ArticleScientific reports2023
Accurate sex prediction of cisgender and transgender individuals without brain size bias.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 23 citations in OpenAlex.
- Brain anatomy in major hormonal transition phases: Longitudinal and cross-sectional volume associations with menarche and menopause.bioRxiv : the preprint server for biology · 2026Article
- Can we predict sleep health based on brain features? A large-scale machine learning study using the UK Biobank.Brain communications · 2026Article
- Inter- and intra-individual differences in brain sex map to neuroendocrine profiles.bioRxiv : the preprint server for biology · 2025Article
- Longitudinal development of sex differences in the limbic system is associated with age, puberty and mental health.Communications biology · 2025Article
- Representative scale-invariant characteristics of male and female brains in magnetic resonance images.Neuroimage. Reports · 2025Article
- "Precision Medicine" and the Failed Search for Binary Brain Sex Differences to Address Gender Behavioral Health Disparities.American journal of human biology : the official journal of the Human Biology Council · 2025Review
- Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders.Biology of sex differences · 2024Article
- Sex classification from functional brain connectivity: Generalization to multiple datasets.Human brain mapping · 2024Article
- A brief historic overview of sexual and gender diversity in neuroscience: past, present, and future.Frontiers in human neuroscience · 2024Article
- Sex differences in brain MRI using deep learning toward fairer healthcare outcomes.Frontiers in computational neuroscience · 2024Article
- Sex and Gender in Population Neuroscience.Current topics in behavioral neurosciences · 2024Review
- Univariate and multivariate sex differences and similarities in gray matter volume within essential language-processing areas.Biology of sex differences · 2023Article
- Sex differences in intrinsic functional cortical organization reflect differences in network topology rather than cortical morphometry.bioRxiv : the preprint server for biology · 2023Article
- Classifying sex with volume-matched brain MRI.Neuroimage. Reports · 2023Article
- Confound-leakage: confound removal in machine learning leads to leakage.GigaScience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
The increasing use of machine learning approaches on neuroimaging data comes with the important concern of confounding variables which might lead to biased predictions and in turn spurious conclusions about the relationship between the features and the target. A prominent example is the brain size difference between women and men. This difference in total intracranial volume (TIV) can cause bias when employing machine learning approaches for the investigation of sex differences in brain morphology. A TIV-biased model will not capture qualitative sex differences in brain organization but rather learn to classify an individual's sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. In this study, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for TIV either through featurewise confound removal or by matching the training samples for TIV. Our results provide strong evidence that models not biased by TIV can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modeling to avoid bias in automated decision making.
Indexed as
Identifiers
What OpenQuestion holds
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.