Neuroimaging　text　mining　extracts　knowledge　from　neuroimaging　texts　and　has　received　widespread　attention.　Topic　learning　is　an　important　research　focus　of　neuroimaging　text　mining.　However,　current　neuroimaging　topic　learning　researches　mainly　used　traditional　probability　topic　models　to　extract　topics　from　literature　and　cannot　obtain　high-quality　neuroimaging　topics.　The　existing　topic　learning　methods　also　cannot　meet　the　requirements　of　topic　learning　oriented　to　full-text　neuroimaging　literature.　In　this　paper,　three　types　of　neuroimaging　research　topic　events　are　defined　to　describe　the　process　and　result　of　neuroimaging　researches.　An　event　based　topic　learning　pipeline,　called　neuroimaging　Event-BTM,　is　proposed　to　realize　topic　learning　from　full-text　neuroimaging　literature.　The　experimental　results　on　the　PLoS　One　data　set　show　that　the　accuracy　and　completeness　of　the　proposed　method　are　significantly　better　than　the　existing　main　topic　learning　methods.