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W4378214891 abstract "Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Methods Data availability References Decision letter Author response Article and author information Metrics Abstract The mouse brain is by far the most intensively studied among mammalian brains, yet basic measures of its cytoarchitecture remain obscure. For example, quantifying cell numbers, and the interplay of sex, strain, and individual variability in cell density and volume is out of reach for many regions. The Allen Mouse Brain Connectivity project produces high-resolution full brain images of hundreds of brains. Although these were created for a different purpose, they reveal details of neuroanatomy and cytoarchitecture. Here, we used this population to systematically characterize cell density and volume for each anatomical unit in the mouse brain. We developed a DNN-based segmentation pipeline that uses the autofluorescence intensities of images to segment cell nuclei even within the densest regions, such as the dentate gyrus. We applied our pipeline to 507 brains of males and females from C57BL/6J and FVB.CD1 strains. Globally, we found that increased overall brain volume does not result in uniform expansion across all regions. Moreover, region-specific density changes are often negatively correlated with the volume of the region; therefore, cell count does not scale linearly with volume. Many regions, including layer 2/3 across several cortical areas, showed distinct lateral bias. We identified strain-specific or sex-specific differences. For example, males tended to have more cells in extended amygdala and hypothalamic regions (MEA, BST, BLA, BMA, and LPO, AHN) while females had more cells in the orbital cortex (ORB). Yet, inter-individual variability was always greater than the effect size of a single qualifier. We provide the results of this analysis as an accessible resource for the community. Editor's evaluation The manuscript provides a new powerful tool as well as a large resource that should be useful both to the neuroscience community and more widely. The authors developed and applied a methodology to automatically estimate volume, cell number, and density of mice brains from multiple regions, by detecting the auto-fluorescence intensities of the cell nuclei. Using this platform, they analyzed a few hundred mouse brains available in the database of the Allen Mouse Brain Connectivity project. They identified strain-specific and sex-specific differences in several brain regions. https://doi.org/10.7554/eLife.82376.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The mammalian brain can be divided into neuroanatomical units (i.e., brain regions) characterized by a shared function, connectivity, developmental origin, and/or cytoarchitecture (i.e., number and density of cells it contains). The mouse brain is the most extensively studied in mammals and its regions are well characterized. Although cytoarchitecture is one of the most prominent features of a brain region, few studies have systematically mapped cell bodies or quantified cell densities in mouse brains, compared to the early, detailed cell mapping of the nematode Caenorhabditis elegans (White et al., 1986). On the other hand, extensive literature explored scaling of cell numbers and densities among mammalian brains (Herculano-Houzel et al., 2006; Herculano-Houzel and Lent, 2005; Azevedo et al., 2009). This however was performed by counting dissociated nuclei at the loss of deeper region-specific resolution. Obtaining an accurate cell count for a brain region is technically challenging. Previous estimates relied heavily on extrapolation from manual counting of 2D sections (stereology), making cell-resolved data for subcortical regions sparse (Keller et al., 2018). Analyzing complete brains using 2D histological sections remains labor-intensive because it requires sectioning, mounting, and accurate alignment with a reference atlas. Furthermore, automated cell counting proved particularly difficult in dense regions, such as the hippocampal formation (HPF) and the cerebellum (Attili et al., 2019). Automated block-face imaging methods solved several of these issues and drastically improved throughput (Ueda et al., 2020), For instance, serial two-photon tomography (STPT) (Ragan et al., 2012) was a technological breakthrough that integrated tissue sectioning with top-view light microscopy. STPT provided high-quality imaging in an optical plane below the sectioning surface and solved many problems of section distortion and atlas alignment, further easing downstream analysis. Yet, STPT typically represents a subsample of the complete volume, and some interpolation is needed. Because of their limited throughput, histological studies cannot supply the number of analyzed brains needed to uncover potential variability between individuals, experimental conditions, and populations. Complementary approaches aimed at evaluating