Sex bias in medicine and computational neuroscience is a growing area of research that highlights the disparities in research, diagnosis, and treatment based on sex differences. Historically, medical studies have disproportionately focused on male participants, leading to gaps in understanding female-specific disease manifestations, drug responses, and neurological conditions. In computational neuroscience, models and algorithms often fail to account for sex differences in brain structure, function, and disease susceptibility. This bias limits the accuracy and applicability of computational tools in personalized medicine, neurodegenerative disorders, and psychiatric conditions. Addressing sex bias in these fields is crucial for developing equitable healthcare solutions and advancing precision medicine.

Despite advances in medicine and computational neuroscience, sex bias remains a critical issue, leading to disparities in diagnosis, treatment, and model accuracy. To address this, researchers must integrate sex as a biological variable in data collection, algorithm design, and clinical trials. By developing sex-sensitive computational models and improving representation in medical research, we can enhance precision medicine, ensure equitable healthcare, and reduce bias in neuroscience applications. Emerging machine-learning and deep-learning techniques are powerful tools to detect nuanced sex-based patterns in large-scale datasets, from genomic profiles to neuroimaging. Ensuring balanced datasets and tracking subgroup-specific outcomes can identify critical biomarkers and pathways underlying disease progression and therapeutic response across different sexes. Furthermore, addressing sex bias involves ethical, regulatory, and policy considerations. Clear reporting standards and the sharing of data disaggregated by sex can mitigate gaps and improve transparency.

This Research Topic addresses themes such as sex biases in brain function modeling, disease modeling and biomarker discovery, treatment responses, and algorithm prediction.

We invite contributions on methodological approaches for integrating sex as a biological variable, sex-aware AI models in neuroscience, and the impact of sex on neurological and psychiatric disorders. Additionally, while the primary focus of this issue is on sex bias, we welcome contributions that also explore intersections with other axes of diversity, such as age, ethnicity, and socioeconomic background. We encourage interdisciplinary work bridging medicine, neuroscience, and computational modeling to address sex bias and advance equitable, personalized healthcare solutions in neuroscience research and clinical applications.

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Sex Bias in Neuroscience: Insights from Computational Models to Clinical Data