Humanized APOE mouse brain volume increases over age irrespective of sex and APOE genotype: Implications for translational validity to the human

Humanized APOE mouse brain volume increases over age irrespective of sex and APOE genotype: Implications for translational validity to the human | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Humanized APOE mouse brain volume increases over age irrespective of sex and APOE genotype: Implications for translational validity to the human View ORCID Profile Adam C. Raikes , View ORCID Profile Avnish Bhattrai , View ORCID Profile Tian Wang , View ORCID Profile Jean-Paul Wiegand , View ORCID Profile Roberta Diaz Brinton doi: https://doi.org/10.1101/2025.10.27.684892 Adam C. Raikes 1 Center for Innovation in Brain Science, University of Arizona , Tucson, AZ, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Adam C. Raikes Avnish Bhattrai 1 Center for Innovation in Brain Science, University of Arizona , Tucson, AZ, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Avnish Bhattrai Tian Wang 1 Center for Innovation in Brain Science, University of Arizona , Tucson, AZ, United States 2 Department of Neurology, University of Arizona , Tucson, AZ, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tian Wang Jean-Paul Wiegand 1 Center for Innovation in Brain Science, University of Arizona , Tucson, AZ, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jean-Paul Wiegand Roberta Diaz Brinton 1 Center for Innovation in Brain Science, University of Arizona , Tucson, AZ, United States 2 Department of Neurology, University of Arizona , Tucson, AZ, United States 3 Department of Pharmacology, College of Medicine Tucson, University of Arizona , Tucson, AZ, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Roberta Diaz Brinton For correspondence: rbrinton{at}arizona.edu Abstract Full Text Info/History Metrics Preview PDF Abstract Translational validity of mouse models of human aging and late-onset Alzheimer’s disease (LOAD) risk are essential for both fundamental mechanistic science and therapeutic development. Given that the strongest risk factors for LOAD are age, female sex, and APOE-ε4 carriership, models must reflect these biological variables and disease phenotypes. The use of mouse models with humanized APOE (hAPOE) is a key strategy to advance translational validity. To initially address translational validity of the hAPOE mouse model, we conducted ex-vivo magnetic resonance imaging analysis of brain volumes in male and female mice across APOE genotypes (ε3/ε3, ε3/ε4, ε4/ε4) and ages corresponding to a human lifespan of approximately 30-70 years (6-25 months in mice). The primary outcomes indicated that total MRI brain volume increased with age (an average of 2.12mm3 per month), irrespective of sex or APOE genotype. Additionally, APOE-ε4 carriers had greater total brain volumes than non-carriers. No sex differences were observed in total brain volume. Voxelwise analyses revealed a pattern of localized morphometric changes independent of differences in total brain volume. Age-related volumetric increases were distributed across subcortical regions (e.g., thalami, hippocampi), while age-related decreases were evident across cortical regions, notably the bilateral parieto-temporal and frontal lobes. Additionally, sex differences were evident after controlling for total brain volume, with females showing greater cortex-dominant volumes while males showed a pattern of greater volumes in regions including cerebellar cortices, olfactory bulbs, and striata. No genotypic effects were observed in the voxelwise analysis after correcting for multiple comparisons, suggesting that APOE genotype does not drive localized volume differences independent of total brain volume. These findings indicate that, at the MRI level of analysis, this humanized APOE mouse model does not recapitulate the volumetric atrophy typically seen in human brain aging and Alzheimer’s disease. The results suggest that humanized APOE alone is insufficient to induce the LOAD atrophy phenotype. This model may better serve as a platform for studying vulnerable aging rather than a primary model for progressive neurodegenerative atrophy. Introduction Approximately 1 in 9 Americans over the age of 65 lives with late-onset Alzheimer’s disease (LOAD) 1 . Without effective treatments, the prevalence is expected to exceed 12.7 million by 2050 with new diagnoses exceeding 1 million per year 1 – 3 . The strongest risk factors for LOAD are age, female sex, and APOE-ε4 carriership 4 – 6 . Therapeutic development