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- W2896619215 abstract "Network connectivity analyses measuring tissue degeneration have brain morphological patterns associated with onset and progression of Alzheimer's disease (AD) over time. This degeneration can be evaluated at the region of interest (ROI) in the brain through probabilistic network analysis, however current approaches for finding such networks have the following limitations: 1) analysis at discrete age groups cannot appropriately account for connectivity dynamics over time; and 2) morphological tissue changes are seldom unified within networks, despite known dependencies. To overcome these limitations, we propose a probabilistic wombled model to simultaneously estimate ROI cortical thickness and network continuously over age, and compare this to an age aggregated model. The inclusion of age in the network model was motivated by the interest in investigating the point in time when disease specific connections alter away from normal, as well as the length of time required for changes to occur. The posterior probability (PP) of the connection between the middle temporal and the posterior cingulate gyrus decreased from age 55 (PP < 0.75), and was absent by age 70 for those participants with AD (PP < 0.12). The same connection for those participants who were cognitively normal (CN) remained present throughout ages 55 through to 95 (PP > 0.75)." @default.
- W2896619215 created "2018-10-26" @default.
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- W2896619215 date "2018-07-01" @default.
- W2896619215 modified "2023-10-16" @default.
- W2896619215 title "P3‐365: AGE DEPENDENT BAYESIAN NETWORKS REVEAL SPATIO‐TEMPORAL PATTERNS OF NEURODEGENERATION IN HEALTHY AGEING AND ALZHEIMER'S DISEASE" @default.
- W2896619215 doi "https://doi.org/10.1016/j.jalz.2018.06.1727" @default.
- W2896619215 hasPublicationYear "2018" @default.
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