联邦贝叶斯网络从多站点数据中学习
Shuai Liu1, Xiao Yan1, Xiao Guo2
1School of Management, Xi'an Jiaotong University, Xi'an 710049, China.
Journal of biomedical informatics
|February 5, 2025
概括
本研究介绍了NOTEARS-PFL,这是一种联合学习方法,用于识别主要抑郁症 (MDD) 的大脑连接生物标志物,使用多站点静止状态功能性MRI数据,同时克服数据共享障碍.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 识别主要抑郁障碍 (MDD) 的功能连接生物标志物对于理解这种疾病和实现早期干预至关重要.
- 多站点神经成像数据可以增强统计能力,但由于站点间异质性和数据共享限制而面临挑战.
研究的目的:
- 从多站点静止状态功能磁共振成像 (rs-fMRI) 数据中开发一种学习贝叶斯网络的方法,克服异质性和数据共享障碍.
- 为了确定主要抑郁症 (MDD) 的功能连接生物标志物.
主要方法:
- 建议NOTEARS-PFL,一个联合的联合估计器,将共享和特定地点的信息纳入使用稀疏组拉索惩罚.
- 使用乘数的交替方向方法进行优化,使本地数据处理和中央网络结构更新成为可能.
- 解决了与多站点研究固有的数据共享限制.
主要成果:
- NOTEARS-PFL在合成和现实世界的多站点 rs-fMRI数据集上展示了有效性和准确性.
- 该方法在识别大脑功能连接方面,与其他方法相比,显示出更高的效率和精度.
- 在休息状态功能磁共振成像 (rs-fMRI) 数据上得到验证,来自主要抑郁障碍 (MDD) 患者.
结论:
- NOTEARS-PFL是一个新的工具箱,用于从多站点数据中学习MDD患者的异质大脑功能连接.
- 该方法有效地处理数据共享限制,这对于协作多站点研究至关重要.
- 综合实验证实了NOTEARS-PFL在MDD研究中的卓越疗效.
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