协调1万个连接器:用于多个站点分析网络连接和认知障碍的站点不变表示学习
Nancy R Newlin1, Michael E Kim1, Praitayini Kanakaraj1
1Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States.
Journal of medical imaging (Bellingham, Wash.)
|November 10, 2025
概括
这项研究通过使用条件变异自编码器在不同队列中协调扩散MRI数据. 该方法通过消除扫描器变异性和增强生物信号来改善阿尔茨海默病的诊断.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
背景情况:
- 由于扫描仪和协议的变化,扩散磁共振成像 (dMRI) 数据协调对于多站点研究至关重要.
- 阿尔茨海默病 (AD) 研究面临的挑战是整合具有高生物和基于地点的变异性的多样化数据集.
- 现有的方法难以协调来自不同成像队伍的数据,限制了统计能力和发现.
研究的目的:
- 为dMRI数据开发数据驱动的协调方法,减轻成像采集的混因素.
- 创建站点不变的,结构连接的低维表示.
- 通过协调的神经成像数据,提高认知状态的预测能力.
主要方法:
- 一个有条件变化的自编码器被设计用于学习潜伏表示.
- 这些表示最小化了关于成像因素 (扫描仪,位置,协议) 的信息,并最大化了关于认知状态的信息.
- 该模型整合了来自9个队列和35个独特收购的数据 (6956名参与者,11,927次会议).
主要成果:
- 站点不变表示成功地消除了12个网络连接措施中的显著站点效应 (p < 0.05).
- 认知诊断的预测准确性从68%提高到73%.
- 该模型在15个数据配置中展示了可重现的精度.
结论:
- 代表性学习有效地减轻了神经成像中的获取特定混因素.
- 拟议的方法增强了生物信号检测,并改善了阿尔茨海默病研究中的诊断预测.
- 该方法为大规模神经科学研究整合异构的dMRI数据集提供了强大的解决方案.
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