Brain　ageing　is　followed　by　changes　of　the　connectivity　of　white　matter　(WM)　and　changes　of　the　grey　matter　(GM)　concentration.　Neurodegenerative　disease　is　more　vulnerable　to　an　accelerated　brain　ageing,　which　is　associated　with　prospective　cognitive　decline　and　disease　severity.　Accurate　detection　of　accelerated　ageing　based　on　brain　network　analysis　has　a　great　potential　for　early　interventions　designed　to　hinder　atypical　brain　changes.　To　capture　the　brain　ageing,　we　proposed　a　novel　computational　approach　for　modeling　the　112　normal　older　subjects　(aged　50-79　years)　brain　age　by　connectivity　analyses　of　networks　of　the　brain.　Our　proposed　method　applied　principal　component　analysis　(PCA)　to　reduce　the　redundancy　in　network　topological　parameters.　Back　propagation　artificial　neural　network　(BPANN)　improved　by　hybrid　genetic　algorithm　(GA)　and　Levenberg-Marquardt　(LM)　algorithm　is　established　to　model　the　relation　among　principal　components　(PCs)　and　brain　age.　The　predicted　brain　age　is　strongly　correlated　with　chronological　age　(r=0.8).　The　model　has　mean　absolute　error　(MAE)　of　4.29　years.　Therefore,　we　believe　the　method　can　provide　a　possible　way　to　quantitatively　describe　the　typical　and　atypical　network　organization　of　human　brain　and　serve　as　a　biomarker　for　presymptomatic　detection　of　neurodegenerative　diseases　in　the　future.　(C)　2015　Elsevier　Ireland　Ltd.　All　rights　reserved.