Bias in Neuroscience: Insights from Computational Models to Clinical Data

Bias in medicine and computational neuroscience is a growing area of research that highlights disparities in research, diagnosis, and treatment based on sex, race, ethnicity, age, and socioeconomic status. Historically, medical studies have disproportionately focused on narrow, often male and Western-centric populations, leading to gaps in understanding disease manifestations, drug responses, and neurological conditions across underrepresented groups. In computational neuroscience, models and algorithms often fail to account for these differences in brain structure, function, and disease susceptibility, and this bias limits the accuracy and applicability of computational tools in personalized medicine, neurodegenerative disorders, and psychiatric conditions. Addressing bias across these dimensions is crucial for developing equitable healthcare solutions and advancing precision medicine.Despite advances in medicine and computational neuroscience, bias remains a critical issue, leading to disparities in diagnosis, treatment, and model accuracy. To address this, researchers must integrate sex, race, age, and other relevant demographic variables into data collection, algorithm design, and clinical trials. By developing bias-aware 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, demographically-driven 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 populations. Furthermore, addressing bias involves ethical, regulatory, and policy considerations. Clear reporting standards and the sharing of data disaggregated by demographic variables can mitigate gaps and improve transparency.This Research Topic addresses themes such as bias in brain function modeling, disease modeling and biomarker discovery, treatment responses, and algorithm prediction, across sex, race, ethnicity, age, and socioeconomic dimensions. We particularly welcome work on demographic differences in neuropathic pain, from divergent underlying mechanisms to differential treatment response, and on the use of digital twins to simulate and predict subgroup-specific disease trajectories and treatment outcomes. We’re also keen to see contributions on lifespan and hormonal variation, including how age and hormonal changes shape brain structure, function, and disease risk differently across populations; on the construction of demographically-disaggregated datasets, benchmarks, and algorithmic fairness metrics for computational neuroscience; and on meta-research examining how sex, race, and other demographic variables are (or aren’t) accounted for across the broader neuroscience research ecosystem, from study design to reporting standards.Within this collection, we invite contributions on methodological approaches for integrating sex, race, age, and other relevant variables as biological and social determinants, bias-aware AI models in neuroscience, and the impact of these factors on neurological and psychiatric disorders. We welcome work focused on a single axis of bias, such as sex or race, as well as work exploring their intersections, since disparities rarely operate along one dimension alone. We encourage interdisciplinary work bridging medicine, neuroscience, and computational modeling to address bias and advance equitable, personalized healthcare solutions in neuroscience research and clinical applications.Don't miss the chance to make your manuscript contribution and showcase your research alongside the work of other outstanding colleagues. Register your interest in the link below so our Topic Editors can make sure to extend deadlines for your submission if needed: Participate in this topic Topic editors: Rosanna Turrisi, Natasha Clarke, Gennaro Tartarisco, Ana Luísa Pinho, Jenny Tigerholm Source: https://www.frontiersin.org/research-topics/71289/bias-in-neuroscience-insights-from-computational-models-to-clinical-data