A Novel Method for Effectively Diagnosing Autism

A promising new approach may identify structural differences in the brains of those with autism spectrum disorder and those without. The findings were published in PLoS ONE.

Autism has yet to be linked to a single cause due to its variety of symptoms and severity. Currently, autism research centers on understanding the disorder through the study of its behavioral consequences, using techniques such as functional magnetic resonance imaging (MRI). However, researchers in this study analyzed the use of diffusion MRI, a technique that measures molecular diffusion in biological tissue, to observe how water moves throughout the brain and interacts with cellular membranes. The approach has helped the research team develop mathematical models of brain microstructures that have helped identify structural differences in the brains of those with and without autism.

“It hasn’t been well understood what those differences might be,” said Benjamin Newman, a postdoctoral researcher in the University of Virginia (UVA) Department of Psychology, recent graduate of the UVA School of Medicine’s neuroscience graduate program, and lead author via a press release. “This new approach looks at the neuronal differences contributing to the etiology of autism spectrum disorder.”

This analysis builds on the work of Alan Hodgkin and Andrew Huxley, who won the 1963 Nobel Prize in Medicine for describing the electrochemical conductivity characteristics of neurons. The groundbreaking work presented concepts to understand how that conductivity differs in those with autism and those without. The findings highlight a first-of-its-kind approach to calculating the conductivity of neural axons and their capacity to carry information through the brain. “What we’re seeing is that there’s a difference in the diameter of the microstructural components in the brains of autistic people that can cause them to conduct electricity slower,” Newman said. “It’s the structure that constrains how the function of the brain works.”

One of Newman’s co-authors, John Darrell Van Horn, a professor of psychology and data science at UVA, expressed that while further research is needed to elucidate the findings, the metric appears promising. “We need greater fidelity in terms of the physiological metrics that we have so that we can better understand where those behaviors are coming from. This is the first time this kind of metric has been applied in a clinical population, and it sheds some interesting light on the origins of [autism spectrum disorder].”