Estimating　brain　effective　connectivity　(EC)　from　neuroimaging　data　has　recently　received　wide　interest　and　become　a　new　topic　in　the　neuroinformatics　field.　Currently,　dynamic　Bayesian　networks　(DBN)　have　been　successfully　applied　to　estimating　EC　from　functional　magnetic　resonance　imaging　(fMRI)　time-series　data　as　they　can　capture　the　temporal　characteristics　of　connectivity　among　brain　regions.　However,　DBN　methods　assume　that　activations　of　brain　regions　are　stationary　and　follow　a　Markovian　condition,　which　are　strong　assumptions　that　may　not　be　valid　in　many　cases.　In　this　paper,　we　introduce　a　novel　method　to　estimate　brain　effective　connectivity　networks　from　fMRI　data　using　non-stationary　dynamic　Bayesian　networks,　named　as　EC-nsDBN.　EC-nsDBN　can　not　only　capture　the　non-stationary　temporal　information　from　fMRI　time-series　data　but　also　estimate　how　interactions　among　brain　regions　change　dynamically　over　the　fMRI　experiments.　Systematic　experiments　on　simulated　data　show　that　EC-nsDBN　has　better　direction　identification　ability　compared　with　other　state-ofthe-art　algorithms,　and　can　accurately　capture　the　temporal　characteristics　of　connectivity.　Experiments　on　real-world　data　sets　are　also　provided　to　support　our　analysis.