Global dissection of the impact of Alzheimer’s disease on brain architecture and behavior: High resolution MRH resolves robust regional effects

Global dissection of the impact of Alzheimer’s disease on brain architecture and behavior: High resolution MRH resolves robust regional effects | 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 Global dissection of the impact of Alzheimer’s disease on brain architecture and behavior: High resolution MRH resolves robust regional effects G. Allan Johnson , Yuqi Tian , View ORCID Profile Kathryn Hornburg , James J. Cook , Yi Qi , Leonard E. White , John T. Killmar , View ORCID Profile Catherine Kaczorowski , View ORCID Profile David G. Ashbrook , Robert W. Williams doi: https://doi.org/10.1101/2025.01.08.632011 G. Allan Johnson 1 Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center , Durham, NC 27710, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: gjohnson{at}duke.edu Yuqi Tian 1 Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center , Durham, NC 27710, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathryn Hornburg 1 Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center , Durham, NC 27710, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kathryn Hornburg James J. Cook 1 Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center , Durham, NC 27710, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yi Qi 1 Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center , Durham, NC 27710, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Leonard E. White 2 Department of Neurology, Duke University Medical Center , Durham, NC 27710, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site John T. Killmar 3 Department of Genetics, Genomics and Informatics, University Tennessee Health Science Center , Memphis TN 38163, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Catherine Kaczorowski 4 Department of Neurology, University of Michigan , Ann Arbor MI 48019, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catherine Kaczorowski David G. Ashbrook 3 Department of Genetics, Genomics and Informatics, University Tennessee Health Science Center , Memphis TN 38163, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David G. Ashbrook Robert W. Williams 3 Department of Genetics, Genomics and Informatics, University Tennessee Health Science Center , Memphis TN 38163, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Alzheimer’s disease (AD) affects brain regions with remarkable heterogeneity, but the precise impact of this disease on hundreds of small cortical, subcortical, and brainstem regions remains poorly defined. Here, as a prelude to testing preclinical models to prevent AD, we systematically quantified effects of human AD mutations in APP and PSEN1 on 231 regions and comprehensively evaluated changes in volume with unprecedented resolution in genetically diverse mice as a function of sex and genetic background. We studied 34 5XFAD F1 hybrids and 23 sibling controls at 14 months, evaluating learning and memory behaviors, followed by ex vivo diffusion tensor images (DTI) at 25 µm resolution. We delineated 231 regions of interest (ROIs) bilaterally with high precision. Remarkably, we found bidirectional changes: marked volume increases (up to 10%) in neocortex, hippocampus, amygdala, and sensory nuclei, contrasted with decreases in striatum, pallidum, thalamus, hypothalamus, and most fiber tracts. These opposing effects are unrelated to amyloid load and are likely to reflect temporal gradients in susceptibility of ROIs. Effects are similar in both sexes but far more prominent in females. Genetic background strongly modulates penetrance of the human mutations with the AD-BXD77 F1 type having the greatest sensitivity. Light sheet microscopy and stereological analysis of NeuN+ neurons and amyloid β aggregates in 22 regions revealed up to 20% loss of cells in CA3 and in anterior and intralaminar parts of the thalamus. Volumetric changes correlated with impaired fear acquisition and memory, with cases and controls often showing opposite relations between performance and regional volumes. These findings reveal unprecedented regional heterogeneity in AD progression and suggest therapeutic efficacy may vary substantially across genetic backgrounds and between sexes. INTRODUCTION Diagnostic criteria of Alzheimer’s disease (AD) are heterogeneous and controversial. This complexity arises from complex relations between cognitive and behavioral symptoms, neuroimaging, and neuropathological criteria 3 , 4 , 5 . The same is true of well-characterized mouse models of AD, in which there is considerable disagreement about major consequences of human familial autosomal dominant AD mutations on specific types of structural and functional outcomes 6 – 8 . One important factor emphasized by the work of both Murdy and colleagues and Gurdon and colleagues is that genetic backgrounds of AD mutation vary greatly and in ways that can make replication difficult 9 , 10 . Another problem is that there is limited systematic quantitative case-control data on effects of AD in either humans or mouse models—particularly on the integrity of cortical and subcortical regions as a function of mutation types, sex, age of onset of behavioral symptoms, environment, and treatment history. We do know that the strong role of genetic background effects and sex are key cofactors that must be incorporated into pre-clinical AD mouse models 11 , 12 , 13 . One of the best examples of the vital importance of genetic background and of sex in humans is the major differences in AD risk associated with APOE ε4 allele dosage 14 , 15 , 16 . In Asian ancestries this allele has strong deleterious effects with high odds ratios, but those with African and Hispanic ancestries have much less risk, even those with two copies of the ε4 variant—odds ratios as low as 1.26 15 , 17 . There is also a sex-by-genotype interaction effect of ε3/ε4 heterozygotes across several genetic ancestries with consistently higher risk for females 17 . As in humans, both sex and genetic ancestry (strain genetic background) also have very strong effects and can even reverse directions of effects of mutations on behavior 18 . Reluctance to accept this complexity is a cause of many problems and misunderstandings. For example, in a recent MODEL-AD study of the 5XFAD mutation 19 the authors recommend “the use of 5XFAD mice for AD studies focused on sex-based differences, immune regulation, and Aβ deposition but not for translational studies evaluating the potential of therapeutics to treat cognitive impairments ” (emphasis added). This statement must be qualified by the important modifier: “on the background of the C57BL/6J inbred strain.” We have known since the work of Kaczorowski and colleagues 12 , 20 , 21 that cognitive effects of the bigenic 5XFAD transgene ( APP K670N, M671L, and I716V mutations; PSEN1 M146L and L286V mutations) depends on strain background 22 . For example, Neuner and colleagues discovered 3-fold variation in contextual fear memory in AD-BXD F1s that is dependent on strain background. While AD-BXD81 F1s are impaired in a contextual fear memory even at 6 months of age, the AD-C57BL/6J mice are the least affected of 26 types . The contrast is even greater when quantifying differences between cases and controls at 14 months. Memory function degrades by 20–45% on 10 backgrounds but remains stable on four other backgrounds of these AD-BXD hybrids. Here we extend work by Kaczorowski and colleagues 12 , the MODEL-AD consortium 23 , 24 , and the recent cytological and gene expression analyses of Gurdon and colleagues 10 . We have systematically quantified the impact of the 5XFAD transgene on brain structure in both sexes using a case-control design at 14 months of age. We estimated changes in volumes of 231 cortical and subcortical regions and tracts bilaterally in 57 AD-BXD mice, both cases and sex-matched controls, using diffusion tensor imaging at 25 µm resolution—several hundred-fold higher than in previous work 24 , 19 . Our main goal is to expose the role of the human APP and PSEN1 mutations in a tightly controlled laboratory setting, while evaluating their impact as a function of genetic background 13 and sex. Our second long-term goal is to set the stage for interventional studies to explore the preclinical efficacy of drugs to treat AD as early as possible, similar to the early interventions now being tested in human clinical cohorts 25 , 5 . To do this in a translationally robust way, we need to know much more about those regions that are affected first and most severely by AD. We also need to know which of hundreds of brain regions contribute to different types of cognitive loss and behavioral changes. Here we address four specific questions: Can we create quantitative and comprehensive maps of the impact of the 5XFAD transgene on volumes of both large cortical and very small subcortical CNS regions and fiber tracts? Can we demonstrate significant effects of genetic background and of sex on modulating the impact of the 5XFAD human mutations on regional volumes? Can we demonstrate cellular correlates of changes in volume, in particular ab load and NeuN+ neuron densities and numbers? Can we link volumetric changes in specific ROIs to key behavioral outcomes, particularly learning and memory? RESULTS Overview We have used an analytic workflow similar in sequence to clinical and pathological investigations of humans with AD ( Fig. 1 ; Ref 26 ). We start with behavioral assessments of general function and cognition and progress to MRI scans to confirm and refine diagnoses. In this workflow, we add postmortem analyses and evaluate changes at the cellular level 27 – 29 ). However, unlike conventional studies of AD using mouse models, we do not rely on a single fully inbred strain, but instead incorporate a high level of genetic variation that matches that of human populations 30 , 31 , 32 , 12 , 22 , 20 , 33 , 10 . The AD-BXD F1s are replicable hybrids that can be used for well-balanced case-control designs to study the efficacy of pre-clinical treatments. Download figure Open in new tab Fig. 1. Workflow to evaluate impact of 5XFAD transgene effects on volumes of 231 brain regions. a, Female C57BL/6J mice hemizygous for the dominant 5XFAD transgene (Tg) were bred to males from four different BXD strains to generate genetically diverse, but isogenic F1 offspring—both Tg carriers (cases) and non-transgenic (NTg) controls. F1 offspring from a cross are genetically identical except for the presence of a transgene (red block). b, Behavioral and physiological phenotyping was carried out at 14 months of age. Contextual fear memory acquisition (CFA) was measured, and the contextual fear memory retention (CFM) was tested 24 hours later. Animals are perfusion fixed for MR histology and sent to the CIVM at Duke. c, Intact whole crania were scanned on a 9.4T vertical bore magnet using DTI with compressed sensing. A script controls the scanner and transfers the raw data to the cluster for reconstruction. d, 3D MRIs are generated at 25-µm resolution and at 61 angles (5 interleaved control scans), each with a different gradient. e, an integrated 4D volume of all scans is created, denoised, and transferred to DSI Studio. This 4D data set is used to generate diffusion tensor output types, including eight 3D image modalities generated from the DTI, including axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RA), fractional anisotropy (FA). f, The set of images are passed to the ANTs pipeline to register all volumes into the Duke Mouse Brain Atlas (DMBA) framework using a reduced subset of the Common Coordinate Framework (rCCFv3) labels. Once registration is complete, full volume delineations from the DMBA rCCFv3 are applied for all ROIs. The transformations with all delineations are then back-projected (inverted) to the original specimen image. We extract quantitative volumetric and other data types bilaterally from each ROI (absolute and normalized volume, MD, FA, etc.). i, Volumes with labels are passed back to DSI Studio and ROIs are seeded for tractography to generate connectomes. j, The brain is removed from the skull, cleared, stained for NeuN and ab and scanned with selective plane illumination. The LSM data sets (∼1TB total) are mapped into the undistorted MRI volume. Cell counts and ab load for selected ROIs are generated as in Tian et al. 2023. k, All data and metadata are stored in an Oracle database. l, Data sets are exported to MATLAB, JMP, CSV, R, or Excel for downstream analyses. l, We provide FAIR+ data access for full 3D images from the CIVM ImageSpace web service ( https://civmimagespace.civm.duhs.duke.edu ). All summarized ROI-level data are aggregated along with metadata, behavioral and morphometric data in GeneNetwork.org (select Species = Mouse; Group = BXD NIA Alzheimer’s Studies; and choose a data Type ). Results are presented for three major data types: 1. MRH: volume differences for 230 brain regions that we have delineated bilaterally, and of course, the whole brain, for 57 cases and controls; 2. light sheet microscopy: Corroborative whole brain stereology of the cytological impact of 5XFAD at 14 months in AD-BXD77 F1 cases ( n = 9) and sibling controls ( n of 7); and 3. behavioral analyses to probe the relations between learning and memory function with volumetric differences as a function of transgene status, genetic backgrounds, and sex. 1. Volumetric Analysis of the Impact of 5XFAD by MRH The critical importance of accurate measurements of volume phenotypes MRI of postmortem tissues at microscopic resolution of 25 µm—what we refer to as magnetic resonance histology 34 —can produce images at volumetric resolutions more than 1000-fold higher than that of in vivo studies 29 . The difference is striking. Figure 2a is from an optimized in vivo study by Crombé 1 whereas Figure 2b is the much-improved ex vivo resolution we achieve using the current methods ( Fig. 1c–j ). Four major upgrades have made this possible: Spatial resolution that far better than previous work using mouse models ( Fig. 2a,b ) High-angular resolution diffusion imaging, 61 angles in our case Multispectral segmentation of brain regions and tracts using DWI and fractional anisotropy (FA) Whole brain 3D coverage using a set of 231 ROIs, that are consistent with the CCFv3 (Ref 35 ), but with stereotaxic correction 2 ( Fig. 1f ) Download figure Open in new tab Fig. 2. Spatial and contrast resolution increase precision in structure delineation. a,b, Side-by-side comparison of optimized fractional anisotropy (FA) in vivo MRI (left) and ex vivo MRH used in the present work (right) to emphasize the improvements in spatial resolution. Figure 2a is of a 5XFAD B6 congenic mouse from Ref 1 at a voxel dimensions of 0.082 x 0.082 x 0.203 mm and voxel volume of 0.0014 mm 3 . b, the right side is a closely matched level of our current ex vivo protocol of an AD-BXD77 F1 specimen at an isotropic voxel volume of 25 µm 3 (0.000016 mm 3 )—an 87-fold improvement in resolution. c,d, FA and DWI images are used in the present study to maximize the contrast resolution for the delineation of structural boundaries. Minimum deformation templates are computed for each individual as a function of its own AD-BXD F1 genetic background—four in this study. Images are registered to the FA and DWI consensus volume of the Duke Mouse Brain Atlas 2 as shown in d-h for the minimum deformation template (average) of 7 AD-BXDF1 controls. Fiber tracts and subtle lamination differences in cortical and hippocampal layers are prominent in the FA images, but boundaries such as those between dorsal striatum (STD) and globus pallidus externa (GPE) are much more prominent in d —the DWI contrast (three outlined red arrowheads). e-h, Higher magnification pairs of FA and DWI cross-sections centered on the hippocampal formation. Sets of red arrows mark