Healthy　aging　is　associated　with　a　regionally　spread　network　pattern　of　gray　matter　(GM)　reductions　on　magnetic　resonance　imaging　(MRI)　that　preferentially　involves　frontal　and　selected　temporal　brain　regions.　Down　syndrome　(DS)　provides　a　model　of　abnormal　aging　in　which　there　is　increased　beta　amyloid　deposition　and　risk　for　Alzheimer＇s　dementia　(AD)　by　the　over　expression　of　genes　on　triplicated　regions　of　human　chromosome　21.　To　identify　the　age-related　network　pattern　of　MRI　GM　in　non-demented　adults　with　DS,　we　used　a　multivariate　spatial　covariance　model,　the　Scaled　Subprofile　Model　(SSM).　High　resolution　T1-weighted　brain　volumetric　MRI　scans　from　36　DS　adults　(mean　age　=　42.3　±　8.3　years;　M/F　=　16/20),　29-62　years　of　age　and　clinically　screened　to　exclude　dementia　were　included.　Statistical　parametric　mapping　(SPM8)　Diffeomorphic　anatomical　registration　using　exponentiated　Lie　algebra　(Dartel)　voxel-based　morphometry　(VBM)　was　used　to　segment　brain　images　into　GM,　white　matter　(WM),　and　cerebrospinal　fluid　(CSF)　partitions,　standardize　all　the　images　to　the　template　stereotactic　space　using　linear　affine　transformation　and　non-linear　warping,　modulate　and　smooth　the　GM　maps.　SSM　analysis　was　performed　on　the　GM　maps　to　determine　the　regional　network　associated　with　age　in　the　DS　group.　Greater　subject　expression　of　the　first　two　SSM　component　patterns　were　correlated　with　increasing　age　(R2　=　0.31,　p　≤　0.001)　in　the　DS　subjects　and　this　association　remained　significant　after　we　controlled　for　gender,　total　intracranial　volume　(eTIV),　and　general　intellectual　ability　on　the　Peabody　Picture　Vocabulary　Test　(PPVT-R).　The　age-related　pattern　was　characterized　mainly　by　extensive　reductions　in　bilateral　parietal,　precuneus,　perisylvian,　temporal　regions.　After　PPVT-R　has　been　controlled,　higher　expression　of　the　age　pattern　was　associated　with　poorer　cognitive　performance.　These　findings　indicate　that　aging　in　DS　is　characterized　by　a　regionally　distributed　pattern　of　GM　reductions　in　brain　regions　that　have　been　associated　with　a　greater　extent　the　progressive　effects　of　AD　type　pathology.　Spatial　covariance　modeling　may　help　to　distinguish　the　effects　of　normal　aging　from　pathological　aging;　and　may　potentially　assist　in　the　evaluation　of　interventions　for　age-related　cognitive　decline.　©　2014　IEEE.