variability, for example, magnetic resonance imaging (MRI), can measure some features, such as the volume of given brain regions, and can even track individuals over time in a noninvasive manner. Yet, MRI lacks the accuracy needed for counting cells or assessing cell densities, and it remains difficult to simultaneously analyze regional volume and cell density with high accuracy brain-wide, especially in the large throughput required for comparing two experimental populations (such as two strains, or males vs. females). Therefore, there is a need for systematic measurement of all cells over hundreds of brains from multiple experimental groups. To address this knowledge gap, we harnessed the Allen Mouse Brain Connectivity Project (AMBCA) dataset, which is largest existing cohort of whole-brain STPT images, produced by the Allen Institute for the different purpose of mapping mouse regional connectivity (Oh et al., 2014; http://help.brain-map.org/display/api/Allen%2BBrain%2BAtlas%2BAPI). We applied a deep neural network (DNN) to discern cell nuclei using the AMBCA background autofluorescence channel. This enabled us to perform a systematic brain-wide cell density estimation across hundreds of mouse brains. Based on the alignment with the Allen Mouse Brain Atlas (AMBA), we were able to simultaneously measure volume and density for each brain, for each region, over a large population. We constructed a comprehensive database that aggregates these results and provides them as an accessible resource to the community. We also discovered nontrivial relationships between densities and volumes, and gained insights into strain- and sex-dependent characteristics across various brain regions. Figure 1 with 2 supplements see all Download asset Open asset Graphical abstract. (A) Analysis is based on a cohort of 507 mouse brains from the Allen Mouse Brain Connectivity Atlas (AMBCA), males and females, of C57BL/6J and FVB.CD1 strains. Each brain was imaged in serial two-photon tomography (STPT) and comprises ~140 coronal sections spaced 100 μm apart along the anterior–posterior axis. (B) Example of nucleus segmentation in the isocortex. Each section was divided into tiles of 312 × 312 pixels (109 × 109 μm) (zoom-ins, right). A deep neural network cell segmentation model (see ‘Methods’) was applied to detect the contours of nuclei for downstream analysis across tiles, sections, and whole brains, as shown. (C) For each brain and region that passed QC (see ‘Methods’), volume, cell density, and cell count were computed, resulting in a comprehensive database (D) available through our GUI. (E) The measured variables displayed region-specific laterality differences, sex and strain differences across the population. Results Autofluorescence of STPT images displays cell nuclei The AMBCA project was first published in 2014 (Oh et al., 2014). The project has systematically imaged 2992 full brains, using serial two-photon tomography, for the purpose of tracing neuronal projections and mapping regional (mesoscale) connectivity with GFP-labeled viral tracers. Each brain in the dataset is covered by 130–140 (median 137) serial coronal sections, with a gap of 100 μm, as reported in the AMBCA study (Oh et al., 2014). We noticed that the red (background) channel of STPT images, taken for the purpose of atlas alignment, typically features dark, round-like objects resembling cell nuclei. This phenomenon was described in previous literature (Wang et al., 2020; Costantini et al., 2021; DaCosta et al., 2005; Kretschmer et al., 2016). In particular, Zipfel et al., 2003 characterized the use of multiphoton-excited native florescence and second harmonic generation for the purpose of staining-free tissue imaging. To confirm that these dark objects indeed represent cell nuclei with lower autofluorescence intensity than the surrounding lipid-rich brain tissue, we performed a standard 4% paraformaldehyde (PFA) perfusion-fixation followed by cryosectioning and nucleus (DAPI) counterstaining. In these sections, we observed the same low-autofluorescent objects using epifluorescence microscopy. 91–98% of detected objects were also marked by DAPI, confirming that dark objects in STPT indeed represent cell nuclei (Figure 1—figure supplement 1). About 2–9% of detected low-autofluorescent objects were additional ‘false positive’ detections that were not obvious in the DAPI image. On the other hand, 26–45% of DAPI-detected nuclei were not observed in the autofluorescent images (false negatives), pointing to an underestimate of cell counts based on low-autofluorescence objects, compared to nuclear staining. A more systematic comparison between autofluorescence images and nuclear staining appears in the next section. Population-wide, regionally resolved exploration of neuroanatomical features To automatically collect cytoarchitecture data for each brain, we trained a DNN model to detect and segment the nuclei (low-autofluorescent objects) in all brain regions, including those