Neural markers of procrastination in white matter microstructures and networks. (14th February 2021)

• Record Type: Journal Article
• Authors: Chen, Zhiyi; Liu, Peiwei; Zhang, Chenyan; Yu, Zeyuan; Feng, Tingyong
• Is Part Of: Psychophysiology. Volume 58:Number 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-14
• Subjects: diffusion tensor imaging -- machine learning -- neural markers -- procrastinators -- white matter microstructure; Psychophysiology -- Periodicals; 612.8
• DOI: 10.1111/psyp.13782
• Languages: English
• ISSNs: 0048-5772

Abstract

Abstract: More than 15% of adults suffer from pathological procrastination, which leads to substantial harm to their mental and psychiatric health. Our previous work demonstrated the role of three neuroanatomical networks as neural substrates of procrastination, but their potential interaction remains unknown. Three large‐scale independent samples (total n = 901) were recruited. In sample A, tract‐based spatial statistics (TBSS) and connectome‐based graph‐theoretical analysis was conducted to probe association between topological properties of white matter (WM) network and procrastination. In sample B, the above analysis was reproduced to demonstrate replicability. In sample C, machine learning models were built to predict individual procrastination. TBSS results showed a negative association between procrastination and WM integrity of limbic‐prefrontal connection, and a positive relationship between intra‐connection within the limbic system and procrastination. Also, both the efficiency and integrity of limbic WM network were found to be linked to procrastination. The above findings were all confirmed to replicate in an independent sample; prediction models demonstrated that these WM features can predict procrastination accurately in sample C. In conclusion, this study moves forward our understanding of procrastination by clarifying the role of interplay of self‐control and emotional regulation with it. Abstract : Our findings address a long‐standing gap in the literature regarding the neural model of procrastination: we found neural evidence that procrastination is driven by an interplay between self‐control and emotional processes. This also revealed novel biomarkers of procrastination, including the connection of white matter fiber tracts in the frontoparietal‐limbic connectome and graph‐topological properties of limbic networks. Finally, we can accurately predict individual procrastination by using these biomarkers in a machine learning model.