Researchers at Stanford University in California have unveiled an artificial intelligence model that assesses gender by analyzing brain activity, achieving about 90 percent accuracy. The work was reported in the Proceedings of the National Academy of Sciences, a respected scientific journal.
The findings support the idea that brain activity patterns differ between men and women in meaningful ways.
Past investigations have shown that gender can influence brain development across the lifespan, from early childhood through adolescence and into aging. There is also a notable gap in how neuropsychiatric disorders present and occur across genders. Women show higher susceptibility to depression, anxiety, and eating disorders, while men tend to have higher rates of autism, attention-deficit/hyperactivity disorder, and schizophrenia.
The study combined a comprehensive spatiotemporal deep neural network with an explainable AI framework to examine resting-state functional MRI data. The dataset included a large group of about one thousand young adults aged 20 to 35. The researchers pinpointed brain regions and networks that contribute to observed sex differences in how the brain is functionally organized.
Key regions linked to the brain’s default or passive state, such as the posterior cingulate cortex, the precuneus, and the ventromedial prefrontal cortex, stood out as significant differentiators between male and female brains.
Further analysis showed notable distinctions in the striatum and the limbic system, networks tied to reward processing, learning from reinforcement, and regulating emotions. These differences could influence gender-specific risks for mental and neurological conditions and may guide the design of more targeted prevention and intervention strategies.
Earlier work in this field has explored how various biological and environmental factors interact with gender to shape brain function over time. This new approach adds a high-resolution view of where and how sex differences manifest in neural activity, offering a reference point for clinicians and researchers pursuing personalized approaches to brain health.
Questions remain about how these findings translate to diverse populations, how they might be affected by age, and what ethical safeguards are needed when using brain data for classification tasks. The researchers emphasize careful interpretation and the importance of corroborating results across independent cohorts and imaging modalities.
Overall, the study advances the discussion on sex-specific brain organization and its implications for mental health, highlighting both the potential benefits of tailored interventions and the necessity of responsible, privacy-conscious research practices.