requires disease models that reflect the biological risk factors as well as disease-like phenotypes. As noted in two recent reviews, the majority of mouse models used are the 5xFAD and the 3xTg, which represent the disease process well in terms of amyloid deposition and atrophy, but are suboptimal in terms of their representativeness of human disease 7 , 8 . Despite the fact that these mice replicate the disease process, the individual genetic mutations required are rare and not observed in combination in the human population 9 , 10 . Single mutations result in an early age of disease onset (translating to approximately 30 years of age in the human, mimicking early-onset AD, rather than late-onset), an inconsistent disease profile in male mice, and mouse Apoe rather than human APOE, which does not confer the same disease mechanisms 7 – 9 , 11 . Given that the APOE-ε4 allele is the strongest genetic risk factor for LOAD, rodent models that include humanized APOE are essential for translationally relevant therapeutic development. To date there are multiple targeted replacement, knock-out, and knock-in models for APOE mice. Although incorporating a major genetic risk factor for AD, these mice do recapitulate key aspects of disease, including cognitive impairment 12 , 13 . To address the paucity of AD relevant pathology, multiple APOE models have been developed that include other non-dominant mutations, including Trem2 14 , 15 , humanized NOS 16 , 17 , or humanized APP 18 . Although beneficial for driving pathology, the isolated role of APOE in AD risk and progression is not clearly identifiable in these models. To this end, a knock-in mouse model expressing humanized APOE-ε3/ε3 and APOE-ε4/ε4 has been developed by the MODEL-AD Consortium ( model-ad.org ). Recent work with this model demonstrates that the APOE-ε4/ε4 mice have reduced cerebral metabolism and neurovascular uncoupling 19 , altered peripheral metabolism 20 , 21 , immunologic profiles 12 , 20 , 22 , and transcriptomic profiles 21 , 23 , decreased cortical microglial responsiveness 24 , accelerated endocrinological aging 25 , as well as subtle cognitive differences 12 , 13 . Many of these effects are sex-specific 21 – 23 , 25 , which is consistent with differences in the sex distribution of Alzheimer’s disease. These findings suggest that, in some respects, this mouse model may be a suitable risk model for Alzheimer’s disease. To date, there are no studies that deeply characterize MRI brain imaging phenotypes of aged humanized APOE-ε3/ε3 and APOE-ε4/ε4. Past work with mouse models that include humanized APOE in addition to other disease- and inflammation-promoting genetic profiles has demonstrated volumetric decreases associated with the APOE-ε4 allele 14 , 16 , 26 , 27 as well as decreased fractional anisotropy in major white matter tracts, the piriform, and hippocampus 14 , 16 . However, the extent of these differences is not consistently reported. For example, Badea et al (2022) and Badea et al (2019) reported no decreases in hippocampal volume, while Yin et al (2014) reported lower volume for APOE-ε4 mice 16 , 26 , 27 . These contradictory findings are similar to those reported for APOE-ε4 FAD models 8 as well as 3xTg and 5xFAD models 7 . Important confounding effects should be noted for these studies, including other genetic mutations, inconsistent control groups, and varied methods for adjusting regional volumes for total brain volume. Collectively, these factors limit the interpretability of APOE-related effects in these mouse models as well as the translational potential for therapeutic development. The purpose of the present study was to investigate the effects of age, sex, and APOE genotype on total brain and localized brain volumes in the MODEL-AD humanized APOE mouse model. As a candidate AD risk model, we hypothesized that humanized APOE-ε4/ε4 mice would exhibit evidence of atrophy and that this effect would be exacerbated in female mice compared to males. Materials and Methods Animals All animal studies were performed following National Institutes of Health guidelines on the use of laboratory animals and all protocols were approved by the University of Arizona Institutional Animal Care and Use Committee. Homozygous humanized APOE-ε4 targeted replacement mice were obtained from Jackson Laboratory (#027894). Humanized APOE-ε3 target replacement heterozygous mice were obtained from Jackson Laboratory (#029018) and bred to get homozygous APOE-ε3 mice. Mice were housed in sterile conditions on 14-hour light, 10-hour dark