the start and end of the pyramidal cell layer extending from CA1 to CA3 and into the hilus of the dentate gyrus (labels in g ). The final delineations combine information from these and other modalities. Three blue arrows in f , g , and h mark the caudal-lateral end of the perirhinal cortex (PRC). This boundary is well defined in by FA in g , but not by DWI in h . In h the green arrow points at a lamination pattern in the LGD that is rarely seen in any other dMRI modality. Other abbreviations: LGD: Lateral geniculate nucleus, dorsal; fim: fimbria; STD: Striatum, dorsal; AUC: Auditory cortex; ECT: Ectorhinal cortex; ENT: Entorhinal cortex; SUB: subiculum; CA1 and CA3 regions of hippocampus; DGG: Dentate gyrus. DMBA anterior-posterior (AP) and dorso-ventral (DV) levels of these sections are given in b and d . No effects of 5XFAD mutation on total brain volume on any of four AD-BXD genetic backgrounds We imaged 57 cases and controls belonging to four types of crosses of 5XFAD-by-BXD F1s, what we refer to as AD-BXD F1s, both the cases and their controls. Numbers of males and females are reasonably well-matched, 26 versus 31. However, the distribution is uneven among the AD-BXD F1 subtypes. The main consequence of the unevenness is variation in statistical power when analyses are stratified by genetic background. We intentionally included 50% more 5XFAD cases than controls (34 vs 23) because the transgene does increase within-class variance—neuroanatomical and behavioral. and sex. Brain volumes of 5XFAD cases and their controls are 475.2 ± 3.45 mm 3 SEM and 478.6 ± 6.14 mm 3 SEM, respectively. Given that females are more affected by FAD in both humans and mice, we controlled for or stratified most statistical tests by sex. There is a –9.4 mm 3 absolute difference between female cases and controls and a +1.4 mm 3 difference between males. Neither difference is significant. Similarly, volumes of 26 males and 31 females are 472.9 ± 4.0 mm 3 and 479.6 ± 4.8 mm 3 ; again, not significant. Finally, even when stratified by the four F1 types (AD-BXD32 F1, AD-BXD65 F1, AD-BXD77 F1, and AD-BXD101 F1), there are still no significant differences in brain volume within type. Our conclusion is that here is no evidence of a global effect of the 5XFAD transgene on brain volume in AD-BXD mice at 14 months. In contrast, there is a strong genetic effect of strain background on overall brain volume. The AD-BXD32 F1s have the largest brain volumes (495 ± 4.9 mm 3 ) whereas the AD-BXD101 F1s have the smallest (447 ± 4.1 mm 3 ). However, there is again no significant effect of the 5XFAD transgene within any of the four AD-BXD types. Strong effects of the 5XFAD transgene on regional volumes on all AD-BXD backgrounds We compute CVs of independently segmented right and left ROIs—the standard deviation of the values divided by their mean—the y axis on figure 3c . The average CV of left-right comparisons of all 231 ROIs, including the two halves of the whole brain (BRN) is 1.8% ( Fig. 3c ). As expected, larger ROIs such as entire hemispheres, can be delineated more accurately, and consequently the correlation between volumes of ROIs and their CVs is –0.7—and this correlation is almost certainly due to technical error rather than true asymmetry. The minimum CV is 0.32% for brain hemispheres (BRN). The highest CVs are 5.7% for ROIs for very small nuclei such as the trapezoid body (NTB) and the paraventricular nucleus of the hypothalamus (PVH). These two ROIs have bilateral summed volumes of 0.084 mm 3 and 0.25 mm 3 , respectively ( Fig. 3c ). However, at a volume of merely 1.0 mm 3 or 1 mg of tissue, the CV of ROIs average about 2%, equivalent to 20 µg or 20 nanoliters of tissue. This level of replicability of right and left delineations gives us unprecedented accuracy to detect much smaller changes and much more accurately than in our previous work 36 . Download figure Open in new tab Fig. 3 Diffusion tensor delineations of cases and controls are accurate and detect many effects of 5XFAD. MRH data types for a set of AD-BXD77-F1 controls. a, Fractional anisotropy at four axial levels of brains of a set of seven AD-BXDF1 controls that have been merged into a common framework using the DMBA. b. Mean diffusivity (MD) provides images with good delineation of nuclei. c, Volume of regions (bilateral sums) have low average right-versus-left coefficients of variation (1.8%) of ROI volumes across all 57 cases and controls that range from a high of 6% for small structure such as the NTB to a low of 0.5% for whole brain (mean and correlation given to the left near the y axis). The two vertical green lines highlight small (0.16 mm 3 ) and larger ROIs (6.3 mm 3 ). Those marked in bold blue font along near the green lines were studied using LSM and stereology to estimate amyloid deposition and neuron numbers ( Fig. 5 ) of AD-BXD77 cases and their controls. All structures are labeled using DMBA acronyms. Most nuclei are labeled using black font, and other fiber tracts and the ventricular volume using red font. d, Significance of effects of 5XFAD on the volumes of 230 regions. The Y axis gives the –log P of the multiple-test corrected P value of an ANOVA model of 5XFAD effects using three cofactors: sex, genetic background, and scanner system. X axis is the rank of the Cohen’s D effect size. There is a strong rostral-to-caudal gradient in the impact of the 5XFAD transgene on gray matter volume. The great majority of ROIs in rostral telencephalic grey matter structures increase in volume—only the caudate-putamen and pallidum are exceptions ( Fig. 3a ). These apparently swelling ROIs include almost all parts of the olfactory system, the entire neocortex, the amygdala complex, and the hippocampal formation. In contrast, almost all diencephalic, mesencephalic and hindbrain structures decrease in volume, as well as most fiber tracts, including the fimbria and fornix ( Fig. 4a,b ). Two notable exceptions from the trend toward shrinkage are the dorsal lateral geniculate nucleus (LGD) and the inferior colliculus (ICC and ICO)—both crucial in sensory processing. Download figure Open in new tab Fig. 4 Regional changes in volume associated with Alzheimer’s disease mutations. a, Columns of brain regions organized roughly from rostral to caudal as an ontology of stacked colored blocks, each representing an ROI. The colored blocks to the far left define P value levels of differences in volume between 5XFAD and control siblings (see Methods). The color scale is defined in the center of the figure beneath the brain section. Green indicates P < .05, FDR corrected. Corresponding pink and blue columns to the right in a define percentage gains (pinks) and losses (blues) in volume. b, Horizontal schematic section of a mouse brain taken from the rCCF3 segmentation of structures (see the Duke Mouse Brain Atlas of C57BL/6J ). This section is at a level –2.37 mm ventral to the lambda-bregma plane. Color codes as in a . The olfactory bulb (OFB) is at the top (rostral) and the vermis (VER) of the cerebellum is at the bottom (caudal). A small number of ROIs are marked for orientation. c, Strain genetic background effects for the set of 137 ROIs that achieve a global P < .05, now with the significance level (left) and effect size (right) for each of the four AD-BXDF1 types. The genetic background of the sire of each F1 is indicated at the top— BXD32 etc.). d, Sex differences and effect sizes for females and males with encoding as in c . e, A decoding of the three letter acronyms with short descriptions of ROIs that we use in the Duke Mouse Brain Atlas that are derived from CCFv3. The complete list of 231ROIs, their precise P values and effect sizes, GeneNetwork.org accession numbers (including right and left sides), and matched Wikipedia entries are provided in Supplementary Table 1 . These striking regional changes in volume are corrected for minimal within group variation in brain size and a total of 137 ROIs have significant fraction volumetric changes at P < .05 with a Benjamini and Hochberg corrections ( Fig. 3d , Fig. 4 ). The sizes of the 5XFAD effects are modest in absolute terms, amounting to differences generally less than 10%. While 5XFAD also causes a general decrease in fiber tract volumes, only three tracts reach significance—the corticospinal tract (cst), the fimbria (fim), and post-commissural fornix (fop). The anterior commissure (aco) and the optic chiasm and tract (oct) are the least affected. The total ventricular volume (Vnt) is reduced in volume, this effect is not even close to being significant. The 5XFAD transgenes increase variability in volumes between right and left sides of the brain Asymmetry between right and left ROI volumes is greater in 5XFAD cases than controls—1.83% ± 0.029 SEM versus 1.72% ± 0.038. This small 6.4% increase could be caused by ontogenetic differences, but we think that a more likely cause is asymmetry in β amyloid deposition that is notable, for example, in figures 5a–d . Download figure Open in new tab Fig. 5 Impact of 5XFAD on aβ aggregates and density of NeuN-positive neuron number. a, 3D light sheet microscopy volume-rendered image of β amyloid deposition. b, Matched FA volume-rendered image of the same 5XFAD AD-BXD77 F1 brain. c, Superposition of β amyloid and FA images demonstrates the accuracy of volume registrations. d, Zoomed-in image of the hippocampus (boxed area in c ) with intense β amyloid in subiculum and entorhinal cortex in the right hemisphere. e, NeuN of the same region. f, Volume differences of 22 ROIs between AD-BXD77 F1 cases and their controls. ROIs have been ranked from left to right based on the percentage increase or decrease in volumes (left y-axis) with three fibers tracts to the right side. Single asterisk for nominal P values <.05, and two asterisks for <.01 (all two-tailed). Volumes were compensated for differences in brain weight among individuals by computing fractional volumes of ROIs given whole brain volume. The basolateral amygdala (BLA) to the left side has the greatest percentage increase in volume in the 5XFAD cases; the posterior nucleus of the thalamus (POT) has the greatest percentage decrease in volume in the 5XFAD cases. g, Amyloid β aggregates are 1.7-to 3.8-fold higher in 5XFAD cases than controls. h, Neuron densities are lower in 10 of 11 regions that have increased volumes in 5XFAD cases, but only the CA3 has a nominally significant reduction in density. Neuron density changes in ROIs that are smaller in 5XFAD cases (blue) are generally noisy due to their much smaller volumes ( Fig. 3c ). i, Neuron numbers are reduced in CA3, PFT, and AVT at nominal P values of <.05. The apparent increase in neuron numbers in secondary visual cortex (VCS) of 5XFAD cases is likely to be an artifact caused by interactions between protocols and β amyloid deposition and cellular stress (see text). NeuN counts in fiber tracts were very low on an absolute scale, and the fold changes are likely to reflect low-level mislabeling and erroneous counts. We computed the Fisher’s combined P value for the 7 ROIs with volumetric reduction (tracts excluded) and numbers are collectively lower in 5XFAD cases (Fisher’s combined P of .02). All stereology from left sides only. Strong sex effects of the 5XFAD transgenes We initiated this analysis by testing our ability to resolve the well-known sex difference in the volume of the medial preoptic area. The bilateral volume of the MPO is 0.428 ± 0.033 SD mm 3 in females and 0.468 ± 0.024 mm 3 in males—about a 9% difference. In an ANOVA fitting strain, transgene status, sex, the sex-by-transgene interaction, only sex is a strong predictor of variance (F = 38.3, P < .0001). Of the 137 ROI highlighted in the overall n-way ANOVA as being modulated by transgene status, at least 28 have significant sex differences at a Bonferroni criterion of P < .05 ( Fig. 4d and Supplementary Table 6, column 7 ). In contrast, and somewhat surprisingly, not a single ROI has a significant 5XFAD-by-sex interaction). Genetic background strongly modulates severity of 5XFAD effect Genetic background is a highly significant predictor of variance in 5XFAD transgene action on the fractional volumes of ROIs that correct for overall differences in brain size ( F = 6.60, P < .0008). Thirty-one ROIs have significant differences in fractional volume even with aggressive Bonferroni correction at P < .05 ( Fig 4c , Supplementary Table 5, column 6 ). By far the strongest effect of the 5XFAD transgene in on the AD-BXD77 F1 background. However, the trends of effects of all four strains tend in the same direction and there is no significant 5XFAD-by-strain interaction. 2. Light sheet stereological analysis of changes in NeuN+ cell densities and numbers in AD-BXD77 cases and controls (see Supplementary Table 2 for acronyms) The volumes of 11 of 12 telencephalic ROIs are larger in AD-BXD77 F1 cases than in their sibling controls (left side of Fig. 5f ). This difference is nominally significant for half of all regions. The Fisher’s combined P value is highly significant ( P < .0001). This pattern is replicated, but with the opposite polarity, for all thalamic and brainstem structures. Figure 5g and h address the question of whether there is evidence in AD-BXD77 cases of loss of NeuN-positive cells. Gurdon and colleagues estimated the intensity of NeuN staining in 6- and 14-month-old AD-BXD F1s and detected notable loss of intensity in the hippocampus proper (CA1–CA3), in the dentate gyrus, in the posterior amygdala nucleus (PAN), and in the striatum-like amygdalar nuclei (the central nucleus of the amygdala and parts of the medial nucleus of the amygdala) 10 . We have counted neurons in both CA1 and CA3, and in both we detect appreciable loss of NeuN+ cell densities—about 20% in both cases that is neatly matched to absolute loss of numbers ( Fig. 5h ). In the case of CA3, the loss in density of cells is nominally significant ( P =.013, two-tailed), but the loss of numbers of cells does not reach this level ( P = .076, two-tailed). Given that the null hypothesis is polarized (no loss of neurons), a one-tailed test is justifiable. Our best estimate is that by 14 months about 20% of NeuN+ cells have died in CA3 of AD-BXD77 F1s. We also have evidence of cells loss, especially in smaller thalamic, mesencephalic, and hindbrain structures that have not been well studied in either mouse models of AD or in human AD patients. In six of seven ROIs that have smaller volumes in 5XFAD cases there is also a significant reduction in cell numbers using Fisher’s combined P test ( P of .017 two-tailed). Numbers are 35% lower in the anteroventral thalamic nucleus (AVT, P = .009, two-tailed), and 25% lower in the parafascicular thalamic nucleus (PFT, P = .044, two-tailed). There is one finding we struggle to explain—the apparent increase in NeuN+ cells in the secondary visual cortex ( Fig. 5i , VCS). Cases have an average population of 363,000 ± 8200 SE cells versus 311,000 ± 9500 SE in controls ( P = .006). We have reviewed the delineations of VCS and recounted all cases and controls, and we did not identify any operational errors. Busser and colleagues 37 discovered that AD in humans can induce aberrant expression of cell cycle genes due to cellular stress, and the RBFOX3 protein to which the NeuN antibodies normally bind is known to be involved in both cellular stress, as well as linked to cell cycle proteins in the nucleus 38 . It is therefore possible that ectopic or artifactual NeuN+ labeling leads to increased detection of NeuN+ cells. Finally, NeuN can cross-react with synapsin1 protein 39 and is also expressed in GFAP-positive astrocytes in cell culture 40 . 