Artificial intelligence has the ability to distinguish between the brain patterns of boys and girls aged 9 to 10 years old based on their sex, and possibly their gender. However, there are doubts about the accuracy of these results.
The study conducted by Elvisha Dhamala and her colleagues at the Feinstein Institutes for Medical Research in New York analyzed MRI data from over 4700 children participating in the Adolescent Brain Cognitive Development project. The children, aged 9 to 10, were divided equally between sexes. Sex was determined by anatomy, genetics, and hormones at birth, while gender was assessed based on attitudes, feelings, and behaviors.
The researchers created scores based on questions asked to parents and children to determine gender identity. Different brain networks were associated with sex and gender, showing distinct patterns of functional connectivity. The visual cortex, areas controlling movement, and the limbic system were linked to sex, while gender was associated with networks involved in attention, emotional processing, and higher-order thinking.
An AI model trained on MRI data could predict a child’s sex based on brain connectivity patterns accurately, but gender prediction was less precise and based on parental reports rather than the children’s self-identification. Understanding these differences in brain activity patterns could provide insights into conditions like ADHD, which vary between boys and girls.
The implications of the study suggest that sex and gender should be considered separately in biomedical research to improve data collection, analysis, interpretation, and communication of results. However, some experts, like Ragini Verma from the University of Pennsylvania, question the study’s findings, suggesting that the large sample size may have influenced the results and that gender prediction accuracy is low.
Overall, the research highlights the need for further exploration of how brain activity varies between sexes and genders to enhance scientific understanding of neurological conditions and improve research methodologies. By recognizing the differences in brain patterns, scientists can advance knowledge in gender-specific health issues and refine approaches to studying the human brain.
