Washington: Neuroscientists have come out with an efficient and reliable method of analyzing brain activity to detect autism in children.
The researchers from Case Western Reserve University School of Medicine and the University of Toronto recorded and analyzed dynamic patterns of brain activity with magnetoencephalography (MEG) to determine the brain`s functional connectivity - that is, its communication from one region to another. MEG measures magnetic fields generated by electrical currents in neurons of the brain.
Roberto Fernandez Galan, PhD, an assistant professor of neurosciences at Case Western Reserve and an electrophysiologist seasoned in theoretical physics led the research team that detected autism spectrum disorder (ASD) with 94 percent accuracy.
The new analytic method offers an efficient, quantitative way of confirming a clinical diagnosis of autism.
"We asked the question, `Can you distinguish an autistic brain from a non-autistic brain simply by looking at the patterns of neural activity?` and indeed, you can," Galan said.
"This discovery opens the door to quantitative tools that complement the existing diagnostic tools for autism based on behavioral tests," he added.
In a study of 19 children-nine with ASD-141 sensors tracked the activity of each child`s cortex. The sensors recorded how different regions interacted with each other while at rest, and compared the brain`s interactions of the control group to those with ASD.
Researchers found significantly stronger connections between rear and frontal areas of the brain in the ASD group; there was an asymmetrical flow of information to the frontal region, but not vice versa.
The new insight into the directionality of the connections may help identify anatomical abnormalities in ASD brains. Most current measures of functional connectivity do not indicate the interactions` directionality.
Their approach also allows them to measure background noise, or the spontaneous input driving the brain`s activity while at rest. A spatial map of these inputs demonstrated there was more complexity and structure in the control group than the ASD group, which had less variety and intricacy. This feature offered better discrimination between the two groups, providing an even stronger measure of criteria than functional connectivity alone, with 94 percent accuracy.
Their findings appeared in the online journal PLOS ONE.