3. Behavioral differences covary with MRH ROI volumes We have overlapping behavioral and neuroimaging data for 52 AD-BXD individuals—31 cases and 21 controls; 27 females and 25 males. We focused attention on contextual fear acquisition (CFA) and contextual fear memory (CFM). We computed correlations of these two traits with variation of all 230 ROI volumes (bilateral sums, whole brain excluded). Correlations of both the hilus of the dentate gyrus (DGH) and the posterior amygdala (PAN) with contextual fear memory are negative ( Fig. 6b and 6d ). In other words, paradoxically, the larger these ROIs, the worse an individual’s memory retention. In contrast, figure 6a highlights a positive correlation of r = 0.49 between performance and greater volume in the fimbria (fim) with acquisition, and in figure 6f a positive correlation of r of 0.49 of the dorsal thalamus (THA) volume with memory. All correlations are significant ( P < .05) whether using Pearson or Spearman methods and are also corrected for multiple tests ( Supplementary Table 8 ). Download figure Open in new tab Fig. 6 Volume changes in 5XFAD cases are linked to worse behavioral performance. a, Volume of the fimbria is smaller in cases than controls and fear acquisition is generally worse. Pearson correlation lines for cases (green) and for controls (purple) are given with the combined r correlation with P values in each panel. We also provide the rank order rho correlations separately for case and controls. b, Volume of the hilus of the dentate gyrus is larger in 5XFAD cases than controls. The 5XFAD cases have higher mean Y axis values and worse contextual fear memory (lower percentage freezing). c, Volume of the paraventricular nucleus of the thalamus correlates with slightly worse CFA in cases than controls. This ROI is so small that the y axis scale has been modified and a true value of 0.00028% is labeled 0.28. One outlier measurement is flagged. d, Volume of the posterior amygdala in 5XFAD cases is greater but there is minimal impact on CFM. e, Volume of the visual cortex is negatively correlated with CFA in cases but is positively correlated in controls. e, Volume of the VCX is negatively correlated with CFA in cases but not controls. f, Volume of the thalamus is decreased in 5XFAD cases, but there is no significant difference between cases and control either by Pearson correction (lines) or the rho correlations (text values in panels). A more interesting question is whether there are differences in correlations between the two groups—5XFAD cases and their controls. We therefore stratified the analysis by transgene status ( Supplementary Table 8 ) and tested for significant differences between rank-order correlations for all ROIs with both contextual fear traits— CFA and CFM. A test of this sort with modest sample sizes ( ns from 21–31) is not well powered. Nonetheless, we detected 46 ROIs for which correlations between cases and controls have opposite polarities that are nominally significant with fear acquisition—a measure of learning efficacy (e.g., Fig. 6c,e ). None meets FDR corrections at P < .05. Remarkably, the visual cortex ( Fig. 6e ) has among the highest differences in correlations between cases and controls for both CFA and CFM. Both temporal association cortex and the paraventricular nucleus of the thalamus also differ markedly ( Fig. 6c , PVT where rho is –0.38 for cases and +0.44 for controls). In contrast, we detected only three structures with differences for CFM in correlations between cases and controls—the medial septal nucleus (MSN, rho of +0.40 for cases vs –0.29 for controls), the visual cortex (VCX, rho –0.41 cases vs +0.18 for controls), and the ventromedial hypothalamic nucleus (VMH, rho +0.11 for cases vs –0.45 for controls). The detection of a large difference in correlations in the MSN is particularly interesting given its critical role in CFM via modulation of CA1 function 41 , 42 . The detection of differential correlations with visual cortex is also intriguing given its critical role in contextual fear memory via modulation by microglial tone 43 . Given the large number of tests, even the strongest results will benefit from larger sample sizes. Nominal and FDR-corrected P values are provided for all 230 ROIs for both CFM and CFA in Supplementary Table 8 . Rather than making claims about these correlations we prefer to regard them as proof-of-concept that support the hypothesis that we are technically able to detect correlations between ROI volumes, changes in neuron densities and numbers, and changes in specific behaviors known to be diagnostic of features of human AD. The apparent link to visual system ROIs is surprising but is consistent with a prodromal form of posterior cerebral atrophy (PCA) type of early onset AD in humans 44 . DISCUSSION Until recently the most definitive diagnosis of human AD has involved specific patterns of cognitive decline, often corroborated by postmortem evaluation of amyloid aggregates and loss of brain mass and neuron numbers 45 , 46 . Over the last 25 years MRI has become one of the more widely used methods for diagnoses of AD subtypes 47 , 48 , 44 , 45 , 49 . Initial MRI diagnoses of AD has focused on morphometric measures of global and regional CNS atrophy. Spin lattice (T1) and spin-spin relaxation (T2) have been exploited to estimate loss of myelin integrity. Diffusion tensor imaging (DTI) has added additional quantitative indices of tissue cytoarchitecture—the DTI scalar diffusion metrics, namely axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD), and fractional anisotropy (FA). DTI can be extended to a systems level by the computation of whole brain connectomes that provide global evaluation of structural connectivity. In combination with advanced statistical methods, it is now practical to link volumetric changes and those of the structural connectomes to behavioral decline. Even the earliest-onset familial forms of Alzheimer’s disease with well-known genetic causes 50 , 51 , 52 are complex in terms of their progression and symptomatology. For example, highly penetrant mutations of PSEN1 are associated with a 30-year range of onset even in single families 53 , 54 , 55 , 56 . There are also striking differences in the range of behavioral deficits with brain regions that either swell or atrophy 57 , 58 , 59 , including posterior cortical atrophy (PCA)—a relatively common variant of AD (∼10% of early onset cases) that causes a wide range of visual and other function deficits 60 , 44 , 61 , 62 , 63 . This complexity has many causes, but two are critical: genetic ancestry and sex. In the present work we have addressed both genetic background and sex and have detected pervasive effects of human AD mutations on volumes of 137 of 230 well defined cortical and subcortical brain regions in mice, including in nuclei as small as the perifascicular nucleus (0.21 mm 3 unilateral, Fig. 3c,d , Fig. 4 , Fig. 5 ). One major finding of our work is a strong rostral-to-caudal gradient in effects of the 5XFAD transgene that causes an increase in volumes in most of the telencephalon and cortex, but a shrinkage of almost all other regions of the brain. We also corroborate that there is a loss of neurons in the CA3 of the hippocampus in the most severely affected AD-BXD77 cases. While not yet sufficiently robust to make strong claims, we suspect that there is appreciable neuron loss also in anterior and midline nuclei of the thalamus on this genetic background. Our results also confirm and extend previous work by Kaczorowski and colleagues that the effects of 5XFAD are highly variable as a function of genetic background 10 , 12 . We confirm generally stronger effects of AD in females that is also a consistent characteristic of FAD in humans 64 , 65 . Our strong positive findings are in contrast to negative findings of the MODEL-AD consortium 24 and several other recent studies 9 , 66 using both conventional T1/T2 MRI and DTI (overview of these papers in Tables 2 and 3 ). One possible biological explanation for some failures is that the impact of 5XFAD has often been studied only one the C57BL/6J genetic background—a background known to be resistant to the effects of this transgene 12 . But three key technical explanations are even more important: 1. the higher resolution of the ex vivo MR images we have generated ( Fig. 2a,b ); 2. the use of more sophisticated multiangle DTI methods that enhances contrast and the precision of delineations; and, 3. the much larger number ROIs that we have been able to delineate. View this table: View inline View popup Download powerpoint Table 1 Numbers of cases and control classes View this table: View inline View popup Download powerpoint Table 2. Recent MRI studies of rodent models of AD using T1/T2 weighted MRI View this table: View inline View popup Download powerpoint Table 3. Representative MRI/DTI studies of mouse models of AD The increase in volumes of ROIs in cortical regions has, to the best of our knowledge, never been reported in humans with either PSEN1 or the APP mutations. Quite the opposite—a common denominator of FAD is cortical thinning even in presymptomatic individuals 83 , 84 . We consider some technical factors below but given the consistency of the apparent swelling of most telencephalic regions and the ease with which many of these large ROIs can be delineated, we currently hypothesize that the increased volume is an initial and perhaps an inflammatory response—potentially compensatory—of these ROIs to beta amyloid aggregation—a finding supported by the work of Andersen, Westi, and colleagues 85 , 66 . Whatever processes are involved in olfactory, cortical, and hippocampal ROI volume increases, they are not shared by thalamic, hypothalamic, and brainstem regions. The regional specificity is a major clue and constraint on potential models of this rostral-to-caudal gradient of 5XFAD effects in AD-BXD F1 mice. Sex differences in the impact of 5XFAD on regional brain volume The core of what we need to say on this important topic is summarized in figure 3d . Of 137 ROIs with volumes that are significantly affected by the 5XFAD transgene, only nine have stronger effects in males than in females (MOS, AHY, PVH, for, LGD, mbh, PAG, PHG, and NTS), and many of these apparent differences may be due to sampling error. However, this list does highlight hypothalamic and other midline regions for scrutiny as sex-dependent targets of the human APP and PSEN1 mutations 8 , 64 , 83 . (See Supplementary table 2 for all ROI acronyms.) Relationships of β-amyloid burden and NeuN+ cell densities and numbers We demonstrate significant volumetric changes across brain regions in AD-BXD F1 cases—especially on the AD-BXD77 hybrid background. This striking regional variation in volumetric effects leads to several questions about underlying molecular and cellular processes. We can provide some initial insights into the quantitative cellular correlates, but we cannot yet provide clear answers. The autosomal dominant mutations in APP and PSEN1 are known to cause aberrant APP cleavage and clearance 86 , 6 and this leads to neuroinflammation mediated by the activation of astrocytes and microglia, as well as fundamental changes in cortical and hippocampal bioenergetics 85 , 66 . We detect what could be considered parenchymal swelling in many telencephalic ROIs. This increase in volume in cortex initially puzzled us, but there are strong precedents in human neuroimaging studies, particularly in presymptomatic PSEN1 humans in which several cortical areas are initially approximately 250 µm thicker than control but are 250 µm thinner in when symptomatic (see figure 5 of Ref 57 ). ROI volumes also initially increase in asymptomatic cases relative to controls, but this is followed by severe loss in symptomatic cases. These PSEN1 subjects carried either M139T, K239N, or L286P mutations. The 5XFAD transgene incorporates the L286V mutation, and to add insult to injury, also incorporates the M146L mutation. The increase in volume of the hippocampus is more than “prodromal” and can be linked to significant loss of neurons. This is most evident in CA3, and perhaps also in CA1 ( Fig. 4i ). Here our results align with the work of Gurdon and colleagues 10 who detected a decrease in NeuN+ intensity in the hippocampus. We estimate that 10–20% of pyramidal neurons are lost in AD-BXD77 F1 cases. To the best of our knowledge this is the first, stereological estimate of absolute neuron loss in 5XFAD mice (not just a reduction in density). In sum, this work suggests that the increase in volume of some ROIs is a prodromal stage of disease that can be associated, or not, with neuron loss. A surprising coda is that an increase in the volume of the hippocampus (+7% in CA3 of AD-BXD77 F1 cases) is associated with a significant loss of neurons (∼18%). The light sheet analysis can also dissociate amyloid levels from the polarity and amplitude of volumetric change ( Fig. 5f,g ). Amyloid burden is high across all 22 ROIs in AD-BXD77 cases, but this high expression does not covary with volume change (rho = +0.27, P = 0.2). This is also true of many other ROIs. Gurdon and colleagues 10 demonstrate that both astrocytes and microglia are activated in AD-BXD F1 mice in all brain regions, including striatum, pallidum, thalamus, and brainstem (Ref 10 , their figure 4 ). In our work, these same regions have reduced ROI volumes—a clear dissociation between volumetric changes and classic markers of neuroinflammation. If molecular drivers of AD ultimately converge on neuroinflammation, then we need better causal explanations for spatial heterogeneity. Many differences in expression, cell types, and patterns of activity might explain volume increases and decreases and neuron loss or sparing. We also need to explain both the broad rostral-caudal gradient in swelling and shrinkage, as well as the sharp differences between neighboring ROIs. One concrete example may help make this point—the dorsal lateral geniculate nucleus (LGD) is the only thalamic volume that is larger because of the 5XFAD transgene at 14 months of age. However, its immediate neighbor, the lateral posterior nucleus gets smaller (LPT, this is the homolog of the human pulvinar, see Fig. 4b ). Both nuclei are crucial components of the visual system, and both have dense reciprocal connections to the visual cortex—both VCP and VCS. But these two thalamic nuclei have radically different physiology and function. It is intriguing that of all nuclei in the dorsal thalamus of mice, the LGD has by far the highest concentration of GABAergic interneurons 87 . Two thalamic ROIs with significant volumetric losses also lose many neurons in AD-BXD77 mice—the perifascicular nucleus (PFT) and the anterior ventral nucleus (AVT) ( Fig. 4i ). Both nuclei have roles in cognition and memory. The anterior nuclear group, including the AVT, have well known roles in memory 88 . The thalamus is one of the first regions to have high amyloid levels and volumetric atrophy in presymptomatic carriers of PSEN1 mutations 89 . Given our strong signal in anterior and midline thalamic regions, these regions are worth more intense study as early targets of AD. Linkage of ROI volumes with behavioral variation Even in the relatively small subset of animals in which we have both neuroanatomical and behavioral data, we see significant correlations with FDR correction. A remarkable set of 29 regions are correlated with CFA—a key learning metric. Twelve of these ROIs are thalamic (AVT, ILM, PCN, PFT, PIT, POT, THA, TPA, TSM, VAL, VGT, VMT), 2 are hypothalamic (SUT, ZIN, lbu), 2 are tracts associated with hippocampus and many other ROIs (for, fim), 1 is the basomedial amygdala, and the remaining 7 are motor-related (ION, MRN, MBM, PNM, PRT, RED, TMN, cst). Collectively this set highlights many linked sensory, perceptual, fear and affect, and motor integration that will be critical in fear conditioning 90 , 91 , 92 , 93 With respect to CFM, the data highlight both the entire dorsal thalamus (THA) and 3 of its components (LAT, TPA, DGT), 3 amygdalar regions (PAN, LAA, BLA), the hilus of dentate gyrus (DGH) and the cortical