用fMRI数据发现大脑功能连接的动态特征可精准区分不同认知状态
Dynamic Functional Connectivity Features for Brain State Classification: Insights from the Human Connectome Project
- 基于时间动态功能连接特征，仅用线性模型实现高精度脑状态分类
- 运动与语言任务分类准确率达当前最优水平，关键区域具有功能特异性
- 揭示非相关脑区在分类中作用最小，凸显时间结构对神经网络调控的重要性
我们分析人类连接组计划（HCP）的静息态和任务态功能磁共振成像（fMRI）数据，以匹配不同认知任务下的脑活动。结果表明，即使使用基础线性机器学习模型，也能有效分类脑状态，并在运动功能与语言处理任务上达到当前最优准确率。特征重要性排序识别出与特定认知功能显著关联的脑区组合，为皮层与皮层下区域的功能分工假说提供有力支持。此外，我们研究了这些脑区的时间动态特性，发现fMRI信号的时变结构对区域间功能连接的形成至关重要：不相关的脑区对分类贡献最小。这一时间视角深化了对认知加工中神经网络构建与调控机制的理解。
原文摘要 · Abstract (English)
We analyze functional magnetic resonance imaging (fMRI) data from the Human Connectome Project (HCP) to match brain activities during a range of cognitive tasks. Our findings demonstrate that even basic linear machine learning models can effectively classify brain states and achieve state-of-the-art accuracy, particularly for tasks related to motor functions and language processing. Feature importance ranking allows to identify distinct sets of brain regions whose activation patterns are uniquely associated with specific cognitive functions. These discriminative features provide strong support for the hypothesis of functional specialization across cortical and subcortical areas of the human brain. Additionally, we investigate the temporal dynamics of the identified brain regions, demonstrating that the time-dependent structure of fMRI signals are essential for shaping functional connectivity between regions: uncorrelated areas are least important for classification. This temporal perspective provides deeper insights into the formation and modulation of brain neural networks involved in cognitive processing.
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