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

doi:10.1007/s11571-021-09765-z

Hybrid brain model accurately predict human procrastination behavior

Cogn Neurodyn. 2022 Oct;16(5):1107-1121. doi: 10.1007/s11571-021-09765-z. Epub 2022 Jan 24.

Authors

Zhiyi Chen^#^{1

2}, Rong Zhang^#^{1

2}, Jiawei Xie³, Peiwei Liu⁴, Chenyan Zhang⁵, Jia Zhao^{1

2}, Justin Paul Laplante⁶, Tingyong Feng^{1

2}

Affiliations

¹ Faculty of Psychology, School of Psychology, Southwest University, Tian Sheng RD, No.2, Beibei, ChongQing, 400715 China.
² Key Laboratory of Cognition and Personality, Ministry of Education, ChongQing, China.
³ Department of Psychology, The University of Sheffield, Sheffield, UK.
⁴ Department of Psychology, University of Florida, Gainesville, USA.
⁵ Cognitive Psychology Unit, Faculty of Social and Behavioural Sciences, The Institute of Psychology, Leiden University, Leiden, Netherlands.
⁶ Francis L Hyatt School of Psychology, Clark University, Massachusetts, USA.

^# Contributed equally.

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.

Keywords: Diagnostic biomarkers; Fusion data; Machine learning; Multiple canonical correlation analysis; Procrastinators.