subplate (CTS) 94 . All of these ROIs have been previously associated with fear memory 95 , 96 , 97 , 98 , 99 , 92 , 100 , 101 , 102 and this gives us confidence in the validity of both methods and our findings. Volumes of the whole thalamus (THA) and the polymodal association cortex-related part of the thalamus (TPA) correlate positively with both CFA and CFM, indicating linkage with both learning (acquisition) and memory. This is in good agreement with much work done at the circuit level 98 . That we see the thalamic regions so prominently is important, because these regions are being increasingly recognized as important in AD 88 , 103 and reduced volume of several thalamic regions have been associated with both early- and late-onset disease 104 . Several hippocampal and amygdalar ROIs have positive correlations – that is, larger volumes are associated with worse function—against naive expectations 105 , 106 , 107 . One explanation mentioned above is parenchymal swelling early in AD progression, and a possible compensatory mechanism 108 , 109 . However, the above correlations represent processes taking place collectively across both cases and controls. Of greater interest are the subset of correlations that are significantly different between cases and controls. We identify 56 ROIs for which correlations differ in directions or magnitude for CFA, and 13 for CFM. Of these, 11 are nominally significant for both CFA and CFM (VCX, VCP, PSV, VCS, ECT, pcm, MGD, RSC, mlf, PTT, cn3). Five of these are involved considered sensory or perceptual (VCX, VCP, PSV, VCS, MGD), and deficits of these types are often symptom of AD, often occurring before the more diagnostic cognitive complaints 110 , 111 , 112 , 113 . Three other ROIs (ECT, RSC, PTT) have been previously linked to learning and memory function. Finally, two tracts—the medial longitudinal fascicle (mlf) and the central part of the oculomotor nerve (cn3) are both involved with eye movement control. Oculomotor cranial neuropathy has recently been identified as an early indicator of AD 114 . Preclinical studies of mice require high resolution to detect 5XFAD effect on brain structure Preclinical protocols that parallel clinical MRI have obvious benefits because they enable much tighter control of genetics, age, diet, and treatments. Unfortunately, most preclinical studies of AD have been undertaken using a single strain of mouse, and this severely limits translational relevance of results to clinical populations. The second problem is that brains of mice are roughly 3000 times smaller than those of humans. Scaling spatial resolution to achieve the same voxel density has been challenging. But increasing spatial resolution is not the only requirement. It is also essential to be able to accurately measure physical changes caused by mutations and transgene on brain structure at the level of discrete ROIs. There are multiple candidate measures: volume changes, changes in diffusion properties, changes in connectomes and/or network properties (derived from diffusion images). Previous work in mouse models of AD ( Table 2 ) has generally used simple T1 and T2 weighted images to measure structural differences and changes in relaxation times. Table 3 summarizes some of the more recent preclinical studies using DTI. These studies compare diffusion properties, more specifically FA and MD, or connectomes and their network architectures. In both tables we provide spatial resolution metrics in absolute voxel volume (mm 3 ) and as relative resolution normalized to the 25 µm 3 resolution that we are now using routinely. Data for the two previous high-resolution studies 79 , 82 were acquired in the CIVM at Duke using our earlier protocol 115 , 36 . Both focused on changes in diffusion and network properties but did not measure ROI volume changes. Kelser and colleagues studied eight individuals at an isotropic resolution of 0.094 mm (voxels of 0.00083 mm 3 ) with b = 1000 s/mm 2 and with 20 angular acquisitions 78 . Their analysis of structural connectomes between 32 ROIs showed no differences between cases and controls. Westi and colleagues describe a comprehensive analysis of the 5XFAD model employing functional MRI, diffusion MRI, and immunohistochemistry 66 . They employed a multi-shell dMRI sequence to measure diffusion kurtosis, a second-order measure of diffusion properties in eight manually delineated ROIs. They also detected no significant difference in metrics between cases and controls, and no differences in volumes for any ROI at 2 and 6 months—perhaps too early in disease progression. The study by Murdy and colleagues 9 is most closely aligned with the present work. This in vivo structural MRI study included a very high level of genetic diversity among cases and controls and included balanced sets of sexes. Their sample size was unusually large—a total of 90 cases and controls. They also used an innovative NODDI protocol—a more nuanced model of DTI and delineated a set of 83 ROIs derived from the Allen Brain Atlas. Their primary results highlighted many differences in NODDI parameters dependent on genetic background, but they only detected one difference caused by 5XFAD on axial diffusivity signal in the stria medullaris (STM) of cases relative to controls. As noted by the authors, the minimal impact of the 5XFAD transgene in this study is probably due to the early age of imaging (90 days). Of particular interest, these authors also probed the relationship between changes in CFA and CFM and the MRI metrics (their Figure 9) and they also noted strong correlations with visual cortex (their area prostriata is equivalent to our VCS). Hippocampal and cortical atrophy were noted as some of the earliest clinical biomarkers for AD 116 , 117 , 118 , 119 using T1, T2 weighted MRI. DTI has yielded additional opportunities to track changes in tissue microstructure through diffusion scalar measures of FA and MD and connectomes and network analysis derived from tractography. Replication of these results in preclinical settings has been limited. We suggest that the problems in preclinical reproducibility stem from radical differences in methods. For example, spatial resolution in clinical MRI varies across studies at most by < 10 (Ref 120 ) but the results in tables 2 and 3 for preclinical studies vary over a much wider range. Methods for reproducible processing in clinical MRI have been routine for nearly 20 years 121 . But application of these methods (over this wide range of variation) have not been as thoroughly validated. Caveats and limitations of study Expression of the bigenic 5XFAD transgene (Tg6799) is driven by neuron-specific Thy1 cassette vectors 122 , and this raises the possibility that rostral-to-caudal effects we have detected are downstream of regional differences in promotor activity. This can probably be ruled out because many teams have shown strong expression of the 5XFAD transgene and high levels of amyloid deposition in the thalamus and midbrain 122 — regions in which we have demonstrated highly significant reductions in volume in 5XFAD cases (see Ref 23 , their figure 4 ; and Ref 24 , their figure 5 ). Furthermore, the volume reduction in diencephalic, midbrain, and hindbrain regions is just as strong and just as ROI-specific as volume expansions in most telencephalic structures. The only major brain region that appears to equivocate in its responses to the transgene is the striatopallidal complex, in which ventral striatum (e.g., NAC) increases in volume but globus pallidus externa (GPE) decreases in volume—in both cases with significant P values. Our analyses of cell densities and numbers in 3D light sheet image data sets are of course subject to all of the usual caveats associated with immunohistochemical protocols when applied to stereology; primarily issues related to penetration and specificity of NeuN antibodies, non-specific background signal, and in our case to possible interactions of these factors with the consequences of the APP and PSEN1 mutations on tissue integrity and on epitope levels and stability. Nonetheless, the numerical analyses are relatively robust in our opinion and in both cases and controls our count align reasonably well with neuron numbers (also see Tian et al., 2023). However, the high CVs of cell counts will compel us to increase samples sizes by at least a factor of two in order to provide stronger evidence for or against neuronal losses within specific ROIs. For our most ambitious work of linking ROI volumetric changes to specific alterations in behavior, the main limitation is sample size. We rely on correlations within the cases and the controls and then we estimate the statistical significance of the difference between correlations, region by region. This is an inherently hard problem and to achieve phenome-wide significance across 231 ROIs and nearly 100 traits of interest (TOIs) these will require on the order of 100–200 individuals in each group to be able to gain sufficient power to reach down to FDRs of under 5%. However, with our current data set we are close to having sufficient power to detect differences in ROI-to-TOI correlations between cases and controls with an FDR of 10%. Future directions Perhaps most obviously—we need to carry out similar statistical analyses of key scalar MRI metrics (FA, MD, AD, RD, etc.) and of connectome. These metrics have great promise as neuroanatomical biomarkers of the progression and treatment of AD 29 , 9 This work is already in progress using the same set of cases and controls. Given the success and throughput of the new MRH methods, another obvious direction is to do more of the same. Our main motivation is two-fold: to gain statistical power and to more precisely define architectural changes caused by FAD mutations as a function of age, sex, background, and treatments; and second, to establish causal links between changes at brain systems to degraded behaviors of many types. Both goals are shared by both clinical and preclinical neuroscience research communities. We add one more atypical goal—namely to increase numbers of AD-BXD F1 types so that we can use quantitative genetic methods to define causal loci that account for susceptibility and resistance to neurodegenerative diseases 123 , 124 , 21 , 125 , 126 . We currently have MRH data for additional 400 mice with matched behavioral data, but for well powered mapping studies we will probably need data for 40 AD-BXD F1 types and 640 individuals—a heavy but necessary lift. Finally, it is already practical to use AI-driven methods to refine and extend ROI delineations to ever smaller and more problematic CNS structures. For example, we do not yet delineate CA2 as a discrete ROI, but as suggested by figure 2g , this should soon be possible. We should anticipate a doubling of the numbers of well delineated ROIs. METHODS AD-BXD F1: a genetically diverse replicable isogenic model C57BL/6J congenic mice (B6.Cg-Tg(APPSwFlLon,PSEN1*M146L*L286V)6799Vas/Mmjax; RRID:MMRRC_034848-JAX), were obtained from the Mutant Mouse Resource and Research Center (MMRRC) at The Jackson Laboratory 122 . These mice are hemizygous for the dominant 5XFAD transgenes, 127 which consists of five human missense mutations that cause familial AD: three in the amyloid precursor protein gene ( APP ; Swedish: K670N, M671L, Florida: I716V, and London: V717I) and two in presenilin 1 ( PSEN1 ; M146L and L286V). Female B6.Cg-Tg mice were crossed to male BXD mice to produce what we call AD-BXD F1 progeny ( Table 1 ). We used four types of sires—BXD32/TyJ (RRID:IMSR_JAX:000078), BXD65/RwwJ (RRID:IMSR_JAX:007110), BXD77/RwwJ (RRID:IMSR_JAX:007121), and BXD101/RwwJ (RRID:IMSR_JAX:007144), all sourced from the UTHSC BXD colony. The terminology of these F1 hybrids is complex, but to give the reader some guidance, a 5XFAD female crossed to a male BXD65 has the charming official designation: (B6.Cg-Tg(APPSwFlLon,PSEN1*M146L*L286V)6799Vas/Mmjax x BXD65/RwwJ)F1/J. In table 1 we highlight only the sire of the cross, because the dam is always a C57BL/6J congenic female who carries a single hemizygous copy of the 5XFAD transgenes ( Fig. 1 , Methods). All five of the parental strains have been deeply sequenced and all F1 hybrids therefore have completely defined genomes 13 , 128 . The BXD32 strain has an unusual genetic history and due to a backcross early in its lineage has a genome of which 75% is derived from DBA/2J. The set of all four types of AD-BXD F1s (AD-BXD32 F1, AD-BXD65 F1, AD-BXD77 F1, and AD-yBXD101 F1) segregate for approximately 5.7 million SNPs and other short variants. They inherit one copy of all autosomes, Chr X, and their mitochondrial genome from the C57BL/6J congenic dam. Other autosomes and Chr Y in males are inherited from the BXD sire ( Fig. 1a ). At any locus F1 progeny (cases and controls) are either homozygous for the B reference allele or heterozygous for B and D alleles. On average half of progeny will inherit the hemizygous bigenic 5XFAD transgene located on Chr 3 (Tg cases) and half will not—the non-transgenic controls (NTg). We refer to both types of F1s as AD-BXD F1s. Female maternal congenic mice were used so that the mitochondrial genome was constant, and to control for strain-specific differences in pre- and postnatal maternal effects, such as maternal behavior. Male and female offspring were group housed (2–5 per cage) and maintained on a 12 hr light/dark cycle with ad libitum access to food and water. All mice were genotyped at weaning in-house using JAX Protocol 31769 ( www.jax.org/protocol?stocknumber=008730&protocolid=31769 ). All mouse experiments were carried out at University of Tennessee Health Science Center in accordance with the standards of the Association for the Assessment and Accreditation of Laboratory Animal Care (AAALAC), as well as the recommendations of the National Institutes of Health Guide for the Care and Use of Laboratory Animals. The protocol was approved by the Institutional Animal Care and Use Committee (IACUC) at the University of Tennessee Health Science Center. Behavioral phenotyping Mice were phenotyped at approximately 14 months of age using a battery of physiological and behavioral assays over a two-week period, including in order of testing: body weight, grip strength, open-field testing, what-where-which testing, rotarod testing, Y-maze testing, snowflake maze testing, and finally, context fear memory acquisition (CFA) and context fear memory consolidation (CFM) testing. In this paper, we mainly report on CFA and CFM. Contextual fear conditioning. At the end of the behavioral battery, and after at least 10 days of habituation to transportation and the testing room, mice were trained on a standard contextual fear conditioning (CFC) paradigm. The protocol used matches that previously used in the AD-BXD F1s 12 , 126 . Training consisted of a 180 s baseline period followed by four mild foot shocks (1 s, 0.9 mA), separated by 115 ± 20 s. A 40 s interval following each foot shock was defined as the post-shock interval. The percentage of time spent freezing during the final post-shock interval (PS4) was used as an index of CFA, a measure of fear learning. Twenty-four hours later, CFM was tested by returning the mouse to the testing chamber for 10 min. The percentage of time spent freezing during this trial was used as a measure of CFM, a measure known to be dependent on hippocampal function 129 . Videos of training and test trials were captured using FreezeFrame software (Coulbourn Instruments, PA, USA). A DeepLabCut model was trained to recognize 13 points on a mouse, following the labelling system of 130 , and DLCanalyzer 130 was used to measure freezing events. All current behavioral data are available on GeneNetwork.org by selecting SPECIES = Mouse; Group = BXD NIA Alzheimer’s Study; Type = Traits and Cofactors. Entering the string “CFM” in the Get Any field will retrieve GN Record ID 5XF_10474, data