ArticleeLife2023
Sex, strain, and lateral differences in brain cytoarchitecture across a large mouse population.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Hemispheric asymmetries in rodent models of neurological diseases: a systematic review.Frontiers in integrative neuroscience · 2026Pooled it
- The lipidomic architecture of the mouse brain.Nature · 2026Article
- Neonatal estradiol influences regional gene expression for early sex-specific inflammatory responses in the 5xFAD mouse model.Biology of sex differences · 2026Article
- Article
- Gonadal and sex chromosomal contributions to sex differences in mammalian brain organization.bioRxiv : the preprint server for biology · 2026Article
- Looking at Both Sides: Integrating Data From Both Hemispheres Is Crucial in Rodent Neuroscience.The European journal of neuroscience · 2025Review
- The Balance in the Head: How Developmental Factors Explain Relationships Between Brain Asymmetries and Mental Diseases.Brain sciences · 2025Review
- The universe is asymmetric, the mouse brain too.Molecular psychiatry · 2025Article
- Article
- Cell density quantification of high resolution Nissl images of the juvenile rat brain.Frontiers in neuroanatomy · 2024Article
- Multi-Omics Data Integration Reveals Sex-Dependent Hippocampal Programming by Maternal High-Fat Diet during Lactation in Adult Mouse Offspring.Nutrients · 2023Article
- Sexual dimorphism in synaptic inputs to the mouse amygdala and orbital cortex.Frontiers in neuroscience · 2023Article
- Regional sex differences in neurochemical profiles of healthy mice measured by magnetic resonance spectroscopy at 9.4 tesla.Frontiers in neuroscience · 2023Article
- Experimental quality control induces changes in Allen mouse brain connectomes.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.