Structure analysis for fMRI brain data by using mutual information and interaction

The authors propose a novel structure analysis method for fMRI data by using mutual information and interaction, based on Shannon´s information theory. First, we introduce a structure analysis that assumes one directional information flow schema: stimulus variate→state variate→response variate. Next, we present alternative structure analysis methods that focus on the common information in variates. These methods are useful in the case where the direction of information flow is not obvious, just like in higher brain areas. We apply these analysis methods to artificially generated data, and show some kinds of classification error. However, intensive analysis that uses many kinds of information measurements can make information structure clear. Finally we apply these methods to fMRI data and show our methods are useful