These are the differences between the brains of girls and boys with autism




Research explains why autism symptoms differ between the sexes and may pave the way for better diagnoses for girls.

According to the study of
Stanford University School of Medicine (USA)
the differences, identified by analyzing hundreds of brain scans with artificial intelligence techniques, are unique to autism and were not found in typically developing boys and girls.

Autism is a developmental disorder. Affected children have social and communication deficits, show restricted interests, and exhibit repetitive behaviors. The original description of autism, published in 1943 by Leo Kanner, MD, was biased toward male patients. Currently, the disorder is diagnosed four times more often in boys than girls, and most autism research has focused on men.

“When a condition is described in a biased way, diagnostic methods are also biased,” says the study’s lead author, Kaustubh Supekar. “This study suggests we need to think differently.”

Most autism research has focused on men.

“We detected significant differences between the brains of boys and girls with autism and obtained individualized predictions of clinical symptoms in girls,” says researcher Vinod Menon. “We know that camouflaging symptoms is a major challenge in diagnosing autism in girls, leading to delays in diagnosis and treatment.”

Girls with autism generally have fewer overt repetitive behaviors than boys, which could contribute to delays in diagnosis, says the work published in The British Journal of Psychiatry.

The study looked at fMRI brain scans of 773 children with autism: 637 boys and 136 girls. Collecting enough data to include a sizable number of girls in the study was a challenge, Supekar acknowledges, noting that the small number of girls historically included in autism research has been a barrier to learning more about them.

The study was based on data collected at Stanford and on public databases containing brain scans from research sites around the world.

The preponderance of children in brain scan databases also posed a mathematical challenge: Standard statistical methods used to find differences between groups require groups to be roughly the same size. These methods, which underlie machine learning techniques in which algorithms can be trained to find patterns in very large and complex data sets, cannot be adapted to a real-world situation where a group is four times larger than a group. the other.

The algorithm couldn’t distinguish between them, confirming that the sex differences the scientists found were unique to autism.

“When we try to identify the differences [con los métodos tradicionales]the algorithm told us that each brain is a man with autism, “says Supekar.

The researchers used a method developed by researcher Tengyu Ma that could reliably compare complex data sets, such as brain scans, from groups of different sizes. The new technique provided the breakthrough scientists needed.

Using 678 brain scans of children with autism, the researchers developed an algorithm that could distinguish between boys and girls with 86% accuracy. When they checked the algorithm on the remaining 95 brain scans of children with autism, they maintained the same accuracy in distinguishing boys from girls.

The scientists also tested the algorithm on 976 brain scans of typically developing boys and girls. The algorithm was unable to distinguish between them, confirming that the sex differences the scientists found were unique to autism.

Among children with autism, girls had different patterns of connectivity than boys in various brain centers, including motor, language, and visuospatial attention systems. Differences in a group of motor areas, including the primary motor cortex, supplementary motor area, parietal and lateral occipital cortex, and middle and superior temporal gyri, were largest between the sexes. Among girls with autism, differences in motor centers were related to the severity of their motor symptoms, meaning that girls whose brain patterns were most similar to those of boys with autism tended to have the most pronounced motor symptoms.

The algorithm couldn’t distinguish between them, confirming that the sex differences the scientists found were unique to autism.

The researchers also identified areas of language that differ between boys and girls with autism, noting that previous studies have identified greater language problems in boys.

Taken together, the findings should be used to guide future efforts to improve girls’ diagnosis and treatment, the researchers said.

“We may need different tests for women compared to men. The artificial intelligence algorithms we developed can help improve the diagnosis of autism in girls”, Supekar explains. At the treatment level, interventions for girls could start earlier, she adds.

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