Zhang Jingxuan, Li Kuiliang, Xue Yitong, Feng Zhengzhi
Department of Medical Psychology, Army Medical University, Chongqing, China.
Front Psychiatry. 2022 Jun 17;13:916332. doi: 10.3389/fpsyt.2022.916332. eCollection 2022.
Both impulsiveness and trait depression are the trait-level risk factors for depressive symptoms. However, the two traits overlap and do not affect depressive symptoms independently. This study takes impulsiveness and trait depression into a whole construct, aiming to find the complex associations among all facets and explore their relative importance in a trait network. It can help us find the key facets that need consideration in preventing depression.
We used the Barratt Impulsiveness Scale (BIS) and Trait Depression Scale (T-DEP) as measuring tools, conducted network analysis, and applied the Graphic Least Absolute Shrinkage and Selection Operator (GLASSO) algorithm to estimate the network structure and compute the linkage and centrality indexes. The accuracy and stability of the indexes were estimated through bootstrapping. All the computations were performed by R script and packages.
We found that "trait anhedonia" was connected with "non-planning" and "cognitive" impulsiveness, while "trait dysthymia" was connected with "motor" impulsiveness. "Cognitive" impulsiveness had a statistically significant higher expected influence than "motor" impulsiveness and had the trend to be dominant in the network. "Trait dysthymia" had a statistically significant higher bridge expected influence than "cognitive" impulsiveness and had the trend to be the key facet linking impulsiveness with trait depression. "Non-only children" had higher network global strength than "only children." All indexes were accurate and stable.
The present study confirms the complex associations among facets of trait depression and impulsiveness, finding that "cognitive" impulsiveness and "trait dysthymia" are the two key factors in the network. The results imply that different facets of impulsiveness should be considered respectively regarding anhedonia and dysthymia. "Cognitive" impulsiveness and "trait dysthymia" are critical to the prevention of depression.
冲动性和特质性抑郁都是抑郁症状的特质水平风险因素。然而，这两种特质相互重叠，并非独立影响抑郁症状。本研究将冲动性和特质性抑郁纳入一个整体结构，旨在找出所有方面之间的复杂关联，并在特质网络中探索它们的相对重要性。这有助于我们找到预防抑郁症时需要考虑的关键方面。
我们使用巴雷特冲动性量表（BIS）和特质抑郁量表（T-DEP）作为测量工具，进行网络分析，并应用图形最小绝对收缩和选择算子（GLASSO）算法来估计网络结构并计算连接性和中心性指标。通过自抽样估计指标的准确性和稳定性。所有计算均由R脚本和软件包完成。
我们发现“特质性快感缺失”与“非计划性”和“认知性”冲动性相关，而“特质性心境恶劣”与“运动性”冲动性相关。“认知性”冲动性在统计学上具有比“运动性”冲动性更高的预期影响力，并且在网络中具有主导趋势。“特质性心境恶劣”在统计学上具有比“认知性”冲动性更高的桥梁预期影响力，并且具有成为将冲动性与特质性抑郁联系起来的关键方面的趋势。“非独生子女”的网络全局强度高于“独生子女”。所有指标均准确且稳定。
本研究证实了特质性抑郁和冲动性各方面之间的复杂关联，发现“认知性”冲动性和“特质性心境恶劣”是网络中的两个关键因素。结果表明，对于快感缺失和心境恶劣，应分别考虑冲动性的不同方面。“认知性”冲动性和“特质性心境恶劣”对预防抑郁症至关重要。
