Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA.
Addiction. 2022 May;117(5):1312-1325. doi: 10.1111/add.15772. Epub 2022 Feb 27.
Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol.
Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics.
A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)-Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe.
Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency).
The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference = -0.0142, 95% confidence interval (CI) = -0.1333, 0.0092; P-value = 0.017], clustering coefficient (AUC difference = -0.0164, 95% CI = -0.1456, 0.0043; P-value = 0.008) and local efficiency (AUC difference = -0.0141, 95% CI = -0.0097, 0.0034; P-value = 0.010), as well as lower average shortest path length (AUC difference = -0.0405, 95% CI = -0.0392, 0.0096; P-value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = -0.0011, 0.0043; P-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = -0.0131, 95% CI = -0.1304, 0.0033; P-value = 0.024), lower average shortest path length (AUC difference = -0.0362, 95% CI = -0.0334, 0.0118; P-value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = -0.0011, 0.0038; P-value = 0.048).
Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking.
结构协变网络（SCN）的图论分析提供了一种尚未应用于酒精依赖（AD）的大脑组织评估方法。我们估计 AD 患者和 19 岁及 14 岁重度饮酒青少年在大量饮酒前是否存在 SCN 差异。
成人的横断面样本和青少年的队列研究。使用图论指标总结了 68 个区域皮质厚度的相关矩阵。
共纳入 24 个合作机构的 ENIGMA-Addiction 联盟的 745 名 AD 成年患者和 979 名非依赖对照者，以及 IMAGEN 研究的 297 名高危饮酒青少年和 594 名对照者，年龄分别为 19 岁和 14 岁，均来自欧洲。
网络分离度（模块度、聚类系数和局部效率）和整合度（平均最短路径长度和全局效率）的度量。
年轻的 AD 患者与非依赖对照组相比，网络分离度较低，整合度较高。与对照组相比，19 岁的高危饮酒者表现出较低的模块度[曲线下面积（AUC）差异=-0.0142，95%置信区间（CI）=-0.1333，0.0092；P 值=0.017]、聚类系数（AUC 差异=-0.0164，95%CI=-0.1456，0.0043；P 值=0.008）和局部效率（AUC 差异=-0.0141，95%CI=-0.0097，0.0034；P 值=0.010），以及较低的平均最短路径长度（AUC 差异=-0.0405，95%CI=-0.0392，0.0096；P 值=0.021）和较高的全局效率（AUC 差异=0.0044，95%CI=-0.0011，0.0043；P 值=0.023）。在 14 岁时也存在相同的模式，表现为较低的聚类系数（AUC 差异=-0.0131，95%CI=-0.1304，0.0033；P 值=0.024）、较低的平均最短路径长度（AUC 差异=-0.0362，95%CI=-0.0334，0.0118；P 值=0.019）和较高的全局效率（AUC 差异=0.0035，95%CI=-0.0011，0.0038；P 值=0.048）。
横断面分析表明，特定的结构协变网络特征是成人酒精依赖的早期标志物。在一个 19 岁且 14 岁时大量饮酒的重度饮酒青少年队列中观察到了类似的影响，这表明这种模式可能是一个潜在的问题饮酒风险因素。
