Cognition　is　the　most　basic　but　complex　process　of　human　beings.　Benefit　from　noninvasive　neuroimaging　technologies,　a　series　of　important　brain　projects　have　been　carried　out　to　model　cognition　from　different　aspects　and　levels.　Because　modeling　such　a　complex　phenomenon　requires　characterizations　of　numerous　entities　and　cannot　only　depend　on　the　efforts　of　one　or　more　laboratories　within　a　project　cycle,　a　lot　of　neuroimaging　text　mining　researches　have　focused　on　curating　neuroimaging-based　brain　cognitive　raw　data,　derived　data　and　result　data,　to　collect　multi-aspect　information　about　brain　cognitive　researches　for　comprehensively　and　objectively　characterizing　key　entities　of　brain　cognition.　However,　the　data-centric　perspective　leads　to　the　shortcomings　of　poor　topic　semantics　and　topic　independent　results.　This　paper　proposes　a　brand-new　perspective　of　big　data　sharing　in　neuroimaging,　that　is,　curating　brain　cognitive　researches.　A　new　task　definition　of　neuroimaging　text　mining　and　a　topic　learning　pipeline　integrating　the　heterogeneous　deep　neural　networks　and　density　clustering　of　topic　relations　are　designed　to　realize　this　new　perspective.　The　experimental　results　on　actual　data　sets　show　that　the　proposed　method　can　obtain　more　accurate　and　complete　research　topics　for　effectively　characterizing　brain　cognition　and　its　researches.