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.
I don’t doubt that there are inherent differences between the brains of most men and women, but “we can measure these differences” and “these differences are inherent” are two different claims. I don’t really get what the article is trying to get at by first claiming the latter and then walking back to the former.
btw can someone post the full PDF I can’t access it via sci-hub yetEdit: Also a tangential nitpick, but looking at their code I can tell that they’re psychiatrists/neuroscientists first and programmers second lol
“CNN Block 1” comment used twice?
They skip layer 5? (Why even keep it in there??)
A linear layer with 2 outputs??? And then they do “
_, predicted = torch.max(outputs.data, 1)
” in the training script??? JUST USE 1 OUTPUT WITH A SIGMOID I’M BEGGING YOUAnd there’s a lot going on in the “utilityFunctions.py” file lol
I would guess clickbait
More like a proof of concept, since they didn’t significantly improve upon the accuracy of their predictions compared to prior models.