Time: 2024-05-17
Artificial intelligence (AI) technology in the field of MRI analysis has unveiled intriguing differences in the organization of male and female brains at a cellular level. These findings shed light on the necessity for diversity in neurological and psychiatric research.Men and women exhibit distinct rates and symptoms of various brain disorders, fueling the importance of understanding how biological sex influences brain structure for enhanced diagnostic and treatment approaches. While previous studies have explored brain size and shape, the intricate cellular layout remains largely uncharted territory.
The study conducted by researchers at NYU Langone Health utilized machine learning algorithms to scrutinize a vast dataset of MRI scans from 471 men and 560 women. The AI programs successfully distinguished between male and female brains by identifying hidden structural patterns unnoticeable to the human eye. These findings, validated by multiple AI models, provide crucial insights into the complexity and organization of the human brain.
Lead author Yvonne Lui, MD, emphasized that the study offers enhanced understanding of brain microstructure, potentially unveiling novel insights into the development and manifestation of psychiatric and neurological conditions in men and women. By employing machine learning techniques, the study minimized biases associated with subjective region selection prevalent in previous research.
The research, published in Scientific Reports, demonstrated the efficacy of AI models in accurately determining the biological sex of brain scans. The innovative approach of training algorithms to differentiate sex without relying on brain size or shape signifies a significant advancement in neurological research.
Co-lead author Junbo Chen highlighted the critical need for diversity in brain disorder research to avoid overlooking valuable insights by using men as the standard model. The study underscores the importance of considering biological sex in the study of brain-related diseases.
While AI tools could identify differences in brain-cell organization, they could not attribute specific features to a particular sex. The classification was based on genetic information and encompassed cis-gendered individuals in the study.
Future investigations aim to delve into the evolution of sex-related brain structural variances to unravel the impact of environmental, hormonal, and social factors on these differences.
The study was supported by grants from the National Institutes of Health and the United States Department of Defense. Collaborators from NYU Langone Health and NYU Tandon School of Engineering contributed to the research.
For media inquiries, please contact Shira Polan at 212-404-4279 or [email protected].
SOURCE NYU Grossman School of Medicine and NYU Langone Health
Business
