Hybrid brain model accurately predict human procrastination behavior.

Zhiyi Chen, Rong Zhang, Jiawei Xie, Peiwei Liu, Chenyan Zhang, Jia Zhao, Justin Paul Laplante, Tingyong Feng

Abstract read

In one paragraph

Article in Cognitive neurodynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Modulating task-outcome value to mitigate real-world procrastination via noninvasive brain stimulation.eLife · 2026
Trial
Shared neurogenetic architecture links adolescent neurodevelopmental deviations to adult psychopathological procrastination.Molecular psychiatry · 2026
Article

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5 · Who and what money

Authors and funding

8 authors.

Zhiyi Chen *Faculty of Psychology, School of Psychology, Southwest University, Tian Sheng RD, No.2, Beibei, ChongQing, 400715 China.ORCID 0000-0003-1744-4647

Rong Zhang *Faculty of Psychology, School of Psychology, Southwest University, Tian Sheng RD, No.2, Beibei, ChongQing, 400715 China.

Jiawei XieDepartment of Psychology, The University of Sheffield, Sheffield, UK.

Peiwei LiuDepartment of Psychology, University of Florida, Gainesville, USA.

Chenyan ZhangCognitive Psychology Unit, Faculty of Social and Behavioural Sciences, The Institute of Psychology, Leiden University, Leiden, Netherlands.ORCID 0000-0002-2945-6584

Jia ZhaoFaculty of Psychology, School of Psychology, Southwest University, Tian Sheng RD, No.2, Beibei, ChongQing, 400715 China.

Justin Paul LaplanteFrancis L Hyatt School of Psychology, Clark University, Massachusetts, USA.

Tingyong FengFaculty of Psychology, School of Psychology, Southwest University, Tian Sheng RD, No.2, Beibei, ChongQing, 400715 China.ORCID 0000-0001-9278-6474

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Procrastination behavior is quite ubiquitous, and should warrant cautions to us owing to its significant influences in poor mental health, low subjective well-beings and bad academic performance. However, how to identify this behavioral problem have not yet to be fully elucidated. 1132 participants were recruited as distribution of benchmark. 81 high trait procrastinators (HP) and matched low trait procrastinators (LP) were screened. To address this issue, we have built upon the hybrid brain model by using hierarchical machine learning techniques to classify HP and LP with multi-modalities neuroimaging data (i.e., grey matter volume, fractional anisotropy, static/dynamic amplitude of low frequency fluctuation and static/dynamic degree centrality). Further, we capitalized on the multiple Canonical Correlation Analysis (mCCA) and joint Independent Component Analysis algorithm (mCCA + jICA) to clarify its fusion neural components as well. The hybrid brain model showed high accuracy to discriminate HP and LP (accuracy rate = 87.04%, sensitivity rate = 86.42%, specificity rate = 85.19%). Moreover, results of mCCA + jICA model revealed several joint-discriminative neural independent components (ICs) of this classification, showing wider co-variants of frontoparietal cortex and hippocampus networks. In addition, this study demonstrated three modal-specific discriminative ICs for classification, highlighting the temporal variants of brain local and global natures in ventromedial prefrontal cortex (vmPFC) and PHC in HP. To sum-up, this research developed a hybrid brain model to identify trait procrastination with high accuracy, and further revealed the neural hallmarks of this trait by integrating neuroimaging fusion data. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-021-09765-z.

Indexed as

Diagnostic biomarkersFusion dataMachine learningMultiple canonical correlation analysisProcrastinators

Identifiers

PMID36237406

DOI10.1007/s11571-021-09765-z

PMCPMC9508313

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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.