Analysis　of　the　structural　connectomes　based　on　diffusion　magnetic　resonance　imaging　(MRI)　can　show　network　attributes　for　segregation　and　integration　in　the　human　brain.　However,　it　is　reported　that　there　are　large　discrepancies　from　those　network　measures,　which　may　result　from　the　network　construction　methodology　(such　as　brain　parcellations,　DWI　acquisition　model,　tractography　algorithms　and　network　weighting　schemes).　The　influence　of　parcellation　atlas　on　the　topological　network　measures　of　diffusion　tensor　imaging　(DTI)　network　with　the　typical　setting　used　in　many　clinical　applications　were　evaluated　in　this　paper.　The　data　of　research　were　acquired　in　75　healthy　older　adults　who　underwent　several　neuropsychological　tests　and　structural　MRI.　It　shows　that　those　network　measures　are　very　sensitive　to　the　spatial　scales　of　brain　parcellation.　When　the　scale　is　close,　the　principle　of　parcellation　doesn＇t　change　those　measures　that　much.　The　modularity　of　brain　network　is　relatively　stable　on　different　kinds　of　brain　parcellation.　The　mesoscale　atlas,　which　shows　higher　sensitivity　in　ageing　study,　may　be　an　optimal　choice　for　typical　DTI　network　analysis.　©　2018,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.