Autistic Infants May Be Diagnosed Early Through Neuroimaging

Autistic Infants May Be Diagnosed Early Through Neuroimaging A medical technique known as functional connectivity magnetic resonance imaging (fcMRI) has been discovered to predict which among high-risk infants are most likely to develop autism spectrum disorder (ASD) by the age of two. This medical breakthrough, funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the National Institute of Mental Health (NIMH), was recently published in the Science Translational Medicine.

In the United States alone, autism is said to affect 1 out of 68 children. Siblings of those who have been diagnosed with the disorder are at high risk of developing it. Early diagnosis and intervention can help reduce the impact of autism on children, but currently there’s no means to diagnose the disorder before a child starts to exhibit the symptoms.

According to the NICHD director, Diana Bianchi, previous findings established that brain-related symptoms occur before the behavioral symptoms with autism. If this is confirmed in future studies, it will be possible for physicians to diagnose and treat autism earlier than they are able to today through detecting brain differences.

In a recent study conducted to determine the human brain’s functional connectivity or the aspect that deals with how the brain works as a whole when rendering different tasks and when it is at rest. The study was conducted on 59 high-risk infants whose siblings had been diagnosed with autism. The study revealed that at age 2, 11 out of the 59 infants were diagnosed with autism.

A computer-based technology known as machine learning was used by the researchers. This machine enabled them to look at the differences that helped them separate the neuroimaging results into groups: autism and non-autism, which is helpful in predicting future diagnosis. Each analysis of an infant’s future diagnosis used the data of the 58 other infants. This was implemented to train the computer program. The said method identified 82 percent of the infants who would develop autism and it also identified all the infants who did not develop the disorder. There was a different analysis that tested the accuracy of the results that could be applied to other cases. In this case, machine learning predicted accurately the diagnoses of groups of ten infants with an accuracy rate of 93 percent.

According to Joshua Gordon, the NIMH director, the findings may still be in the early stage, but it clearly suggests that in the future neuroimaging may be considered as useful for autism diagnosis and for health care givers to easily evaluate a child’s risk for developing the disorder.

The research team found 974 functional connections inside the brains of six-month-old infants that were associated with behaviors related to autism. It is proposed by the researchers that a single neuroimaging scan may be able to predict autism among infants who are at high risk of it. However, they cautioned that the findings have to be replicated in a bigger group.

For the benefit of those who are not familiar with neuroimaging in recent years, there are two types of neuroimaging techniques that are used today. One is the structural brain imaging which is capable of providing static images of the brain at rest. This technique allows for localizing lesions and damage in the brain, measurement and comparison of various brain regions, and also for monitoring over time some areas of the brain that have been impacted by damage. Examples of this are CT scans and MRI. The second type of neuroimaging is functional neuroimaging which measures the activity of the brain while performing a task. It allows the researchers to determine the areas of the brain that are activated during this period. Examples of this type of neuroimaging technique are positron emission tomography (PET) scans, magnetoencephalography (MEG), electroencephalograms (EEG), and others.


    Ava Wadaby

    Ava Wadaby researches and writes about autism as she works to understand the challenges of her son who was diagnosed with Autism and ADHD. She also regularly conducts activities with children in her neighborhood, focusing on their learning and development.