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Hybrid brain model accurately predict human procrastination behavior.

Zhiyi Chen1,2, Rong Zhang1,2, Jiawei Xie3

  • 1Faculty of Psychology, School of Psychology, Southwest University, Tian Sheng RD, No.2, Beibei, ChongQing, 400715 China.

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|October 14, 2022
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Summary
This summary is machine-generated.

This study developed a hybrid brain model using machine learning to accurately identify high trait procrastinators (HP) and low trait procrastinators (LP). The model revealed distinct neural patterns associated with procrastination, offering insights into its underlying brain mechanisms.

Keywords:
Diagnostic biomarkersFusion dataMachine learningMultiple canonical correlation analysisProcrastinators

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Area of Science:

  • Neuroscience
  • Psychology
  • Artificial Intelligence

Background:

  • Procrastination is common and linked to poor mental health and academic outcomes.
  • Identifying procrastination behavior remains a challenge.

Purpose of the Study:

  • To develop and validate a hybrid brain model for accurately classifying high trait procrastinators (HP) and low trait procrastinators (LP).
  • To elucidate the neural underpinnings of trait procrastination using multi-modal neuroimaging data.

Main Methods:

  • Recruited 1132 participants, screening 81 HP and matched LP.
  • Employed hierarchical machine learning on multi-modal neuroimaging data (grey matter volume, fractional anisotropy, ALFF, DC).
  • Utilized multiple Canonical Correlation Analysis (mCCA) and joint Independent Component Analysis (jICA) to identify fusion neural components.

Main Results:

  • The hybrid brain model achieved high classification accuracy (87.04%), sensitivity (86.42%), and specificity (85.19%) for HP vs. LP.
  • mCCA+jICA identified joint-discriminative neural components involving frontoparietal cortex and hippocampus networks.
  • Three modal-specific components highlighted temporal variations in vmPFC and PHC in HP.

Conclusions:

  • A novel hybrid brain model accurately identifies trait procrastination.
  • This research reveals key neural hallmarks of procrastination, integrating multi-modal neuroimaging data.