on CFM for over 500 mice. Every trait can be accessed with URLs of this general form where the number in the URL is the taken from the Record ID: https://genenetwork.org/show_trait?trait_id=10474&dataset=BXD-NIA-ADPublish Sacrifice and perfusion Following behavioral testing, mice were deeply anesthetized with isoflurane administered by a Somno Suite system (Kent Scientific). A 5% mixture will be used until the animal is deeply anesthetized, and the surgical procedure will not begin until the animal is no longer able to respond to pain (toe/tail pinches) and trancardially perfused with heparinized PBS for 5 min followed by PBS-4% formaldehyde to which ProHance gadolinium (Gadoteridol, Bracco, Inc.) for another 5 min. ProHance reduces the spin-lattice relaxation time (T1), enabling a shorter repetition time and greatly reducing scan times. The cranium without skin was stored for 24 hours at 4°C in 13 ml of neutral buffered formalin. The following day the mandible and muscles were trimmed away, and the crania and parts of the maxillary region including the eyes and periorbital regions were placed in 1% PBS with 1% ProHance and shipped by air from UTHSC to Duke in on ice in pressure-proof containers. Crania remained in ProHance for at least an addition two weeks prior to scanning. MR histology Details of imaging methods are provided in Ref. 29 and are outlined in Fig. 1c-m ). Diffusion tensor imaging (DTI) was performed with a 9.4T vertical bore MRI system designed for MR histology ( Fig. 1c ).This system features a high-performance gradient (Resonance Research, Inc. BFG 88-41) providing peak gradients of 3000 mT/m. 3D volumes were acquired using a Stejskal-Tanner spin echo sequence 131 at 25 µm isotropic resolution ( Fig. 1d ). Compressed sensing with an 8X acceleration 115 and high angular resolution diffusion encoding (HARDI) 132 was used with gradients (b = 3000 s/mm²) sampled along 61 axes to obtain uniform angular coverage of a unit sphere. Baseline (b0) images were generated after every 10th diffusion-weighted scan (except the last 10), resulting in a total of 66 3D volumes. During the first two years of data gathering (2021– 2022), the scanner was controlled by an Agilent Direct Drive console. The system was upgraded in 2023 to an MR Solutions console. Automated imaging pipelines were crucial for managing the large data volumes. A script initiated on the scanner ( Fig. 1c ) triggered the scan and started data transfer to the high-performance compute cluster ( Fig. 1g ). When all the data for the first volume had been transferred to the cluster, a Fourier transform was performed along the read-out axis separating the 3D volume into many (∼800) 2D files which were then distributed across the cluster cores for parallel iterative reconstruction. At the same time, the script loaded the next set of acquisition parameters (e.g., b value, angle) and initiated the next scan. This process ran unattended for approximately 22 h facilitating high throughput with minimal operator errors. Once the scans were completed, a pipeline built on ANTs 133 registered the 66 volumes into a common space to correct for small displacements caused by eddy currents in the magnet, which vary between volumes. This step was critical due to the small voxel size and the strength of the gradients (up to 50 times stronger than standard clinical DTI). The 4D volume was then denoised using a machine learning algorithm 134 and processed through DSI Studio ( dsi-studio.labsolver.org ), where diffusion scalar images were generated using a DTI algorithm 135 ( Fig. 1e ). After all scans were complete, each specimen was labeled using another automated pipeline based on ANTs 133 . A hybrid 3D volume was created for each specimen by combining the diffusion-weighted image (DWI)—that is to say, the average of all the diffusion images and the fractional anisotropy image. To minimize bias from individual specimens, a pairwise minimum deformation template (MDT) was generated using the hybrid images for all individuals of a particular genetic background type. This MDT was then registered to the Duke Mouse Brain Atlas, a stereotaxic population atlas of the 90-day male C57BL/6J mouse, which was created from high-resolution (15 µm) DTI data, microCT, and complementary light-sheet imaging 2 . Labels were placed on the MDT, and the transformations for each specimen were inverted. The reduced common coordinate reference labels (RCCFv3) 29 are a subset of the Allen Mouse Brain Common Coordinate Framework (CCFv3) 35 which have been put into a framework with stereotaxic accuracy 2 . Empirical estimation of the precision of ROI delineations using left-right coefficients of variation We compute CVs of independently segmented right and left volumes of each ROI—the standard deviation of the two values divided by their mean. Like standard deviations, CVs are biased downward by about 25% with a sample size of only two 136 but we did not apply this correction because the technical components of CVs are overestimated by biological sources of asymmetry. Note that we claim precision of delineation rather than accuracy . Accuracy would require an additional step to confirm that delineations are not only precise but that they conform to consensus boundaries established by a group of skilled neuroanatomists—a sociological enterprise that has proved to be problematic for over 150 years. But we have spot-checked consistency of MRH-driven delineations of volumes with those generated by skilled neuroscientists and stereologists (e.g., Figs. 2 , 3). For example, our delineations of the dorsal lateral geniculate nucleus (LGD) of AD-BXD F1s have mean unilateral volumes of approximately 0.35 ± 0.04 SD mm 3 , whereas our previous stereological estimates of adult B6D2 F1 hybrid from celloidin-sectioned Nissl-stained material are 0.32 ± 0.03 mm 3 with correction for shrinkage 137 . Those of Evangelino and colleagues 87 are about 0.26 mm 3 for adult C57BL/6J mice as delineated in serial Nissl-stained sections without correction for shrinkage. We estimate that the LGD volume of this particular strain with correction of shrinkage is about 15% higher (0.30 ± 0.02 mm 3 , see Ref 137 ). From genetic and statistical perspectives precision and replicability are operationally more critical than accuracy in terms of detecting causes and effects of volumetric changes. Light Sheet Microscopy Brains were removed from the skull, placed in buffered formalin, and shipped to LifeCanvas Technologies (Cambridge, Mass) in airtight containers. Specimens were cleared using SHIELD 138 and labeled using eFLASH technology which integrates stochastic electrotransport 139 and SWITCH methods 140 ). Brains were labeled using 180 µg Cell Signaling Technology rabbit beta amyloid (aβ) (RRID:AB_2798671), 120 µg EnCor Biotechnology mouse NeuN (RRID:AB_2572267), and 480 µL Millipore goat ChAT (RRID:AB_2079751). After index matching the samples were imaged using a SmartSPIM axially-swept light sheet microscope using a 3.6x objective (0.2 NA) (LifeCanvas Technologies, Cambridge, MA) using three different excitation sources (488 nm for beta amyloid; 561 nm for NeuN; and 647 nm for ChAT) producing three tiff files each of about 300 GB. Data were transmitted via Globus to a dedicated alignment server at the CIVM ( Fig. 1g ). The inevitable distortions in light sheet processed tissue and images were removed by aligning the NeuN (561 nm) volumes to the original DWI volumes of the same individuals ( Fig 1h ). Details of this workflow are included in Ref. 27 . The transforms generated in this alignment were applied to the other two channels—aβ and ChAT. The delineations of the ROIs were then transferred to the LSM volumes. We are able to estimate densities of aβ, and NeuN+ cells in anatomically correct stereotaxic space using a digital version of direct 3-dimensional counting, also known as the optical dissector 28 , 141 . Statistical Analyses ANOVA was performed for each terminal ROI and for all parent ROIs ( Fig. 4a ), for example all ROIs contributing to the thalamus (THA). We used a workflow developed with MATLAB 2021b. The primary metrics that we evaluated were absolute ROI volumes and the fractional volume of the whole brain—the ratio of the bilateral volume of each ROI for each individual brain given the total volume of that brain. The overall ANOVA model used in most analyses ( Fig. 4 ) evaluates sources of variance of fractional volumes (bilateral sums of ROIs) as a function of transgene status, genetic background, sex, and MRI scanner. The last factor was added because we upgraded the scanner console during the study, but we note that this factor was insignificant in all analyses. Benjamini-Hochberg (BH) corrections were used to define the P values at FDRs ranging from 0.05 in figure 3 , to more lenient values for exploratory and comparative analyses in figure 4 . Additional stratified ANOVA tests were performed to consider the sex and strain effects separately on the most significantly affected ROIs at several BH corrected P values from the overall ANOVA test. For the strain-stratified ANOVAs, we used the model: fractional volume ∼ transgene status + sex + scanner + error. The AD-BXD101 cases and controls were only scanned on the newer console from MR Solutions Group ( www.mrsolutions.com ),and therefore does not have a scanner covariate. For the sex-stratified ANOVAs, we used the model: fractional volume ∼ transgene status + strain + scanner. Effect size measures are provided for all ANOVA tests as Cohen’s F of the transgene status and as Cohen’s D providing a pairwise comparison of key classes (cases vs controls, males vs. females). Correlations between ROI fraction volume and either CFA or CFM were calculated using Spearman rank correlations, implemented in R ( www.R-project.org ). To compare the correlations in cases vs controls, we used the cocor R package 142 , which makes use of Fisher’s r -to-Z transformation which has been found to be a robust way to compare Spearman rank correlations. Data can be accessed at the individual level at GeneNetwork.org 143 , under the group heading of BXD NIA Alzheimer’s Studies (BXD-NIA-AD) (see Supplementary Table READ ME). All brain volume data are available under assertion GN1068, and behavioral phenotypes used in this study are CFA (5XF_10594) and CFM (5XF_10474). All data are freely available under FAIR guidelines 144 . Footnotes https://www.genenetwork.org References 1. ↵ Crombé A , Nicolas R , Richard N , Tourdias T , Hiba B . High B-value diffusion tensor imaging for early detection of hippocampal microstructural alteration in a mouse model of multiple sclerosis . Sci Rep . 2022 ; 12 ( 1 ): 12008 . Epub 20220714. doi: 10.1038/s41598-022-15511-0 . PubMed PMID: 35835801 ; PMCID: PMC9283448 . OpenUrl CrossRef PubMed 2. ↵ Mansour M. AR , Cook J. J. , Hornburg K.J. , Qi Y. , Tian Y. WRW , Yeh F.C. , White L.E. , Johnson G.A. An Open Resource: MR and light sheet microscopy s 1 tereotaxic atlas of the mouse brain . Science Advances . 2025 ;in press. 3. ↵ Negash S , Wilson RS , Leurgans SE , Wolk DA , Schneider JA , Buchman AS , Bennett DA , Arnold SE . Resilient brain aging: characterization of discordance between Alzheimer’s disease pathology and cognition . Curr Alzheimer Res . 2013 ; 10 ( 8 ): 844 – 51 . doi: 10.2174/15672050113109990157 . PubMed PMID: 23919768 ; PMCID: PMC4060425 . OpenUrl CrossRef PubMed 4. ↵ Habes M , Grothe MJ , Tunc B , McMillan C , Wolk DA , Davatzikos C . Disentangling Heterogeneity in Alzheimer’s Disease and Related Dementias Using Data-Driven Methods . Biol Psychiatry . 2020 ; 88 ( 1 ): 70 – 82 . Epub 20200131. doi: 10.1016/j.biopsych.2020.01.016 . PubMed PMID: 32201044 ; PMCID: PMC7305953 . OpenUrl CrossRef PubMed 5. ↵ Arnold SE , Hyman BT , Betensky RA , Dodge HH . Pathways to personalized medicine-Embracing heterogeneity for progress in clinical therapeutics research in Alzheimer’s disease . Alzheimers Dement . 2024 ; 20 ( 10 ): 7384 – 94 . Epub 20240906. doi: 10.1002/alz.14063 . PubMed PMID: 39240044 ; PMCID: PMC11485305 . OpenUrl CrossRef PubMed 6. ↵ Ringman JM , Goate A , Masters CL , Cairns NJ , Danek A , Graff-Radford N , Ghetti B , Morris JC . Genetic heterogeneity in Alzheimer disease and implications for treatment strategies . Curr Neurol Neurosci Rep . 2014 ; 14 ( 11 ): 499 . doi: 10.1007/s11910-014-0499-8 . PubMed PMID: 25217249 ; PMCID: PMC4162987 . OpenUrl CrossRef PubMed 7. Ryman DC , Acosta-Baena N , Aisen PS , Bird T , Danek A , Fox NC , Goate A , Frommelt P , Ghetti B , Langbaum JB , Lopera F , Martins R , Masters CL , Mayeux RP , McDade E , Moreno S , Reiman EM , Ringman JM , Salloway S , Schofield PR , Sperling R , Tariot PN , Xiong C , Morris JC , Bateman RJ . Symptom onset in autosomal dominant Alzheimer disease: a systematic review and meta-analysis . Neurology . 2014 ; 83 ( 3 ): 253 – 60 . Epub 20140613. doi: 10.1212/wnl.0000000000000596 . PubMed PMID: 24928124 ; PMCID: PMC4117367 . OpenUrl CrossRef PubMed 8. ↵ Ryan NS , Nicholas JM , Weston PSJ , Liang Y , Lashley T , Guerreiro R , Adamson G , Kenny J , Beck J , Chavez-Gutierrez L , de Strooper B , Revesz T , Holton J , Mead S , Rossor MN , Fox NC . Clinical phenotype and genetic associations in autosomal dominant familial Alzheimer’s disease: a case series . Lancet Neurol . 2016 ; 15 ( 13 ): 1326 – 35 . Epub 20161021. doi: 10.1016/s1474-4422(16)30193-4 . PubMed PMID: 27777022 . OpenUrl CrossRef PubMed 9. ↵ Murdy TJ , Dunn AR , Singh S , Telpoukhovskaia MA , Zhang S , White JK , Kahn I , Febo M , Kaczorowski CC . Leveraging genetic diversity in mice to inform individual differences in brain microstructure and memory . Front Behav Neurosci . 2022 ; 16 : 1033975 . Epub 20230110. doi: 10.3389/fnbeh.2022.1033975 . PubMed PMID: 36703722 ; PMCID: PMC9871587 . OpenUrl CrossRef PubMed 10. ↵ Gurdon B , Yates SC , Csucs G , Groeneboom NE , Hadad N , Telpoukhovskaia M , Ouellette A , Ouellette T , O’Connell KMS , Singh S , Murdy TJ , Merchant E , Bjerke I , Kleven H , Schlegel U , Leergaard TB , Puchades MA , Bjaalie JG , Kaczorowski CC . Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model . Commun Biol . 2024 ; 7 ( 1 ): 605 . Epub 20240520. doi: 10.1038/s42003-024-06242-1 . PubMed PMID: 38769398 ; PMCID: PMC11106287 . OpenUrl CrossRef PubMed 11. ↵ Williams RW . Herding cats: the sociology of data integration . Front Neurosci . 2009 ; 3 ( 2 ): 154 – 6 . Epub 20090915. doi: 10.3389/neuro.01.016.2009 . PubMed PMID: 20228863 ; PMCID: PMC2751652 . OpenUrl CrossRef PubMed 12. ↵ Neuner SM , Heuer SE , Huentelman MJ , O’Connell KMS , Kaczorowski CC . Harnessing Genetic Complexity to Enhance Translatability of Alzheimer’s Disease Mouse Models: A Path toward Precision Medicine . Neuron . 2019 ; 101 ( 3 ): 399 – 411 .e5. Epub 20181227. doi: 10.1016/j.neuron.2018.11.040 . PubMed PMID: 30595332 ; PMCID: PMC6886697 . OpenUrl CrossRef PubMed 13. ↵ Ashbrook DG , Arends D , Prins P , Mulligan MK , Roy S , Williams EG , Lutz CM , Valenzuela A , Bohl CJ , Ingels JF , McCarty MS , Centeno AG , Hager R , Auwerx J , Lu L , Williams RW . A platform for experimental precision medicine: The extended BXD mouse family . Cell Syst . 2021 ; 12 ( 3 ): 235 – 47 .e9. Epub 20210119. doi: 10.1016/j.cels.2020.12.002 . PubMed PMID: 33472028 ; PMCID: PMC7979527 . OpenUrl CrossRef PubMed 14. ↵ Farrer LA , Cupples LA , Haines JL , Hyman B , Kukull WA , Mayeux R , Myers RH , Pericak-Vance MA , Risch N , van Duijn CM . Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease . A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. Jama . 1997 ; 278 ( 16 ): 1349 – 56 . PubMed PMID: 9343467 . OpenUrl CrossRef PubMed Web of Science 15. ↵ Rajabli F , Feliciano BE , Celis K , Hamilton-Nelson KL , Whitehead PL , Adams LD , Bussies PL , Manrique CP , Rodriguez A , Rodriguez V , Starks T , Byfield GE , Sierra Lopez CB , McCauley JL , Acosta H , Chinea A , Kunkle BW , Reitz C , Farrer LA , Schellenberg GD , Vardarajan BN , Vance JM , Cuccaro ML , Martin ER , Haines JL , Byrd GS , Beecham GW , Pericak-Vance MA . Ancestral origin of ApoE ε4 Alzheimer disease risk in Puerto Rican and African American populations . PLoS Genet . 