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Moving beyond the canonical functional networks and descriptive brain network modeling methods, we attempted to construct a node-and-edge network of Alzheimer’s disease (AD) constrained by disease-specific grey-matter volume perturbations.
The resting-state functional network structure was estimated by computing edges between voxel-based morphometry (VBM) data-derived 8 nodes with Bayesian estimation methods suitable for time-series data.
Independently computed group-wise network models were comparable; 10 out 12 paths were common to both AD and control groups (fig). Major difference in the networks of two groups were in terms of connectivity strengths reflected by edge coefficients. In AD group, connectivity strength measures could significantly predict AD disease severity measured by CDR-SB (r2=0.77, p=0.015).
This novel and data driven network modeling can quantify resting state connectivity strengths and provide a reliable marker of disease identification, progression and treatment response.