Learning　brain　effective　connectivity　(EC)　networks　from　functional　magnetic　resonance　imaging　(fMRI)　data　has　become　a　new　hot　topic　in　the　neuroinformatics　field.　However,　how　to　accurately　and　efficiently　learn　brain　EC　networks　is　still　a　challenging　problem.　In　this　paper,　we　propose　a　new　algorithm　to　learn　the　brain　EC　network　structure　using　ant　colony　optimization　(ACO)　algorithm　combining　with　voxel　activation　information,　named　as　VACOEC.　First,　VACOEC　uses　the　voxel　activation　information　to　measure　the　independence　between　each　pair　of　brain　regions　and　effectively　restricts　the　space　of　candidate　solutions,　which　makes　many　unnecessary　searches　of　ants　be　avoided.　Then,　by　combining　the　global　score　increase　of　a　solution　with　the　voxel　activation　information,　a　new　heuristic　function　is　designed　to　guide　the　process　of　ACO　to　search　for　the　optimal　solution.　The　experimental　results　on　simulated　datasets　show　that　the　proposed　method　can　accurately　and　efficiently　identify　the　directions　of　the　brain　EC　networks.　Moreover,　the　experimental　results　on　real-world　data　show　that　patients　with　Alzheimers　disease　(AD)　exhibit　decreased　effective　connectivity　not　only　in　the　intra-network　within　the　default　mode　network　(DMN)　and　salience　network　(SN),　but　also　in　the　inter-network　between　DMN　and　SN,　compared　with　normal　control　(NC)　subjects.　The　experimental　results　demonstrate　that　VACOEC　is　promising　for　practical　applications　in　the　neuroimaging　studies　of　geriatric　subjects　and　neurological　patients.