Several neuroimaging markers have been established for the early diagnosis of Alzheimer's disease, among them amyloid-beta deposition, glucose metabolism, and gray matter volume. Up to now, these imaging modalitieswere mostly analyzed separately from each other, and little is known about the regional interrelation and dependency of these markers. Gaussian graphical models (GGMs) are able to estimate the conditional dependency between many individual random variables. We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load  , glucose metabolism  , and gray matter volume  was calculated for each brain region.  GGMs were estimated using a Bayesian framework and for each individual diagnosis, graph-theoretical statistics were calculated to determine structural changes associated with disease severity. Highly inter-correlated regions, e.g. adjacent regions in the same lobes, formed distinct clusters but included only regions within the same imaging modality. Hardly any associations were found between different modalities, indicating almost no conditional dependency of brain regions across modalities when considering the covariance explained by all other regions. Network measures clustering coefficient and path length were significantly altered across diagnostic groups, with a biphasic u-shape trajectory  . GGMs showed almost no conditional dependencies between modalitieswhen at the same time considering various other regions within the same modalities. However, this approach could be used as a clustering method to derive graph statistics in future studies omitting the need to binarize the network as currently being done for connections based on Pearson correlation .
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 .
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Several neuroimaging markers have been established for the early diagnosis of
A sequence of pathological changes takes place in
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among them amyloid-beta deposition, glucose metabolism, and gray matter volume. Up to now, these imaging modalitieswere mostly analyzed separately from each other, and little is known about the regional interrelation and dependency of these markers.
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
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are able to estimate
for analyzing
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between many individual random variables. We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal
networks of multimodal neuroimaging data and assessed alterations of the network structure in mild cognitive impairment (MCI) and
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disease
dementia (AD) compared to cognitively healthy controls
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with mild cognitive impairment, dementia, and cognitively healthy controls
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ADNI
Alzheimer's Disease Neuroimaging Initiative
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(AV45-PET)
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(FDG-PET)
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(MRI)
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Separate
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and for each individual diagnosis, graph-theoretical
for the combined multimodal data for each diagnostic category. Graph-theoretical
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structural changes
network alterations
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Highly inter-correlated regions, e.g. adjacent regions in the same lobes, formed distinct clusters but included only regions within the same imaging modality. Hardly any associations were found between different modalities, indicating almost no conditional dependency of brain regions across modalities when considering the covariance explained by all other regions.
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and path length
, path length and small-world coefficient
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, 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
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GGMs showed almost no conditional dependencies between modalitieswhen at the same time considering various other regions within the same modalities. However, this approach could be used as a clustering method to derive graph statistics in future studies omitting the need to binarize the network as currently being done for connections based on Pearson correlation
GGMs provide a useful methodology to analyze the conditional dependency networks of multimodal neuroimaging data