Healthy aging is associated with brain volume reductions that involve the frontal cortex, but also affect other brain regions. We sought to identify an age-related network pattern of MRI gray matter using a multivariate statistical model of regional covariance, the Scaled Subprofile Model (SSM) with voxel based morphometry (VBM) in 29 healthy adults, 23-84 years of age (Group 1). In addition, we evaluated the reproducibility of the age-related gray matter pattern derived from a prior SSM VBM study of 26 healthy adults, 22-77 years of age (Group 2; Alexander et al., 2006) in relation to the current sample and tested the ability of the network analysis to extract an age-related pattern from both cohorts combined. The SSM VBM analysis of Group 1 identified a regional pattern of gray matter atrophy associated with healthy aging (R(2)=0.64, p<0.000001) that included extensive reductions in bilateral dorsolateral and medial frontal, anterior cingulate, insula/perisylvian, precuneus, parietotemporal, and caudate regions with areas of relative preservation in bilateral cerebellum, thalamus, putamen, mid cingulate, and temporal pole regions. The age-related SSM VBM gray matter pattern, previously reported for Group 2, was highly expressed in Group 1 (R(2)=0.52, p<0.00002). SSM analysis of the combined cohorts extracted a common age-related pattern of gray matter showing reductions involving bilateral medial frontal, insula/perisylvian, anterior cingulate and, to a lesser extent, bilateral dorsolateral prefrontal, lateral temporal, parietal, and caudate brain regions with relative preservation in bilateral cerebellum, temporal pole, and right thalamic regions. The results suggest that healthy aging is associated with a regionally distributed pattern of gray matter atrophy that has reproducible regional features. Whereas the network patterns of atrophy included parietal, temporal, and subcortical regions, involvement of the frontal brain regions showed the most consistently extensive and reliable reductions across samples. Network analysis with SSM VBM can help detect reproducible age-related MRI patterns, assisting efforts in the study of healthy and pathological aging.