BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia.
Research School of Psychology, ANU College of Health and Medicine, The Australian National University, Canberra, Australia.
Transl Psychiatry. 2022 Jan 10;12(1):10. doi: 10.1038/s41398-021-01773-1.
Compulsivity is a poorly understood transdiagnostic construct thought to underlie multiple disorders, including obsessive-compulsive disorder, addictions, and binge eating. Our current understanding of the causes of compulsive behavior remains primarily based on investigations into specific diagnostic categories or findings relying on one or two laboratory measures to explain complex phenotypic variance. This proof-of-concept study drew on a heterogeneous sample of community-based individuals (N = 45; 18-45 years; 25 female) exhibiting compulsive behavioral patterns in alcohol use, eating, cleaning, checking, or symmetry. Data-driven statistical modeling of multidimensional markers was utilized to identify homogeneous subtypes that were independent of traditional clinical phenomenology. Markers were based on well-defined measures of affective processing and included psychological assessment of compulsivity, behavioral avoidance, and stress, neurocognitive assessment of reward vs. punishment learning, and biological assessment of the cortisol awakening response. The neurobiological validity of the subtypes was assessed using functional magnetic resonance imaging. Statistical modeling identified three stable, distinct subtypes of compulsivity and affective processing, which we labeled "Compulsive Non-Avoidant", "Compulsive Reactive" and "Compulsive Stressed". They differed meaningfully on validation measures of mood, intolerance of uncertainty, and urgency. Most importantly, subtypes captured neurobiological variance on amygdala-based resting-state functional connectivity, suggesting they were valid representations of underlying neurobiology and highlighting the relevance of emotion-related brain networks in compulsive behavior. Although independent larger samples are needed to confirm the stability of subtypes, these data offer an integrated understanding of how different systems may interact in compulsive behavior and provide new considerations for guiding tailored intervention decisions.
强迫性是一种理解不足的跨诊断结构，被认为是多种障碍的基础，包括强迫症、成瘾和暴食症。我们对强迫行为原因的理解主要仍基于对特定诊断类别的调查，或依赖一两个实验室测量来解释复杂的表型变异。这项概念验证研究利用了一个基于社区的个体异质样本（N=45；18-45 岁；25 名女性），这些个体在饮酒、饮食、清洁、检查或对称方面表现出强迫性行为模式。利用多维标记物的数据驱动统计建模来识别与传统临床表型无关的同质亚型。标记物基于情感处理的明确定义测量，包括对强迫性、行为回避和压力的心理评估，对奖励与惩罚学习的神经认知评估，以及对皮质醇觉醒反应的生物学评估。使用功能磁共振成像来评估亚型的神经生物学有效性。统计建模确定了三种稳定、不同的强迫性和情感处理亚型，我们将其标记为“强迫性非回避”、“强迫性反应”和“强迫性应激”。它们在情绪、不确定性容忍度和紧迫性的验证测量上有显著差异。最重要的是，亚型在基于杏仁核的静息态功能连接上捕获了神经生物学变异，这表明它们是潜在神经生物学的有效代表，并强调了情绪相关脑网络在强迫行为中的相关性。尽管需要更大的独立样本来确认亚型的稳定性，但这些数据提供了对不同系统如何在强迫行为中相互作用的综合理解，并为指导针对性干预决策提供了新的考虑因素。
