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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连接网络中使用后向边缘消除和图形核对大脑状态进行解码

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摘要

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：在当前的研究中，我们提出了一种基于从功能磁共振成像（fMRI）数据中提取的连接图来解码大脑状态的新方法。 fMRI连接图在不同的大脑状态下构建，并馈入迭代支持向量分类器，该分类器由最短路径内核丰富。分类器通过后向边缘消除过程修剪不重要边缘的图。发生最大分类性能的迭代被视为最佳迭代。在最佳迭代中幸存的边缘和节点形成状态之间的判别网络。我们应用“一对一”方法将所提出的方法扩展到一个多类分类器中。该分类器用于从受阻的设计fMRI数据中区分出五个认知脑状态：（1）固定，（2）单个刺激的检测，（3）知觉匹配，（4）注意提示和（5）延迟匹配样品。所提方法对多类分类的准确率为86.32％。后扣带回皮层被识别为将固定与任务分开的网络中枢。上顶叶具有相同的作用来区分不同的任务。右后骨和上顶叶之间的连通性有助于在固定任务和任务任务情况下进行区分。 ？ 2012年Elsevier B.V.

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