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AI Detects Gender-Specific Brain Traits Which Could Deepen Our Understanding of Psychitric Disorders

February 19, 2024 from GEN


Researchers from Stanford Medicine have presented a revolutionary artificial intelligence model that can identify, with over 90% accuracy, whether MRI brain scans belong to a man or a woman. This research was the subject of a pioneering study that was published in the Proceedings of the National Academy of Sciences (PNAS). The research, led by Professor of Psychiatry and Behavioural Sciences Vinod Menon, PhD, found that the long-debated issue of sex-based variations in the human brain was clarified by their findings.


Stressing the importance of comprehending sex-based differences in brain function, Dr. Menon said, “Sex plays a crucial role in human brain development, aging, and the manifestation of psychiatric and neurological disorders.” In terms of neuroscience research, the study, titled “Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization,” represents a major breakthrough.


Using a spatiotemporal deep neural network (stDNN) model, the researchers employed cutting-edge artificial intelligence techniques to interpret brain pictures obtained from resting-state functional magnetic resonance imaging (rsfMRI). This novel strategy outperformed other methodologies in enabling the identification of minor patterns suggestive of sex differences. According to Dr. Menon, the model “uncovered reliable sex differences with over 90% cross-validation classification accuracies, outperforming previous studies,”.


Beyond scholarly curiosity, the study’s consequences offer intriguing insights into neuropsychiatric diseases that present differently in men and women. Through identifying specific brain networks linked to sex differentiation, such as the striatum-limbic network and the default mode network (DMN), scientists hope to gain a better understanding of disorders like addiction, depression, and autism that have sex-specific prevalence rates and consequences.


In addition, the group used explainable AI (XAI) methods to clarify the model’s decision-making process and pinpoint the brain areas that are essential for differentiating between the brains of men and women. Interestingly, these regions align with regions linked to other mental diseases, pointing to a possible connection between vulnerability to specific neuropsychiatric problems and changes in brain chemistry based on sex.


The results of the study highlight the need of taking sex-based differences in brain structure into account while examining cognitive functions and behavioural characteristics. The creation of sex-specific models to forecast cognitive function emphasizes the behavioural significance of gender-specific functional brain features.


The researchers conclude that their methodology offers a strong framework for detecting, reproducing, and interpreting sex differences in the structure of the human brain across a variety of datasets and cohorts. Through recognition and exploration of these differences, scientists can progress tailored methods for cognitive neuroscience studies and therapeutic interventions.


Dr. Menon highlighted the AI models’ wide applicability, imagining its application in investigating brain variations linked to disparities in social functioning and a range of cognitive disorders. The team intends to openly share their model in an effort to spur additional progress in comprehending the complex interplay among brain connectivity, cognitive capacities, and behavioural outcomes.


Although the study does not conclusively link early developmental factors, hormonal influences, or social contexts to sex-based brain variations, it does set the stage for future focused research into the vulnerabilities associated with sex in neurological and psychiatric illnesses.





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