 Gaussian graphical models (GGMs) are able to estimate the conditional dependency between many individual random variables. We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load  , glucose metabolism  , and gray matter volume  was calculated for each brain region.
<clarity>  Gaussian graphical models (GGMs) are able to estimate the conditional dependency between many individual random variables. We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load  , glucose metabolism  , and gray matter volume  was calculated for each brain region.
 Gaussian graphical models (GGMs) are able to estimate the conditional dependency networks of multimodal neuroimaging data and assessed alterations of the network structure in mild cognitive impairment (MCI) and Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load , glucose metabolism , and gray matter volume was calculated for each brain region.