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A method based on the granger causality and graph kernels for discriminating resting state from attentional task

机译：一种基于GRANGER因果关系和图形内核的方法，用于辨别休息状态的注意力

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Exploring the directional connections between brain regions is of great importance in understanding the brain function. As a method of this exploration, Granger causality is defined in terms of the amount of improvement in the estimation of a signal by past samples of another signal (cause). This method produced reliable results in various applications. In current study, we use connections of directed graphs as the features for discriminating two brain states, rest and attentional cueing task, in a block design fMRI dataset. We apply a support vector machine (SVM) which is enriched by graph kernels like random walk, graphlet and sub-tree kernels on directed graphs of different brain states. Graph kernel methods are a branch of graph matching methods and have recently been proposed as a theoretically sound and promising approach to the problem of graph comparison. They measure the inexact similarity between graphs. For the first time, we apply graph kernels on graphs of brain's effective connectivity. We achieved classification accuracy of 100% in discrimination of resting state from attentional task. We also obtain one graph for each brain state representing causal connections between brain regions. From the networks obtained for each state, we can infer that caudate is the source of information in both states and Left ventromedial prefrontal is the sink of information in the resting state.
机译：探索大脑区域之间的方向连接在理解大脑功能方面具有重要意义。作为该探索的方法，Ganger因果关系是根据过去的另一种信号的过去样本估计信号的改进量（原因）。该方法在各种应用中产生可靠的结果。在目前的研究中，我们使用定向图形的连接作为识别两个脑状态，休息和注意力决定在块设计FMRI数据集中的功能。我们应用了一个支持向量机（SVM），它通过图形内核丰富，如随机步行，石墨和子树内核，不同脑状态的指导图。图形内核方法是图形匹配方法的分支，最近已被提出为图形比较问题的理论上和有希望的方法。它们测量图之间的不精确相似性。首次，我们将图形内核应用于大脑有效连接的图表。我们从注意事项判断休息状态的歧视率为100％的分类准确性。我们还获得一个图表，用于代表大脑区域之间的因果关系。从为每个状态获得的网络，我们可以推断尾部是两个状态的信息源，左侧介文预称是静止状态中信息的汇。

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