Application of neurovascular uncoupling and whole brain network connectomics to assess the role of genetic and dietary risk factors in the LOAD2 mouse model

Abstract Background As research efforts to discover and develop promising future therapeutics continues, the necessity to develop clinically significant Alzheimer’s disease (AD) mouse models and analysis is more important than ever. Method The study utilizes a novel MODEL‐AD (Model Organism Development and Evaluation for Late‐onset AD) mouse model that incorporates APOE4, Trem2 * R47H, and humanized amyloid‐beta (Aβ) allele into C57BL/6J (B6) mice to produce LOAD2. Two cohorts of male and female LOAD2 mice received either control or high fat diet (HFD) followed by brain metabolism imaging using 18F‐FDG PET/CT. Brain regions were segmented using the Paxinos‐Franklin atlas and analyzed using a whole brain neurovascular uncoupling and connectivity approach. To do this, we computed z‐score statistics for all mice relative their control diet group, and conducted hierarchical modularization of all 28 brain regions and statically compared results across sex and high‐risk diet in 12‐month‐old mice. Result An initial network analysis detected metabolic hypo‐perfusion and metabolism for female mice, while males showed neurovascular uncoupling. Hierarchical analysis revealed primary modules within the whole brain network. These primary modules were then further modularized to yield a final breakdown of six secondary sub‐modules consisting of 3‐7 brain regions. Predominate functional associations of the sub‐modules’ brain regions were sensory (S) and learning (L), while sub‐module 2.1 had the strongest visual (V) representation. Statistical analysis of the six sub‐modules exhibited significant, module‐, sex‐, and dietary‐dependent effect for LOAD2 mice on high fat diet (HFD) by 12mo. Conclusion The incorporation of APOE4, Trem2 * R47H, and humanized Ab sequence in combination with HFD induced age‐dependent LOAD‐relevant changes in neurovascular coupling and whole brain network connectivity consistent with known brain circuit variations observed clinically. These findings support application of this newer LOAD2 mouse model to improve knowledge regarding disease mechanism. Additionally, our connectivity analysis approach shows promise for implementation in future therapeutic development and testing.