An analysis of microscopic movements is being used by researchers to diagnose autism spectrum disorder (ASD) and determine its severity in children and young adults.
The research is the work of Jorge V. José, Ph.D., of Indiana University, and Elizabeth Torres, Ph.D., of Rutgers University who presented the new technique at the 2013 Society for Neuroscience annual meeting.
Their work builds on earlier findings involving the random nature of movements of people with autism.
Earlier research looked at the speed maximum and randomness of movement during a computer exercise that involved tracking the motions of youths with ASD when touching an image on the screen to indicate a decision.
That research was reported in the Nature journal Frontiers of Neuroscience.
In the new study, the researchers looked at the entire movement involved in raising and extending a hand to touch a computer screen.
The device they use can record 240 frames per second, which allows them to measure speed changes in the millisecond range.
“We looked at the curve going up and the curve going down and studied the micromovements,” said José.
“When a person reaches for an object, the speed trajectory is not one smooth curve; it has some irregular random movements we call ‘jitter,’” he said. “We looked at the properties of those very small fluctuations and identified patterns.”
Those patterns or signatures also identify the degree of the severity of the person’s ASD, he said.
“Often in movement research, such fluctuations are considered a nuisance,” José said.
“People averaged them away over repeated movements, but we decided instead to analyze the movements on a smaller time scale and found they hold lots of information to help diagnose the continuum of autism spectrum disorder.
“Looking at the speed versus time curves of the motion in much more detail, we noticed that in general many smaller oscillations or fluctuations occur even when the hand is resting in the lap. We decided to carefully study that jitter.
“Our remarkable finding is that the fluctuations in this jitter are not just random fluctuations, but they do correspond to unique characteristics of the degree of autism each child has.”
The work was presented by Ph.D. graduate student Di Wu, who said the more detailed information allows subtyping of ASD and helps to identify typically developing individuals much better than previously.
The new refinement may help advance research in ASD to develop treatments tailored to the individual’s needs and capabilities.
Source: Indiana University