of the highest density, such as the dentate gyrus (DG). Because of computing constraints, we applied the model systematically to segment a subset of the AMBCA dataset comprising 507 brains (Figure 1A and B and ‘Methods’). The model performed with an estimated 97% cell detection accuracy on a test set, with a false positive rate of <0.01 (see ‘Methods’) whenever image quality was sufficient (for exclusion criteria of whole brains or certain regions within sections, see ‘Methods’). Using detected cells in each section, we obtained a local estimate of the volumetric cell density (see ‘Methods’), which, combined with the pixel-wise registration of brain regions provided by the AMBA, allowed us to estimate the average cell density per region for each brain. Similarly, we evaluated the per-region volume of each brain by linear interpolation across all sections (see ‘Methods’). The anterior-most olfactory bulb (MOB) and posterior-most cerebellum (CB) were truncated in imaging, which likely led to an underestimate in their quantifications, and slightly higher variance compared to other regions of similar volume (Figure 2—figure supplement 1D and E). We further corrected a batch effect in the AMBCA dataset showing a small difference in overall brain volume across experimental batches (see ‘Methods,’ Figure 2—figure supplement 1B and C). In sum, we simultaneously estimated the 3D cell density (D) and volume (V) of each region for each brain (see ‘Methods’). In total, we estimated per-region D and V for 532 basic regions annotated in the AMBA, which corresponds to levels 6–8 of the AMBA region hierarchy. Cell count (N) is the product V×D; therefore, it was not considered an independent variable. The median male C57BL/6J mouse brain contained a total of 76 ± 11 × 106 cells, in 380 mm3 of gray matter, at a density of 2.05 × 105 cells/mm3. A pie chart of the volume and cell count of the main regions (level 4 of region hierarchy) calculated across 507 brains appear in Figure 2B, and absolute cell counts for C57BL/6J male mouse representative regions are shown in Figure 2C. We quantified each level of the hierarchical tree structure of the AMBA and found good correlation (ρ = 0.86 and ρ = 0.98 for log and linear scale, respectively; interclass correlation coefficient 0.98–0.99) with a recent 3D whole-brain single-cell resolved light-sheet microscopy study (Murakami et al., 2018; Figure 2D). The diameter of detected objects (nuclei) varied between 7 and 9.5 μm (Figure 2E, left), which at a nucleus/soma volumetric ratio of 0.08 (Neumann and Nurse, 2007; Huber and Gerace, 2007) corresponds to median cell body diameters from 16.25 μm in the RSPv6a, to 22 μm in the ENTl3. The regional variability of cell densities was high, ranging from 1 × 105 mm–3 in layer 1 isocortex (e.g., Mos1) to 6 × 105 mm–3 in the dentate gyrus granule layer (DG-sg). We show examples of regional distributions across the full cohort of 507 brains in the inset of Figure 2E, right. Figure 2 with 3 supplements see all Download asset Open asset Survey of neuroanatomic properties of the mouse brain. (A) Segmentation of several sections of one particular brain; segmented nuclei are colored using the Allen Mouse Brain Atlas (AMBA) region convention. (B) Pie charts of the median volumes and cell counts across all 507 brains in the main brain regions, colored using AMBA nomenclature. (CTX: cerebral cortex; CNU: cerebral nuclei; MB: midbrain; IB: interbrain; HB: hindbrain; CBN: cerebellar nuclei; CBX: cerebellar cortex). (C) Median cell counts for selected brain regions in C57BL/6J males (number near bars in thousands; SEM is displayed per region yet values are very small). (D) Comparison of region cell counts between this study and Murakami et al., over C57BL/6J males; dots above/below the dashed lines represent regions with greater than twofold difference. The correlation coefficient in both linear and log scales is displayed. The intraclass correlation coefficient (ICC) values were 0.98–0.99 for six ICC forms. (E) Ranking of 532 regions by nucleus diameter (left) and density (right). Each dot corresponds to the median value of one region over 507 brains. Red dashed line, median across regions. Inset shows distributions of density over 507 brains for selected regions. (F) Distribution of cell density (left), brain volume (middle), and cell count (right), comparing C57BL/6J males and FVB.CD1 males across basic cell groups and regions (‘gray’). Step-like dashed lines represent histograms while full lines correspond to kernel estimations of the probability density function. Dispersion values correspond to standard deviations. Source data for panels (D, E) is provided in Figure 2—source data 1. Figure 2—source data 1 Comparison of cell count per region (male C57BL/6J) with Murakami et al., 2018; median cell diameter and cell density per region over all 507 brains in this study. https://cdn.elifesciences.org/articles/82376/elife-82376-fig2-data1-v3.xlsx Download