Brain decoding is a hot spot in cognitive science, which focuses on reconstructing perceptual images from brain activities. Analyzing the correlations of collected data from human brain activities and representing activity patterns are two key problems in brain decoding based on functional magnetic resonance imaging signals. However, existing correlation analysis methods mainly focus on the strength information of voxel, which reveals functional connectivity in the cerebral cortex. They tend to neglect the structural information that implies the intracortical or intrinsic connections; that is, structural connectivity. Hence, the effective connectivity inferred by these methods is relatively unilateral. Therefore, we propose in this paper a correlation network (CorrNet) framework that could be flexibly combined with diverse pattern representation models. In the CorrNet framework, the topological correlation is introduced to reveal structural information. Rich correlations can be obtained, which contribute to specifying the underlying effective connectivity. We also combine the CorrNet framework with a linear support vector machine and a dynamic evolving spike neuron network for pattern representation separately, thus provide a novel method for decoding cognitive activity patterns. Experimental results verify the reliability and robustness of our CorrNet framework, and demonstrate that the new method can achieve significant improvement in brain decoding over comparable methods.
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