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首页> 外文会议>Proceedings of the 17th IASTED international conference on robotics applications ; Proceedings of the 11th IASTED international conference on biomedical engineering >LARGE SCALE FUNCTIONAL CONNECTIVITY FOR BRAIN DECODING
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LARGE SCALE FUNCTIONAL CONNECTIVITY FOR BRAIN DECODING

机译：大规模的大脑解码功能连接

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摘要

Functional Magnetic Resonance Imaging (fMRI) data consists of time series for each voxel recorded during a cognitive task. In order to extract useful information from this noisy and redundant data, techniques are proposed to select the voxels that are relevant to the underlying cognitive task. We propose a simple and efficient algorithm for decoding the brain states by modelling the correlation patterns between the voxel time series. For each stimulus during the experiment, a separate functional connectivity matrix is computed in voxel level. The elements in connectivity matrices are then filtered out by making use of a minimum spanning tree formed using a global connectivity matrix for the entire experiment in order to reduce dimensionality. For a recognition memory experiment with nine subjects, functional connectivity matrices are computed for encoding and retrieval phases. The class labels of the retrieval samples are predicted within a k-nearest neighbour space constructed by the traversed entries in the functional connectivity matrices for encoding samples. The proposed method is also adapted to large scale functional connectivity tasks by making use of graphics boards. Classification performance in ten categories is comparable and even better compared to both classical and enhanced methods of multi-voxel pattern analysis techniques.
机译：功能磁共振成像（fMRI）数据由在认知任务期间记录的每个体素的时间序列组成。为了从这种嘈杂的数据中提取有用的信息，提出了一些技术来选择与基础认知任务相关的体素。我们提出了一种简单有效的算法，通过对体素时间序列之间的相关模式进行建模来解码大脑状态。对于实验期间的每个刺激，将在体素级别上计算一个单独的功能连接矩阵。然后，通过使用为整个实验使用全局连通性矩阵形成的最小生成树来滤除连通性矩阵中的元素，以降低维数。对于具有九个主题的识别记忆实验，计算功能连接矩阵以进行编码和检索阶段。在由用于编码样本的功能连接矩阵中遍历的条目构成的k个最近邻空间内，预测检索样本的类别标签。通过使用图形板，所提出的方法还适用于大规模功能连接任务。与经典和增强的多体素模式分析技术相比，十个类别的分类性能相当，甚至更好。

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