Title: Assessing inter-modal and inter-regional dependencies in prodromal Alzheimer's disease using multimodal MRI/PET and Gaussian graphical models,
Abstract: A sequence of pathological changes takes place in Alzheimer's disease, which
can be assessed in vivo using various brain imaging methods. Currently, there
is no appropriate statistical model available that can easily integrate
multiple imaging modalities, being able to utilize the additional information
provided from the combined data. We applied Gaussian graphical models (GGMs)
for analyzing the conditional dependency networks of multimodal neuroimaging
data and assessed alterations of the network structure in mild cognitive
impairment (MCI) and Alzheimer's dementia (AD) compared to cognitively healthy
controls.
Data from N=667 subjects were obtained from the Alzheimer's Disease
Neuroimaging Initiative. Mean amyloid load (AV45-PET), glucose metabolism
(FDG-PET), and gray matter volume (MRI) was calculated for each brain region.
Separate GGMs were estimated using a Bayesian framework for the combined
multimodal data for each diagnostic category. Graph-theoretical statistics were
calculated to determine network alterations associated with disease severity.
Network measures clustering coefficient, path length and small-world
coefficient were significantly altered across diagnostic groups, with a
biphasic u-shape trajectory, i.e. increased small-world coefficient in early
MCI, intermediate values in late MCI, and decreased values in AD patients
compared to controls. In contrast, no group differences were found for
clustering coefficient and small-world coefficient when estimating conditional
dependency networks on single imaging modalities.
GGMs provide a useful methodology to analyze the conditional dependency
networks of multimodal neuroimaging data.