Photo: flickr
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 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.
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."
Category(s):Autism spectrum disorders
Source material from Brown University
