Provenances　are　a　research　focus　of　neuroimaging　resources　sharing.　An　amount　of　work　has　been　done　to　construct　high-quality　neuroimaging　provenances　in　a　standardized　and　convenient　way.　However,　besides　existing　processed-based　provenance　extraction　methods,　open　research　sharing　in　computational　neuroscience　still　needs　one　way　to　extract　provenance　information　from　rapidly　growing　published　resources.　This　paper　proposes　a　literature　mining-based　approach　for　research　sharing-oriented　neuroimaging　provenance　construction.　A　group　of　neuroimaging　event-containing　attributes　are　defined　to　model　the　whole　process　of　neuroimaging　researches,　and　a　joint　extraction　model　based　on　deep　adversarial　learning,　called　AT-NeuroEAE,　is　proposed　to　realize　the　event　extraction　in　a　few-shot　learning　scenario.　Finally,　a　group　of　experiments　were　performed　on　the　real　data　set　from　the　journal　PLOS　ONE.　Experimental　results　show　that　the　proposed　method　provides　a　practical　approach　to　quickly　collect　research　information　for　neuroimaging　provenance　construction　oriented　to　open　research　sharing.