cycles and provided ad libitum access to food and water. Image acquisition Mice were sacrificed at prespecified ages (6, 9, 15, and 23–25 months) for ex vivo MRI. Each animal underwent transcardial perfusion with phosphate-buffered saline (PBS) for 4–6 minutes, followed by perfusion with 4% paraformaldehyde (PFA) in PBS for an additional 4–6 minutes. After perfusion, skulls were cleaned of soft tissue and stored in 4% PFA in PBS for 48 hours to ensure complete fixation and enable in-skull imaging. Samples were then transferred to 0.01% sodium azide for long-term storage. Prior to MRI, fixed skulls were placed in vacuum-desiccated 10 mL syringes filled with Fluorinert (FC-3283) for imaging. High resolution structural images were acquired with a T2-weighted rapid acquisition with relaxation enhancement (RARE) sequence (TE: 30 ms; TR: 1800 ms; flip angle 180°; RARE factor: 8; number of averages: 2; FOV: 2.4 × 1.44 × 0.96; acquisition matrix 320×192×120; reconstructed voxel size 75μm; total acquisition time: 3 hours and 4 minutes). Image minimal pre-processing All preprocessing on and analyses were conducted on the high performance computing cluster at the University of Arizona. All data were preprocessed using a custom pipeline written in Bash and Python. A custom Apptainer container was created with necessary softwares for version control and was used unless otherwise noted. This included ANTs 28 (v. 2.4.4), FSL 29 (v. 6.0.6.4), MRtrix 30 (v. 3.0.4), minc-toolkit-extras ( https://github.com/CoBrALab/minc-toolkit-extras ; commit 544485d), optimized_antsMultivariateTemplateConstruction ( https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction ; commit ca4ba15) and Python (v 3.10.12). Anatomical preprocessing T2-weighted images underwent initial automated quality control 31 . Outlier images were examined visually and images with significant non-uninform intensities (n = 4) were excluded. All included images were reoriented to RAS ordering and intensity-normalized using percentile-based histogram rescaling. Foreground estimation was performed via Otsu thresholding, followed by centering, padding, and cropping to a constrained field of view. Bias field correction was applied using N4BiasFieldCorrection 32 , with masks refined through iterative affine and nonlinear registration to the DSURQE 33 – 36 mouse brain template ( https://github.com/IBT-FMI/mouse-brain-templates ) resampled to 0.075mm isotropic voxel size. Non-local means denoising 37 preceded final bias correction and intensity rescaling. Brain masks were generated by inverse warping of the template mask, yielding a final preprocessed T2w image and corresponding brain mask. Deformation based analysis An initial minimum deformation template was built using optimized_antsMultivariateTemplateConstruction . A total of 96 mice were used to build the template and included 4 mice from each age (6, 9, 15, 24 months), genotype (hAPOE-ε3/3, hAPOE-ε3/4, hAPOE-ε4/4), and sex (male, female) combination. The process began by building an initial template using six iterations of rigid-only transformations to align the images. The resulting average from the final rigid iteration was then used as the target for the next stage, which built a new template using six iterations of similarity transformations. This procedure was sequentially repeated for six iterations of affine transformations and finally for six iterations of non-linear Symmetric Normalization (SyN) transformations. At each iteration of every stage, all subjects were registered to the target template; a new template was then generated by averaging the warped images, sharpening the result, and applying the scaled, inverted average of the transformations for use in the next iteration. A final consensus mask was also generated, computed as the mean of the template-space transformed brain masks and thresholded to include voxels in at least 50% of the masks. After this process, all of the brains (n = 159) were independently re-registered to the minimum deformation template using a single iteration step identical to the template building process, which included both linear (rigid, affine) and nonlinear (SyN) registration components. Deformation based morphometry was implemented through optimized_antsMultivariateTemplateConstruction to produce absolute and relative log-transformed Jacobian determinants of the deformation fields transforming each individual mouse brain into the study-specific template space. Log-transformed Jacobians describe the amount of deformation to warp the template voxel to