Department of Psychology, University of Michigan, Ann Arbor, USA.
Department of Psychology, University at Buffalo, Buffalo, USA.
Psychol Med. 2020 Oct;50(14):2397-2405. doi: 10.1017/S0033291719002563. Epub 2019 Oct 10.
An ongoing challenge in understanding and treating personality disorders (PDs) is a significant heterogeneity in disorder expression, stemming from variability in underlying dynamic processes. These processes are commonly discussed in clinical settings, but are rarely empirically studied due to their personalized, temporal nature. The goal of the current study was to combine intensive longitudinal data collection with person-specific temporal network models to produce individualized symptom-level structures of personality pathology. These structures were then linked to traditional PD diagnoses and stress (to index daily functioning).
Using about 100 daily assessments of internalizing and externalizing domains underlying PDs (i.e. negative affect, detachment, impulsivity, hostility), a temporal network mapping approach (i.e. group iterative multiple model estimation) was used to create person-specific networks of the temporal relations among domains for 91 individuals (62.6% female) with a PD. Network characteristics were then associated with traditional PD symptomatology (controlling for mean domain levels) and with daily variation in clinically-relevant phenomena (i.e. stress).
Features of the person-specific networks predicted paranoid, borderline, narcissistic, and obsessive-PD symptom counts above average levels of the domains, in ways that align with clinical conceptualizations. They also predicted between-person variation in stress across days.
Relations among behavioral domains thought to underlie heterogeneity in PDs were indeed associated with traditional diagnostic constructs and with daily functioning (i.e. stress) in person-specific networks. Findings highlight the importance of leveraging data and models that capture person-specific, dynamic processes, and suggest that person-specific networks may have implications for precision medicine.
理解和治疗人格障碍（PD）的一个持续挑战是障碍表现的显著异质性，源于潜在动态过程的可变性。这些过程在临床环境中经常讨论，但由于其个性化和时间性，很少进行实证研究。本研究的目的是将密集的纵向数据收集与个体特定的时间网络模型相结合，以产生人格病理的个体化症状水平结构。然后将这些结构与传统的 PD 诊断和压力（以指数日常功能）联系起来。
使用大约 100 次对 PD 下的内化和外化领域（即负性情绪、超脱、冲动、敌意）的日常评估，使用时间网络映射方法（即团体迭代多次模型估计）为 91 名患有 PD 的个体创建个体特定的领域之间时间关系网络。然后将网络特征与传统的 PD 症状学（控制平均领域水平）以及与临床相关现象（即压力）的日常变化相关联。
个体特定网络的特征预测了偏执型、边缘型、自恋型和强迫型 PD 症状计数高于领域的平均水平，这与临床概念化一致。它们还预测了个体间压力在多天内的变化。
被认为是 PD 异质性基础的行为领域之间的关系确实与传统的诊断结构以及个体特定网络中的日常功能（即压力）相关。研究结果强调了利用数据和模型捕捉个体特定的、动态过程的重要性，并表明个体特定的网络可能对精准医学有影响。
