Brain structural covariance network features are robust markers of early heavy alcohol use

IF 5.2 1区医学 Q1 PSYCHIATRY

Addiction Pub Date : 2023-09-19 DOI:10.1111/add.16330

Jonatan Ottino-González, Renata B. Cupertino, Zhipeng Cao, Sage Hahn, Devarshi Pancholi, Matthew D. Albaugh, Ty Brumback, Fiona C. Baker, Sandra A. Brown, Duncan B. Clark, Massimiliano de Zambotti, David B. Goldston, Beatriz Luna, Bonnie J. Nagel, Kate B. Nooner, Kilian M. Pohl, Susan F. Tapert, Wesley K. Thompson, Terry L. Jernigan, Patricia Conrod, Scott Mackey, Hugh Garavan

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Abstract

Background and Aims

Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.

Design and Setting

Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14–22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17–22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22–37 years).

Cases

Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.

Measurements

Graph theory metrics of segregation and integration were used to summarize SCN.

Findings

Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = −0.029, P = 0.002], lower modularity (AUC = −0.14, P = 0.004), lower average shortest path length (AUC = −0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = −0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.

Conclusion

Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

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大脑结构协方差网络特征是早期大量饮酒的可靠标记

背景与目的最近，我们证实了通过磁共振成像（MRI）测量大脑皮层厚度得出的结构协方差网络（SCN）的独特模式，该模式可描述患有酒精使用障碍（AUD）的年轻成人的特征，并可预测与对照组相比青少年当前和未来的问题性饮酒。在此，我们在另外三项独立研究中确定了 SCN 在识别重度饮酒者方面的稳健性和价值。设计和设置利用儿科成像、神经认知和遗传学（PING）研究（n = 400，年龄范围 = 14-22岁）、全国青少年酒精和神经发育联合会（NCANDA）（n = 272，年龄范围 = 17-22岁）和人类连接组项目（HCP）（n = 375，年龄范围 = 22-37岁）的数据进行横断面和纵向研究。病例病例根据大量饮酒模式或曾被诊断为酒精使用障碍（AUD）来定义：分别确定了 50、68 和 61 例病例。对照组不饮酒或少饮酒或无 AUD：分别选取了 350、204 和 314 名对照组。测量采用分离和整合的图式理论指标来概括 SCN。研究结果与我们之前的研究结果一致，在三组数据中，病例的聚类系数较低[曲线下面积（AUC）= -0.029，P = 0.002]，模块化程度较低（AUC = -0.14，P = 0.004），平均最短路径长度较低（AUC = -0.078，P = 0.017），整体效率较高（AUC = 0.007，P = 0.010）。局部效率差异很小（AUC = -0.017，P = 0.052）。也就是说，病例表现出较低的网络分离度和较高的整合度，这表明相邻节点（即脑区）的厚度相似度较低，而空间距离较远的节点相似度较高。结论在三组新数据中，大脑结构协方差网络（SCN）差异似乎构成了大量饮酒的早期标志，更广泛地说，证明了 SCN 衍生指标在检测与大脑相关的精神病理学方面的实用性。

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来源期刊

Addiction 医学-精神病学

CiteScore

10.80

自引率

6.70%

发文量

319

审稿时长

3 months

期刊介绍： Addiction publishes peer-reviewed research reports on pharmacological and behavioural addictions, bringing together research conducted within many different disciplines. Its goal is to serve international and interdisciplinary scientific and clinical communication, to strengthen links between science and policy, and to stimulate and enhance the quality of debate. We seek submissions that are not only technically competent but are also original and contain information or ideas of fresh interest to our international readership. We seek to serve low- and middle-income (LAMI) countries as well as more economically developed countries. Addiction’s scope spans human experimental, epidemiological, social science, historical, clinical and policy research relating to addiction, primarily but not exclusively in the areas of psychoactive substance use and/or gambling. In addition to original research, the journal features editorials, commentaries, reviews, letters, and book reviews.