可卡因使用障碍中的功能连接性改变:三重网络模型和成神经临床评估框架的见解
Ziyang Xu1, Lie Li1, Ruobing Liu1
1Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, NE, 68198, USA.
bioRxiv : the preprint server for biology
|November 28, 2024
概括
使用突出网络 (SN) 和中央执行网络 (CEN) 的机器学习模型准确地识别了可卡因使用障碍 (CUD). 这突出了SN和CEN的存在.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 计算精神病学是一种计算精神病学.
背景情况:
- 可卡因使用障碍 (CUD) 与大脑网络中的功能连接受损有关.
- 三重网络模型,包括默认模式网络 (DMN),突出网络 (SN) 和中央执行网络 (CEN),与精神病相关.
- 了解CUD中的网络变化对于开发诊断和治疗策略至关重要.
研究的目的:
- 研究DMN,SN和CEN功能连接在区分患有CUD的人与健康对照 (HC) 中的有用性.
- 评估基于这些网络的不同组合的机器学习模型,用于CUD分类.
- 在成的背景下探索特定网络相互作用的作用.
主要方法:
- 机器学习分类器使用DMN,SN和CEN的功能连接数据进行训练.
- 使用这些网络的各种组合构建了分类器,以区分CUD主题与HC主题.
- 进行了功能连接分析,以检查CUD患者的网络内部变化.
主要成果:
- 结合SN和CEN的分类器在区分CUD和HC受试者方面获得了最高的准确性 (73.4%).
- 这种SN-CEN模型的性能优于使用全三重网络 (DMN,SN,CEN) 的模型.
- 在CUD患者中,DMN和SN连接性下降,CEN连接性增加.
结论:
- SN和CEN是识别CUD的关键网络组件,特别是在成的狂饮/中毒阶段.
- 在DMN,SN和CEN内部的功能连接性改变可能作为成严重程度的潜在生物标志物.
- 这些发现与成神经临床评估 (ANA) 框架一致,强调了SN和CEN在成中的作用.
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