Shi Yudong, Zhang Qing, Wang Song, Zhang Ling, Liu Zhiwei, Wang Jiawei, Luo Xiangfen, Wen Xiangwang, Liu Huanzhong
Department of Psychiatry, Chaohu Hospital of Anhui Medical University, Hefei, Anhui, China.
Anhui Psychiatric Center, Anhui Medical University, Hefei, Anhui, China.
Front Psychiatry. 2025 Apr 16;16:1543651. doi: 10.3389/fpsyt.2025.1543651. eCollection 2025.
Suicidal ideation is prevalent in major depressive disorder (MDD) and is closely related to empathy and alexithymia. While traditional approaches (e.g., regression models) focus on linear associations, network analysis provides unique advantages by mapping dynamic symptom interactions and identifying pivotal nodes that may drive suicidal risk. This study investigates these relationships through a network lens to reveal actionable intervention targets.
The study included 329 adolescents with MDD (ages 12-18). The Alexithymia Scale (TAS-20), Interpersonal Reactivity Index (IRI), and the Positive and Negative Suicide Ideation scale (PNSI) were used to assess alexithymia, empathy, and suicidal ideation levels, respectively. Network analysis was conducted to model the relationships between symptoms and calculate centrality and stability indices.
Network analysis revealed strong stability with Emotional Identification Difficulty (DIF) and Personal Distress (PD) identified as the most influential core symptoms, exhibiting the strongest bridging roles between emotional dysfunction and suicidal ideation. DIF showed particularly robust connections to both PD and suicidal ideation, while comparative subgroup analyses indicated no significant differences in network patterns between first-episode and recurrent MDD patients, suggesting consistent symptom dynamics across illness stages.
By revealing DIF and PD as central therapeutic targets, this study demonstrates how network analysis can uncover intervention opportunities missed by traditional approaches. Clinically, targeting these nodes through emotion recognition training and distress tolerance interventions may disrupt the pathway to suicidality in adolescents with MDD.
自杀观念在重度抑郁症(MDD)中很常见,且与共情和述情障碍密切相关。虽然传统方法(如回归模型)侧重于线性关联,但网络分析通过绘制动态症状相互作用并识别可能驱动自杀风险的关键节点提供了独特优势。本研究通过网络视角调查这些关系,以揭示可采取行动的干预靶点。
该研究纳入了329名患有MDD的青少年(年龄在12 - 18岁之间)。分别使用述情障碍量表(TAS - 20)、人际反应指数(IRI)以及正负性自杀观念量表(PNSI)来评估述情障碍、共情和自杀观念水平。进行网络分析以建立症状之间的关系模型,并计算中心性和稳定性指数。
网络分析显示出很强的稳定性,情绪识别困难(DIF)和个人痛苦(PD)被确定为最具影响力的核心症状,在情绪功能障碍和自杀观念之间发挥着最强的桥梁作用。DIF与PD和自杀观念均表现出特别紧密的联系,而比较亚组分析表明首发和复发性MDD患者之间的网络模式没有显著差异,这表明疾病各阶段的症状动态具有一致性。
通过将DIF和PD揭示为核心治疗靶点,本研究证明了网络分析如何能够发现传统方法错过的干预机会。在临床上,通过情绪识别训练和痛苦耐受干预针对这些节点,可能会中断MDD青少年的自杀途径。