Brain　science　research　has　entered　the　era　of　wconnectome.　The　era　of　connectome　has　brought　new　challenges　and　opportunities　for　brain　science　research.　Many　studies　have　reported　the　structural　and　functional　connections　of　the　brain,　but　extracting　scientific　evidences　from　them　is　not　easy.　Traditional　neuroimaging　text　mining　methods　based　on　terms　are　not　suitable　for　the　complex　experimental　designs　and　results　analysis　of　brain　connectome　studies.　Therefore,　this　paper　proposes　a　novel　method　for　event-level　neuroimaging　text　mining,　which　aims　to　extract　causal　event-event　relations　of　brain　connectome.　The　method　uses　a　deep　learning　model　that　combines　BiLSTM　and　MLP,　and　incorporates　constraints　learning　to　enhance　the　model＇s　performance　on　few-shot　datasets.　The　constraints　include　common　sense　constraints　and　domain　constraints,　which　help　the　model　to　learn　from　prior　knowledge　and　domain　expertise.　The　experiments　on　a　brain　connectome　article　dataset　show　that　the　proposed　method　can　effectively　extract　the　causal　event-event　relations　of　brain　connectome　with　low　resource　requirements.