A team of researchers at Boston Children’s Hospital and Boston University have made s major breakthrough in the early diagnosis of autism spectrum disorder (ASD). Their new study suggests that simply measuring a baby’s brain activity through an electroencephalogram (EEG) from as early as three months of age could accurately predict the likelihood of the child developing ASD.
A growing body of research is beginning to suggest that early intervention and education for infants with ASD can be significantly beneficial in modulating the onset of symptoms as they grow older. But ASD is notoriously difficult to clearly diagnose, with symptoms generally not appearing until the age of two, and official diagnoses coming several years later.
From blood tests to eye-tracking, researchers are currently working hard to find ways to objectively detect the condition as early as possible. One of the most promising recent research avenues came in a study last year from the University of North Carolina, which revealed a potentially accurate way to use fMRI scans to predict ASD in babies as young as six months.
Despite the research still being at an early stage, fMRI scans are expensive, time-consuming and difficult to undertake on young babies, so not the ideal diagnostic tool to easily roll out widely. Now the Boston-based team has revealed that successful diagnostic results could be obtained from a simple EEG, something already often used in developmental pediatric settings.
“EEGs are low-cost, non-invasive and relatively easy to incorporate into well-baby checkups,” says Charles Nelson, a co-author on the new study.
As part of a long-running infant screening project, 188 infants had EEG measurements were taken at three, six, nine, 12, 18, 24 and 36 months of age. Just over half the group were considered at high-risk of developing ASD (due to having an older diagnosed sibling), while 89 acted as low-risk controls. Computer algorithms were then developed to deeply analyze the six different wave components of the EEG measurements and the results were incredibly promising.
“The results were stunning,” explains William Bosl, from the Computational Health Informatics Program (CHIP) at Boston Children’s. “Our predictive accuracy by nine months of age was nearly 100 percent. We were also able to predict ASD severity, as indicated by the ADOS Calibrated Severity Score, with quite high reliability, also by nine months of age.”
This kind of neural connectivity study is vitally important in the search for an early diagnosis measure for ASD. As the condition is incredibly complex, its onset is most likely due to an abstract combination of environmental conditions and genetics. This means that a simple biological biomarker found in blood, saliva or urine may not be an accurate or reliable measure of the condition at very early ages.
“We believe that infants who have an older sibling with autism may carry a genetic liability for developing autism,” says Nelson. “This increased risk, perhaps interacting with another genetic or environmental factor, leads some infants to develop autism – although clearly not all, since we know that four of five ‘infant sibs’ do not develop autism.”
The ability to track a baby’s neurodevelopment through simple EEG measures could offer physicians an accurate way to monitor the growing brain and allow for early interventions in cases of potential ASD diagnoses.