Li Yashuang, Li Guangfei, Yang Lin, Yan Yan, Zhang Ning, Gao Mengdi, Hao Dongmei, Ye-Lin Yiyao, Li Chiang-Shan R
Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China.
Quant Imaging Med Surg. 2025 Mar 3;15(3):2405-2419. doi: 10.21037/qims-24-2131. Epub 2025 Feb 26.
Alcohol use impacts brain structure, including white matter integrity, which can be quantified by fractional anisotropy (FA) in diffusion tensor imaging (DTI). This study explored the relationship between the severity of alcohol consumption and white matter FA changes, and its sex differences, in young adults, using data from the Human Connectome Project.
We analyzed DTI data from 949 participants (491 females) and used principal component analysis (PCA) of 15 drinking metrics to quantify drinking severity. Connectome-based predictive modeling (CPM) was employed to predict the principal component of drinking severity from network FA values in a matrix of 116×116 regions. Mediation analyses were conducted to explore the interrelationships among networks identified by CPM, drinking severity, and rule-breaking behavior.
Significant correlations were found between drinking severity and network FA values. Both men and women showed significant correlations between negative network connectivity and drinking severity (men: r=0.15, P=0.001; women: r=0.30, P<0.001). Sex differences were observed in the brain regions contributing to drinking severity predictions. Mediation analyses revealed significant inter-relationships between network features, drinking severity, and rule-breaking behavior.
The connectomics of white matter FA can predict the severity of alcohol consumption, and by incorporating brain network pathways, identify sex differences. This approach provides new clues to the biological basis of alcohol abuse and evaluates how these regions interact in broader brain networks for understanding alcohol misuse and its comorbidities.
饮酒会影响大脑结构，包括白质完整性，这可以通过扩散张量成像（DTI）中的分数各向异性（FA）来量化。本研究利用人类连接组计划的数据，探讨了年轻成年人饮酒严重程度与白质FA变化之间的关系及其性别差异。
我们分析了949名参与者（491名女性）的DTI数据，并使用15种饮酒指标的主成分分析（PCA）来量化饮酒严重程度。基于连接组的预测模型（CPM）被用于从116×116区域矩阵中的网络FA值预测饮酒严重程度的主成分。进行中介分析以探索CPM确定的网络、饮酒严重程度和违规行为之间的相互关系。
发现饮酒严重程度与网络FA值之间存在显著相关性。男性和女性在负性网络连通性与饮酒严重程度之间均显示出显著相关性（男性：r = 0.15，P = 0.001；女性：r = 0.30，P < 0.001）。在有助于饮酒严重程度预测的脑区观察到了性别差异。中介分析揭示了网络特征、饮酒严重程度和违规行为之间的显著相互关系。
白质FA的连接组学可以预测饮酒的严重程度，并通过纳入脑网络通路来识别性别差异。这种方法为酒精滥用的生物学基础提供了新线索，并评估了这些区域在更广泛的脑网络中如何相互作用，以理解酒精滥用及其合并症。
