Summary: University of Virginia researchers present a pioneering study utilizing diffusion MRI to reveal structural brain differences in autism, offering insights into neuronal conductivity and dynamic networks, potentially advancing precision medicine for autism and other neurological conditions.

Key Takeways:

  1. UVA’s diffusion MRI reveals brain differences in autism, surpassing traditional methods.
  2. Inspired by Nobel laureates, the study uncovers neural conductivity variations linked to autism, deepening our understanding.
  3. Investigating dynamic neural networks, the research aims to advance autism treatment through NIH’s Autism Center of Excellence.


Autism spectrum disorder remains elusive in terms of a single cause due to its diverse symptoms and severity. Yet, a groundbreaking study by University of Virginia researchers proposes a fresh approach to unraveling its mysteries, offering insights that could extend to other neurological conditions.

Uncovering Structural Brain Differences in Autism

Traditionally, autism research delves into behavioral aspects using techniques like functional MRI, which assesses brain responses but overlooks underlying causes. However, UVA researchers have pioneered the use of diffusion MRI, revealing structural disparities in the brains of autistic versus non-autistic individuals. By examining how water moves within the brain, they’ve crafted mathematical models pinpointing these differences.

“It hasn’t been well understood what those differences might be,” says Benjamin Newman, a postdoctoral researcher with UVA’’s Department of Psychology, recent graduate of UVA School of Medicine’s neuroscience graduate program and lead author of a paper published this month in PLOS: One. “This new approach looks at the neuronal differences contributing to the etiology of autism spectrum disorder.”

New Understanding of Autism Neural Function

Drawing 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, Newman and his co-authors applied those concepts to understand how that conductivity differs in those with autism and those without, using the latest neuroimaging data and computational methodologies. 

The result is a first-of-its-kind approach to calculating the conductivity of neural axons and their capacity to carry information through the brain. The study also offers evidence that those microstructural differences are directly related to participants’ scores on the Social Communication Questionnaire, a common clinical tool for diagnosing autism.

“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 says. “It’s the structure that constrains how the function of the brain works.”

Delving Deeper into Autism Brain Function

One of Newman’s co-authors, John Darrell Van Horn, PhD, a professor of psychology and data science at UVA, says, there’s been a lot of work done with functional MRI, looking at blood oxygen related signal changes in autistic individuals. But this research, he says, “Goes a little bit deeper.”

“It’s asking not if there’s a particular cognitive functional activation difference; it’s asking how the brain actually conducts information around itself through these dynamic networks,” Van Horn says. “And I think that we’ve been successful showing that there’s something that’s uniquely different about autistic-spectrum-disorder-diagnosed individuals relative to otherwise typically developing control subjects.”

Newman, Van Horn, along with co-authors Jason Druzgal and Kevin Pelphrey from UVA School of Medicine, are part of the National Institute of Health’s Autism Center of Excellence (ACE). This initiative aims to understand autism’s causes and treatments through large-scale studies. 

Pelphrey, a neuroscientist and the study’s lead, highlights ACE’s goal of pioneering precision medicine for autism. “This study provides the foundation for a biological target to measure treatment response and allows us to identify avenues for future treatments to be developed,” he says.