MIC as an Appropriate Method to Construct the Brain Functional Network

Figure 2

The flowchart summarizes the process of our comparison scheme on two aspects: consistency and robustness. Looking from the top to the bottom following the arrow and the branches, we can have a clear vision of our steps and the steps can be easily realized. We will conduct ten comparisons, four for consistency and six for robustness. To make it clear, “voting” is a metaphor. For instance, when we use 6 methods to extract the top 9 important nodes, we think that the 6 methods are “holding” a vote for their top 9 important nodes from the 90 nodes. The same goes for our samples. In the left branch of consistency part, the six methods have their own important nodes sets after “voting,” but the sets are different; that is to say, the six methods have different “opinions” on important nodes. We give each method a score, respectively, to decide which method’s “opinion” is the best. One method’s score is the total votes it receives from other methods, including itself. The method which acquires the most votes pools other methods’ “opinions” together and is considered more reliable. In the first left branch of the robustness part, the 13 samples “vote”    important nodes from the 90 nodes. After “voting,” the 90 nodes have their voting numbers and voting rates (voting number/sum of vote). We can calculate the entropy according to the probability distribution induced by the voting rates. If the entropy is small, it means that the 13 samples have consensus on important nodes, and the corresponding method is more robust.