In human Alzheimer’s disease (AD) patients and AD mouse models, both differential pre-disease brain features and differential disease-associated memory decline are observed, suggesting that certain neurological features may protect against AD-related cognitive decline. The combination of these features is known as brain reserve, and understanding the genetic underpinnings of brain reserve may advance AD treatment in genetically diverse human populations. One potential source of brain reserve is brain microstructure, which is genetically influenced and can be measured with diffusion MRI (dMRI). To investigate variation of dMRI metrics in pre-disease-onset, genetically diverse AD mouse models, we utilized a population of genetically distinct AD mice produced by crossing the 5XFAD transgenic mouse model of AD to 3 inbred strains (C57BL/6J, DBA/2J, FVB/NJ) and two wild-derived strains (CAST/EiJ, WSB/EiJ). At 3 months of age, these mice underwent diffusion magnetic resonance imaging (dMRI) to probe neural microanatomy in 83 regions of interest (ROIs). At 5 months of age, these mice underwent contextual fear conditioning (CFC). Strain had a significant effect on dMRI measures in most ROIs tested, while far fewer effects of sex, sex*strain interactions, or strain*sex*5XFAD genotype interactions were observed. A main effect of 5XFAD genotype was observed in only 1 ROI, suggesting that the 5XFAD transgene does not strongly disrupt neural development or microstructure of mice in early adulthood. Strain also explained the most variance in mouse baseline motor activity and long-term fear memory. Additionally, significant effects of sex and strain*sex interaction were observed on baseline motor activity, and significant strain*sex and sex*5XFAD genotype interactions were observed on long-term memory. We are the first to study the genetic influences of brain microanatomy in genetically diverse AD mice. Thus, we demonstrated that strain is the primary factor influencing brain microstructure in young adult AD mice and that neural development and early adult microstructure are not strongly altered by the 5XFAD transgene. We also demonstrated that strain, sex, and 5XFAD genotype interact to influence memory in genetically diverse adult mice. Our results support the usefulness of the 5XFAD mouse model and convey strong relationships between natural genetic variation, brain microstructure, and memory.

BackgroundAlzheimer’s disease (AD) is a neurodegenerative disease that affects 50 million people worldwide (Breijyeh & Karaman, 2020). Among individuals with AD, cognitive dysfunction varies, giving rise to the hypothesis that neural function and anatomical substrates provide “cognitive reserve” against AD. This idea inspired the brain reserve hypothesis, which states that structural features of the brain may protect against AD‐related cognitive decline (Stern, 2012). One potential source of brain reserve is neural microanatomy, which varies across individuals (Wang et al., 2020) and may then explain the differential cognitive outcomes in AD associated with cognitive reserve.MethodWe utilized a population of genetically distinct AD mice produced by crossing the 5XFAD model of AD to five mouse strains (C57BL/6J, DBA/2J, FVB/NJ, CAST/EiJ, and WSB/EiJ). At 3 months of age, these mice underwent diffusion magnetic resonance imaging (dMRI), a collection of techniques that characterizes brain microstructure (Campbell & Pike, 2019), to probe variation of neural microanatomy in 430 regions of interest (ROIs). At 5 months of age, when cognitive decline is expected (Oakley et al., 2006; Devi & Ohno, 2010), these mice underwent contextual fear conditioning (CFC) and contextual fear memory (CFM) paradigms. Hierarchical clustering was performed on the correlations between dMRI measurements in each brain region influenced by strain and CFC and CFM performance.Result362 ROIs had dMRI metrics with significant effects of sex, strain, and/or 5XFAD genotype. Of these ROIs, 348 had a significant effect of strain, 68 had a significant effect of sex, and 30 or fewer had a significant effect of genotype or any variable interactions. We identified one network of ROIs in which nearly all dMRI parameters correlated positively and strongly to CFC performance and two networks whose dMRI measures correlated strongly to CFM performance, although the directionality of these correlations varied within these two networks.ConclusionOur work identifies three neural networks that are influenced by strain and correlate strongly to CFC and CFM performance. Further analyses should explore genetic factors influencing these networks and the structural and functional connectivity of brain regions in these networks.