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神经标记物与大脑白质微观结构和网络中的拖延行为。

Neural markers of procrastination in white matter microstructures and networks.

机构信息

Faculty of Psychology, Southwest University, Chongqing, China.

Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China.

出版信息

Psychophysiology. 2021 May;58(5):e13782. doi: 10.1111/psyp.13782. Epub 2021 Feb 14.

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.

摘要

超过 15%的成年人患有病理性拖延症,这对他们的精神和心理健康造成了严重的伤害。我们之前的工作已经证明了三个神经解剖网络作为拖延症的神经基础的作用,但它们之间的潜在相互作用仍然未知。我们招募了三个大型独立样本(总样本量为 901 人)。在样本 A 中,我们进行了基于束的空间统计学(TBSS)和基于连接体的图论分析,以探讨白质(WM)网络的拓扑性质与拖延症之间的关联。在样本 B 中,我们复制了上述分析以验证可重复性。在样本 C 中,我们构建了机器学习模型来预测个体的拖延症。TBSS 结果表明,拖延症与边缘-前额叶连接的 WM 完整性呈负相关,而边缘系统内的连接与拖延症呈正相关。此外,边缘 WM 网络的效率和完整性都与拖延症有关。这些发现都在独立样本中得到了验证;预测模型表明,这些 WM 特征可以在样本 C 中准确预测拖延症。总之,本研究通过阐明自我控制和情绪调节与拖延症之间相互作用的作用,推进了我们对拖延症的理解。

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