Sharing　various　neuroimaging　digital　resources　have　received　widespread　attention　in　FAIR　(Findable,　Accessible,　Interoperable　and　Reusable)　neuroscience.　In　order　to　support　a　comprehensive　understanding　of　brain　cognition,　neuroimaging　provenance　should　be　constructed　to　characterize　both　research　processes　and　results,　and　integrates　various　digital　resources　for　quick　replication　and　open　cooperation.　This　brings　new　challenges　to　neuroimaging　text　mining,　including　fragmented　information,　lack　of　labelled　corpora,　and　vague　topics.　This　paper　proposes　a　text　mining　pipeline　for　enabling　the　FAIR　neuroimaging　study.　In　order　to　avoid　fragmented　information,　the　Brain　Informatics　provenance　model　is　redesigned　based　on　NIDM　(Neuroimaging　Data　Model)　and　FAIR　facets.　It　can　systematically　capture　the　provenance　requests　from　the　FAIR　neuroimaging　study　and　then　transform　them　into　a　group　of　text　mining　tasks.　A　neuroimaging　text　mining　pipeline　combining　deep　adversarial　learning　with　interaction　based　topic　modeling,　called　neuroimaging　interaction　topic　model　(Neuroimaging-ITM),　is　proposed　to　automatically　extract　neuroimaging　provenance　and　identify　research　topics　in　the　few-shot　scenario.　Finally,　a　group　of　experiments　is　completed　by　using　real　data　from　the　journal　PloS　One.　The　experimental　results　show　that　Neuroimaging-ITM　can　systematically　and　accurately　extract　provenance　information　and　obtain　high-quality　research　topics　from　the　full　text　of　neuroimaging　articles.　Most　of　the　mean　F1　values　of　provenance　extraction　exceed　0.9.　The　topic　coherence　and　KL　(Kullback-Leibler)　divergence　reach　9.95　and　0.96　respectively.　The　results　are　obviously　better　than　baseline　methods.