Research Article

MIC as an Appropriate Method to Construct the Brain Functional Network

Table 4

In Table 4(a), we used degree centrality (DC) to measure the importance of node in network. In Table 4(b), we used Shannon-Parry centrality (SPC) to measure the importance of node in network. So each method got a 1 90 vector to measure the importance of nodes by calculating nodes’ average importance from 13 sample networks. The value in the middle of table was the Euclidean distance between vectors from different method. The value of last row was the sum of Euclidean distance from one method to the others. If this sum of distance was smaller, corresponding method had better consistency. From Table 4(a), CF and MIC had relatively small and very similar sum of distance, so MIC had better consistency than other methods. From Table 4(b), MIC had the smallest sum of distance, so MIC had the best consistency with other methods.
(a)

MethodCFPCFMICMIWCFCH

CF048.7713.9011.1022.0030.70
PCF48.77047.4850.3644.3240.34
MIC13.9047.48014.8523.8027.81
MI11.1050.3614.85022.6131.73
WCF22.0044.3223.8022.61034.30
CH30.7040.3427.8131.7334.300

Total126.4231.27127.84130.65147.03164.88

(b)

MethodCFPCFMICMIWCFCH

CF00.04540.01170.00940.01910.0299
PCF0.045400.04250.04420.04130.0332
MIC0.01170.042500.01150.01920.0267
MI0.00940.04420.011500.01800.0288
WCF0.01910.04130.01920.018000.0328
CH0.02990.03320.02670.02880.03280

Total0.11550.20660.11160.11190.13040.1514