  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 problems in brain decoding based on functional
magnetic resonance imaging (fMRI) 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
proposed a correlation network (CorrNet) framework that could be flexibly
combined with diverse pattern representation models. In the CorrNet framework,
the topological correlation was introduced to reveal structural information.
Rich correlations were obtained, which contributed to specifying the underlying
effective connectivity. We also combined the CorrNet framework with a linear
support vector machine (SVM) and a dynamic evolving spike neuron network (SNN)
for pattern representation separately, thus providing a novel method for
decoding cognitive activity patterns. Experimental results verified the
reliability and robustness of our CorrNet framework and demonstrated that the
new method achieved significant improvement in brain decoding over comparable
methods.
