Identifying　brain　effective　connectivity　(EC)　networks　from　neuroimaging　data　has　become　an　effective　tool　that　can　evaluate　normal　brain　functions　and　the　injuries　associated　with　neurodegenerative　diseases.　So　far,　there　are　many　methods　used　to　identify　EC　networks.　However,　most　of　the　research　currently　focus　on　learning　EC　networks　from　single　modal　imaging　data　such　as　functional　magnetic　resonance　imaging　(fMRI)　data.　This　paper　proposes　a　new　method,　called　ACOEC-FD,　to　learn　EC　networks　from　fMRI　and　diffusion　tensor　imaging　(DTI)　using　ant　colony　optimization　(ACO).　First,　ACOEC-FD　uses　DTI　data　to　acquire　some　positively　correlated　relations　among　regions　of　interest　(ROI),　and　takes　them　as　anatomical　constraint　information　to　effectively　restrict　the　search　space　of　candidate　arcs　in　an　EC　network.　ACOEC-FD　then　achieves　multi-modal　imaging　data　integration　by　incorporating　anatomical　constraint　information　into　the　heuristic　function　of　probabilistic　transition　rules　to　effectively　encourage　ants　more　likely　to　search　for　connections　between　structurally　connected　regions.　Through　simulation　studies　on　generated　datasets　and　real　fMRI-DTI　datasets,　we　demonstrate　that　the　proposed　approach　results　in　improved　inference　results　on　EC　compared　to　some　methods　that　only　used　fMRI　data.