Evidence map›Paper›PMID 39126347›Full record

ArticleHuman brain mapping2024

The computational and neural substrates of individual differences in impulsivity under loss framework.

Keying Jiang, Guang Zhao, Qian Feng, Shunping Guan, Hohjin Im, Bin Zhang, Pinchun Wang, Xuji Jia, Haidong Zhu, Ye Zhu and 2 more

Abstract read
In one paragraph

Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

9 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

12 authors.

Keying JiangFaculty of Psychology, Tianjin Normal University, Tianjin, China.
Guang ZhaoFaculty of Psychology, Tianjin Normal University, Tianjin, China.
Qian FengEpilepsy Center, Tsinghua University Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China.
Shunping GuanFaculty of Psychology, Tianjin Normal University, Tianjin, China.
Hohjin ImIndependent Researcher, USA.
Bin ZhangFaculty of Psychology, Tianjin Normal University, Tianjin, China.
Pinchun WangFaculty of Psychology, Tianjin Normal University, Tianjin, China.
Xuji JiaFaculty of Psychology, Tianjin Normal University, Tianjin, China.
Haidong ZhuNormal College, Shihezi University, Shihezi, China.
Ye ZhuFaculty of Psychology, Tianjin Normal University, Tianjin, China.
He WangInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Qiang WangFaculty of Psychology, Tianjin Normal University, Tianjin, China.ORCID 0000-0002-1081-6690

Funding

Beijing Key Laboratory of Mental Disorders 2023JSJB04Chinese Academy of Medical Sciences Initiative for Innovative Medicine 2021-I2M-1-015Chinese Academy of Medical Sciences Initiative for Innovative Medicine 2021-I2M-1-058Humanities and Social Science Fund Project of the Ministry of Education 20YJC190018Huzhou Key Laboratory of Brain Science and Child Learning 2024HNE001National Natural Science Foundation of China 32000786Natural Science Foundation of Tianjin 23JCYBJC00910Open Research Fund of the Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention SYS2024A05Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning CNLYB2202Postgraduate Innovative Research Project of Tianjin Normal University 2024KYCX139F
6 · The paper itself

Abstract

Numerous neuroimaging studies have identified significant individual variability in intertemporal choice, often attributed to three neural mechanisms: (1) increased reward circuit activity, (2) decreased cognitive control, and (3) prospection ability. These mechanisms that explain impulsivity, however, have been primarily studied in the gain domain. This study extends this investigation to the loss domain. We employed a hierarchical Bayesian drift-diffusion model (DDM) and the inter-subject representational similarity approach (IS-RSA) to investigate the potential computational neural substrates underlying impulsivity in loss domain across two experiments (n = 155). These experiments utilized a revised intertemporal task that independently manipulated the amounts of immediate and delayed-loss options. Behavioral results demonstrated positive correlations between the drift rate, measured by the DDM, and the impulsivity index K in Exp. 1 (n = 97) and were replicated in Exp. 2 (n = 58). Imaging analyses further revealed that the drift rate significantly mediated the relations between brain properties (e.g., prefrontal cortex activations and gray matter volume in the orbitofrontal cortex and precuneus) and K in Exp. 1. IS-RSA analyses indicated that variability in the drift rate also mediated the associations between inter-subject variations in activation patterns and individual differences in K. These findings suggest that individuals with similar impulsivity levels are likely to exhibit similar value processing patterns, providing a potential explanation for individual differences in impulsivity within a loss framework.

Indexed as

Impulsive BehaviorIndividualityMagnetic Resonance ImagingAdultBayes TheoremBrainBrain MappingDelay DiscountingFemaleHumansMaleYoung Adultindividual variabilityinter‐subject representational similarity analysisintertemporal choiceloss framework

Identifiers

PMID39126347
PMCPMC11316248

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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.