Gaussian graphical models reveal inter-modal and inter-regional conditional dependencies of brain alterations in Alzheimer's disease
Alzheimer's disease (AD) is characterized by a sequence of pathological changes, which are commonly assessed in vivo using MRI and PET. Currently, the most approaches to analyze statistical associations between brain regions rely on Pearson correlation. However, these are prone to spurious correlations arising from uninformative shared variance. Notably, there are no appropriate multivariate statistical models available that can easily integrate dozens of variables derived from such data, being able to use the additional information provided from the combination of data sources. Gaussian graphical models (GGMs) can estimate the conditional dependency from given data, which is expected to reflect the underlying causal relationships. We applied GGMs to assess multimodal regional brain alterations in AD. We obtained data from N=972 subjects from the Alzheimer's Disease Neuroimaging Initiative. The mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) were calculated. GGMs were estimated using a Bayesian framework for the combined multimodal data to obtain conditional dependency networks. Conditional dependency matrices were much sparser (10% density) than Pearson correlation matrices (50% density). Within modalities, conditional dependency networks yielded clusters connecting anatomically adjacent regions. For associations between 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 among multimodal measures of regional brain alterations in MCI and AD, and yielded sparser matrices compared to Pearson correlation networks. Therefore, GGMs may be used as alternative to thresholding-approaches typically applied to correlation networks to obtain the most informative relations between variables.
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