In　recent　years,　the　discovery　of　brain　effective　connectivity　(EC)　networks　through　computational　analysis　of　functional　magnetic　resonance　imaging　(fMRI)　data　has　gained　prominence　in　neuroscience　and　neuroimaging.　However,　owing　to　the　influence　of　diverse　factors　during　data　collection　and　processing,　fMRI　data　typically　exhibit　high　noise　and　limited　sample　characteristics,　consequently　leading　to　the　suboptimal　performance　of　current　methods.　In　this　paper,　we　propose　a　novel　brain　effective　connectivity　discovery　method　based　on　meta-reinforcement　learning,　called　MetaRLEC.　The　method　mainly　consists　of　three　modules:　actor,　critic,　and　meta-critic.　MetaRLEC　first　employs　an　encoder-decoder　framework:　The　encoder　utilizing　a　transformer　converts　noisy　fMRI　data　into　a　state　embedding,　and　the　decoder　employing　bidirectional　LSTM　discovers　brain　region　dependencies　from　the　state　and　generates　actions　(EC　networks).　Then,　a　critic　network　evaluates　these　actions,　incentivizing　the　actor　to　learn　higher-reward　actions　amidst　the　high-noise　setting.　Finally,　a　meta-critic　framework　facilitates　online　learning　of　historical　state-action　pairs,　integrating　an　action-value　neural　network　and　supplementary　training　losses　to　enhance　the　model＇s　adaptability　to　small-sample　fMRI　data.　We　conduct　comprehensive　experiments　on　both　simulated　and　real-world　data　to　demonstrate　the　efficacy　of　our　proposed　method.