the individual mouse such that values > 0 indicate larger individual volumes and values < 0 indicate larger template volumes. The relative Jacobians, which exclude the affine and residual affine components of the transformations, were used for all analyses and intrinsically control for total brain size. All images were smoothed with a 4 voxel full width at half maximum kernel prior to analyses. Statistical analyses Primary analyses were conducted in template voxel space using Convoxel and ModelArray 38 , 39 (v. 0.1.5). ModelArray runs in R 40 (v. 4.1.2). Voxelwise linear additive models were fit using ModelArray to examine the independent effects of age, sex, and genotype with sum to zero contrast coding for interpretable coefficients. Specifically, age was Z-scored (mean = 13.4 ± 5.94 months), sex compared males - females, and genotypic effects compared hAPOE-ε3/4 - hAPOE-ε4/4 and then hAPOE-ε3/3 - hAPOE-ε4 carriers. Voxelwise statistical significance was set at a two-sided p FDR female, negative: female > male), localized using the Dorr mouse brain atlas which had been transformed to study-specific template space and summarized as the percent of the region occupied by significant voxels (regional specificity) and the percent of the significant voxels represented by the region (pattern representation). Total brain volumes were additionally analyzed by inverse warping the template space mask to the individual mouse brains, summing the voxels and fitting a linear model with the same covariates as the voxelwise models. Plots were generated using a combination of software including ggprism 41 ( Figure 1 ), nilearn 42 ( Figure 2 ), and tidyplots 43 ( Figures 3 - 4 ). Download figure Open in new tab Figure 1. Sex, genotype, and age effects on total brain volume. A) There were no statistically significant differences between male and female mice. B) APOE-ε4 carriers exhibited greater volume than non-carriers. C) A statistically significant increase in brain volume as observed with increasing age. Group mean and error bars are model-derived marginal means + 95% CI (A,B). Line and ribbon (C) represents model fit + 95% CI. Download figure Open in new tab Figure 2. Voxelwise t-statistic maps for main effects of age (A), sex (B), APOE-ε4 hetero-vs. homozygosity (C), and APOE-ε4 carriership (D). Statistically significant findings (pFDR < 0.05) are outlined and opaque for age (A) and sex (B). Genotypic effects (C, D) did not survive multiple comparisons correction. Download figure Open in new tab Figure 3. Pattern representation in gray (A) and white (b) matter. For each effect (age decrease, increase; sex differences), the heatmap indicates the proportion of statistically significant voxels within each region (i.e. >20% of the voxels in demonstrating an age-related decrease in volume were located in the the parieto-temporal lobe). Download figure Open in new tab Figure 4. Regional specificity of effects for gray (A) and white (b) matter. For each effect (age decrease, increase; sex differences), the heatmap indicates the proportion of the regions voxels that are statistically significant (i.e., >80% of the voxels in the bilateral cerebral peduncles were larger in males compared to females. Results Cohort characteristics A total of 158 mice were included in the present analyses. Distributions by age, sex, and genotype are presented in Figure 1 . The linear model for the total brain volumes demonstrated statistically significant increases in brain volumes over the range of ages (6-25 months), with an average increase of 2.12mm 3 per month. After controlling for all other parameters, APOE-ε4 carriers had greater total brain volumes than non-carriers (mean difference: 9.78mm 3 ). No statistically significant differences were observed in total brain volumes between APOE-ε3/4 and APOE-ε4/4 mice or between males and females. Total brain volume analyses are summarized in Figure 1 . Aging is associated with cortical decreases and subcortical preservation Age-specific effects are presented in Figure 2A . Age-related increases were primarily observed in subcortical regions – including corpus callosum, thalami, hippocampi, superior colliculi, hypothalami, and amydalae – as well as a portion of the bilateral parieto-temporal lobe accounting for approximately 40% of the total number of significant voxels. Notably while this distribution of age-positive voxels was observed across multiple regions, the regional specificity (proportion of a region with significant voxels) was relatively smaller. Smaller gray matter nuclei (e.g., superior olivary complex, pontine nuclei) as well as smaller white matter pathways (anterior commissure, stria terminalis) and gray matter substructures (dentate and stratum granulosum of the hippocampus) demonstrated age-related regional specificity, with significant voxels in > 60% of those individual regions. Figures 3 and Figure 4 provide comparative heatmaps representing regional pattern representation and specificity. The age-related pattern of decreasing volume was substantially represented by the bilateral parieto-temporal cortex (21.7% of significant voxels), cerebellar cortex (16.66% of significant voxels), and frontal lobes (13.39% of significant voxels). To a lesser extent, other regions, including the bilateral striata, hippocampi, entorhinal cortex, amygdala, and thalami were also represented in the overall pattern. Notably, these cortical and cerebellar cortical regions, as well as the occipital lobe, also demonstrated high regional specificity, with up to 50% of the regional voxels demonstrating age-related decrease. Figure 3 and Figure 4 provide comparative heatmaps representing regional pattern representation and specificity. Cortical and subcortical structures exhibit sexual dimorphism Sex-specific effects are presented in Figure 2B . The regional pattern of male positive effects included the bilateral cerebellar cortices, olfactory bulbs, striata, frontal cortex, hippocampi, and corpus callosum. High regional specificity was observed in the bilateral olivary complexes, white matter pathways (cerebral peduncles, corticospinal tract pyramids, medial longitudinal fasciculus) as well as subcortical structures (globus pallidus, bilateral stratum granulosa and dentate gyri of the hippocampus). Figure 3 and Figure 4 provide comparative heatmaps representing regional pttern representation and specificity. In contrast, females exhibited greater cortex-dominant regional patterns, with bilateral parieto-temporal, frontal, cerebellar, and entorhinal cortices all substantially contributing to the overall female-specific pattern. White matter tracts (mammilothalamic, stria terminalis, olfactory, cerebellar peduncle superior, anterior commissure), bilateral subependymale zones rhinocele, and the entorhinal cortex further contributed to this female-specific pattern. Figure 3 and Figure 4 provide comparative heatmaps representing regional pattern representation and specificity. APOE genotype does not drive localized volumetric differences No voxelwise differences survived multiple comparisons correction for any of the APOE genotype analyses, after accounting for age and sex effects ( Figure 2C-D ). Discussion Key findings The purpose of this work was to determine the translational relative of MRI-based volumetric profiles of the humanized APOE male and female brain. This mouse model was developed as a candidate for translationally relevant research in Alzheimer’s disease addressing the hypothesis that APOE genotype would be a significant driver of Alzheimer’s-like morphological phenotypes. Brain MRI imaging findings reported herein indicate A) that age and APOE-ε4 carriership significantly increase total brain volumes in this mouse model; B) that localized age-related changes in brain morphometry are independent of the global age-related increase, with widespread decreases particularly in cortical volume; and C) that sexually dimorphic patterns of local volume differences exist. However, contrary to expectations, APOE genotype did not impact regional volumes in the same manner observed in human studies. Total brain volume The present findings indicate that brain volumes in this mouse model continue to increase over the lifespan. This is consistent with findings in different mouse models, including the background C57/B6 strain, where rapid brain growth is observed over the first few months of life followed by a much slower increase or plateauing of brain volumes over the course of aging 7 . These findings are dissimilar to findings in human aging, where the brain reaches peak volume within the first two decades of life, followed by plateauing or decline in volume over the next 50-70 years 44 . Normal whole brain volume may decline at rates of up to < 0.5% per year, depending on decade of life 45 and requires between 30 and 35 years for volume to decrease by two standard deviations from its value at age 40 during healthy aging 46 . In AD, the rate of decline is accelerated 44 . The cross-sectional nature of our mouse data does not permit an estimate of rates of growth or atrophy, but