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首页> 外文期刊>Journal of Neuroscience Methods >Decoding brain states using backward edge elimination and graph kernels in fMRI connectivity networks
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Decoding brain states using backward edge elimination and graph kernels in fMRI connectivity networks

机译：在FMRI连接网络中使用反向边缘消除和图形内核解码脑状态

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In the current study, we present a new approach for decoding brain states based on the connectivity graphs extracted from functional magnetic resonance imaging (fMRI) data. fMRI connectivity graphs are constructed in different brain states and fed into an iterative support vector classifier that is enriched by shortest-path kernel. The classifier prunes the graphs of insignificant edges via a backward edge elimination procedure. The iteration in which maximum classification performance occurs is considered as optimum iteration. The edges and nodes that survive in the optimum iteration form discriminant networks between states. We apply "one-versus-one" approach to extend the proposed method into a multi-class classifier. This classifier is used to distinguish between five cognitive brain states from a blocked design fMRI data: (1) fixation, (2) detection of a single stimulus, (3) perceptual matching, (4) attentional cueing, and (5) delayed match-to-sample. The proposed method results in multi-class classification accuracy of 86.32%. Posterior cingulate cortex is identified as a hub in the networks that separate fixation from tasks. Superior parietal lob has the same role to distinguish between different tasks. Connectivity between right retrosplential and superior parietal lobe contributes to discrimination in the fixation-task and task-task cases. ? 2012 Elsevier B.V.
机译：在目前的研究中，我们基于从功能磁共振成像（FMRI）数据中提取的连接图来提出了一种对脑状态进行解码的新方法。 FMRI连接图形在不同的大脑状态中构建，并进入迭代支持向量分类器，该分类器由最短路径内核丰富。分类器通过向后边缘消除过程修剪微不足道边缘的图。最大分类性能发生的迭代被认为是最佳迭代。在最佳迭代中生存的边缘和节点在状态之间判别网络。我们应用“一对一对”方法，将所提出的方法扩展到多级分类器中。该分类器用于区分五个认知脑状态，从阻塞设计FMRI数据：（1）固定，（2）检测单个刺激，（3）感知匹配，（4）注意力匹配，（5）延迟匹配-to样品。所提出的方法导致多级分类精度为86.32％。后刺铰接皮质被识别为单独的任务固定的网络中的集线器。卓越的Paretal Lob在不同的任务之间具有相同的作用。右旋转旋转和上部叶片之间的连接有助于在固定任务和任务任务案例中歧视。还2012年Elsevier B.v.

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