Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be considered as a multi-objective pattern classification problem with the aim to optimize response patterns, in which informative voxels interacting with each other are selected, achieving high classification accuracy associated with cognitive stimulus conditions. To solve the problem, we propose a feature interaction detection framework, integrating hierarchical heterogeneous particle swarm optimization and support vector machines, for voxel selection in MVPA. In the proposed approach, we first select the most informative voxels and then identify a response pattern based on the connectivity of the selected voxels. The effectiveness of the proposed approach was examined for the Haxby's dataset of object-level representations. The computational results demonstrated higher classification accuracy by the extracted response patterns, compared to state-of-the-art feature selection algorithms, such as forward selection and backward selection.

中文翻译：

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使用基于粒子群优化的方法对fMRI数据进行脑反应模式识别。

许多神经科学研究致力于使用功能磁共振成像（fMRI）来了解与认知相关的大脑神经反应。与识别响应模式的单变量分析相比，fMRI数据的多体素模式分析（MVPA）已成为最近文献中使用机器学习技术的一种相对有效的方法。MVPA可以被认为是一个多目标模式分类问题，旨在优化响应模式，其中选择了相互交互的信息性体素，从而实现了与认知刺激条件相关的高分类精度。为了解决该问题，我们提出了一种功能交互检测框架，该框架集成了分层的异构粒子群优化算法和支持向量机，用于MVPA中的体素选择。在提出的方法中，我们首先选择信息最丰富的体素，然后根据所选体素的连通性确定响应模式。针对对象级别表示的Haxby数据集检查了所提出方法的有效性。与最新的特征选择算法（例如，前向选择和后向选择）相比，计算结果表明，通过提取的响应模式，可以实现更高的分类精度。