A new method in using brain scans to distinguish between adults diagnosed with autism and people without it could lead the way to developing a new tool to diagnose the disorder, researchers say.

Diagnosing autism spectrum disorder (ASD) can be difficult, since there is no medical test, like a blood test, to diagnose it.  In children, doctors look at behavior and development to make a diagnosis and process can be even more challenging in adults. 

Scientists say they could improve the diagnosis and understanding of autism spectrum disorders if they had reliable means to identify specific abnormalities in the brain. Such "biomarkers" have proven elusive, often because methods that show promise with one group of patients fail when applied to another.

In a new study in Nature Communications, however, researchers from both Japan and Brown University in Providence, R.I., have come up with a biomarker which they say works with a comparably high degree of accuracy in assessing autism in adults.

They have developed a computer algorithm called a "classifier" because it can classify sets of subjects -- those with an autism spectrum disorder and those without -- based on functional magnetic resonance imaging (fMRI) brain scans.

By analyzing thousands of connections of brain network connectivity in scores of people with and without autism, the software found information that allowed it to tell, with about 80 percent accuracy, who had been traditionally diagnosed with autism and who had not.

While the accuracy range is not yet high enough – and they do not know how well the device will work in children – the researchers believe that with further refinements they may come up with not only a useful diagnostic tool, but one that can also be used to monitor the success of autism treatments.