Inferring　brain-effective　connectivity　networks　from　neuroimaging　data　has　become　a　very　hot　topic　in　neuroinformatics　and　bioinformatics.　In　recent　years,　the　search　methods　based　on　Bayesian　network　score　have　been　greatly　developed　and　become　an　emerging　method　for　inferring　effective　connectivity.　However,　the　previous　score　functions　ignore　the　temporal　information　from　functional　magnetic　resonance　imaging　(fMRI)　series　data　and　may　not　be　able　to　determine　all　orientations　in　some　cases.　In　this　article,　we　propose　a　novel　score　function　for　inferring　effective　connectivity　from　fMRI　data　based　on　the　conditional　entropy　and　transfer　entropy　(TE)　between　brain　regions.　The　new　score　employs　the　TE　to　capture　the　temporal　information　and　can　effectively　infer　connection　directions　between　brain　regions.　Experimental　results　on　both　simulated　and　real-world　data　demonstrate　the　efficacy　of　our　proposed　score　function.