Yoon Sujung, Kim Jungyoon, Hong Gahae, Kim Tammy D, Hong Haejin, Ha Eunji, Ma Jiyoung, Lyoo In Kyoon
Ewha Brain Institute, Ewha W. University, Seoul, South Korea.
Department of Brain and Cognitive Sciences, Ewha W. University, Seoul, South Korea.
Front Syst Neurosci. 2020 Mar 3;14:9. doi: 10.3389/fnsys.2020.00009. eCollection 2020.
The propensity to engage in risky behaviors including excessive alcohol consumption may impose increased medical, emotional, and psychosocial burdens. Personality and behavioral traits of individuals may contribute in part to the involvement in risky behaviors, and therefore the classification of one's traits may help identify those who are at risk for future onset of the addictive disorder and related behavioral issues such as alcohol misuse. Personality and behavioral characteristics including impulsivity, anger, reward sensitivity, and avoidance were assessed in a large sample of healthy young adults ( = 475). Participants also underwent diffusion tensor imaging for the analysis of structural brain networks. A data-driven clustering using personality and behavioral traits of the participants identified four subtypes. As compared with individuals clustered into the neutral type, individuals with a high level of impulsivity (A subtype) and those with high levels of reward sensitivity, impulsivity, anger, and avoidance (B subtype) showed significant associations with problem drinking. In contrast, individuals with high levels of impulsivity, anger, and avoidance but not reward sensitivity (C subtype) showed a pattern of social drinking that was similar to those of the neutral subtype. Furthermore, logistic regression analysis with ridge estimators was applied to demonstrate the neurobiological relevance for the identified subtypes according to distinct patterns of structural brain connectivity within the addiction circuitry [neutral vs. A subtype, the area under the receiver operator characteristic curve (AUC) = 0.74, 95% CI = 0.67-0.81; neutral vs. B subtype, AUC = 0.74, 95% CI = 0.66-0.82; neutral vs. C subtype, AUC = 0.77, 95% CI = 0.70-0.84]. The current findings enable the characterization of individuals according to subtypes based on personality and behavioral traits that are also corroborated by neuroimaging data and may provide a platform to better predict individual risks for addictive disorders.
参与包括过度饮酒在内的危险行为的倾向可能会增加医疗、情感和心理社会负担。个体的人格和行为特征可能部分导致其参与危险行为，因此对个体特征进行分类可能有助于识别那些未来有患成瘾性障碍及相关行为问题（如酒精滥用）风险的人。在一大群健康的年轻成年人（(n = 475)）中评估了包括冲动性、愤怒、奖励敏感性和回避等人格和行为特征。参与者还接受了扩散张量成像以分析脑结构网络。使用参与者的人格和行为特征进行数据驱动的聚类，确定了四种亚型。与聚类为中性类型的个体相比，冲动性水平高的个体（A亚型）以及奖励敏感性、冲动性、愤怒和回避水平高的个体（B亚型）与问题饮酒存在显著关联。相比之下，冲动性、愤怒和回避水平高但奖励敏感性不高的个体（C亚型）表现出与中性亚型相似的社交饮酒模式。此外，应用带有岭估计器的逻辑回归分析，根据成瘾回路内不同的脑结构连接模式，证明所确定的亚型具有神经生物学相关性[中性与A亚型，受试者操作特征曲线下面积（AUC）= 0.74，95%置信区间 = 0.67 - 0.81；中性与B亚型，AUC = 0.74，95%置信区间 = 0.66 - 0.82；中性与C亚型，AUC = 0.77，95%置信区间 = 0.70 - 0.84]。当前的研究结果能够根据基于人格和行为特征的亚型对个体进行特征描述，这些特征也得到了神经影像学数据的证实，并且可能为更好地预测个体患成瘾性障碍的风险提供一个平台。
