University of Missouri. US: Autism is a spectrum of closely related disorders diagnosed in patients
who exhibit a shared core of symptoms, including delays in learning to
communicate and interact socially. Early detection of autism in children
is the key for treatments to be most effective and produce the best
outcomes. Using advanced three-dimensional imaging and statistical
analysis techniques, researchers at the University of Missouri
have identified facial measurements in children with autism that may
lead to a screening tool for young children and provide clues to its
genetic causes.
“We want to detect the specific facial traits of the face of a child with autism,” said Ye Duan, associate professor of computer science in the College of Engineering
at MU. “Doing so might help us define the facial structures common to
children with autism and potentially enable early screening for the
disorder.”
Expanding upon previous studies using two-dimensional imaging, Duan,
working with Judith Miles, professor emerita of child health-genetics in
the MU Thompson Center for Autism and Neurodevelopmental Disorders, used a system of cameras to photograph and generate three-dimensional images of children’s faces.
The children selected were between 8 and 12 years old. One group of
children had been diagnosed with autism by the Thompson Center; the
other group consisted of typically developing children. Researchers
photographed the faces of children using three-dimensional imaging,
which allowed scientists to measure distances along the curvature of the
face rather than in a straight line as had been done in previous tests.
Duan then ran sophisticated statistical analyses to measure minute
differences in the facial measurements of each group.
“It all started from a clinical observation,” Miles said. “Over years
of treating children, I noticed that a portion of those diagnosed with
autism tend to look alike with similar facial characteristics. I thought
perhaps there was something more than coincidence at play. The
differences were not abnormal, rather they appeared analogous to
similarities observed among siblings. Using three-dimensional images and
statistical analysis, we created a ‘fine-tuned map’ of children’s faces
and compared those measurements to the various symptoms they exhibit.
By clustering the groups based on their facial measurements and
recording their autism symptoms, we wanted to determine whether
subgroups based on facial structure correlate with autism symptoms and
severity.”
The group’s analyses revealed three distinct subgroups of children
with autism who had similar measurement patterns in their facial
features. These subgroups also shared similarities in the type and
severity of their autism symptoms.
Miles said that next steps include inviting other research groups to
replicate our findings and to perform DNA analyses to look for specific
genes associated with each subgroup. Identifying genes associated with
each subtype of autism could potentially lead to the development of more
effective treatments and drug therapies, she said.
Duan and Miles worked with Tayo Obafemi-Ajayi, postdoctoral fellow in
the College of Engineering at MU, as well as Kristina Aldridge,
associate professor of pathology and anatomy, and T. Nicole Takahashi,
research core administrator at the Thompson Center. The team’s paper, “Facial structure analysis separates autism spectrum disorders into meaningful clinical subgroups,” was funded in part by the Department of Defense’s Medical Research and Development Program and was published in the Journal of Autism and Developmental Disorders.