This article describes a new study using AI to identify sex differences in the brain with over 90% accuracy.
Key findings:
- An AI model successfully distinguished between male and female brains based on scans, suggesting inherent sex-based brain variations.
- The model focused on specific brain networks like the default mode, striatum, and limbic networks, potentially linked to cognitive functions and behaviors.
- These findings could lead to personalized medicine approaches by considering sex differences in developing treatments for brain disorders.
Additional points:
- The study may help settle a long-standing debate about the existence of reliable sex differences in the brain.
- Previous research failed to find consistent brain indicators of sex.
- Researchers emphasize that the study doesn’t explain the cause of these differences.
- The research team plans to make the AI model publicly available for further research on brain-behavior connections.
Overall, the study highlights the potential of AI in uncovering previously undetectable brain differences with potential implications for personalized medicine.
Found myself a copy of the paper for a read-through and it’s immediately obvious to me why they couldn’t get above 90% accuracy.
The word “Gender” occurs exactly zero times in the text and the datasets they worked with were divided into a strict sex binary. As a result, the accuracy of their models’ predictions could not significantly improve upon prior work in the field.
The only new info here is that their XAN is able to point out the specific brain features that influenced its predictions. Potentially useful with regards to the development of treatments for gendered brain issues in neurotypical people, but anyone who falls outside of the 90th percentile of sexually dimorphic normativity won’t see any benefit here.
10% seems a bit more than was predicted, but would that account for those who don’t fit the peaks for the sexual dimorphism definitions, you think?
I think so. With a more diverse dataset and fewer binary assumptions baked into the analysis I think we’d start seeing the bimodal contours of a spectrum between the masculine and feminine peaks. The graphics included in the study seem to hint at this, showing nodes of similarity with a tapering tail toward the middle of the distribution for all three sets of data they analyzed: