Aided by AI, Study Uncovers Hidden Sex Differences in Dynamic Brain Function

Posted on February 29th, 2024 by Dr. Monica M. Bertagnolli

We’re living in an especially promising time for biomedical discovery and advances in the delivery of data-driven health care for everyone. A key part of this is the tremendous progress made in applying artificial intelligence to study human health and ultimately improve clinical care in many important and sometimes surprising ways.¹ One new example of this comes from a fascinating study, supported in part by NIH, that uses AI approaches to reveal meaningful sex differences in the way the brain works.

As reported in the Proceedings of the National Academy of Sciences, researchers led by Vinod Menon at Stanford Medicine, Stanford, CA, have built an AI model that can—nine times out of ten—tell whether the brain in question belongs to a female or male based on scans of brain activity alone.² These findings not only help resolve long-term debates about whether reliable differences between sexes exist in the human brain, but they’re also a step toward improving our understanding of why some psychiatric and neurological disorders affect women and men differently.

The prevalence of certain psychiatric and neurological disorders in men and women can vary significantly, leading researchers to suspect that sex differences in brain function likely exist. For example, studies have found that females are more likely to experience depression, anxiety, and eating disorders, while autism, Attention-Deficit/Hyperactivity Disorder, and schizophrenia are seen more often in males. But earlier research to understand sex differences in the brain have focused mainly on anatomical and structural studies of brain regions and their connections. Much less is known about how those structural differences translate into differences in brain activity and function.

To help fill those gaps in the new study, Menon’s team took advantage of vast quantities of brain activity data from MRI scans from the NIH-supported Human Connectome Project. The data was captured from hundreds of healthy young adults with the goal of studying the brain and how it changes with growth, aging, and disease. To use this data to explore sex differences in brain function, the researchers developed what’s known as a deep neural network model in which a computer “learned” how to recognize patterns in brain activity data that could distinguish a male from a female brain.

This approach doesn’t rely on any preconceived notions about what features might be important. A computer is simply shown many examples of brain activity belonging to males and females and, over time, can begin to pick up on otherwise hidden differences that are useful for making such classifications accurately. One of the things that made this work different from earlier attempts was it relied on dynamic scans of brain activity, which capture the interplay among brain regions.

After analyzing about 1,500 brain scans, a computer could usually (although not always) tell whether a scan came from a male or female brain. The findings also showed the model worked reliably well in different datasets and in brain scans for people in different places in the U.S. and Europe. Overall, the findings confirm that reliable sex differences in brain activity do exist.

Where did the model find those differences? To get an idea, the researchers turned to an approach called explainable AI, which allowed them to dig deeper into the specific features and brain areas their model was using to pick up on sex differences. It turned out that one set of areas the model was relying on to distinguish between male and female brains is what’s known as the default mode network. This area is responsible for processing self-referential information and constructing a coherent sense of the self and activates especially when people let their minds wander.³ Other important areas included the striatum and limbic network, which are involved in learning and how we respond to rewards, respectively.

Many questions remain, including whether such differences arise primarily due to inherent biological differences between the sexes or what role societal circumstances play. But the researchers say that the discovery already shows that sex differences in brain organization and function may play important and overlooked roles in mental health and neuropsychiatric disorders. Their AI model can now also be applied to begin to explain other kinds of brain differences, including those that may affect learning or social behavior. It’s an exciting example of AI-driven progress and good news for understanding variations in human brain functions and their implications for our health.

References:

[1] Bertagnolli, MM. Advancing health through artificial intelligence/machine learning: The critical importance of multidisciplinary collaboration. PNAS Nexus. DOI: 10.1093/pnasnexus/pgad356 (2023).

[2] Ryali S, et al. Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization. Proc Natl Acad Sci. DOI: 10.1073/pnas.2310012121 (2024).

[3] Menon, V. 20 years of the default mode network: A review and synthesis. Neuron.DOI: 10.1016/j.neuron.2023.04.023 (2023).

NIH Support: National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute on Aging