Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230027, China.
School of Computer and Information Engineering, Chuzhou University, Chuzhou, 239099, Anhui, China.
Exp Brain Res. 2022 Oct;240(10):2595-2605. doi: 10.1007/s00221-022-06447-y. Epub 2022 Aug 27.
Alterations in brain reactions to alcohol-related cues are a neurobiological characteristic of alcohol dependence (AD) and a prospective target for achieving substantial treatment effects. However, a robust prediction of the differences in inpatients' brain responses to alcohol cues during the treatment process is still required. This study offers a data-driven approach for classifying AD inpatients undertaking alcohol treatment protocols based on their brain responses to alcohol imagery with and without drinking actions. The brain activity of thirty inpatients with AD undergoing treatment was scanned using functional magnetic resonance imaging (fMRI) while seeing alcohol and matched non-alcohol images. The mean values of brain regions of interest (ROI) for alcohol-related brain responses were obtained using general linear modeling (GLM) and subjected to hierarchical clustering analysis. The proposed classification technique identified two distinct subgroups of inpatients. For the two types of cues, subgroup one exhibited significant activation in a wide range of brain regions, while subgroup two showed mainly decreased activation. The proposed technique may aid in detecting the vulnerability of the classified inpatient subgroups, which can suggest allocating the inpatients in the classified subgroups to more effective therapies and developing prognostic future relapse markers in AD.
大脑对与酒精相关线索的反应改变是酒精依赖（AD）的一种神经生物学特征，也是实现显著治疗效果的一个有前景的目标。然而，仍需要对治疗过程中住院患者对酒精线索的大脑反应差异进行稳健预测。本研究提供了一种基于对接受酒精治疗方案的 AD 住院患者的大脑对酒精意象的反应（有无饮酒行为）进行分类的方法。通过功能磁共振成像（fMRI）对 30 名接受治疗的 AD 住院患者的大脑活动进行扫描，同时观察酒精和匹配的非酒精图像。使用广义线性模型（GLM）获得与酒精相关的大脑反应的感兴趣区域（ROI）的平均值，并对其进行层次聚类分析。所提出的分类技术识别出了两个不同的住院患者亚组。对于两种类型的线索，亚组一在广泛的大脑区域表现出显著的激活，而亚组二则主要表现出激活减少。该技术可用于检测分类住院患者亚组的脆弱性，这有助于为分类亚组中的住院患者分配更有效的治疗方法，并为 AD 开发预后未来复发的标志物。
