分因式二进制搜索:在多变量高维时间序列的网络结构中检测变化点
Martin Ondrus1, Emily Olds2, Ivor Cribben1,2
1Neuroscience and Mental Health Institute, University of Alberta, Alberta, Canada.
Imaging neuroscience (Cambridge, Mass.)
|August 13, 2025
概括
我们开发了FaBiSearch,这是一种使用功能磁共振成像 (fMRI) 数据检测大脑活动模式变化的新方法. 这个工具有助于理解休息和任务期间的动态大脑连接和网络变化.
科学领域:
- 神经科学是一个神经科学.
- 数据科学数据科学数据科学
- 计算生物学 计算生物学
背景情况:
- 从功能磁共振成像 (fMRI) 时间序列数据中了解动态大脑连接在神经科学中至关重要.
- 现有的模型经常与全脑fMRI数据的高维度和复杂动态作斗争.
- 需要精确的变化点检测和网络估计来分析这些复杂的大脑机制.
研究的目的:
- 引入一种新的方法,FabiSearch,用于在高维fMRI数据的网络结构中准确检测变化点.
- 开发一种新的网络估计技术,用于在检测到的变化点之间分析大脑数据.
- 在休息状态和基于任务的fMRI实验中研究动态功能连接.
主要方法:
- 开发了FaBiSearch,该方法结合了非负矩阵分解 (NMF) 和用于多个变化点检测的新型二进制搜索算法.
- 提出了一种新的网络估计方法,用于变化点之间的多变量时间序列数据.
- 将这些方法应用于静止状态和基于任务的fMRI数据集,包括阅读任务 ("哈利波特").
主要成果:
- 在高维fMRI数据的网络结构中,FaBiSearch有效地识别了多个变化点.
- 对静止状态数据的分析揭示了动态功能连接的测试-重新测试行为.
- 基于任务的fMRI分析探索了阅读期间的网络动态,将变化点与叙事事件关联起来,并确定动态枢纽节点.
结论:
- "FabiSearch"为了解大规模的大脑动态和网络变化提供了一个强大的框架.
- 这些方法为休息状态和任务唤起的大脑活动模式提供了洞察力.
- 该FaBiSearch R包和相关的实验是公开可用于进一步的研究.
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