超越功能连接:基于fMRI的脑疾病分类时间序列建模.
IEEE transactions on medical imaging
|February 6, 2026
概括
从功能磁共振成像 (fMRI) 分析血氧水平依赖 (BOLD) 信号的新时代模型显著改善了对传统方法的脑疾病分类. 这些先进的技术更有效地捕捉复杂的大脑动态.
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:
- 功能磁共振成像 (fMRI) 使用血氧水平依赖 (BOLD) 信号进行非侵入性脑疾病分类.
- 当前的方法通常依赖于通过皮尔森相关的功能连接 (FC),这简化了4D BOLD数据变成静态的2D矩阵,失去时间动态和线性关系.
研究的目的:
- 将最先进的时间模型与基于FC的传统方法进行fMRI脑疾病分类的比较.
- 引入DeCI,一个新的框架,集成循环漂移分解和道独立,用于增强的fMRI分析.
主要方法:
- 在五个公共fMRI数据集的原始BOLD信号上对先进的时间序列模型 (PatchTST,TimesNet,TimeMixer) 的基准测试.
- 开发和应用DeCI框架,包括每个感兴趣区域 (ROI) 的循环和漂移分解和频道独立建模.
- 与传统的基于FC的方法和其他时间基线进行比较分析.
主要成果:
- 最先进的时间模型在分类准确性方面始终优于传统的基于FC的方法.
- 与FC和时间基线相比,提出的DeCI框架显示出更高的分类准确性和概括能力.
- 直接建模时间信息,包括振荡波动和缓慢的基线趋势,对于提高性能至关重要.
结论:
- 对BOLD信号的端到端时间建模为基于fMRI的脑疾病分类提供了比静态FC方法更有效的方法.
- DeCI框架为捕捉复杂的大脑动态提供了强大而准确的方法,推进了神经影像分析领域.
- 研究结果主张在fMRI分析中转向时间建模的范式转变,以更好地了解大脑功能和功能障碍.
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