A map of the brain connections that proved useful in distinguishing patients diagnosed with autism from people without an autism diagnosis.
Credit: Nature Communications
Brain scan method may help detect autism
- Date:
- April 14, 2016
- Source:
- Brown University
- Summary:
- Scientists report a new degree of success in using brain scans to distinguish between adults diagnosed with autism and people without the disorder, an advance that could lead to the development of a diagnostic tool.
Many doctors and scientists think 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, 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.
The technology, principally developed at the Advanced Telecommunications Research Institute International in Kyoto, Japan, with the major contributions from three co-authors at Brown University, is 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 team developed the classifier with 181 adult volunteers at three sites in Japan and then applied it in 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 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.
“These results indicate that although we developed a highly reliable classifier using the training data only in Japan, it is sufficiently universal to classify [autism] in the U.S.A. validation cohort,” wrote the team of clinicians and basic researchers led by Mitsuo Kawato of ATR.
ARTICLE CONTINUES AT SOURCE: https://www.sciencedaily.com/releases/2016/04/160414114255.htm
