Autistic children may be generating more brain activity

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The brains of autistic children generate more information at rest – a 42% increase on average – over normally developing children. The study offers an explanation for the withdrawal that many autistic children have, a symptom considered a hallmark of the condition. The excess amount of information may explain a child’s detachment from his environment. This new study is a follow up to one which found that brain connections in autistic children are different. This study shows the increased complexity is what makes the brain operate differently.

Generating more information when resting

“Our results suggest that autistic children are not interested in social interactions because their brains generate more information at rest, which we interpret as more introspection in line with early descriptions of the disorder,” explained Roberto Fernandez Galan, PhD, senior author and associate professor of neurosciences at Case Western Reserve School of Medicine. This may explain their lack of interest in external stimuli, even interest in people.

New way to examine the brain

Researchers also looked at the interactions between brain regions, or the brain’s functional connectivity and were able to identify the amount of activity when resting. “This is a novel interpretation because it is a different attempt to understand the children’s cognition by analyzing their brain activity,” noted Jose L. Pérez Velazquez, PhD, first author and professor of neuroscience at University of Toronto Institute of Medical Science and Department of Pediatrics, Brain and Behavior Center. “Measuring cognitive processes is not trivial; yet, our findings indicate that this can be done to some extent with well-established mathematical tools from physics and engineering.”

This study provides some support for the “Intense World Theory” of autism which describes the disorder as the result of hyper-functioning neural circuitry, leading to a stat of over-arousal.

Source: MedicalNewsToday, Frontiers in Neuroinformatics

 
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