大脑结构共变网络的特征是早期重度饮酒的强有力的标志物
Jonatan Ottino-González1,2, Renata B Cupertino3, Zhipeng Cao2
1Division of Endocrinology, The Saban Research Institute, Children's Hospital Los Angeles, Los Angeles, CA, USA.
Addiction (Abingdon, England)
|September 19, 2023
概括
在脑MRI扫描中识别的结构共变网络 (SCN) 揭示了酒精使用障碍 (AUD) 患者的独特模式. 这些SCN差异是重度饮酒和相关大脑疾病的早期标志物.
科学领域:
- 神经成像是一种神经成像.
- 大脑网络 大脑网络
- 成研究 研究成研究
背景情况:
- 以前的研究表明,在患有酒精使用障碍 (AUD) 的年轻成年人中,有明显的结构性共变网络 (SCN) 模式.
- 这些SCN模式被证明可以预测青少年的问题饮酒.
研究的目的:
- 确定SCN的稳定性和价值,用于识别重度酒精使用者.
- 在独立数据集中验证SCN衍生指标.
主要方法:
- 利用了来自三个独立研究的横截面和纵向数据:PING,NCANDA和HCP.
- 基于大量饮酒或AUD诊断的确定的病例,以及基于低/无酒精使用的对照.
- 应用图形理论指标 (分离和整合) 来总结SCN.
主要成果:
- 在所有数据集中,重度饮酒者表现出较低的网络隔离 (较低的聚类系数,模块化和平均最短路径长度).
- 沉重的酒精使用者显示出更高的网络集成 (更高的全球效率).
- 这些发现表明,相邻的大脑区域的皮层厚度不太相似,而远处的区域更相似.
结论:
- SCN差异是重度酒精使用的早期标志物.
- 来自SCN的指标对于检测与大脑相关的精神病理学是有价值的,特别是在饮酒的背景下.
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