elife-82376-fig2-data1-v3.xlsx The large number of AMBCA brains in our analysis enabled us to compare variabilities of macroscopic properties between subsets of the cohort, for example, to compare strains. We compared distributions of volume, cell density, and cell count at the coarsest hierarchical atlas level, that is, across gray matter cell groups in the brains of male C57BL/6J vs. male FVB.CD1 mice (Figure 2F). Median cell density was similar for the two strains, with considerably larger variance in FVB.CD1 males. FVB.CD1, however, had a 7.6% larger gray matter volume (GMV) than C57BL/6J. Combining these two features revealed an ~10% increase in the median cell count in FVB.CD1 vs. C57BL/6J (Figure 2F, right panel). These results suggest that (1) there is no simple relationship between volume and density, therefore, both properties should be measured simultaneously; and (2) a large cohort enables detection of relatively small differences. To expand on our technical comparison between quantification based on autofluorescence vs. staining (Figure 1—figure supplement 1), we performed nuclear staining (Hoechst 33342) and whole-brain STPT imaging on nine brains of female C57BL/6J mice. We trained a DNN of the same topology using tiles from the resulting Hoechst images, registered with the AMBA coordinates, and repeated our per-region estimates of volume, density, and cell count (see ‘Methods’). As expected, correlation between this in-house dataset and the AMBCA analysis was very high when comparing the volume across regions (ρ = 0.99, Figure 2—figure supplement 2A). In addition, cell count correlations were also high (ρ = 0.99, Figure 2—figure supplement 2B), but median cell count in Hoechst was 65% higher; recapitulating the trend toward false negatives observed in Figure 1—figure supplement 1 (epifluorescence microscopy). However, agreement in cell density varied by region. The correlations in density were fair across cortical regions of layers 2/3, 4, 5, and 6a (ρ = 0.78, Figure 2—figure supplement 2C1): they displayed comparable densities between the methods (Figure 2—figure supplement 2D1). The correlation value across cortical regions 1 and 6b was similar (ρ = 0.79), yet the error in absolute values was significantly higher (Figure 2—figure supplement 2C2), at about twofold higher density using Hoechst (Figure 2—figure supplement 2D1). The correlation across brain stem regions was also lower (ρ = 0.56, Figure 2—figure supplement 2C3), yet rank order across regions was similar to cortical regions 1 and 6b. Cerebellar regions, which are significantly denser, displayed a twofold density difference (ρ = 0.65, Figure 2—figure supplement 2C4). This detection bias may therefore stem from several underlying causes; (1) inaccurate registration of border regions (CTX L1 and 6b); (2) physical detection limits of the low-autofluorescent objects compared to stained objects; and (3) region or cell type-inherent differences in autofluorescence, resulting in lower detection of cells in glial-rich (e.g., CTX L1, brainstem), compared to neuron-rich (e.g., other cortex layers) regions. To test the power of our model, we explored the densities and nucleus diameter of cortical regions (Figure 2—figure supplement 3). First, we considered the HPF because imaging-based quantification of its denser regions (pyramidal layers of Ammon’s horn and the granule layer of the dentate gyrus) has been difficult (Attili et al., 2019) and was achieved only recently (Murakami et al., 2018; Seiriki et al., 2017). Analyzing 195 C57BL/6J male brains, we found that the pyramidal layer of CA1 was denser than that of CA3 and CA2, whereas nucleus size was larger in CA3. In the dentate gyrus, the granule layer had the highest density of all regions, with >6.5 × 105 cells/mm3, and nuclei were largest in the polymorph layer (Figure 2—figure supplement 3, upper panels). In the isocortex, we examined the extent to which the cortical layers across cortical divisions differed in density and size (Figure 2—figure supplement 3, lower panels). Layer 1 was consistently underpopulated, having a density of about 105 cells/mm3. The overall rank order from densest to sparsest was maintained, with layer 4 > layer 6a > layer 5 > layer 2/3 > layer 1, suggesting a similarity in cytoarchitecture between cortical regions. Layer 4 of the primary visual and somatosensory cortices had higher density than did the auditory and visceral cortices. Nucleus diameters showed less distinct distributions between layers, although layer 2/3 and layer 5 tended to have larger nuclei than did layers 4 and 6a. Density differences between left and right hemispheres Brain laterality has been discussed in the literature since Broca and Wernike found language dominance on the left side of the brain. Although evidence for brain laterality in mice is more scarce, Levy et al., 2019 have suggested functional and circuit differences in the auditory cortex. We performed a systematic comparison of the left and