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Record W4411857787 · doi:10.1186/s13293-025-00728-1

A cross-species analysis of neuroanatomical covariance sex differences in humans and mice

2025· article· en· W4411857787 on OpenAlexafffund
Linh Pham, Elisa Guma, Jacob Ellegood, Jason P. Lerch, Armin Raznahan

Bibliographic record

VenueBiology of Sex Differences · 2025
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenOccupational Cancer Research CentreHolland Bloorview Kids Rehabilitation Hospital
FundersNational Institute of General Medical SciencesNational Institute of Mental HealthCanadian Institutes of Health ResearchMcDonnell Center for Systems NeuroscienceNational Institutes of HealthCompute CanadaWellcome Trust
KeywordsCovarianceAnalysis of covarianceBrain sizeBiologyNeuroimagingPsychologyStatisticsNeuroscienceMedicineMathematicsMagnetic resonance imaging

Abstract

fetched live from OpenAlex

BACKGROUND: Structural covariance within the brain is thought to reflect inter-regional sharing of developmental influences. This hypothesis has proved difficult to test but can be informatively probed by the study of sex differences. Here, we use neuroimaging in humans and mice to study sex-differences in anatomical covariance- asking (1) are there sex differences in structural covariance and (2) do regions that share the same developmental influences, as exhibited by shared sex differences in volume, also show shared sex differences in volume covariance. This study design illuminates both the biology of sex-differences and theoretical models for anatomical covariance- benefitting from tests of inter-species convergence. METHODS: Brain volume correlations for males and females across 255 regions in mice (n = 423) and 378 regions in humans (n = 436) were calculated using volumetric measures obtained from structural MRI. Mean correlations for each sex were compared within species to determine whether covariance sex differences exist. Specific covariances with strong sex differences in each species were identified via permutation tests for statistical significance. Brain maps of regional average structural covariance sex-bias were generated for mice and humans. Regional average structural covariance sex-bias and volumetric sex-bias were correlated to identify whether these features align in their direction of sex-bias. RESULTS: We find that volumetric structural covariance is stronger in adult females than males for both wild-type mice and healthy human subjects: 98% of comparisons with statistically significant covariance sex differences in mice are female-biased, while 76% of such comparisons are female-biased in humans (q < 0.05). Regional covariance and volumetric sex-biases have weak inverse relationships to each other in both species: volumetrically male-biased regions contain more female-biased covariations, while volumetrically female-biased regions have more male-biased covariations (mice: r = -0.185, p = 0.002; humans: r = -0.189, p = 0.001). CONCLUSIONS: Our results identify a tendency for females to show stronger neuroanatomical covariance across species. These structural covariance sex differences are also partially related to regional sex differences in volume for both species, suggesting that stronger structural covariance in females could be an evolutionarily conserved feature - partially shaped by the same developmental influences that mediate volumetric sex-biases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.307
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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