A Novel Brain Decoding Method: a Correlation Network Framework for Revealing Brain Connections

TL;DR

This paper introduces a correlation network framework that integrates structural and functional brain connectivity to improve decoding of perceptual images from fMRI data, demonstrating enhanced accuracy over existing methods.

Contribution

The novel CorrNet framework combines topological correlation with pattern models, capturing structural connectivity to advance brain decoding techniques.

Findings

Achieved significant improvement in decoding accuracy.

Verified robustness and reliability of the CorrNet framework.

Demonstrated effectiveness with SVM and SNN models.

Abstract

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.