the observed increase in volumes across the 18 months represented here indicates an estimated 7% increase in total volume. Notably, the brain mask for estimating total volume includes the ventricles, which may contribute to this increase, whereas ex-vivo imaging here does not permit accurate evaluation of ventricular size 47 . Independent of the observed aging effects, a small, statistically significant amount of the variability in total brain volume was explained by genotypic variation, with APOE-ε4 carriers having greater brain volumes than non-carriers. The observed magnitude (9.72mm 3 ) is small and may not represent a translationally relevant magnitude of difference. For reference, this magnitude of difference is approximately the same size as the left thalamus distributed over the entire mouse brain. While APOE-ε4 mediated whole brain atrophy is evident in Alzheimer’s disease, recent studies using large cohorts reflecting presumably healthy aging have revealed subtle or absent APOE genotypic effects on total gray matter and total brain volume 48 – 50 , which is broadly consistent with the findings here. Interestingly, after accounting for age and APOE genotype, no sex differences were evident in total brain volumes. Sex differences in total brain volume are robustly reported across the human literature, yet absent in the present analyses 44 , 51 – 53 . MRI findings from the background strain (C57BL6) show the same lack of total brain volume differences as observed here 54 , suggesting that this is not a by-product of the inclusion of humanized APOE but rather a mouse-specific phenotype. Collectively, these results indicate that this mouse model may better represent aging processes at the whole brain level rather than genetic trait related phenotypes. Aging results in decreased cortical and preserved or increased subcortical volume While overall increases in brain volume occurred over the aging process, local changes in volume occurred independently of this increase. Specifically, the cortex exhibited decreased volume while subcortical regions exhibited more frequently preserved or increased volumes. This age-related pattern is evident in both human 44 , 55 and mouse 56 literature. Prior work in a related but different mouse model suggests the presence of both metabolic deficits and altered neurovascular coupling in the cortex, which may provide a plausible explanation for the decreased cortical volume with advancing age 15 , 19 . Notably, the present mouse model does not display any of the hallmark pathological features of neurodegenerative processes, and thus cortical-subcortical shift may better reflect non-pathological aging rather than neurodegenerative atrophy. However, the localization and regional specificity of some of the volumetric decline – particularly the parieto-temporal and entorhinal cortices – does reflect early atrophy sites in Alzheimer’s disease 57 – 61 . This specific decline may, therefore, reflect some AD-like vulnerability, rather than pathology. Cortical and subcortical structures exhibit sexual dimorphism Interestingly, sex differences were observed in local volumes. Females generally had greater cortical volumes, independent of any age-related decline, than males. These findings are consistent with the pattern of sex differences observed in a comparative study of humans and C57BL6 mice 54 , in which consistent sex differences were observed across species with subcortical and deep gray brain structures exhibiting a male bias where cortical regions exhibited a female bias. These findings are generally consistent with other human 51 , 62 and mouse studies 63 of sex differences. These sex-biased regional variations may likely reflect sex hormone exposure and timing in both species 63 , 64 . APOE genotype does not drive local volume variation Contrary to expectation, APOE genotype did not exert significant voxel-level effects. Analyses herein controlled for the total brain volume indicating that total brain volume differences were more global in nature rather than driven by regionally focal genotypic variation. Prior work with this specific mouse model has revealed APOE-ε4/ε4 decreases in cerebral metabolism with aging 19 , alterations in neuronal 65 and peripheral metabolism 20 , 21 , immunologic profiles 12 , 20 , 22 , cortical transcriptomic profiles 21 , 23 , heterogeneous weight trajectories across aging 66 , and accelerated endocrine aging 25 . Mutations ( Trem 2 15 ; Trem2 + hAPP 67 ) in addition to humanized APOE coupled with environmental factors (high fat