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Alzheimer's disease (AD) is characterized by a sequence of pathologicalchanges, which are commonly assessed in vivo using MRI and PET. Currently, themost approaches to analyze statistical associations between brain regions relyon Pearson correlation. However, these are prone to spurious correlationsarising from uninformative shared variance. Notably, there are no appropriatemultivariate statistical models available that can easily integrate dozens ofvariables derived from such data, being able to use the additional informationprovided from the combination of data sources. Gaussian graphical models (GGMs)can estimate the conditional dependency from given data, which is expected toreflect the underlying causal relationships. We applied GGMs to assessmultimodal regional brain alterations in AD. We obtained data from N=972subjects from the Alzheimer's Disease Neuroimaging Initiative. The mean amyloidload (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI)were calculated. GGMs were estimated using a Bayesian framework for thecombined multimodal data to obtain conditional dependency networks. Conditionaldependency matrices were much sparser (10% density) than Pearson correlationmatrices (50% density). Within modalities, conditional dependency networksyielded clusters connecting anatomically adjacent regions. For associationsbetween different modalities, only few region-specific connections remained.Graph-theoretical network statistics were significantly altered between groups,with a biphasic u-shape trajectory. GGMs removed shared variance amongmultimodal measures of regional brain alterations in MCI and AD, and yieldedsparser matrices compared to Pearson correlation networks. Therefore, GGMs maybe used as alternative to thresholding-approaches typically applied tocorrelation networks to obtain the most informative relations betweenvariables.

Gaussian graphical models reveal inter-modal and inter-regional conditional dependencies of brain alterations in Alzheimer's disease