Gene-Modulated Network Diffusion for Improved Modeling of Amyloid- β Spread in Alzheimer's Disease

Understanding the pathogenesis of amyloid- $\beta$ pathology in Alzheimer's Disease (AD) proves to be a challenge. In this work, we expand upon the application of network diffusion models (NDM) to study pathophysiological spread of amyloid- $\beta$ throughout white matter structural brain networks. We found that the NDM successfully recaptures subpopulation-level spatial patterns (Pearson's R = 0.45-0.48, P_FDR < 0.01) of amyloid- $\beta$ deposition in the Alzheimer's Disease Neuroimaging Cohort at a regional level, but with drawbacks in mechanism interpretability. We then moved to an extended NDM framework (eNDM), including a protein synthesis term to better reflect the role of amyloid- $\beta$ metabolism, as well as including regional vulnerability using spatial transcriptomics from the Allen Human Brain Atlas to modulate the region-level rate parameters of the synthesis term. The novel gene eNDMs exhibited significant performance increases in Pearson's correlation (Steiger's Z, P_FDR < 0.10) over baseline NDM performance in mild cognitive impairment and AD groups using APOE, SORL1, and FGL2 for gene modulation. The results were robust and replicable when testing on an external cohort of the Alzheimer's Disease Sequencing Project. The study thus demonstrates the importance of regional genetic vulnerability, in conjunction with network diffusion mechanisms, in improving the modelling and prediction of amyloid- $\beta$ pathophysiological spread.