Emerging research suggests there may be a day when abnormalities in the brain associated with autism can be detected with brain scans.
Early detection of these specific brain abnormalities could lead to improved diagnosis and enhanced understanding of autism spectrum disorders.
Discovering the biomarkers associated with autism has been challenging, often because methods that show promise with one group of patients fail when applied to another.
In a new study, however, scientists report a new degree of success. Their proposed biomarker worked with a comparably high degree of accuracy in assessing two diverse sets of adults.
Scientists 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 16 key interregional functional connections that allowed it to tell, with high accuracy, who had been traditionally diagnosed with autism and who had not.
The technology was principally developed at the Advanced Telecommunications Research Institute International in Kyoto, Japan, with major contributions from three co-authors at Brown University in Rhode Island.
Researchers studied 181 adult volunteers at three sites in Japan and then applied the algorithm to a group of 88 American adults at seven sites. All the study volunteers with autism diagnoses had no intellectual disability.
“It is the first study to [successfully] apply a classifier to a totally different cohort,” said co-corresponding author Dr. Yuka Sasaki, a research associate professor of cognitive, linguistic and psychological sciences at Brown.
“There have been numerous attempts before. We finally overcame the problem.”
The classifier, which blends two machine-learning algorithms, worked well in each population, averaging 85 percent accuracy among the Japanese volunteers and 75 percent accuracy among the Americans.
The researchers calculated that the probability of seeing this degree of cross-population performance purely by chance was 1.4 in a million.
The researchers validated the efficacy of the classifier in another manner by comparing the classifier’s prediction of an autism diagnosis to the main diagnostic method currently available to clinicians, the Autism Diagnostic Observation Schedule (ADOS).
ADOS is based not on markers of biology or physiology, but instead on a doctor’s interviews and observations of behavior. The classifier was able to predict scores on the ADOS communications component with a statistically significant correlation of 0.44. The correlation suggests that the 16 connections identified by the classifier relate to attributes of importance in ADOS.
Researchers then discovered the connections were associated with a brain network responsible for brain functions such as acknowledging other people, face processing and emotional processing. This anatomical alignment is consistent with symptoms associated with autism spectrum disorders such social and emotional perceptions.
Finally, the team looked to see whether the classifier appropriately reflects the similarities and differences between autism spectrum disorders and other psychiatric conditions.
When applied to patients with each of these other disorders compared to similar people without the conditions, the classifier showed moderate but statistically significant accuracy in distinguishing schizophrenia patients, but not depression or ADHD patients.
The MRI scans required to gather the data were simple, Sasaki said. Subjects only needed to spend about 10 minutes in the machine and didn’t have to perform any special tasks. They just had to stay still and rest.
Despite that simplicity and even though the classifier performed unprecedentedly well as a matter of research, Sasaki said, it is not yet ready to be a clinical tool. While the future may bring that development, refinements will be necessary first.
“The accuracy level needs to be much higher,” Sasaki said. “Eighty percent accuracy may not be useful in the real world.”
It’s also not clear how it would work among children, as the volunteers in this study were all adults.
Although the classifier is not ready for current diagnostics, as accuracy improves the scans and analysis may not only be a physiology-based diagnostic tool, but also an approach to monitor the effectiveness of treatment.
Doctors perhaps will be able to use the tool someday to monitor whether therapies produce changes in brain connectivity, Sasaki said.
The research is published in the journal Nature Communications.