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 dataand 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 calculatedfor 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 ADpatients compared to controls. In contrast, no group differences were found for clustering coefficient and small-world coefficient when estimating conditional dependency networkson single imaging modalities. GGMs provide a useful methodology to analyze the conditional dependency networks of multimodal neuroimaging data .
 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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networks of multimodal neuroimaging dataand assessed alterations of the network structure in mild cognitive impairment (MCI) and Alzheimer's dementia (AD) compared to cognitively healthy controls. Data
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667 subjects were obtained
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for each diagnostic category.
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.
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statistics were calculated to determine network alterations associated with disease severity. Network measures clustering coefficient, path length and small-world coefficient
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across diagnostic
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, i. e. increased small-world coefficient in early MCI , intermediate values in late MCI, and decreased values in ADpatients compared to controls. In contrast, no group differences were found for clustering coefficient and small-world coefficient when estimating conditional dependency networkson single imaging modalities. GGMs provide a useful methodology to analyze the conditional dependency networks of multimodal neuroimaging data
. 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