Network analysis yields insight into autism

Children with autism have multiple, redundant neural connections between neighboring sections of the brain, but fewer with more distant areas, which can help in understanding some classic behaviors in autism patients.

Researchers from Children's Hospital Boston used EEGs to track the brain's electrical cross-talk in four groups of children: 16 classic autism, 14 children whose autism is part of a genetic syndrome known as tuberous sclerosis complex (TSC), 46 healthy children with neither, and 29 children with TSC but not autism. Their work was published this week in BMC Medicine, and accompanied by a commentary.

The researchers used a network analysis similar to that used to study airlines or electrical grids, according to an announcement.

"We examined brain networks as a whole in terms of their capacity to transfer and process information," says Jurriaan Peters, MD, of the Department of Neurology at Boston Children's. "What we found may well change the way we look at the brains of autistic children."

The network that favors short-range connections fits the profile of classic autism, in which the child excels at specific, focused tasks such as memorizing streets, but struggles to put that information together with other data into higher-order concepts. While a lot of work is being done locally, there's less communication with the rest of the brain.

The network analysis also demonstrated a quality called "resilience" in children with autism, in which the brain finds multiple ways for information to get from point A to point B because of the redundant pathways. This may indicate a brain that is less able to focus on the stimuli that are most important.

The children with tuberous sclerosis complex had reduced connectivity overall, but only those who also had autism had the increased short-range connections.

New genomic technology called high-throughput sequencing has found potentially hundreds of genetic mutations associated with autism spectrum disorder. A better understanding of the many genetic roots of this disorder are expected to lead to more tailored treatments for patients.

Meanwhile, researchers at the University of Minnesota have found that using Microsoft's Kinect sensors can help in identifying behaviors among preschoolers that can lead to an autism diagnosis earlier.

What's more, researchers at the Georgia Institute of Technology have developed a system that includes special gaze-tracking glasses and facial-analysis software to help detect harmful "problem behaviors" among autism patients.

To learn more:
- find the research
- here's the commentary
- read the announcement

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