Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA.
Mol Psychiatry. 2021 Sep;26(9):4931-4943. doi: 10.1038/s41380-020-0771-z. Epub 2020 May 12.
Binge drinking is associated with disease and death, and developing tools to identify risky drinkers could mitigate its damage. Brain processes underlie risky drinking, so we examined whether neural and psychosocial markers could identify binge drinkers. Reward is the most widely studied neural process in addiction, but processes such as emotion, social cognition, and self-regulation are also involved. Here we examined whether neural processes apart from reward contribute to predicting risky drinking behaviors. From the Human Connectome Project, we identified 177 young adults who binged weekly and 309 nonbingers. We divided the sample into a training and a testing set and used machine-learning algorithms to classify participants based on psychosocial, neural, or both (neuropsychosocial) data. We also developed separate models for each of the seven fMRI tasks used in the study. An ensemble model developed in the training dataset was then applied to the testing dataset. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) and differences between models were assessed using DeLong's test. The three models performed better than chance in the test sample with the neuropsychosocial (AUC = 0.86) and psychosocial (AUC = 0.84) performing better than the neural model (AUC = 0.64). Two fMRI-based models predicted binge drinking status better than chance, corresponding to the social and language tasks. Models developed with psychosocial and neural variables could contribute as diagnostic tools to help classify risky drinkers. Since social and language fMRI tasks performed best among the neural discriminators (including those from gambling and emotion tasks), it suggests the involvement of a broader range of brain processes than those traditionally associated with binge drinking in young adults.
binge 饮酒与疾病和死亡有关，开发识别高危饮酒者的工具可以减轻其危害。大脑过程是高危饮酒的基础，因此我们研究了神经和心理社会标志物是否可以识别高危饮酒者。奖励是成瘾研究中最广泛研究的神经过程，但也涉及情绪、社会认知和自我调节等过程。在这里，我们研究了除奖励之外的神经过程是否有助于预测高危饮酒行为。我们从人类连接组计划中确定了 177 名每周 binge 饮酒的年轻人和 309 名非 binge 饮酒者。我们将样本分为训练集和测试集，并使用机器学习算法根据心理社会、神经或两者（神经心理社会）数据对参与者进行分类。我们还为研究中使用的七个 fMRI 任务中的每一个都开发了单独的模型。然后将在训练数据集中开发的集成模型应用于测试数据集。通过接收者操作特征曲线下的面积 (AUC) 评估模型性能，并使用 DeLong 检验评估模型之间的差异。三个模型在测试样本中的表现优于随机水平，神经心理社会模型 (AUC=0.86) 和心理社会模型 (AUC=0.84) 的表现优于神经模型 (AUC=0.64)。两个基于 fMRI 的模型预测 binge 饮酒状态优于随机水平，对应于社会和语言任务。基于心理社会和神经变量开发的模型可以作为诊断工具，有助于对高危饮酒者进行分类。由于社会和语言 fMRI 任务在神经判别器中表现最佳（包括赌博和情绪任务的判别器），这表明在年轻人中，与 binge 饮酒相关的大脑过程比传统上认为的更为广泛。
