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 (n = 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.

中文翻译：

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从结构性脑网络识别酒精滥用倾向

从事包括过度饮酒在内的危险行为的倾向可能会增加医疗、情感和社会心理负担。个人的个性和行为特征可能部分地导致参与危险行为，因此对一个人的特征进行分类可能有助于识别那些有成瘾性障碍和相关行为问题（如滥用酒精）风险的人。在大量健康年轻人样本（n = 475）中评估了人格和行为特征，包括冲动、愤怒、奖励敏感性和回避。参与者还接受了扩散张量成像以分析结构性大脑网络。使用参与者的个性和行为特征的数据驱动聚类确定了四种亚型。与聚集成中性类型的个体相比，具有高水平冲动的个体（A 亚型）和具有高水平奖励敏感性、冲动、愤怒和回避的个体（B 亚型）显示出与饮酒问题的显着关联。相比之下，具有高水平冲动、愤怒和回避但不具有奖励敏感性（C 亚型）的个体表现出与中性亚型相似的社交饮酒模式。此外，根据成瘾回路中结构性大脑连接的不同模式，使用脊估计器进行逻辑回归分析来证明已识别亚型的神经生物学相关性 [中性与 A 亚型，接收者操作特征曲线下的面积 (AUC) = 0.74, 95% CI = 0.67–0.81；中性 vs. B 亚型，AUC = 0.74，95% CI = 0.66–0.82；中性与 C 亚型，AUC = 0.77，95% CI = 0.70–0.84]。目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表征，神经影像数据也证实了这些特征，并可能提供一个平台来更好地预测成瘾性疾病的个体风险。