diet) 67 revealed reduced cerebral glucose metabolism and neurovascular coupling. In hAPOE-ε4s carrying both Trem2 and hAPP mutations, a high fat diet induced volumetric decreases compared to a normal control diet at multiple age points 67 . Given those findings, results reported herein indicate that humanized APOE-ε4 alone is insufficient to induce Alzheimer’s-like MRI phenotypes of atrophy. Collectively, and in the absence of discernible pathology, these findings point to an aging process characterized by widespread cortical volumetric reductions that are coincident with increased subcortical volumes. These changes are further modified by sex, with female preservation of cortical volumes and male preservation of subcortical volumes, without any specific impact of humanized APOE genotype. These findings partially echo findings from more aggressive AD mouse models that do exhibit pathological hallmarks (amyloid B deposition, tau) 7 , 8 . In those models, age-related cortical thinning and volumetric decrease are greater and more accelerated compared to control mice 7 , 8 . However, the present humanized APOE model, without other aggressive disease-causing mutations, fails to recapitulate the extent of progressive atrophy observed in aggressive, but translationally constrained, mouse models. Rather, these mice appear to represent a model of vulnerable aging, particularly with APOE-ε4/ε4 females demonstrating accelerated aging 25 , and may serve as a reasonable aging control group when evaluating more aggressive disease models. Strengths and limitations This study capitalizes on a number of strengths. Here we pool from a large number of humanized APOE mice across an extensive age range, reflecting an estimated human age range of 30-70. Our imaging pipeline capitalizes on open-source softwares to enable high-throughput processing, robust brain masking and template registration, as well as voxel-wise analyses and ultimately reproducibility within and across research groups. The template brains built from this analytic process can serve as a basis for future target registrations and comparative analyses with other mouse models. However, several limitations should be acknowledged. First, the humanized APOE-ε3/3 mouse was our control group, rather than a wild-type mouse. While the inclusion of wild type mice may have revealed greater hAPOE effects, the comparison would then have been between mouse-type Apoe and human APOE and we did not view this as a clinically or translationally relevant model. Second, we did not perform behavioral phenotyping in all of the mouse cohorts, thus we could not investigate whether these brain morphometric differences were related to relevant behavioral outcomes. Third, all animals were housed in sterile environments with a controlled, standard diet. This housing strategy decreases the potential influence of environmental confounders (e.g., bacterial and viral contagions, pollution) on the genetic risk factors at the sacrifice of real world environmental factors that may contribute to disease onset and progression in humans 9 , 68 . Finally, nuclear (i.e. FDG-PET) and molecular (i.e. IHC) assays were not available for the full cohort of animals. Therefore, mechanistically linking macroscale changes in MRI features to more granular causal factors was not feasible. Conclusions Volumetric analyses of the humanized APOE mouse model indicate that these mice do not present with an Alzheimer’s disease-like phenotype, despite disease-related risk factors of age, female sex, and APOE genotype. These findings suggest that, for these mice, APOE-ε4 alone does not confer macrostructural atrophy and likely requires additional risk factors (genetic and/or environmental). Analyses are underway with a mouse model that combines humanized APOE genotypes with humanized APP, which may better model the amyloid-generating process and ultimately disease progression. Funding This work was supported by the NIH/National Institute on Aging grants P01AG026572 and R01AG057931 to RDB and the Center for Innovation in Brain Science to RDB. Neuroimaging was supported by the University of Arizona’s Translational Bioimaging Resource (TBIR) and NIH small instrumentation grant (S10 OD025016). Neuroimaging preprocessing and statistical analyses were supported by High Performance Computing (HPC) resources supported by the University of Arizona TRIF, UITS, and Research, Innovation, and Impact (RII) and maintained by the UArizona Research Technologies department. 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