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Artificial intelligence (AI) appears capable of identifying the brain patterns associated with the sex of children aged 9 to 10. However, it still struggles to accurately predict their gender identities. 

Elvisha Dhamala and her team at the Feinstein Institutes for Medical Research in New York analyzed thousands of MRI scans from over 4,700 children participating in the Adolescent Brain Cognitive Development project. In their study, they used AI to explore the neurological differences between sexes and genders in children, a field with limited existing knowledge.

For their research published in Science Advances, sex was defined biologically, encompassing anatomy, genetics, hormones, and physiology, while gender was assessed based on attitudes, feelings, and behaviors. The team used detailed questionnaires answered by both parents and children to determine gender identity. The questions included how often children imitated characters of different sexes from media and their personal feelings about their gender identity.

The AI model developed by the researchers analyzed the MRI data, revealing that brain connectivity patterns could distinguish between sexes. Specifically, connectivity differences were observed in the visual cortex, motor control areas, and the limbic system, which regulates emotions, behavior, and memory. These patterns were consistent and distinct for boys and girls.

Gender-related brain connectivity was more complex, involving networks spread across the cerebral cortex, responsible for higher-order thinking, memory, movement, and sensation. The researchers noted that gender patterns varied significantly, with differences in attention, emotional processing, and motor control in assigned females and additional higher-order thinking and visual processing in assigned males.

Despite the findings, the AI model's ability to predict gender based on brain patterns was less accurate. It could only reliably predict gender using parent-reported data, not the children's self-reported data. This discrepancy pointed to the nuanced and multifaceted nature of gender identity, which may not be as easily discerned through brain connectivity alone.

The study's results suggest that neurological research must consider sex and gender as distinct variables. This approach could provide deeper insights into conditions that exhibit sex-based prevalence differences, such as ADHD, and improve the accuracy and relevance of biomedical research, according to New Scientist.

Dhamala and her colleagues believe that the findings show the importance of separating sex and gender in research methodologies. By refining data collection, analysis, and interpretation practices, scientists can enhance their understanding of the human brain and its complex interplay with identity factors.