed greater DMPFC and LFPC activity as the amount of rewards increased but showed lower DMPFC and DLPFC activity as the delay time increased. Lastly, RCA suggested that the topological metrics (i.e., global and local efficiency) of the functional connectome subserving the delay time dimension inversely predicted individual discounting rate. These findings provide novel insights into neural representations of the two attributes in intertemporal decisions, and offer a new approach to construct task-based functional brain networks whose topological properties are related to impulsivity.

Indexed as

Brain MappingRewardAdultDelay DiscountingDorsolateral Prefrontal CortexFemaleHumansMagnetic Resonance ImagingMalePrefrontal CortexPsychomotor PerformanceTime FactorsYoung Adultdorsal medial prefrontal cortexgraph theoryintertemporal decision-makingmultivariate pattern analysisrepresentational similarity analysis

Identifiers

PMID33934449
PMCPMC8249888
OpenAlexW3159662626

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.