基于大规模的大脑网络的多变量分类在早期戒断期间预测了男性解毒酒精依赖患者的失效
Xia Yang1, Hong-Ru Zhu1, Yu-Jie Tao1
1Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
Asian journal of psychiatry
|September 17, 2023
概括
预测酒精依赖 (AD) 复发是一个挑战. 休息状态fMRI的神经成像生物标志物准确识别了可能在六个月内复发的患者,有助于预防策略.
科学领域:
- 神经科学是一个神经科学.
- 成 药物 药物 药物 药物
- 生物标志物发现发现
背景情况:
- 预测酒精依赖 (AD) 复发对于有效的治疗和预防至关重要.
- 确定AD失效的可靠生物标志物仍然是一个重大的临床挑战.
研究的目的:
- 为了确定神经成像特征,可以预测男性酒精依赖患者的失效.
- 使用静止状态功能磁共振成像 (fMRI) 数据开发AD复发的预测模型.
主要方法:
- 66名男性阿尔茨海默症患者在戒毒后的早期戒断期间接受了休息状态的fMRI.
- 在基线大规模大脑网络数据上进行了相关性矢量机器 (RVM) 分析.
- 对患者进行了六个月的随访,以确定复发状态.
主要成果:
- 该RVM模型有效地区分了复发患者 (n=38) 和禁酒者.
- 预测模型实现了曲线下的面积 (AUC) 为0.912.
- 交叉验证结果为0.833.3的预测准确度.
结论:
- 来自静止状态fMRI的神经连接体生物标志物显示出预测AD失效的前景.
- 开发的RVM模型为识别高复发风险的个体提供了一个潜在的工具.
- 这些发现支持在个性化成治疗策略中使用神经成像.
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