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24-01-2022 | Research Article

Hybrid brain model accurately predict human procrastination behavior

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

Published in: Cognitive Neurodynamics | Issue 5/2022

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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.

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Appendix
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Metadata
Title
Hybrid brain model accurately predict human procrastination behavior
Authors
Zhiyi Chen
Rong Zhang
Jiawei Xie
Peiwei Liu
Chenyan Zhang
Jia Zhao
Justin Paul Laplante
Tingyong Feng
Publication date
24-01-2022
Publisher
Springer Netherlands
Published in
Cognitive Neurodynamics / Issue 5/2022
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-021-09765-z

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