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Explaining Autism Spectrum Disorder via Placenta Research

Autism Spectrum Disorder (ASD) is one of the fastest-growing developmental disorders in the United States. About one in 68 children in the United States and one percent of the world population has been identified with ASD, according to a 2016 estimate from the Center for Disease Control. The lifelong cost of ASD in the United States is about $2.4 million for a person with an intellectual disability, or $1.4 million for a person without intellectual disability.

Professor Change giving a Presentation

Professor Chang giving a presentation during the 2017 Mathematical Biosciences Institute Emphasis Workshop: Women Advancing Mathematical Biology in Columbus, Ohio.

While the causes of ASD are not definitive and include both genetic and non-inherited factors and exposures, one thing we know for certain is that the brain is most responsive to treatments in the early years of life, making early intervention the key to help children diagnosed with ASD. However, most of the diagnosis of ASD are not made until the child is three years or older, the best opportunities for intervention have already been lost. Therefore, it is critical to develop reliable biomarkers in assessing prenatal and neonatal risk for ASD. My collaborators and I do this by studying the locus of maternal-fetal interactions – placenta.

When I tell people that I study placenta, their first reaction is typically, “is it really beneficial to eat the placenta?” I usually chuckle at a statement like that since I wouldn’t know the answer to that considering that I have never eaten one myself. When I tell people that I study mathematics and placenta, their reaction is always, “what does math have to do with placenta?” Well, the answer is, EVERYTHING! The shapes and the fetal surface of the placentas are full of interesting patterns, something that mathematicians are deeply drawn to.

Professor Change working with a student

Professor Chang explaining a programming problem on the computer.

What I do is to come up with mathematically sound measures to quantify the differences between placentas associated with high risks for ASD and those associated with normal risks for ASD, and make predictions about which new placentas are most likely associated with high risks for ASD based on those measures. The methodologies I use fall under the general area of machine learning.

For example, one of the things I found was that placental chorionic surface vascular networks associated with placentas of high-risk ASD pregnancies generally had fewer branch points and thicker and less tortuous arteries than their population-based counterparts. Imagine that one day, the doctors can simply take a picture of the placenta in the delivery room and know whether to recommend an ASD intervention treatment based on the looks of the surface vascular networks.

This research has been challenging, yet rewarding. It is challenging because acquiring workable and a large quantity of human placenta requires extensive human and monetary resources; it is rewarding because I know that what I do could contribute to the overall understanding of the causes of ASD, thereby improving the quality of life for many people in the world.

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