Stanford University Medical Center used AI to detect structural differences in female and male brains.
The study achieved over 90% accuracy in identifying the sex of people based on brain activity scans, shedding light on the long-debated topic of sex-specific structural differences within the brain.
The research, published within the Proceedings of the National Academy of Sciences (PNAS), states these differences will enhance our understanding and treatment of neuropsychiatric conditions that manifest distinctly in men and women.
Dr. Vinod Menon, director of the Stanford Cognitive and Systems Neuroscience Laboratory, explained the importance of recognizing sex differences within the brain.
“Sex plays a vital role in human brain development, in aging, and within the manifestation of psychiatric and neurological disorders,” Menon stated, highlighting the study’s purpose to advance our understanding of sex-specific mental and neurological vulnerabilities.
For example, we all know that ladies are nearly twice as likely as men to be diagnosed with depression, whereas men usually tend to be diagnosed with ADHD.
Other mental illnesses like personality disorders, bipolar, and schizophrenia also manifest otherwise in women and men.
Accurately identifying and classifying differences between female and male brain anatomy is vital to understanding whether there may be a biological explanation.
Here are six steps to explain how the study worked:
- The study investigated sex-related differences in brain function, which is crucial for understanding behavioral impacts and mental health conditions, by analyzing functional MRI (fMRI) data from about 1,500 young adults.
- Advanced AI, specifically a spatiotemporal deep neural network (stDNN), was employed to scrutinize brain scans, revealing distinct patterns in how female and male brains are organized.
- This AI model, over 90%, demonstrated impressive accuracy in distinguishing male from female brains based on functional dynamics, highlighting its effectiveness across multiple sessions and independent cohorts.
- Key brain networks—resembling the default mode network, striatum, and limbic system—showed significant sex differences, with effect sizes greater than 1.5, indicating robust distinctions in brain organization.
- The study’s use of explainable AI (XAI) techniques allowed for the identification of specific brain features liable for these differences, and these features could predict cognitive profiles specific to every sex.
- These findings challenge previous beliefs a few continuous spectrum of male-female brain organization, emphasizing sex as a fundamental consider brain structure and performance, with implications for personalized medical approaches in treating mental and neurological disorders.
The researchers took their investigation further by asking whether or not they could predict individuals’ performance on cognitive tasks based on the sex-specific brain features they’d identified.
To do that, they created two specialized AI models: one tailored to predict cognitive abilities in men and one other for ladies. These models were informed by the distinct brain patterns related to sex that the team had previously uncovered.
The success of those models was notable. The model designed for men accurately predicted their cognitive performance, nevertheless it didn’t work for ladies, and vice versa. This strongly suggests that the functional differences in brain organization between sexes have real-world impacts on behavior and cognitive abilities.
Menon explained the importance of those findings: “These models worked very well because we successfully separated brain patterns between sexes,” he explained.
This separation led to a deeper understanding of how overlooking sex differences in brain organization could lead to missing crucial elements that contribute to neuropsychiatric disorders.
Menon also highlighted the broader potential of their AI model. Beyond exploring sex differences, the model will be applied to numerous questions on brain connectivity and its relation to cognitive functions or behaviors.
AI’s role in neuroscience is well-established. A recent study used machine learning to ‘retrieve’ images from MRI scans, and one other used brain cells to perform speech recognition tasks.
AI has also been used to research speech patterns in individuals with schizophrenia and develop novel 3D therapy avatars.
In the longer term, it is going to probably be possible to accurately ‘read someone’s mind’ in real-time by applying ML models to neurological data.
The Stanford team intends to make their model accessible to the research community to encourage further research into mental illnesses and learning disabilities.
Menon’s vision is for these AI tools to know and address the challenges individuals face on account of these brain differences.
Sophisticated brain imaging models could eventually assist a brand new era of precision psychiatry. As Menon summarizes, “Our AI models have very broad applicability.”
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