Recent　advancements　in　AI,　big　data　analytics,　and　magnetic　resonance　imaging　(MRI)　have　revolutionized　the　study　of　brain　diseases　such　as　Alzheimer＇s　Disease　(AD).　However,　most　AI　models　used　for　neuroimaging　classification　tasks　have　limitations　in　their　learning　strategies,　that　is　batch　training　without　the　incremental　learning　capability.　To　address　such　limitations,　the　systematic　Brain　Informatics　methodology　is　reconsidered　to　realize　evidence　combination　and　fusion　computing　with　multi-modal　neuroimaging　data　through　continuous　learning.　Specifically,　we　introduce　the　BNLoop-GAN　(Loop-based　Generative　Adversarial　Network　for　Brain　Network)　model,　utilizing　multiple　techniques　such　as　conditional　generation,　patch-based　discrimination,　and　Wasserstein　gradient　penalty　to　learn　the　implicit　distribution　of　brain　networks.　Moreover,　a　multiple-loop-learning　algorithm　is　developed　to　combine　evidence　with　better　sample　contribution　ranking　during　training　processes.　The　effectiveness　of　our　approach　is　demonstrated　through　a　case　study　on　the　classification　of　individuals　with　AD　and　healthy　control　groups　using　various　experimental　design　strategies　and　multi-modal　brain　networks.　The　BNLoop-GAN　model　with　multi-modal　brain　networks　and　multiple-loop-learning　can　improve　classification　performance.