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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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基于隐藏的半马尔科夫模型进行协同激活模式分析,用于大脑时空动力学.

Zihao Yuan, Jiaqing Chen, Han Qiu

    IEEE transactions on medical imaging
    |September 8, 2025
    PubMed
    概括

    这项研究介绍了HSMM-CAP,这是一种用于分析大脑活动动态的新框架. 它更强大地揭示了时空协同激活模式 (stCAPs),即使信号与噪声比率较低的数据.

    科学领域:

    • 神经科学是一个神经科学.
    • 计算神经科学是一种神经科学.
    • 数据科学数据科学数据科学

    背景情况:

    • 分析人类自发大脑活动揭示了功能组织.
    • 同激活模式 (CAP) 分析特征神经网络,但忽视时间动态,对噪声敏感.
    • 现有的CAP方法缺乏对低信号噪声比 (SNR) 数据和时间可重现性的稳定性.

    研究的目的:

    • 提出一种新的计算框架,用于研究大脑活动中的时空联合激活模式 (stCAPs).
    • 增强人类大脑中动态功能组织的分析.
    • 为了解决现有的农业政策方法的局限性,特别是在时间动态和低SNR数据方面.

    主要方法:

    • 基于隐藏的半马尔科夫模型 (HSMM) 开发了一个新的协同激活模式 (CAP) 框架,称为HSMM-CAP分析.
    • 在半马尔科夫过程框架内使用stCAPs的实证空间分布作为排放模型.
    • 构建了HSMM-CAP-K-means方法来推断stCAP状态序列和过渡参数,利用稀疏性和异质性假设.

    主要成果:

    • 在HSMM-CAP分析中,成功地研究了大脑活动中的时空共激活模式 (stCAPs).
    • 该方法在变化的信号噪声比 (SNR) 水平上显示出稳定性,模拟研究证实了这一点.

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  • 在现实世界休息状态fMRI数据中,HSMM-CAP揭示了stCAP的时空动态.

    • 结论:

    • HSMM-CAP提供了一个强大的,数据驱动的计算框架,用于分析大脑的时空动态.
    • 拟议的方法克服了传统的CAP分析的局限性,通过结合时间信息和提高噪声弹性.
    • 这个框架通过对静止状态fMRI数据的动态分析,为人类大脑的功能组织提供了新的见解.