2018 ; 14 ( 12 ): e1007791 . Epub 20181205. doi: 10.1371/journal.pgen.1007791 . PubMed PMID: 30517106 ; PMCID: PMC6281216 . OpenUrl CrossRef PubMed 16. ↵ Rajabli F , Seixas AA , Akgun B , Adams LD , Inciute J , Hamilton KL , Whithead PG , Konidari I , Gu T , Arvizu J , Golightly CG , Starks TD , Laux R , Byrd GS , Haines JL , Beecham GW , Griswold AJ , Vance JM , Cuccaro ML , Pericak-Vance MA . African Ancestry Individuals with Higher Educational Attainment Are Resilient to Alzheimer’s Disease Measured by pTau181 . J Alzheimers Dis . 2024 ; 98 ( 1 ): 221 – 9 . doi: 10.3233/jad-231116 . PubMed PMID: 38393909 ; PMCID: PMC11091636 . OpenUrl CrossRef PubMed 17. ↵ Belloy ME , Andrews SJ , Le Guen Y , Cuccaro M , Farrer LA , Napolioni V , Greicius MD . APOE Genotype and Alzheimer Disease Risk Across Age, Sex, and Population Ancestry . JAMA Neurol . 2023 ; 80 ( 12 ): 1284 – 94 . doi: 10.1001/jamaneurol.2023.3599 . PubMed PMID: 37930705 ; PMCID: PMC10628838 . OpenUrl CrossRef PubMed 18. ↵ Sittig LJ , Carbonetto P , Engel KA , Krauss KS , Barrios-Camacho CM , Palmer AA . Genetic Background Limits Generalizability of Genotype-Phenotype Relationships . Neuron . 2016 ; 91 ( 6 ): 1253 – 9 . Epub 20160908. doi: 10.1016/j.neuron.2016.08.013 . PubMed PMID: 27618673 ; PMCID: PMC5033712 . OpenUrl CrossRef PubMed 19. ↵ Kotredes KP , Pandey RS , Persohn S , Elderidge K , Burton CP , Miner EW , Haynes KA , Santos DFS , Williams SP , Heaton N , Ingraham CM , Lloyd C , Garceau D , O’Rourke R , Herrick S , Rangel-Barajas C , Maharjan S , Wang N , Sasner M , Lamb BT , Territo PR , Sukoff Rizzo SJ , Carter GW , Howell GR , Oblak AL . Characterizing molecular and synaptic signatures in mouse models of late-onset Alzheimer’s disease independent of amyloid and tau pathology . Alzheimers Dement . 2024 ; 20 ( 6 ): 4126 – 46 . Epub 20240512. doi: 10.1002/alz.13828 . PubMed PMID: 38735056 ; PMCID: PMC11180851 . OpenUrl CrossRef PubMed 20. ↵ Neuner SM , Heuer SE , Zhang JG , Philip VM , Kaczorowski CC . Identification of Pre-symptomatic Gene Signatures That Predict Resilience to Cognitive Decline in the Genetically Diverse AD-BXD Model . Front Genet . 2019 ; 10 : 35 . Epub 20190206. doi: 10.3389/fgene.2019.00035 . PubMed PMID: 30787942 ; PMCID: PMC6372563 . OpenUrl CrossRef PubMed 21. ↵ Heuer SE , Neuner SM , Hadad N , O’Connell KMS , Williams RW , Philip VM , Gaiteri C , Kaczorowski CC . Identifying the molecular systems that influence cognitive resilience to Alzheimer’s disease in genetically diverse mice . Learn Mem . 2020 ; 27 ( 9 ): 355 – 71 . Epub 20200817. doi: 10.1101/lm.051839.120 . PubMed PMID: 32817302 ; PMCID: PMC7433658 . OpenUrl Abstract / FREE Full Text 22. ↵ Hyman B , Tanzi RE . Effects of Species-Specific Genetics on Alzheimer’s Mouse Models . Neuron . 2019 ; 101 ( 3 ): 351 – 2 . doi: 10.1016/j.neuron.2019.01.021 . PubMed PMID: 30731053 . OpenUrl CrossRef PubMed 23. ↵ Forner S , Kawauchi S , Balderrama-Gutierrez G , Kramár EA , Matheos DP , Phan J , Javonillo DI , Tran KM , Hingco E , da Cunha C , Rezaie N , Alcantara JA , Baglietto-Vargas D , Jansen C , Neumann J , Wood MA , MacGregor GR , Mortazavi A , Tenner AJ , LaFerla FM , Green KN. Systematic phenotyping and characterization of the 5xFAD mouse model of Alzheimer’s disease . Sci Data . 2021 ; 8 ( 1 ): 270 . Epub 20211015. doi: 10.1038/s41597-021-01054-y . PubMed PMID: 34654824 ; PMCID: PMC8519958 . OpenUrl CrossRef PubMed 24. ↵ Oblak AL , Lin PB , Kotredes KP , Pandey RS , Garceau D , Williams HM , Uyar A , O’Rourke R , O’Rourke S , Ingraham C , Bednarczyk D , Belanger M , Cope ZA , Little GJ , Williams SG , Ash C , Bleckert A , Ragan T , Logsdon BA , Mangravite LM , Sukoff Rizzo SJ , Territo PR , Carter GW , Howell GR , Sasner M , Lamb BT. Comprehensive Evaluation of the 5XFAD Mouse Model for Preclinical Testing Applications: A MODEL-AD Study . Front Aging Neurosci . 2021 ; 13 : 713726 . Epub 20210723. doi: 10.3389/fnagi.2021.713726 . PubMed PMID: 34366832 ; PMCID: PMC8346252 . OpenUrl CrossRef PubMed 25. ↵ Pang M , Gabelle A , Saha-Chaudhuri P , Huijbers W , Gafson A , Matthews PM , Tian L , Rubino I , Hughes R , de Moor C , Belachew S , Shen C . Precision medicine analysis of heterogeneity in individual-level treatment response to amyloid beta removal in early Alzheimer’s disease . Alzheimers Dement . 2024 ; 20 ( 2 ): 1102 – 11 . Epub 20231026. doi: 10.1002/alz.13431 . PubMed PMID: 37882364 ; PMCID: PMC10917030 . OpenUrl CrossRef PubMed 26. ↵ Dickerson BC , Atri A , Clevenger C , Karlawish J , Knopman D , Lin PJ , Norman M , Onyike C , Sano M , Scanland S , Carrillo M . The Alzheimer’s Association clinical practice guideline for the Diagnostic Evaluation, Testing, Counseling, and Disclosure of Suspected Alzheimer’s Disease and Related Disorders (DETeCD-ADRD): Executive summary of recommendations for specialty care . Alzheimers Dement . 2024 . Epub 20241223. doi: 10.1002/alz.14337 . PubMed PMID: 39713957 . OpenUrl CrossRef PubMed 27. ↵ Tian Y , Cook JJ , Johnson GA . Restoring morphology of light sheet microscopy data based on magnetic resonance histology . Front Neurosci . 2022 ; 16 : 1011895 . Epub 20230104. doi: 10.3389/fnins.2022.1011895 . PubMed PMID: 36685227 ; PMCID: PMC9846533 . OpenUrl CrossRef PubMed 28. ↵ Tian Y , Johnson GA , Williams RW , White L . A rapid workflow for neuron counting in combined light sheet microscopy and magnetic resonance histology . Frontiers in Neuroscience . 2023 ( 17 : 1223226 .). Epub 20230517. doi: 10.3389/fnins.2023.1223226 . PubMed PMID: 37292796 ; PMCID: PMC10245654 . OpenUrl CrossRef PubMed 29. ↵ Johnson GA , Tian Y , Ashbrook DG , Cofer GP , Cook JJ , Gee JC , Hall A , Hornburg K , Kaczorowski CC , Qi Y , Yeh FC , Wang N , White LE , Williams RW . Merged magnetic resonance and light sheet microscopy of the whole mouse brain . Proc Natl Acad Sci U S A . 2023 ; 120 ( 17 ): e2218617120 . Epub 20230417. doi: 10.1073/pnas.2218617120 . PubMed PMID: 37068254 ; PMCID: PMC10151475 . OpenUrl CrossRef PubMed 30. ↵ Badea A , Johnson GA , Williams RW . Genetic dissection of the mouse CNS using magnetic resonance microscopy . Curr Opin Neurol . 2009 ; 22 ( 4 ): 379 – 86 . doi: 10.1097/WCO.0b013e32832d9b86 . PubMed PMID: 19542887 ; PMCID: PMC2734144 . OpenUrl CrossRef PubMed Web of Science 31. ↵ Ashbrook DG , Williams RW , Lu L , Stein JL , Hibar DP , Nichols TE , Medland SE , Thompson PM , Hager R . Joint genetic analysis of hippocampal size in mouse and human identifies a novel gene linked to neurodegenerative disease . BMC Genomics . 2014 ; 15 ( 1 ): 850 . Epub 20141003. doi: 10.1186/1471-2164-15-850 . PubMed PMID: 25280473 ; PMCID: PMC4192369 . OpenUrl CrossRef PubMed 32. ↵ Wang X , Pandey AK , Mulligan MK , Williams EG , Mozhui K , Li Z , Jovaisaite V , Quarles LD , Xiao Z , Huang J , Capra JA , Chen Z , Taylor WL , Bastarache L , Niu X , Pollard KS , Ciobanu DC , Reznik AO , Tishkov AV , Zhulin IB , Peng J , Nelson SF , Denny JC , Auwerx J , Lu L , Williams RW . Joint mouse-human phenome-wide association to test gene function and disease risk . Nat Commun . 2016 ; 7 : 10464 . Epub 20160202. doi: 10.1038/ncomms10464 . PubMed PMID: 26833085 ; PMCID: PMC4740880 . OpenUrl CrossRef PubMed 33. ↵ O’Connell KMS , Ouellette AR , Neuner SM , Dunn AR , Kaczorowski CC . Genetic background modifies CNS-mediated sensorimotor decline in the AD-BXD mouse model of genetic diversity in Alzheimer’s disease . Genes Brain Behav . 2019 ; 18 ( 8 ): e12603 . Epub 20190819. doi: 10.1111/gbb.12603 . PubMed PMID: 31381246 ; PMCID: PMC6899779 . OpenUrl CrossRef PubMed 34. ↵ Johnson GA , Benveniste H , Black RD , Hedlund LW , Maronpot RR , Smith BR . Histology by magnetic resonance microscopy . Magn Reson Q . 1993 ; 9 ( 1 ): 1 – 30 . PubMed PMID: 8512830 . OpenUrl CrossRef PubMed Web of Science 35. ↵ Wang Q , Ding SL , Li Y , Royall J , Feng D , Lesnar P , Graddis N , Naeemi M , Facer B , Ho A , Dolbeare T , Blanchard B , Dee N , Wakeman W , Hirokawa KE , Szafer A , Sunkin SM , Oh SW , Bernard A , Phillips JW , Hawrylycz M , Koch C , Zeng H , Harris JA , Ng L . The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas . Cell . 2020 ; 181 ( 4 ): 936 – 53 .e20. Epub 20200507. doi: 10.1016/j.cell.2020.04.007 . PubMed PMID: 32386544 ; PMCID: PMC8152789 . OpenUrl CrossRef PubMed 36. ↵ Wang N AR , Ashbrook DG , Gopalakrishnan V , Park Y , Priebe CE , Laoprasert R , Vogelstein JT , Williams RW , Johnson GA . Variability and Heritability of Mouse Brain Structure: Microscopic MRI Atlases and Connectomes for Diverse Strains . NeuroImage . 2020 ; 222 . doi: 10.1016/j.neuroimage.2020.117274 . OpenUrl CrossRef PubMed 37. ↵ Busser J , Geldmacher DS , Herrup K . Ectopic cell cycle proteins predict the sites of neuronal cell death in Alzheimer’s disease brain . J Neurosci . 1998 ; 18 ( 8 ): 2801 – 7 . doi: 10.1523/jneurosci.18-08-02801.1998 . PubMed PMID: 9525997 ; PMCID: PMC6792587 . OpenUrl Abstract / FREE Full Text 38. ↵ Park C , Choi S , Kim YE , Lee S , Park SH , Adelstein RS , Kawamoto S , Kim KK . Stress Granules Contain Rbfox2 with Cell Cycle-related mRNAs . Sci Rep . 2017 ; 7 ( 1 ): 11211 . Epub 20170911. doi: 10.1038/s41598-017-11651-w . PubMed PMID: 28894257 ; PMCID: PMC5593835 . OpenUrl CrossRef PubMed 39. ↵ Kim KK , Adelstein RS , Kawamoto S . Identification of neuronal nuclei (NeuN) as Fox-3, a new member of the Fox-1 gene family of splicing factors . J Biol Chem . 2009 ; 284 ( 45 ): 31052 – 61 . Epub 20090827. doi: 10.1074/jbc.M109.052969 . PubMed PMID: 19713214 ; PMCID: PMC2781505 . OpenUrl Abstract / FREE Full Text 40. ↵ Gusel’nikova VV , Korzhevskiy DE . NeuN As a Neuronal Nuclear Antigen and Neuron Differentiation Marker . Acta Naturae . 2015 ; 7 ( 2 ): 42 – 7 . PubMed PMID: 26085943 ; PMCID: PMC4463411 . OpenUrl CrossRef PubMed 41. ↵ Müller C , Remy S. Septo-hippocampal interaction . Cell Tissue Res . 2018 ; 373 ( 3 ): 565 – 75 . Epub 20171218. doi: 10.1007/s00441-017-2745-2 . PubMed PMID: 29250747 ; PMCID: PMC6132648 . OpenUrl CrossRef PubMed 42. ↵ Sans-Dublanc A , Razzauti A , Desikan S , Pascual M , Monyer H , Sindreu C . Septal GABAergic inputs to CA1 govern contextual memory retrieval . Sci Adv . 2020 ; 6 ( 44 ). Epub 20201030. doi: 10.1126/sciadv.aba5003 . PubMed PMID: 33127668 ; PMCID: PMC7608800 . OpenUrl FREE Full Text 43. ↵ Xie X , Gong S , Sun N , Zhu J , Xu X , Xu Y , Li X , Du Z , Liu X , Zhang J , Gong W , Si K . Contextual Fear Learning and Extinction in the Primary Visual Cortex of Mice . Neurosci Bull . 2023 ; 39 ( 1 ): 29 – 40 . Epub 20220615. doi: 10.1007/s12264-022-00889-8 . PubMed PMID: 35704211 ; PMCID: PMC9849540 . OpenUrl CrossRef PubMed 44. ↵ Crutch SJ , Schott JM , Rabinovici GD , Murray M , Snowden JS , van der Flier WM , Dickerson BC , Vandenberghe R , Ahmed S , Bak TH , Boeve BF , Butler C , Cappa SF , Ceccaldi M , de Souza LC , Dubois B , Felician O , Galasko D , Graff-Radford J , Graff-Radford NR , Hof PR , Krolak-Salmon P , Lehmann M , Magnin E , Mendez MF , Nestor PJ , Onyike CU , Pelak VS , Pijnenburg Y , Primativo S , Rossor MN , Ryan NS , Scheltens P , Shakespeare TJ , Suárez González A , Tang-Wai DF , Yong KXX , Carrillo M , Fox NC . Consensus classification of posterior cortical atrophy . Alzheimers Dement . 2017 ; 13 ( 8 ): 870 – 84 . Epub 20170302. doi: 10.1016/j.jalz.2017.01.014 . PubMed PMID: 28259709 ; PMCID: PMC5788455 . OpenUrl CrossRef PubMed 45. ↵ Jack CR , Jr ., Bennett DA , Blennow K , Carrillo MC , Dunn B , Haeberlein SB , Holtzman DM , Jagust W , Jessen F , Karlawish J , Liu E , Molinuevo JL , Montine T , Phelps C , Rankin KP , Rowe CC , Scheltens P , Siemers E , Snyder HM , Sperling R. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease . Alzheimers Dement . 2018 ; 14 ( 4 ): 535 – 62 . doi: 10.1016/j.jalz.2018.02.018 . PubMed PMID: 29653606 ; PMCID: PMC5958625 . OpenUrl CrossRef PubMed 46. ↵ Herrup K . How not to study a disease . Cambridge , Mass: MIT Press ; 2021 . 47. ↵ McKhann GM , Knopman DS , Chertkow H , Hyman BT , Jack CR , Jr. , Kawas CH , Klunk WE , Koroshetz WJ , Manly JJ , Mayeux R , Mohs RC , Morris JC , Rossor MN , Scheltens P , Carrillo MC , Thies B , Weintraub S , Phelps CH . The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease . Alzheimers Dement . 2011 ; 7 ( 3 ): 263 – 9 . Epub 20110421. doi: 10.1016/j.jalz.2011.03.005 . PubMed PMID: 21514250 ; PMCID: PMC3312024 . OpenUrl CrossRef PubMed Web of Science 48. ↵ Persson K , Eldholm RS , Barca ML , Cavallin L , Ferreira D , Knapskog AB , Selbæk G , Brækhus A , Saltvedt I , Westman E , Engedal K . MRI-assessed atrophy subtypes in Alzheimer’s disease and the cognitive reserve hypothesis . PLoS One . 2017 ; 12 ( 10 ): e0186595 . Epub 20171016. doi: 10.1371/journal.pone.0186595 . PubMed PMID: 29036183 ; PMCID: PMC5643102 . OpenUrl CrossRef PubMed 49. ↵ Dubois B , Villain N , Frisoni GB , Rabinovici GD , Sabbagh M , Cappa S , Bejanin A , Bombois S , Epelbaum S , Teichmann M , Habert MO , Nordberg A , Blennow K , Galasko D , Stern Y , Rowe CC , Salloway S , Schneider LS , Cummings JL , Feldman HH . Clinical diagnosis of Alzheimer’s disease: recommendations of the International Working Group . Lancet Neurol . 2021 ; 20 ( 6 ): 484 – 96 . Epub 20210429. doi: 10.1016/s1474-4422(21)00066-1 . PubMed PMID: 33933186 ; PMCID: PMC8339877 . OpenUrl CrossRef PubMed 50. ↵ Goate A , Chartier-Harlin MC , Mullan M , Brown J , Crawford F , Fidani L , Giuffra L , Haynes A , Irving N , James L , et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease . Nature . 1991 ; 349 ( 6311 ): 704 -6. doi: 10.1038/349704a0 . PubMed PMID: 1671712 . OpenUrl CrossRef PubMed Web of Science 51. ↵ Murrell J , Farlow M , Ghetti B , Benson MD . A mutation in the amyloid precursor protein associated with hereditary Alzheimer’s disease . Science . 1991 ; 254 (5028): 97 – 9 . doi: 10.1126/science.1925564 . PubMed PMID: 1925564 . OpenUrl Abstract / FREE Full Text 52. ↵ Corder EH , Saunders AM , Strittmatter WJ , Schmechel DE , Gaskell PC , Small GW , Roses AD , Haines JL , Pericak-Vance MA . Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families . Science . 1993 ; 261 ( 5123 ): 921 – 3 . doi: 10.1126/science.8346443 . PubMed PMID: 8346443 . OpenUrl Abstract / FREE Full Text 53. ↵ Lopera F , Ardilla A , Martínez A , Madrigal L , Arango-Viana JC , Lemere CA , Arango-Lasprilla JC , Hincapíe L , Arcos-Burgos M , Ossa JE , Behrens IM , Norton J , Lendon C , Goate AM , Ruiz-Linares A , Rosselli M , Kosik KS . Clinical features of early-onset Alzheimer disease in a large kindred with an E280A presenilin-1 mutation . Jama . 1997 ; 277 ( 10 ): 793 – 9 . PubMed PMID: 9052708 . OpenUrl CrossRef PubMed Web of Science 54. ↵ Axelman K , Basun H , Lannfelt L . Wide range of disease onset in a family with Alzheimer disease and a His163Tyr mutation in the presenilin-1 gene . Arch Neurol . 1998 ; 55 ( 5 ): 698 – 702 . doi: 10.1001/archneur.55.5.698 . PubMed PMID: 9605727 . OpenUrl CrossRef PubMed Web of Science 55. ↵ Larner AJ , Doran M . Clinical phenotypic heterogeneity of Alzheimer’s disease associated with mutations of the presenilin-1 gene . J Neurol . 2006 ; 253 ( 2 ): 139 – 58 . Epub 20051104. doi: 10.1007/s00415-005-0019-5 . PubMed PMID: 16267640 . OpenUrl CrossRef PubMed Web of Science 56. ↵ Vélez JI , Lopera F , Patel HR , Johar AS , Cai Y , Rivera D , Tobón C , Villegas A , Sepulveda-Falla D , Lehmann SG , Easteal S , Mastronardi CA , Arcos-Burgos M . Mutations modifying sporadic Alzheimer’s disease age of onset . Am J Med Genet B Neuropsychiatr Genet . 2016 ; 171 ( 8 ): 1116 – 30 . Epub 20160830. doi: 10.1002/ajmg.b.32493 . PubMed PMID: 27573710 . OpenUrl CrossRef PubMed 57. ↵ Fortea J , Sala-Llonch R , Bartrés-Faz D , Bosch B , Lladó A , Bargalló N , Molinuevo JL , Sánchez-Valle R . Increased cortical thickness and caudate volume precede atrophy in PSEN1 mutation carriers . J Alzheimers Dis . 