Kuceyeski et al. (bioRxiv preprint, Feb 2026 — not peer reviewed) report that sex differences in brain connectivity are minimal in childhood but increase "drastically" at puberty and continue diverging through adulthood. They scanned 1,286 people aged 8–100 and used an AI tool (Krakencoder) to identify sex-linked connectivity patterns. Nature covered it enthusiastically. The methodology does not support the claims.

Cross-sectional data cannot show developmental trajectories

This study did not follow anyone over time. It took snapshots of different people at different ages and connected the dots. This design fundamentally confounds individual development with cohort effects and survivorship bias. The 70-year-olds in this study grew up in the 1950s with radically different nutrition, education, physical activity, environmental exposures, and gender socialization than the 10-year-olds measured in the 2020s. Older participants represent survival-enriched cohorts with systematically different health profiles.

Describing these cross-sectional age comparisons as showing how sex differences "evolve over the lifespan" is misleading. Only longitudinal studies following the same individuals through puberty and into adulthood can make such claims. This is a basic methodological distinction.

The brain mosaic problem

Joel et al. (2015) demonstrated that individual brains are heterogeneous mosaics of features — not internally consistent "male" or "female" types. This finding has been more robustly supported than strict dimorphism models. More critically, many reported sex differences in brain connectivity are eliminated when total brain volume is controlled for, suggesting allometric scaling — not sex itself — drives much of the variance. Men have larger brains on average; larger brains have different connectivity patterns regardless of sex.

The actual percentage of variance in brain connectivity explained by sex, compared to age, brain volume, socioeconomic status, and education, is typically small. The study does not report this comparison, which would contextualize whether sex is a major or minor contributor relative to other factors.

The neuroplasticity confound is not a footnote — it is a validity crisis

Brain connectivity is shaped by experience through neuroplasticity. Men and women have systematically different life trajectories: physical activity patterns, occupational exposures, caregiving responsibilities, stress profiles, social roles, and daily cognitive demands. These experiential differences accumulate over decades.

The study has no data on participants' gender identity, occupation, physical activity, or daily experience. It used sex assigned at birth. Without controlling for the neural signatures of gendered socialization, the study cannot distinguish biological sex effects from experience-driven connectivity differences. Finding that connectivity diverges increasingly after puberty — exactly when gender socialization intensifies — is equally consistent with socialization as with hormonal programming.

Observed connectivity differences in youth may also reflect different rates of brain maturation between sexes (developmental tempo) rather than fixed dimorphic endpoints — a confound the cross-sectional design cannot resolve.

Krakencoder: unvalidated AI on small samples

AI classifiers can achieve high classification accuracy by aggregating many small, potentially noise-driven effects. With ~1,286 subjects, complex pattern recognition, and no apparent pre-registration, overfitting risk is substantial. High accuracy at classifying sex from brain scans does not prove biological dimorphism — the classifier may be learning sample-specific noise, head-size correlates, or socialization signatures. Without independent validation on external datasets, the claims are premature.

The depression extrapolation is reverse inference

The suggestion that stronger default mode network connectivity in women explains their higher depression rates is a textbook reverse inference error. Does DMN hyperconnectivity cause depression, or does depression/rumination produce hyperconnectivity? Without prospective longitudinal studies showing connectivity changes precede symptom onset, or intervention studies demonstrating causal manipulation (e.g., TMS targeting DMN reduces depression), this is correlation masquerading as mechanism.

Bottom line

The underlying question — how brain connectivity relates to sex and development — is legitimate and important. But a cross-sectional, non-peer-reviewed study with no experiential controls, an unvalidated AI tool, no brain-volume correction, and causal extrapolations to mental health does not answer it. The study describes age-stratified group averages in a convenience sample. Everything beyond that — developmental trajectories, hormonal causation, mental health mechanisms — is interpretive overreach.

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