Study shows differences between brains of girls, boys with autism

Date:
February 17, 2022
Source:
Stanford Medicine
Summary:
Girls with autism differ in several brain centers compared with
boys with autism, suggesting gender-specific diagnostics are needed,
a new study using artificial intelligence finds.



FULL STORY ========================================================================== Brain organization differs between boys and girls with autism, according
to a new study from the Stanford University School of Medicine.


==========================================================================
The differences, identified by analyzing hundreds of brain scans with artificial intelligence techniques, were unique to autism and not found
in typically developing boys and girls. The research helps explain why
autism symptoms differ between the sexes and may pave the way for better diagnostics for girls, according to the scientists.

Autism is a developmental disorder with a spectrum of severity. Affected children have social and communication deficits, show restricted
interests and display repetitive behaviors. The original description
of autism, published in 1943 by Leo Kanner, MD, was biased toward male patients. The disorder is diagnosed in four times as many boys as girls,
and most autism research has focused on males.

"When a condition is described in a biased way, the diagnostic methods are biased," said the study's lead author, Kaustubh Supekar, PhD, a clinical assistant professor of psychiatry and behavioral sciences. "This study
suggests we need to think differently." The study was published online
Feb. 15 in The British Journal of Psychiatry.

"We detected significant differences between the brains of boys and
girls with autism, and obtained individualized predictions of clinical
symptoms in girls," said the study's senior author, Vinod Menon, PhD,
a professor of psychiatry and behavioral sciences and the Rachael L. and
Walter F. Nichols, MD, Professor.

"We know that camouflaging of symptoms is a major challenge in the
diagnosis of autism in girls, resulting in diagnostic and treatment
delays." Girls with autism generally have fewer overt repetitive
behaviors than boys, which may contribute to diagnostic delays, the
researchers said.



========================================================================== "Knowing that males and females don't present the same way, both
behaviorally and neurologically, is very compelling," said Lawrence Fung,
MD, PhD, assistant professor of psychiatry and behavioral sciences,
who was not an author of the study.

Fung treats people with autism at Stanford Children's Health, including
girls and women with delayed diagnoses. Many autism treatments work best
during the preschool years when the brain's motor and language centers
are developing, he noted.

"If the treatments can be done at the right time, it makes a big, big difference: For instance, children on the autismspectrum receiving early language intervention will have a better chance of developing language
like everyone else and won't have to keep playing catch-up as they grow
up," Fung said. "If a child cannot articulate themselves well, they fall
behind in many different areas. The consequences are really serious
if they are not getting diagnoses early." New statistical methods
unlock differences The study analyzed functional magnetic resonance
imaging brain scans from 773 children with autism -- 637 boys and 136
girls. Amassing enough data to include a sizeable number of girls in
the study was challenging, Supekar said, noting that the small number
of girls historically included in autism research has been a barrier to learning more about them. The research team relied on data collected at Stanford and on public databases containing brain scans from research
sites around the world.



==========================================================================
The preponderance of boys in the brain-scan databases also set up
a mathematical challenge: Standard statistical methods used to find
differences between groups require that the groups be roughly equal in
size. These methods, which underlie machine-learning techniques in which algorithms can be trained to find patterns in very large and complex
datasets, can't accommodate a real- world situation in which one group
is four times as large as the other.

"When I tried to identify differences [with traditional methods], the
algorithm would tell me every brain is a male with autism," Supekar
said. "It was over- learning and not distinguishing between males and
females with autism." Supekar discussed the problem with Tengyu Ma, PhD, assistant professor of computer science and of statistics at Stanford
and a co-author on the study. Ma had recently developed a method that
could reliably compare complex datasets, such as brain scans, from different-sized groups. The new technique provided the breakthrough the scientists needed.

"We happened to be lucky that this new statistical approach was developed
at Stanford," Supekar said.

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

The scientists also tested the algorithm on 976 brain scans from typically developing boys and girls. The algorithm could not distinguish among
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 did in several 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 the largest between sexes. Among girls with autism, the differences
in motor centers were linked to the severity of their motor symptoms,
meaning girls whose brain patterns were most similar to boys with autism
tended to have the most pronounced motor symptoms.

The researchers also identified language areas that differed between
boys and girls with autism, and noted that prior studies have identified greater language impairments in boys.

"When you see that there are differences in regions of the brain that
are related to clinical symptoms of autism, this seems more real,"
Supekar said.

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

"Our research advances use of artificial intelligence-based techniques
for precision psychiatry in autism," Menon said.

"We may need to have different tests for females compared with males. The artificial intelligence algorithms we developed may help to improve
diagnosis of autism in girls," Supekar said. At the treatment level, interventions for girls could be initiated earlier, he added.

The study's other Stanford Medicine co-authors are scientific data
analyst Carlo de los Angeles; senior research scientist Srikanth Ryali,
PhD; and graduate student Kaidi Cao. Co-authors include members of
Stanford's Maternal and Child Health Research Institute, Stanford Bio-X,
the Stanford Wu Tsai Neurosciences Institute and the Stanford Wu Tsai
Human Performance Alliance, and the Stanford Institute for Human-Centered Artificial Intelligence.

The research was supported by the National Institutes of Health
(grants AG072114, MH084164 and MH221069), the Brain & Behavior
Research Foundation, a Stanford Innovator Award and grants from the
Stanford Maternal and Child Health Research Institutes, including the Transdisciplinary Initiatives Program, the Taube Maternal and Child
Health Research Fund, and the Uytengsu-Hamilton 22q11 Neuropsychiatry
Research Program.

Supekar is a Taube Family Endowed Transdisciplinary Investigator for
Maternal and Child Health.

special promotion Explore the latest scientific research on sleep and
dreams in this free online course from New Scientist -- Sign_up_now_>>> ========================================================================== Story Source: Materials provided by Stanford_Medicine. Original written
by Erin Digitale.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Kaustubh Supekar, Carlo de los Angeles, Srikanth Ryali, Kaidi
Cao, Tengyu
Ma, Vinod Menon. Deep learning identifies robust gender differences
in functional brain organization and their dissociable links to
clinical symptoms in autism. The British Journal of Psychiatry,
2022; 1 DOI: 10.1192/bjp.2022.13 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220217181733.htm

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