2010 ; 22 ( 3 ): 909 – 22 . doi: 10.3233/jad-2010-100678 . PubMed PMID: 20858974 . OpenUrl CrossRef PubMed Web of Science 58. ↵ Graff-Radford J , Yong KXX , Apostolova LG , Bouwman FH , Carrillo M , Dickerson BC , Rabinovici GD , Schott JM , Jones DT , Murray ME . New insights into atypical Alzheimer’s disease in the era of biomarkers . Lancet Neurol . 2021 ; 20 ( 3 ): 222 – 34 . doi: 10.1016/s1474-4422(20)30440-3 . PubMed PMID: 33609479 ; PMCID: PMC8056394 . OpenUrl CrossRef PubMed 59. ↵ Singh NA , Graff-Radford J , Machulda MM , Carlos AF , Schwarz CG , Senjem ML , Jack CR , Jr. , Lowe VJ , Josephs KA , Whitwell JL . Atypical Alzheimer’s disease: new insights into an overlapping spectrum between the language and visual variants . J Neurol . 2024 ; 271 ( 6 ): 3571 – 85 . Epub 20240329. doi: 10.1007/s00415-024-12297-1 . PubMed PMID: 38551740 ; PMCID: PMC11273322 . OpenUrl CrossRef PubMed 60. ↵ Mendez MF , Ghajarania M , Perryman KM . Posterior cortical atrophy: clinical characteristics and differences compared to Alzheimer’s disease . Dement Geriatr Cogn Disord . 2002 ; 14 ( 1 ): 33 – 40 . doi: 10.1159/000058331 . PubMed PMID: 12053130 . OpenUrl CrossRef PubMed Web of Science 61. ↵ Tang-Wai DF , Graff-Radford NR , Boeve BF , Dickson DW , Parisi JE , Crook R , Caselli RJ , Knopman DS , Petersen RC . Clinical, genetic, and neuropathologic characteristics of posterior cortical atrophy . Neurology . 2004 ; 63 ( 7 ): 1168 – 74 . doi: 10.1212/01.wnl.0000140289.18472.15 . PubMed PMID: 15477533 . OpenUrl Abstract / FREE Full Text 62. ↵ Schott JM , Crutch SJ , Carrasquillo MM , Uphill J , Shakespeare TJ , Ryan NS , Yong KX , Lehmann M , Ertekin-Taner N , Graff-Radford NR , Boeve BF , Murray ME , Khan QU , Petersen RC , Dickson DW , Knopman DS , Rabinovici GD , Miller BL , González AS , Gil-Néciga E , Snowden JS , Harris J , Pickering-Brown SM , Louwersheimer E , van der Flier WM , Scheltens P , Pijnenburg YA , Galasko D , Sarazin M , Dubois B , Magnin E , Galimberti D , Scarpini E , Cappa SF , Hodges JR , Halliday GM , Bartley L , Carrillo MC , Bras JT , Hardy J , Rossor MN , Collinge J , Fox NC , Mead S . Genetic risk factors for the posterior cortical atrophy variant of Alzheimer’s disease . Alzheimers Dement . 2016 ; 12 ( 8 ): 862 – 71 . Epub 20160315. doi: 10.1016/j.jalz.2016.01.010 . PubMed PMID: 26993346 ; PMCID: PMC4982482 . OpenUrl CrossRef PubMed 63. ↵ Firth NC , Primativo S , Marinescu RV , Shakespeare TJ , Suarez-Gonzalez A , Lehmann M , Carton A , Ocal D , Pavisic I , Paterson RW , Slattery CF , Foulkes AJM , Ridha BH , Gil-Néciga E , Oxtoby NP , Young AL , Modat M , Cardoso MJ , Ourselin S , Ryan NS , Miller BL , Rabinovici GD , Warrington EK , Rossor MN , Fox NC , Warren JD , Alexander DC , Schott JM , Yong KXX , Crutch SJ . Longitudinal neuroanatomical and cognitive progression of posterior cortical atrophy . Brain . 2019 ; 142 ( 7 ): 2082 – 95 . doi: 10.1093/brain/awz136 . PubMed PMID: 31219516 ; PMCID: PMC6598737 . OpenUrl CrossRef PubMed 64. ↵ Willumsen N , Poole T , Nicholas JM , Fox NC , Ryan NS , Lashley T . Variability in the type and layer distribution of cortical Aβ pathology in familial Alzheimer’s disease . Brain Pathol . 2022 ; 32 ( 3 ): e13009 . Epub 20210728. doi: 10.1111/bpa.13009 . PubMed PMID: 34319632 ; PMCID: PMC9048809 . OpenUrl CrossRef PubMed 65. ↵ Mielke MM , Vemuri P , Rocca WA . Clinical epidemiology of Alzheimer’s disease: assessing sex and gender differences . Clin Epidemiol . 2014 ; 6 : 37 – 48 . Epub 20140108. doi: 10.2147/clep.S37929 . PubMed PMID: 24470773 ; PMCID: PMC3891487 . OpenUrl CrossRef PubMed 66. ↵ Westi EW , Molhemi S , Hansen CT , Skoven CS , Knopper RW , Ahmad DA , Rindshoj MB , Ameen AO , Hansen B , Kohlmeier KA , Aldana BI . Comprehensive Analysis of the 5xFAD Mouse Model of Alzheimer’s Disease Using dMRI, Immunohistochemistry, and Neuronal and Glial Functional Metabolic Mapping . Biomolecules . 2024 ; 14 ( 10 ). Epub 20241013. doi: 10.3390/biom14101294 . PubMed PMID: 39456227 ; PMCID: PMC11505609 . OpenUrl CrossRef PubMed 67. Badea A , Johnson GA , Jankowsky JL . Remote sites of structural atrophy predict later amyloid formation in a mouse model of Alzheimer’s disease . Neuroimage . 2010 ; 50 ( 2 ): 416 – 27 . Epub 20091224. doi: 10.1016/j.neuroimage.2009.12.070 . PubMed PMID: 20035883 ; PMCID: PMC2823970 . OpenUrl CrossRef PubMed Web of Science 68. Spencer NG , Bridges LR , Elderfield K , Amir K , Austen B , Howe FA . Quantitative evaluation of MRI and histological characteristics of the 5xFAD Alzheimer mouse brain . Neuroimage . 2013 ; 76 : 108 – 15 . Epub 20130315. doi: 10.1016/j.neuroimage.2013.02.071 . PubMed PMID: 23507393 . OpenUrl CrossRef PubMed Web of Science 69. Girard SD , Jacquet M , Baranger K , Migliorati M , Escoffier G , Bernard A , Khrestchatisky M , Feron F , Rivera S , Roman FS , Marchetti E . Onset of hippocampus-dependent memory impairments in 5XFAD transgenic mouse model of Alzheimer’s disease . Hippocampus . 2014 ; 24 ( 7 ): 762 – 72 . Epub 20140314. doi: 10.1002/hipo.22267 . PubMed PMID: 24596271 . OpenUrl CrossRef PubMed 70. Micotti E , Paladini A , Balducci C , Tolomeo D , Frasca A , Marizzoni M , Filibian M , Caroli A , Valbusa G , Dix S , O’Neill M , Ozmen L , Czech C , Richardson JC , Frisoni GB , Forloni G . Striatum and entorhinal cortex atrophy in AD mouse models: MRI comprehensive analysis . Neurobiol Aging . 2015 ; 36 ( 2 ): 776 – 88 . Epub 20141025. doi: 10.1016/j.neurobiolaging.2014.10.027 . PubMed PMID: 25433456 . OpenUrl CrossRef PubMed 71. Spencer NG , Lovell DP , Elderfield K , Austen B , Howe FA . Can MRI T(1) be used to detect early changes in 5xFAD Alzheimer’s mouse brain? MAGMA . 2017 ; 30 ( 2 ): 153 – 63 . Epub 20161026. doi: 10.1007/s10334-016-0593-9 . PubMed PMID: 27785640 ; PMCID: PMC5364252 . OpenUrl CrossRef PubMed 72. Frost GR , Longo V , Li T , Jonas LA , Judenhofer M , Cherry S , Koutcher J , Lekaye C , Zanzonico P , Li YM . Hybrid PET/MRI enables high-spatial resolution, quantitative imaging of amyloid plaques in an Alzheimer’s disease mouse model . Sci Rep . 2020 ; 10 ( 1 ): 10379 . Epub 20200625. doi: 10.1038/s41598-020-67284-z . PubMed PMID: 32587315 ; PMCID: PMC7316864 . OpenUrl CrossRef PubMed 73. Zhou XA , Ngiam G , Qian L , Sankorrakul K , Coulson EJ , Chuang KH . The basal forebrain volume reduction detected by MRI does not necessarily link with the cholinergic neuronal loss in the Alzheimer’s disease mouse model . Neurobiol Aging . 2022 ; 117 : 24 – 32 . Epub 20220503. doi: 10.1016/j.neurobiolaging.2022.03.017 . PubMed PMID: 35640461 . OpenUrl CrossRef PubMed 74. Kim ST , Kim HG , Kim YM , Han HS , Cho JH , Lim SC , Lee T , Jahng GH . An aptamer-based magnetic resonance imaging contrast agent for detecting oligomeric amyloid-β in the brain of an Alzheimer’s disease mouse model . NMR Biomed . 2023 ; 36 ( 3 ): e4862 . Epub 20221120. doi: 10.1002/nbm.4862 . PubMed PMID: 36308279 . OpenUrl CrossRef PubMed 75. Kim E , Di Censo D , Baraldo M , Simmons C , Rosa I , Randall K , Ballard C , Dickie BR , Williams SCR , Killick R , Cash D . In vivo multi-parametric manganese-enhanced MRI for detecting amyloid plaques in rodent models of Alzheimer’s disease . Sci Rep . 2021 ; 11 ( 1 ): 12419 . Epub 20210614. doi: 10.1038/s41598-021-91899-5 . PubMed PMID: 34127752 ; PMCID: PMC8203664 . OpenUrl CrossRef PubMed 76. Tomaszewski MR , Sukstanskii AL , Haley H , Meng X , Miller CO , Yablonskiy DA . Quantitative gradient recalled echo (qGRE) MRI enables in vivo measurement of pre-atrophic neurodegeneration in a mouse model of Alzheimer’s disease . Neuroimage . 2024 ; 298 : 120794 . Epub 20240822. doi: 10.1016/j.neuroimage.2024.120794 . PubMed PMID: 39173693 . OpenUrl CrossRef PubMed 77. Grandjean J , Derungs R , Kulic L , Welt T , Henkelman M , Nitsch RM , Rudin M . Complex interplay between brain function and structure during cerebral amyloidosis in APP transgenic mouse strains revealed by multi-parametric MRI comparison . Neuroimage . 2016 ; 134 : 1 – 11 . Epub 20160324. doi: 10.1016/j.neuroimage.2016.03.042 . PubMed PMID: 27033685 . OpenUrl CrossRef PubMed 78. ↵ Kesler SR , Acton P , Rao V , Ray WJ . Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease . Netw Neurosci . 2018 ; 2 ( 2 ): 241 – 58 . Epub 20180601. doi: 10.1162/netn_a_00048 . PubMed PMID: 30215035 ; PMCID: PMC6130552 . OpenUrl CrossRef PubMed 79. ↵ Badea A , Wu W , Shuff J , Wang M , Anderson RJ , Qi Y , Johnson GA , Wilson JG , Koudoro S , Garyfallidis E , Colton CA , Dunson DB . Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer’s Disease . Front Neuroinform . 2019 ; 13 : 72 . Epub 20191210. doi: 10.3389/fninf.2019.00072 . PubMed PMID: 31920610 ; PMCID: PMC6914731 . OpenUrl CrossRef PubMed 80. Falangola MF , Nie X , Ward R , Dhiman S , Voltin J , Nietert PJ , Jensen JH . Diffusion MRI detects basal forebrain cholinergic abnormalities in the 3xTg-AD mouse model of Alzheimer’s disease . Magn Reson Imaging . 2021 ; 83 : 1 – 13 . Epub 20210703. doi: 10.1016/j.mri.2021.06.022 . PubMed PMID: 34229088 ; PMCID: PMC8449815 . OpenUrl CrossRef PubMed 81. Maharjan S , Tsai AP , Lin PB , Ingraham C , Jewett MR , Landreth GE , Oblak AL , Wang N . Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging . Front Neurosci . 2022 ; 16 : 964654 . Epub 20220817. doi: 10.3389/fnins.2022.964654 . PubMed PMID: 36061588 ; PMCID: PMC9428354 . OpenUrl CrossRef PubMed 82. ↵ Winter S , Mahzarnia A , Anderson RJ , Han ZY , Tremblay J , Stout JA , Moon HS , Marcellino D , Dunson DB , Badea A . Brain network fingerprints of Alzheimer’s disease risk factors in mouse models with humanized APOE alleles . Magn Reson Imaging . 2024 ; 114 : 110251 . Epub 20241001. doi: 10.1016/j.mri.2024.110251 . PubMed PMID: 39362319 ; PMCID: PMC11514054 . OpenUrl CrossRef PubMed 83. ↵ Weston PS , Nicholas JM , Lehmann M , Ryan NS , Liang Y , Macpherson K , Modat M , Rossor MN , Schott JM , Ourselin S , Fox NC . Presymptomatic cortical thinning in familial Alzheimer disease: A longitudinal MRI study . Neurology . 2016 ; 87 ( 19 ): 2050 – 7 . Epub 20161012. doi: 10.1212/wnl.0000000000003322 . PubMed PMID: 27733562 ; PMCID: PMC5109950 . OpenUrl Abstract / FREE Full Text 84. ↵ Weston PSJ , Poole T , Nicholas JM , Toussaint N , Simpson IJA , Modat M , Ryan NS , Liang Y , Rossor MN , Schott JM , Ourselin S , Zhang H , Fox NC . Measuring cortical mean diffusivity to assess early microstructural cortical change in presymptomatic familial Alzheimer’s disease . Alzheimers Res Ther . 2020 ; 12 ( 1 ): 112 . Epub 20200917. doi: 10.1186/s13195-020-00679-2 . PubMed PMID: 32943095 ; PMCID: PMC7499910 . OpenUrl CrossRef PubMed 85. ↵ Andersen JV , Skotte NH , Christensen SK , Polli FS , Shabani M , Markussen KH , Haukedal H , Westi EW , Diaz-delCastillo M , Sun RC , Kohlmeier KA , Schousboe A , Gentry MS , Tanila H , Freude KK , Aldana BI , Mann M , Waagepetersen HS . Hippocampal disruptions of synaptic and astrocyte metabolism are primary events of early amyloid pathology in the 5xFAD mouse model of Alzheimer’s disease . Cell Death Dis . 2021 ; 12 ( 11 ): 954 . Epub 20211016. doi: 10.1038/s41419-021-04237-y . PubMed PMID: 34657143 ; PMCID: PMC8520528 . OpenUrl CrossRef PubMed 86. ↵ Nalivaeva NN , Turner AJ . The amyloid precursor protein: a biochemical enigma in brain development, function and disease . FEBS Lett . 2013 ; 587 ( 13 ): 2046 – 54 . Epub 20130516. doi: 10.1016/j.febslet.2013.05.010 . PubMed PMID: 23684647 . OpenUrl CrossRef PubMed 87. ↵ Evangelio M , García-Amado M , Clascá F . Thalamocortical Projection Neuron and Interneuron Numbers in the Visual Thalamic Nuclei of the Adult C57BL/6 Mouse . Front Neuroanat . 2018 ; 12 : 27 . Epub 20180412. doi: 10.3389/fnana.2018.00027 . PubMed PMID: 29706872 ; PMCID: PMC5906714 . OpenUrl CrossRef PubMed 88. ↵ Aggleton JP , Pralus A , Nelson AJ , Hornberger M . Thalamic pathology and memory loss in early Alzheimer’s disease: moving the focus from the medial temporal lobe to Papez circuit . Brain . 2016 ; 139 (Pt 7 ): 1877 – 90 . Epub 20160428. doi: 10.1093/brain/aww083 . PubMed PMID: 27190025 ; PMCID: PMC4939698 . OpenUrl CrossRef PubMed 89. ↵ Ryan NS , Keihaninejad S , Shakespeare TJ , Lehmann M , Crutch SJ , Malone IB , Thornton JS , Mancini L , Hyare H , Yousry T , Ridgway GR , Zhang H , Modat M , Alexander DC , Rossor MN , Ourselin S , Fox NC . Magnetic resonance imaging evidence for presymptomatic change in thalamus and caudate in familial Alzheimer’s disease . Brain . 2013 ; 136 (Pt 5 ): 1399 – 414 . Epub 20130328. doi: 10.1093/brain/awt065 . PubMed PMID: 23539189 ; PMCID: PMC3634199 . OpenUrl CrossRef PubMed Web of Science 90. ↵ Greco JA , Liberzon I . Neuroimaging of Fear-Associated Learning . Neuropsychopharmacology . 2016 ; 41 ( 1 ): 320 – 34 . Epub 20150821. doi: 10.1038/npp.2015.255 . PubMed PMID: 26294108 ; PMCID: PMC4677141 . OpenUrl CrossRef PubMed 91. ↵ Antoniadis EA , McDonald RJ . Amygdala, hippocampus and discriminative fear conditioning to context . Behav Brain Res . 2000 ; 108 ( 1 ): 1 – 19 . doi: 10.1016/s0166-4328(99)00121-7 . PubMed PMID: 10680753 . OpenUrl CrossRef PubMed Web of Science 92. ↵ Keifer OP , Jr ., Hurt RC , Ressler KJ , Marvar PJ . The Physiology of Fear: Reconceptualizing the Role of the Central Amygdala in Fear Learning . Physiology (Bethesda) . 2015 ; 30 ( 5 ): 389 – 401 . doi: 10.1152/physiol.00058.2014 . PubMed PMID: 26328883 ; PMCID: PMC4556826 . OpenUrl CrossRef PubMed 93. ↵ Shin LM , Liberzon I . The neurocircuitry of fear, stress, and anxiety disorders . Neuropsychopharmacology . 2010 ; 35 ( 1 ): 169 – 91 . doi: 10.1038/npp.2009.83 . PubMed PMID: 19625997 ; PMCID: PMC3055419 . OpenUrl CrossRef PubMed Web of Science 94. ↵ Bruguier H , Suarez R , Manger P , Hoerder-Suabedissen A , Shelton AM , Oliver DK , Packer AM , Ferran JL , García-Moreno F , Puelles L , Molnár Z . In search of common developmental and evolutionary origin of the claustrum and subplate . J Comp Neurol . 2020 ; 528 ( 17 ): 2956 – 77 . Epub 20200506. doi: 10.1002/cne.24922 . PubMed PMID: 32266722 . OpenUrl CrossRef PubMed 95. ↵ Venkataraman A , Dias BG. Expanding the canon: An inclusive neurobiology of thalamic and subthalamic fear circuits . Neuropharmacology . 2023 ; 226 : 109380 . Epub 20221223. doi: 10.1016/j.neuropharm.2022.109380 . PubMed PMID: 36572176 ; PMCID: PMC9984284 . OpenUrl CrossRef PubMed 96. ↵ Frontera JL , Sala RW , Georgescu IA , Baba Aissa H , d’Almeida MN , Popa D , Léna C. The cerebellum regulates fear extinction through thalamo-prefrontal cortex interactions in male mice . Nat Commun . 2023 ; 14 ( 1 ): 1508 . Epub 20230317. doi: 10.1038/s41467-023-36943-w . PubMed PMID: 36932068 ; PMCID: PMC10023697 . OpenUrl CrossRef PubMed 97. ↵ Penzo MA , Robert V , Tucciarone J , De Bundel D , Wang M , Van Aelst L , Darvas M , Parada LF , Palmiter RD , He M , Huang ZJ , Li B . The paraventricular thalamus controls a central amygdala fear circuit . Nature . 2015 ; 519 ( 7544 ): 455 – 9 . Epub 20150119. doi: 10.1038/nature13978 . PubMed PMID: 25600269 ; PMCID: PMC4376633 . OpenUrl CrossRef PubMed 98. ↵ Ratigan HC , Krishnan S , Smith S , Sheffield MEJ . A thalamic-hippocampal CA1 signal for contextual fear memory suppression, extinction, and discrimination . Nat Commun . 2023 ; 14 ( 1 ): 6758 . Epub 20231024. doi: 10.1038/s41467-023-42429-6 . PubMed PMID: 37875465 ; PMCID: PMC10598272 . OpenUrl CrossRef PubMed 99. ↵ Sun Y , Gooch H , Sah P . Fear conditioning and the basolateral amygdala . F1000Res . 2020 ; 9 . Epub 20200128. doi: 10.12688/f1000research.21201.1 . PubMed PMID: 32047613 ; PMCID: PMC6993823 . OpenUrl CrossRef PubMed 100. ↵ Bernier BE , Lacagnina AF , Ayoub A , Shue F , Zemelman BV , Krasne FB , Drew MR . Dentate Gyrus Contributes to Retrieval as well as Encoding: Evidence from Context Fear Conditioning, Recall, and Extinction . J Neurosci . 