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.
Unlikely as it might be, maybe the 10% error rate is from gender queer people that haven’t realized/faced it yet.
There are a lot of potential explanations. In essence they built a model to categorize brain features into male and female, and then tested this against their label of male or female on each brain. So this could result from problems with the model predictions—or just as easily from their “correct” labeling of each brain as male or female.
So a big question is how did they define male and female? By genetics? By reproductive anatomy? By self reported identity? This information was not in the article. All of these things are very likely correlated with things happening in the brain, but probably not perfectly. It’s worth noting that many definitions of sex do not consider gender identity at all—if such a definition was used, then a trans-man might be labeled female in their data, whether they have reckoned with their identity or not.
I looked into this, the study analyzed three pre-existing fMRI datasets.
I wasn’t able to find any info on how these projects assessed sex/gender of participants.
Based on this, I’d assume they just used AGAB as that’s how medical professionals approach patients in their care.