  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.