2017 ; 37 ( 26 ): 6359 – 71 . Epub 20170525. doi: 10.1523/jneurosci.3029-16.2017 . PubMed PMID: 28546308 ; PMCID: PMC5490069 . OpenUrl Abstract / FREE Full Text 101. ↵ Zhang Y , Wang Z , Ju J , Liao J , Zhou Q . Elevated activity in the dorsal dentate gyrus reduces expression of fear memory after fear extinction training . J Psychiatry Neurosci . 2021 ; 46 ( 3 ): E390 – e401 . Epub 20210602. doi: 10.1503/jpn.200151 . PubMed PMID: 34077148 ; PMCID: PMC8327976 . OpenUrl Abstract / FREE Full Text 102. ↵ Hernández-Rabaza V , Hontecillas-Prieto L , Velázquez-Sánchez C , Ferragud A , Pérez-Villaba A , Arcusa A , Barcia JA , Trejo JL , Canales JJ . The hippocampal dentate gyrus is essential for generating contextual memories of fear and drug-induced reward . Neurobiol Learn Mem . 2008 ; 90 ( 3 ): 553 – 9 . Epub 20080729. doi: 10.1016/j.nlm.2008.06.008 . PubMed PMID: 18644245 . OpenUrl CrossRef PubMed Web of Science 103. ↵ Braak H , Braak E . Alzheimer’s disease affects limbic nuclei of the thalamus . Acta Neuropathol . 1991 ; 81 ( 3 ): 261 – 8 . doi: 10.1007/bf00305867 . PubMed PMID: 1711755 . OpenUrl CrossRef PubMed Web of Science 104. ↵ Forno G , Saranathan M , Contador J , Guillen N , Falgàs N , Tort-Merino A , Balasa M , Sanchez-Valle R , Hornberger M , Lladó A . Thalamic nuclei changes in early and late onset Alzheimer’s disease . Curr Res Neurobiol . 2023 ; 4 : 100084 . Epub 20230324. doi: 10.1016/j.crneur.2023.100084 . PubMed PMID: 37397807 ; PMCID: PMC10313877 . OpenUrl CrossRef PubMed 105. ↵ Armstrong NM , An Y , Shin JJ , Williams OA , Doshi J , Erus G , Davatzikos C , Ferrucci L , Beason-Held LL , Resnick SM . Associations between cognitive and brain volume changes in cognitively normal older adults . Neuroimage . 2020 ; 223 : 117289 . Epub 20200821. doi: 10.1016/j.neuroimage.2020.117289 . PubMed PMID: 32835822 ; PMCID: PMC9020590 . OpenUrl CrossRef PubMed 106. ↵ Driscoll I , Davatzikos C , An Y , Wu X , Shen D , Kraut M , Resnick SM . Longitudinal pattern of regional brain volume change differentiates normal aging from MCI . Neurology . 2009 ; 72 ( 22 ): 1906 – 13 . doi: 10.1212/WNL.0b013e3181a82634 . PubMed PMID: 19487648 ; PMCID: PMC2690968 . OpenUrl Abstract / FREE Full Text 107. ↵ Boublay N , Bouet R , Dorey JM , Padovan C , Makaroff Z , Fédérico D , Gallice I , Barrellon MO , Robert P , Moreaud O , Rouch I , Krolak-Salmon P . Brain Volume Predicts Behavioral and Psychological Symptoms in Alzheimer’s Disease . J Alzheimers Dis . 2020 ; 73 ( 4 ): 1343 – 53 . doi: 10.3233/jad-190612 . PubMed PMID: 31903989 . OpenUrl CrossRef PubMed 108. ↵ Iacono D , O’Brien R , Resnick SM , Zonderman AB , Pletnikova O , Rudow G , An Y , West MJ , Crain B , Troncoso JC . Neuronal hypertrophy in asymptomatic Alzheimer disease . J Neuropathol Exp Neurol . 2008 ; 67 ( 6 ): 578 – 89 . doi: 10.1097/NEN.0b013e3181772794 . PubMed PMID: 18520776 ; PMCID: PMC2518071 . OpenUrl CrossRef PubMed 109. ↵ Riudavets MA , Iacono D , Resnick SM , O’Brien R , Zonderman AB , Martin LJ , Rudow G , Pletnikova O , Troncoso JC . Resistance to Alzheimer’s pathology is associated with nuclear hypertrophy in neurons . Neurobiol Aging . 2007 ; 28 ( 10 ): 1484 – 92 . Epub 20070628. doi: 10.1016/j.neurobiolaging.2007.05.005 . PubMed PMID: 17599696 ; PMCID: PMC2694127 . OpenUrl CrossRef PubMed Web of Science 110. ↵ Hong S , Baek SH , Lai MKP , Arumugam TV , Jo DG. Aging-associated sensory decline and Alzheimer’s disease . Mol Neurodegener . 2024 ; 19 ( 1 ): 93 . Epub 20241204. doi: 10.1186/s13024-024-00776-y . PubMed PMID: 39633396 ; PMCID: PMC11616278 . OpenUrl CrossRef PubMed 111. ↵ Murphy C . Olfactory and other sensory impairments in Alzheimer disease . Nat Rev Neurol . 2019 ; 15 ( 1 ): 11 – 24 . doi: 10.1038/s41582-018-0097-5 . PubMed PMID: 30532084 . OpenUrl CrossRef PubMed 112. ↵ Zhang NK , Zhang SK , Zhang LI , Tao HW , Zhang GW . Sensory processing deficits and related cortical pathological changes in Alzheimer’s disease . Front Aging Neurosci . 2023 ; 15 : 1213379 . Epub 20230815. doi: 10.3389/fnagi.2023.1213379 . PubMed PMID: 37649717 ; PMCID: PMC10464619 . OpenUrl CrossRef PubMed 113. ↵ Albers MW , Gilmore GC , Kaye J , Murphy C , Wingfield A , Bennett DA , Boxer AL , Buchman AS , Cruickshanks KJ , Devanand DP , Duffy CJ , Gall CM , Gates GA , Granholm AC , Hensch T , Holtzer R , Hyman BT , Lin FR , McKee AC , Morris JC , Petersen RC , Silbert LC , Struble RG , Trojanowski JQ , Verghese J , Wilson DA , Xu S , Zhang LI . At the interface of sensory and motor dysfunctions and Alzheimer’s disease . Alzheimers Dement . 2015 ; 11 ( 1 ): 70 – 98 . Epub 20140709. doi: 10.1016/j.jalz.2014.04.514 . PubMed PMID: 25022540 ; PMCID: PMC4287457 . OpenUrl CrossRef PubMed 114. ↵ Kim J , Han K , Jung JH , Park KA , Oh SY . Early-Onset Ocular Motor Cranial Neuropathy Is a Strong Predictor of Dementia: A Nationwide, Population-Based Cohort Study . Ophthalmology . 2024 ; 131 ( 3 ): 288 – 301 . Epub 20231012. doi: 10.1016/j.ophtha.2023.10.006 . PubMed PMID: 37832727 . OpenUrl CrossRef PubMed 115. ↵ Wang N , Anderson RJ , Badea A , Cofer G , Dibb R , Qi Y , Johnson GA . Whole mouse brain structural connectomics using magnetic resonance histology . Brain Structure and Function . 2018 ; 223 ( 9 ): 4323 – 35 . doi: 10.1007/s00429-018-1750-x . PubMed PMID: 30225830 . OpenUrl CrossRef PubMed 116. ↵ Thompson PM , Hayashi KM , Dutton RA , Chiang MC , Leow AD , Sowell ER , De Zubicaray G , Becker JT , Lopez OL , Aizenstein HJ , Toga AW. Tracking Alzheimer’s disease . Ann N Y Acad Sci . 2007 ; 1097 : 183 – 214 . doi: 10.1196/annals.1379.017 . PubMed PMID: 17413023 ; PMCID: PMC3197831 . OpenUrl CrossRef PubMed Web of Science 117. ↵ Frisoni GB , Fox NC , Jack CR , Jr. , Scheltens P , Thompson PM . The clinical use of structural MRI in Alzheimer disease . Nat Rev Neurol . 2010 ; 6 ( 2 ): 67 – 77 . doi: 10.1038/nrneurol.2009.215 . PubMed PMID: 20139996 ; PMCID: PMC2938772 . OpenUrl CrossRef PubMed Web of Science 118. ↵ Sabuncu MR , Desikan RS , Sepulcre J , Yeo BT , Liu H , Schmansky NJ , Reuter M , Weiner MW , Buckner RL , Sperling RA , Fischl B . The dynamics of cortical and hippocampal atrophy in Alzheimer disease . Arch Neurol . 2011 ; 68 ( 8 ): 1040 – 8 . doi: 10.1001/archneurol.2011.167 . PubMed PMID: 21825241 ; PMCID: PMC3248949 . OpenUrl CrossRef PubMed Web of Science 119. ↵ Wyman BT , Harvey DJ , Crawford K , Bernstein MA , Carmichael O , Cole PE , Crane PK , DeCarli C , Fox NC , Gunter JL , Hill D , Killiany RJ , Pachai C , Schwarz AJ , Schuff N , Senjem ML , Suhy J , Thompson PM , Weiner M , Jack CR , Jr . . Standardization of analysis sets for reporting results from ADNI MRI data . Alzheimers Dement . 2013 ; 9 ( 3 ): 332 – 7 . Epub 20121027. doi: 10.1016/j.jalz.2012.06.004 . PubMed PMID: 23110865 ; PMCID: PMC3891834 . OpenUrl CrossRef PubMed Web of Science 120. ↵ Collij LE , Ingala S , Top H , Wottschel V , Stickney KE , Tomassen J , Konijnenberg E , Ten Kate M , Sudre C , Lopes Alves I , Yaqub MM , Wink AM , Van ‘t Ent D , Scheltens P , van Berckel BNM , Visser PJ , Barkhof F , Braber AD. White matter microstructure disruption in early stage amyloid pathology . Alzheimers Dement (Amst) . 2021 ; 13 ( 1 ): e12124 . Epub 20210401. doi: 10.1002/dad2.12124 . PubMed PMID: 33816751 ; PMCID: PMC8015832 . OpenUrl CrossRef PubMed 121. ↵ Morra JH , Tu Z , Apostolova LG , Green AE , Avedissian C , Madsen SK , Parikshak N , Toga AW , Jack CR , Jr. , Schuff N , Weiner MW , Thompson PM . Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls . Neuroimage . 2009 ; 45 ( 1 Suppl ): S3 – 15 . Epub 20081108. doi: 10.1016/j.neuroimage.2008.10.043 . PubMed PMID: 19041724 ; PMCID: PMC2733354 . OpenUrl CrossRef PubMed Web of Science 122. ↵ Oakley H , Cole SL , Logan S , Maus E , Shao P , Craft J , Guillozet-Bongaarts A , Ohno M , Disterhoft J , Van Eldik L , Berry R , Vassar R . Intraneuronal beta-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer’s disease mutations: potential factors in amyloid plaque formation . J Neurosci . 2006 ; 26 ( 40 ): 10129 – 40 . doi: 10.1523/jneurosci.1202-06.2006 . PubMed PMID: 17021169 ; PMCID: PMC6674618 . OpenUrl Abstract / FREE Full Text 123. ↵ Neuner SM , Garfinkel BP , Wilmott LA , Ignatowska-Jankowska BM , Citri A , Orly J , Lu L , Overall RW , Mulligan MK , Kempermann G , Williams RW , O’Connell KM , Kaczorowski CC . Systems genetics identifies Hp1bp3 as a novel modulator of cognitive aging . Neurobiol Aging . 2016 ; 46 : 58 – 67 . Epub 20160617. doi: 10.1016/j.neurobiolaging.2016.06.008 . PubMed PMID: 27460150 ; PMCID: PMC5018442 . OpenUrl CrossRef PubMed 124. ↵ Chintalapudi SR , Maria D , Di Wang X , Bailey JNC , Hysi PG , Wiggs JL , Williams RW , Jablonski MM . Systems genetics identifies a role for Cacna2d1 regulation in elevated intraocular pressure and glaucoma susceptibility . Nat Commun . 2017 ; 8 ( 1 ): 1755 . Epub 20171124. doi: 10.1038/s41467-017-00837-5 . PubMed PMID: 29176626 ; PMCID: PMC5701146 . OpenUrl CrossRef PubMed 125. ↵ Pan L , Cho KS , Wei X , Xu F , Lennikov A , Hu G , Tang J , Guo S , Chen J , Kriukov E , Kyle R , Elzaridi F , Jiang S , Dromel PA , Young M , Baranov P , Do CW , Williams RW , Chen J , Lu L , Chen DF . IGFBPL1 is a master driver of microglia homeostasis and resolution of neuroinflammation in glaucoma and brain tauopathy . Cell Rep . 2023 ; 42 ( 8 ): 112889 . Epub 20230731. doi: 10.1016/j.celrep.2023.112889 . PubMed PMID: 37527036 ; PMCID: PMC10528709 . OpenUrl CrossRef PubMed 126. ↵ Gómez-Pascual A , Glikman DM , Ng HX , Tomkins JE , Lu L , Xu Y , Ashbrook DG , Kaczorowski C , Kempermann G , Killmar J , Mozhui K , Ohlenschläger O , Aebersold R , Ingram DK , Williams EG , Williams RW , Overall RW , Jucker M , de Bakker DEM . The Pgb1 locus controls glycogen aggregation in astrocytes of the aged hippocampus without impacting cognitive function . bioRxiv . 2024 . Epub 20240708. doi: 10.1101/2023.11.22.567373 . PubMed PMID: 38045339 ; PMCID: PMC10690248 . OpenUrl Abstract / FREE Full Text 127. ↵ Oakley H , Cole SL , Logan S , Maus E , Shao P , Craft J , Guillozet-Bongaarts A , Ohno M , Disterhoft J , Van Eldik L , Berry R , Vassar R. Intraneuronal β-Amyloid Aggregates, Neurodegeneration, and Neuron Loss in Transgenic Mice with Five Familial Alzheimer’s Disease Mutations: Potential Factors in Amyloid Plaque Formation . The Journal of Neuroscience . 2006 ; 26 ( 40 ): 10129 – 40 . doi: 10.1523/jneurosci.1202-06.2006 . OpenUrl Abstract / FREE Full Text 128. ↵ Sasani TA , Ashbrook DG , Beichman AC , Lu L , Palmer AA , Williams RW , Pritchard JK , Harris K . A natural mutator allele shapes mutation spectrum variation in mice . Nature . 2022 ; 605 (7910): 497 -502. Epub 20220511. doi: 10.1038/s41586-022-04701-5 . PubMed PMID: 35545679 ; PMCID: PMC9272728 . OpenUrl CrossRef PubMed 129. ↵ Chen C , Kim JJ , Thompson RF , Tonegawa S . Hippocampal lesions impair contextual fear conditioning in two strains of mice . Behav Neurosci . 1996 ; 110 ( 5 ): 1177 – 80 . doi: 10.1037//0735-7044.110.5.1177 . PubMed PMID: 8919020 . OpenUrl CrossRef PubMed Web of Science 130. ↵ Sturman O , von Ziegler L , Schläppi C , Akyol F , Privitera M , Slominski D , Grimm C , Thieren L , Zerbi V , Grewe B , Bohacek J . Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions . Neuropsychopharmacology . 2020 ; 45 ( 11 ): 1942 – 52 . Epub 20200725. doi: 10.1038/s41386-020-0776-y . PubMed PMID: 32711402 ; PMCID: PMC7608249 . OpenUrl CrossRef PubMed 131. ↵ Stejskal EOaJET . Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient . Journal of Chemical Physics ,. 1965 ; 42 ( 1 ): 288 – 92 . OpenUrl CrossRef Web of Science 132. ↵ Tuch DS , Reese TG , Wiegell MR , Makris N , Belliveau JW , Wedeen VJ . High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity . Magn Reson Med . 2002 ; 48 ( 4 ): 577 – 82 . doi: 10.1002/mrm.10268 . PubMed PMID: 12353272 . OpenUrl CrossRef PubMed Web of Science 133. ↵ Avants BB , Tustison NJ , Stauffer M , Song G , Wu B , Gee JC . The Insight ToolKit image registration framework . Front Neuroinform . 2014 ; 8 : 44 . Epub 20140428. doi: 10.3389/fninf.2014.00044 . PubMed PMID: 24817849 ; PMCID: PMC4009425 . OpenUrl CrossRef PubMed 134. ↵ St-Jean S , Coupé P , Descoteaux M. Non Local Spatial and Angular Matching: Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising . Med Image Anal . 2016 ; 32 : 115 – 30 . Epub 20160318. doi: 10.1016/j.media.2016.02.010 . PubMed PMID: 27082655 . OpenUrl CrossRef PubMed 135. ↵ Basser PJ , Jones DK . Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review . NMR Biomed . 2002 ; 15 ( 7-8 ): 456 – 67 . doi: 10.1002/nbm.783 . PubMed PMID: 12489095 . OpenUrl CrossRef PubMed Web of Science 136. ↵ Gurland J TR . A simple approximation for unbiased estimation of the standard deviation . Am Stat . 1971 ; 25 : 30 – 2 . OpenUrl CrossRef 137. ↵ Seecharan DJ , Kulkarni AL , Lu L , Rosen GD , Williams RW . Genetic control of interconnected neuronal populations in the mouse primary visual system . J Neurosci . 2003 ; 23 ( 35 ): 11178 – 88 . doi: 10.1523/jneurosci.23-35-11178.2003 . PubMed PMID: 14657177 ; PMCID: PMC6741036 . OpenUrl Abstract / FREE Full Text 138. ↵ Park Y-G , Sohn CH , Chen R , McCue M , Yun DH , Drummond GT , Ku T , Evans NB , Oak HC , Trieu W , Choi H , Jin X , Lilascharoen V , Wang J , Truttmann MC , Qi HW , Ploegh HL , Golub TR , Chen S-C , Frosch MP , Kulik HJ , Lim BK , Chung K . Protection of tissue physicochemical properties using polyfunctional crosslinkers . Nature Biotechnology . 2018 ; 37 ( 1 ): 73 – 83 . doi: 10.1038/nbt.4281 . OpenUrl CrossRef PubMed 139. ↵ Kim SY , Cho JH , Murray E , Bakh N , Choi H , Ohn K , Ruelas L , Hubbert A , McCue M , Vassallo SL , Keller PJ , Chung K . Stochastic electrotransport selectively enhances the transport of highly electromobile molecules . Proc Natl Acad Sci U S A . 2015 ; 112 ( 46 ): E6274 – 83 . Epub 20151102. doi: 10.1073/pnas.1510133112 . PubMed PMID: 26578787 ; PMCID: PMC4655572 . OpenUrl Abstract / FREE Full Text 140. ↵ Murray E , Cho JH , Goodwin D , Ku T , Swaney J , Kim S-Y , Choi H , Park Y-G , Park J-Y , Hubbert A , McCue M , Vassallo S , Bakh N , Frosch MP , Wedeen VJ , Seung HS , Chung K. Simple , Scalable Proteomic Imaging for High-Dimensional Profiling of Intact Systems . Cell . 2015 ; 163 ( 6 ): 1500 – 14 . doi: 10.1016/j.cell.2015.11.025 . OpenUrl CrossRef PubMed 141. ↵ Williams RW , Rakic P . Three-dimensional counting: an accurate and direct method to estimate numbers of cells in sectioned material . J Comp Neurol . 1988 ; 278 ( 3 ): 344 – 52 . doi: 10.1002/cne.902780305 . PubMed PMID: 3216047 . OpenUrl CrossRef PubMed Web of Science 142. ↵ Diedenhofen B , Musch J. cocor: a comprehensive solution for the statistical comparison of correlations . PLoS One . 2015 ; 10 ( 3 ): e0121945 . Epub 20150402. doi: 10.1371/journal.pone.0121945 . PubMed PMID: 25835001 ; PMCID: PMC4383486 . OpenUrl CrossRef PubMed 143. ↵ Sloan Z , Arends , D. , W. Broman , K. , Centeno , A. , Furlotte , N. , Nijveen , H. , Yan , L. , Zhou , X. , Williams , R.W. , Prins , P . J Open Source Software . 2016 ; 1 ( 25 ). doi: 10.21105/joss.00025 . OpenUrl CrossRef 144. ↵ Wilkinson MD , Dumontier M , Aalbersberg IJ , Appleton G , Axton M , Baak A , Blomberg N , Boiten JW , da Silva Santos LB , Bourne PE , Bouwman J , Brookes AJ , Clark T , Crosas M , Dillo I , Dumon O , Edmunds S , Evelo CT , Finkers R , Gonzalez-Beltran A , Gray AJ , Groth P , Goble C , Grethe JS , Heringa J , t Hoen PA , Hooft R , Kuhn T , Kok R , Kok J , Lusher SJ , Martone ME , Mons A , Packer AL , Persson B , Rocca-Serra P , Roos M , van Schaik R , Sansone SA , Schultes E , Sengstag T , Slater T , Strawn G , Swertz MA , Thompson M , van der Lei J , van Mulligen E , Velterop J , Waagmeester A , Wittenburg P , Wolstencroft K , Zhao J , Mons B. The FAIR Guiding Principles for scientific data management and stewardship . Sci Data . 2016 ; 3 : 160018 . Epub 20160315. doi: 10.1038/sdata.2016.18 . PubMed PMID: 26978244 ; PMCID: PMC4792175 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted January 10, 2025. 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