Brains of men and women are organized differently, according to artificial intelligence

The degree to which a person's sex affects the organization and brain functioning, has been a source of controversy among scientists. A new study based on artificial intelligence has identified different organizational patterns in women and men.

A team led by Stanford University (USA) publishes in the journal PNAS a study based on a new artificial intelligence model that determined, with an accuracy of more than 90%, whether the brain activity scans came from a woman or of a man.

How did you discover that the brains of men and women are organized differently?

This finding helps resolve a long-standing controversy over whether reliable sex differences exist in the human brain and suggests that understanding these differences may be critical to addressing neuropsychiatric conditions that affect women and men differently, the university said.

Identifying consistent and replicable sex differences in the healthy adult brain is a critical step toward a deeper understanding of sex-specific vulnerabilities in psychiatric and neurological disorders, he said in a statement.

“A key motivation for this study is that sex plays a crucial role in human brain development, aging, and the manifestation of psychiatric and neurological disorders,” said Vinod Menon of Stanford University and lead author of the study. .

The work did not take into account whether sex-related differences arise early in life or whether they may be due to hormonal differences or the different social circumstances that men and women are more likely to face.

The “hot spots” that most helped the model distinguish male brains from female brains include the default mode network (a brain system that helps us process self-referential information), the striatum, and the limbic network, which are involved in learning and how we respond to rewards.

The team took advantage of advances in artificial intelligence and access to multiple large data sets to perform more powerful analysis than ever used before.

The first step was to create a deep neural network model that learns to classify brain imaging data: As the researchers showed scans to the model and told it that it was looking at a male or female brain, it began to “notice” what subtle patterns They could help you distinguish them.

This deep neural network model analyzes dynamic MRI images, thereby capturing the complex interaction between different brain regions.

When the researchers tested the model with about 1,500 brain scans, it could almost always tell whether the scan was from a woman or a man.

The success of the model suggests that there are detectable differences between the sexes in the brain, but that until now they have not been reliably detected, the statement says.

AI explains how a model's decisions are made

The team also turned to explainable AI, which examines large amounts of data to explain how a model's decisions are made.

Thanks to it, the team identified the brain networks which were more important for the model to judge whether a brain scan came from a man or a woman.

Thus, they discovered that the model most frequently searched the default mode network, the striatum, and the limbic network.

The team then developed sex-specific models of cognitive abilities to try to predict how participants would perform on certain tasks based on functional brain characteristics that differ between women and men.

One model effectively predicted cognitive performance in men but not in women, and another did the opposite. The findings indicate that functional brain characteristics that vary between sexes have important behavioral implications.

Menon highlighted that these models worked “very well” because they managed to separate the brain patterns between sexes, which leads us to think that “overlooking sex differences in brain organization could lead us to overlook key factors underlying neuropsychiatric disorders.”

Although the team applied their deep neural network model to questions related to sex differences, Menon said the model will be used to answer questions about the relationship between any aspect of brain connectivity and any type of cognitive ability or behavior.

(With information from EFE)

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