Brain　science　research　has　entered　the　era　of　connectomics,　characterized　by　a　significant　increase　in　published　articles　investigating　brain　structure　and　functional　connections.　Automatically　and　accurately　extracting　scientific　evidence　from　these　articles　has　become　an　urgent　concern.　Unlike　early　brain　mechanism　studies　at　the　functional　area　level,　brain　connectomics　studies　feature　more　intricate　experimental　designs　and　yield　complex　findings.　Traditional　neuroimaging　text　mining　techniques,　operating　at　the　term　level,　are　insufficient　for　effectively　extracting　scientific　evidence　from　brain　connectomics　articles.　This　paper　addresses　a　key　challenge　in　event-level　neuroimaging　text　mining,　i.e.,　event　causal　relation　extraction　in　brain　connectomics.　We　introduce　a　novel　model　named　Brain　Connectomics　Event　Relation　Miner　(BCERM),　leveraging　weighted　joint　constrained　learning.　By　integrating　a　bidirectional　long　short-term　memory　(BiLSTM)　network　with　a　multi-layer　perceptron　(MLP),　we　develop　a　lightweight　model　for　jointly　extracting　multiple　event　causal　relations　from　brain　connectomics　articles.　Given　the　scarcity　of　annotated　brain　connectomics　corpora,　we　propose　a　weighted　joint　constrained　learning　framework.　This　framework　integrates　double　consistency　constraints,　encompassing　common　sense　and　domain　constraints,　and　combines　them　with　adaptive　weight　learning　to　enhance　the　model＇s　few-shot　learning　capability.　Experimental　evaluations　on　a　real　brain　connectomics　article　dataset　demonstrate　that　our　method　achieves　an　F-score　of　70%,　outperforming　state-of-the-art　event　relation　extraction　methods　in　the　low-resource　environment.