大麻和酒精使用障碍的多类分类:识别常见和特定物质的神经回路
Ayaka Kato1,2,3,4, Kaustubh R Kulkarni1,2,5,4, Nikash R Das6
1Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
使用机器学习进行的大脑网络分析,可以准确地区分大麻和酒精使用障碍. 这种方法可以识别每个物质的独特神经特征,帮助开发成生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 计算精神病学是一种计算精神病学.
- 成研究 研究成研究
背景情况:
- 机器学习推进了对物质使用的神经签名识别.
- 大多数研究都集中在单一物质上,限制了对比和对多物质使用者的概括性.
- 现有的方法难以在不同的物质使用情况中比较共享和独特的网络计算.
研究的目的:
- 开发一种可解释的,基于连接性的多类分类框架来对成进行分类.
- 模拟分布式大脑网络组织,以区分共享和特定物质的成机制.
- 为了能够直接比较大麻使用者,酒精使用者和健康对照者的神经特征.
主要方法:
- 利用了来自提示诱导的渴望任务的功能连接功能.
- 为大麻使用者 (n=166),酒精使用者 (n=101) 和健康对照者 (n=238) 开发了一个多类分类框架.
- 基于功能连接的模型与基于激活的模型进行了比较.
主要成果:
- 获得了高的样本外分类准确度:大麻为87%,酒精为69%,对照为73%.
- 功能连接模型显著超过了基于激活的模型.
- 确定了不同的网络配置,包括大麻使用者中增强的前额头合和酒精使用者中更大的岛屿-三角形整合.
结论:
- 不同的大脑网络配置的特征是不同的物质使用情况.
- 基于连接的模型为成神经科学提供可解释的生物标志物.
- 这些发现有助于更好地理解成中共同和特定物质的神经机制.
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