With　the　progress　of　artificial　intelligence,　big　data　and　functional　neuroimaging　technologies,　brain　computing　has　rapidly　advanced　our　understanding　of　brain　intelligence　and　brain　disorders.　We　argue　that　existing　data　analytical　methods　have　become　insufficient　for　brain　computing　when　dealing　with　multiple　brain　big　data　sources,　because　such　methods　mainly　focus　on　flattening　strategies　and　fail　to　work　well　for　systematic　understanding　of　the　constituent　elements　of　cognition,　emotion　and　disease,　as　well　as　the　intra-　and　inter-relations　within　and　among　themselves.　To　address　this　problem,　we　present　in　this　paper　a　novel　multi-source　brain　computing　platform　by　Data-Brain　driven　systematic　fusion.　First,　we　formalize　a　series　of　behaviors　surrounding　the　Brain　Informatics-based　investigation　process,　and　present　a　conceptual　model　to　systematically　represent　content　and　context　of　functional　neuroimaging　data.　Then,　we　propose　the　systematic　brain　computing　framework　with　multi-aspect　fusion　and　inference　to　understand　brain　specificity　and　give　uncertainty　quantification,　as　well　as　its　inspiration　and　applications　for　translational　studies　on　brain　health.　In　particular,　a　graph　matching-based　task　search　algorithm　is　introduced　to　help　systematic　experimental　design　and　data　sampling　with　multiple　cognitive　tasks.　The　study　increases　the　interpretability　and　transparency　of　brain　computing　findings　by　inferring　and　testing　multiple　hypotheses　taking　into　consideration　the　effect　of　evidence　combination.　Finally,　multiple　sources　of　knowledge　(K),　information　(I)　and　data　(D)　are　driven　by　a　KID　loop　as　the　thinking　space　to　inspire　never-ending　learning　and　multi-dimensional　interactions　in　the　connected　social–cyber–physical　spaces.　Experimental　results　have　demonstrated　the　efficacy　of　the　proposed　brain　computing　method　with　systematic　fusion.　©　2021　Elsevier　B.V.