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How to Predict Autism Among High-Risk Infants

How to Predict Autism Among High-Risk Infants

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  • Autism is a neurodevelopmental spectrum disorder that begins early in childhood.
  • Scientists have found a neuroimaging technique called Functional connectivity magnetic resonance imaging (fcMRI) that could predict the risk of infants who would develop autism.
  • The computer-based technology can separate the neuroimaging results and predict the risk of autism.

A neuroimaging technique called Functional connectivity magnetic resonance imaging (fcMRI) may help to predict high-risk 6-month old infants who would develop autism spectrum disorders by age 2 years, finds a new study funded by two components of the National Institutes of Health (NIH).

The research study is published in the journal Science Translational Medicine.


Around 1 out of 68 children in the United States is affected with autism. Siblings of children who are diagnosed with autism are at a higher risk of developing the disorder.

Even though, early diagnosis can improve outcomes for children with autism. There is currently no method to diagnose the disease before the child could show any symptoms.

Diana Bianchi, M.D., NICHD Director, said, "Previous findings suggest that brain-related changes occur in autism before behavioral symptoms emerge."

"If future studies confirm these results, detecting brain differences may enable physicians to diagnose and treat autism earlier than they do today."

Focusing on the Brain's Functional Connectivity
The research study conducted by the NIH-funded University of North Carolina at Chapel Hill and Washington University School of Medicine in St. Louis aimed to focus on the brain's functional connectivity on how the regions of the brain could work together during different tasks and during rest.

The research team had scanned 59 high-risk 6-month old infants when they had slept naturally. These infants were termed high-risk because they have older siblings with autism.

By the age of 2, around 11 of the 59 infants were diagnosed with autism.

Computer-Based Technology
A computer-based technology called machine learning that could train itself for finding the differences can separate the neuroimaging results in two groups autism and non-autism to predict future diagnosis.

The analysis has been able to predict the future diagnosis of each infant by using the other 58 infants data to train computer program.

The method has been identified by 82% of the infants who will have autism and can be correctly identified by all of the infants who do not develop autism.

However, in another analysis, the research team has tested on how well the results could apply to other cases. The computer program has been able to predict diagnose for group of 10 infants which is at an accuracy rate of 93%

Joshua Gordon, M.D., Ph.D., NIMH Director, said, "Although the findings are early-stage, the study suggests that in the future, neuroimaging may be a useful tool to diagnose autism or help health care providers evaluate a child's risk of developing the disorder."

To conclude, the research team had found 974 functional connections in the brain of 6-months old that are associated with autism-related behaviors. The neuroimaging technique could accurately predict autism among high-risk infants but the technique has to be replicated in a larger group.

Autism Spectrum Disorders (ASD)
It is a neurological and developmental disorder that begins early in childhood. Autism is called a spectrum disorder because it can have a range of symptoms. Boys are more likely to be autistic when compared to girls. They may often have problems with social, emotional and communication skills.

  1. Emerson RW, Adams C, Nishino T, Hazlett HC, Wolff JJ, Zwaigenbaum L, Constantino JN, Shen MD, Swanson MR, Elison JT, Kandala S, Estes AM, Botteron KN, Collins L, Dager SR, Evans AC, Gerig G, Gu H, McKinstry RC, Paterson S, Schultz RT, Styner M, the IBIS Network, Schlaggar BL, Pruett, Jr. JR, and Piven J. Functional neuroimaging in high-risk 6-month-old infants predicts later autism. Science Translational Medicine, (2017); DOI: 10.1126/scitranslmed.aag2882
  2. Autism Spectrum Disorder (ASD) - ( https://www.cdc.gov/ncbddd/autism/facts.html)
  3. Facts about Autism - (https://www.autismspeaks.org/what-autism/facts-about-autism)

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