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Analyzing Brain Activity May Help Detect Autism

By Associate News Editor
Reviewed by John M. Grohol, Psy.D. on April 19, 2013

brain activity and autismNeuroscientists have developed a method of analyzing brain activity that may help detect autism in children.

In a small study, researchers from Case Western Reserve University School of Medicine in Ohio and the University of Toronto used magnetoencephalography (MEG) to record and analyze patterns of brain activity. MEG measures magnetic fields generated by electrical currents in the neurons of the brain, the researchers explain.

They wanted to determine the brain’s functional connectivity, which they describe as the brain’s communication from one region to another.

According to Roberto Fernández Galán, Ph.D., an assistant professor of neurosciences at Case Western Reserve who led the research team, the method detected autism spectrum disorder (ASD) with 94 percent accuracy.

“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,” Galán said. “This discovery opens the door to quantitative tools that complement the existing diagnostic tools for autism based on behavioral tests.”

In a study of 19 children, including 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.

The researchers said they found significantly stronger connections between rear and frontal areas of the brain in the children with autism. They noted there was an asymmetrical flow of information to the frontal region, but not vice versa.

Insight into the directionality of the connections may help identify anatomical abnormalities in brains of children with autism, the researchers theorized. Most current measures of functional connectivity do not indicate the interactions’ directionality, they note.

“It is not just who is connected to whom, but rather who is driving whom,” Galán said.

The new method also enabled the researchers to measure background noise, described as “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. According to the researchers, this feature offered better discrimination between the two groups, providing an even stronger measure of criteria than functional connectivity alone, with 94 percent accuracy.

Case Western Reserve’s Office of Technology Transfer has filed a provisional patent application for the analysis’ algorithm, which investigates the brain’s activity at rest. Galán said he and his colleagues hope to collaborate with others in the autism field with an emphasis on translational and clinical research.

Findings from the latest research appear in the online journal PLOS ONE.

Source: Case Western Reserve University

Neuron image available from Shutterstock

 

APA Reference
Wood, J. (2013). Analyzing Brain Activity May Help Detect Autism. Psych Central. Retrieved on December 20, 2014, from http://psychcentral.com/news/2013/04/20/analyzing-brain-activity-may-help-detect-autism/53909.html