right hemispheres seeking differences in region-wise volume and/or cell density. In C57BL/6J (n = 369), we found 229 regions with lateral cell density bias of over 5%, and up to 30%. Both sides showed similar numbers of biased regions in density (left, 100; right, 129, Figure 3A). Neighboring regions in prefrontal cortex ACAv2/3, ACAd2/3, ORBl2/3, and PL/23 were up to 30% denser in the right hemisphere. In addition, a more posterior part of the HPF, the parasubiculum (PAR) also showed a consistent per-brain higher cell density in the right hemisphere (Figure 3B). In contrast, cortical areas GU2/3, RSPv6a, VISC2/3, and AUDv2/3 showed more than 20% higher density in the left hemisphere. Strikingly, we found consistent density bias in left hemisphere cortical regions, specifically in layers 2/3 (Figure 3A, upper inset). This bias was both most consistent and pronounced in a group of neighboring ventrolateral areas: visceral, gustatory, temporal association, and auditory (Figure 3C), where the latter may be consistent with the Levy et al. findings. For example, in Figure 3D we show higher density in the right hemisphere visceral cortex layer 2/3, while corresponding layers 4, 5, and 6a were not biased. Laterally biased regions were consistent across strains (e.g., C57BL/6J vs. FVB.CD1, Figure 3—figure supplement 1A and B). Figure 3 with 1 supplement see all Download asset Open asset Region-wise laterality of cell densities in C57BL/6J. (A) Laterality differences in density (all C57BL/6J brains n = 369) shown for all regions whose volume >0.05 mm3 (excluding layers 1 and 6b) resulting in 559 regions. Regions are sorted by their bias to the left. Red and blue dots show layer 2/3 and layer 5/6a, respectively. Upper inset shows the distribution of layer 2/3 regions and layer 5/6a in red and blue lines, respectively. (B) Scatter plot of the density of the left vs. right hemisphere for parasubiculum (PAR). Each dot corresponds to one brain. Vertical and horizontal cyan lines correspond to the left and right median values, respectively. The dashed dotted line is equi-density value and the dashed line corresponds to a 10% offset by the median of the left density. The inset shows an analogous scatter plot for volume, with no observed lateral bias. (C) Percentage of brains with tendency for left or right hemisphere density across cortical areas (blue, left > right; orange, right > left). (D) Scatter plots of the density of the left vs. right hemisphere for visceral area layers 2/3, 4, 5, and 6A. We note that volume laterality was less pronounced with some regions slightly larger in the right hemisphere (Figure 3—figure supplement 1C). Most regions did not show laterality in both density and volume (e.g., PAR and VISC2/3 in Figure 3B and D display density laterality, while no bias in volume is observed). Regions with volume/density sexual dimorphism in C57BL/6J mice To examine whether differences in overall brain volume or density (Figure 2F) are isotropic, we conducted a region-specific analysis of volume, density, and cell count. Differences between males and females in regional neuroanatomy have been extensively described, including dimorphic volume and cell count in the medial amygdala (MEA) (Morris et al., 2008; Morris et al., 2005) and in the bed nuclei of the stria terminalis (BST) (Garcia-Falgueras et al., 2005). We first compared C57BL/6J males (n = 152) with females (n = 140). Although at the global level (whole-brain gray matter), males and females had similar volume (380 ± 17 mm3 and 380 ± 14 mm3, for females and males, respectively), in density (1.97 ± 0.28 × 105 cells/mm3 and 1.97 ± 0.28 × 105 cells/mm3 for females and males, respectively) and total numbers of cells (75 ± 10 × 106 and 77 ± 9 × 106 for females and males, respectively) (Figure 4A), certain regions displayed sexual dimorphism in one or more property. We conducted rank-sum testing on each region that passed QC (see ‘Methods’) for sex differences, in volume, density, or count (Figure 4B). Most regions were consistent with the overall trend of equal volume, yet MEA and BST, but also the ventral cochlear nucleus (VCO), taenia tecta ventral (TTv), and the medial preoptic nucleus (MPN) were >5% larger in males. A handful of regions, for example, ventrolateral orbital area layer 2/3 (ORBvl2/3), displayed the opposite effect and had larger volume in females. ORBvl2/3 was the only region to also display significantly higher density and cell count in females. In contrast, many regions were 5–20% denser in males, which also resulted in higher cell counts. These regions include other parts of the amygdala (BMA, BLA, and the CEA), hypothalamic regions LPO and AHN, and cortical regions such as the primary auditory layer 2/3 (AUDv2/3). Figure 4—source data 1 specifies whether each region is significantly dimorphic in volume, density, or count. Figure 4 with 1 supplement see all Download asset Open asset Sexual dimorphism in C57BL/6J. (A) Distribution of volume (left), density (middle), and cell count (right) for the whole brain gray matter (‘gray’) in female (dark green), and male (light green). p-Values correspond to a rank-sum test. Step-like dashed lines represent histograms while full lines correspond to kernel estimations of the probability density function. Dispersion values correspond to standard deviations. (B) Volcano plots showing per-region statistical testing for male versus female difference in volume (left), density (middle), and cell count (right), each dot representing one region. Horizontal axis, median differences (%); vertical axis, q-values (FDR-corrected rank-sum p-values by BH procedure in -log10 scale). Red dots highlight regions with an effect size larger than 5% and q < 0.01. Source data for this panel is provided in Figure 4—source data 1. (C) Examples of regions that display sexually dimorphic volume and/or density. Distributions of volumes appear in the upper row, distributions of densities in the lower row. Figure 4—source data 1 Median values per region for cell count, volume and cell density for male and female C57BL/6J or FVB.CD1. https://cdn.elifesciences.org/articles/82376/elife-82376-fig4-data1-v3.xlsx Download elife-82376-fig4-data1-v3.xlsx Next, we looked beyond the rank-sum statistical test, governed by the median of the distribution, at examples of how distributions differ in volume and/or density. For example, ORBvl2/3 showed both larger volumes and slightly higher density in females (Figure 4C, left), resulting in significantly more cells in females (Figure 4A). The opposite was the case for BST, where males had larger volume yet similar density (Figure 4C, middle). As a third example, we showed the case of AUDv2/3, which displayed no difference in region volume, yet density in the male brains was higher (Figure 4C, right). In an independent AMBCA cohort of additional 663 males and 166 females, we could robustly replicate our findings regarding region-specific sexual dimorphism: volumes of MEA, BST, and ORBvl2/3 displayed sex differences of consistent effect size and variance (Figure 4—figure supplement 1A–E). In sum, the population-wide survey revealed a number of sexually dimorphic areas; Yet, to what extent were region volumes or densities predictors of the individual’s sex? To answer this question, we trained linear support vector machine (SVM) classifiers. In brief, we randomly selected 2/3 of the C57BL/6J brains (n = 184), where each brain was represented by a 532-dimensional vector of either volume or density across regions, and trained the SVM. We then tested its performance on the remaining 1/3 of brains (n = 97). This process was repeated 100 times and resulted in an average accuracy of 78 and 90% for density and volume, respectively. From this, we identified the regions that had the highest contribution to the separating hyperplane (Figure 4—figure supplement 1F and H) and trained new classifiers based on these top regions, adding one region at a time. Using volume data, MEA and BST alone performed classification to >78% accuracy. Adding the postpiriform transitional area (TR) and the rostral lateral septal nucleus (LSr), the classifier’s performance saturated to about 90% (Figure 4—figure supplement 1G). Figure 4—figure supplement 1J shows the SVM separating line based on four pairs of regions (e.g., BST and LSr) yielding an accuracy of 79–86%. In contrast, the density-based classifier showed only incremental improvements when adding almost any of the top 10 regions (Figure 4—figure supplement 1I). Together, these results suggest that although many brain regions show significant differences between males and females both in volume and density (Figure 4), the considerable overlap between the male and female population distributions in each of these regions hampers classification. Apart from MEA and BST, the classifier revealed TR, LSr, MPN, and ORBl2/3 as predictors for sex based on volume, while density-based classification was based on amygdala-related regions (COApl, BMAp) and frontal cortical regions (ILA and ORB). Strain differences in volume and density We next investigated the relation between recorded body weight and GMV. To this end, we added the cohort of outbred FVB.CD1 mice, a strain with 40–50% higher body weight than C57BL/6J. As expected, in both strains, males and females showed distinct distributions for body weight, and males were larger than females (Figure 5A). Within each strain, body weight did not correlate with gray volume. We next quantified sex and strain differences in brain volume and density, resolved to neuroanatomical regions. First, we compared strain differences in females with those in males, showing concordance/discordance patterns betwe" @default.
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W4378214891 title "Editor's evaluation: Sex, strain, and lateral differences in brain cytoarchitecture across a large mouse population" @default.
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