Summary: A visual test may be a new tool in the diagnosis of autism. Individuals on the autism spectrum are slower to dampen neural activity in response to visual stimuli in the brain. Using EEG data collected from the visual region, researchers could predict with 87% accuracy whether or not a person had ASD.
Source: Dartmouth College
A Dartmouth-led research team has identified a non-verbal, neural marker of autism. This marker shows that individuals with autism are slower to dampen neural activity in response to visual signals in the brain. This first-of-its kind marker was found to be independent of intelligence and offers an objective way to potentially diagnose autism in the future. The results are published in Current Biology.
“Autism is hard to screen for in children, when the first signs are present. A trained clinician may be able to detect autism at 18-months or even younger; yet, the average age of a diagnosis of autism in the U.S. is about four years old,” explains lead author Caroline Robertson, an assistant professor of psychological and brain sciences at Dartmouth, and director of the Dartmouth Autism Research Initiative. “We need objective, non-invasive screening tools that don’t depend on assessing a child’s behavior. One of the big goals of the field is to develop objective neural markers of autism that can work with non-verbal individuals. This neural marker is just that,” she added.
People with autism have long been thought to have differences in inhibiting the neural signals in the brain. This is thought to underpin symptoms in autism, such as hypersensitivity to sensory input, which includes differences in processing visual information.
When the human brain is presented with two different images at the same time, the images rock back and forth in awareness, toggling between the left and right eye. Prior research led by Robertson has demonstrated that the autistic brain is slower in switching from one image to the next (also known as slower binocular rivalry) due to differences in inhibitory neural transmission in the brain. In the autistic brain, the neurotransmitter, GABA, has difficulty filtering and regulating sensory signals, including in this case, suppressing one of the images.
The new study used brain imaging to measure the slower rate of binocular rivalry in individuals with autism. With these results, the research team was able to accurately determine if participants had autism and predict the severity of autism symptoms, which were measured using traditional clinical assessments.
To obtain the neural data, the study measured brain signals from a single electroencephalography (EEG) electrode that was placed on a participant’s head, over the visual region of the brain. Participants were presented with one of two visual images: red checkerboards in the left eye and green checkerboards in the right eye that flickered back and forth at different rates.
The research revealed that neural data could be used to predict whether or not an individual had autism with 87 percent accuracy. The findings were striking and tracked with clinical measures of autism: participants with a higher level of autism had a slower rate of binocular rivalry, where the brain was slower in switching from one image to the next.
The research offers new promise for the way autism is diagnosed. “This visual test may be a non-verbal marker of autism in adults. Our next steps are to learn whether this test could potentially be used to detect autism in pre-verbal children and non-verbal adults and develop it into a screening tool for the condition. In the meantime, this result gives us new insight into the brain in autism, showing that visual regions of the brain are affected” says Robertson. The researchers also note that the visual sensitivities individuals with autism experience differ significantly among people on the autism spectrum, so while measuring these differences in visual processing may not detect autism in all individuals, it might help to better understand the autism spectrum.
Under Robertson’s direction, the Dartmouth Autism Research Initiative, seeks to understand how people with autism see the world, including differences in sensory perception with respect to neurobiology. The team works with children and adults, who have been diagnosed with autism, Pervasive Development Disorder-Not Otherwise Specified (PDD-NOS), and Asperger’s Syndrome.
Amy D. Olson – Dartmouth College
The image is in the public domain.
Original Research: Open access
“Slower Binocular Rivalry in the Autistic Brain”. Alina Spiegel, Jeff Mentch, Amanda J. Haskins, Caroline E. Robertson.
Current Biology. doi:10.1016/j.cub.2019.07.026
Slower Binocular Rivalry in the Autistic Brain
• Binocular rivalry is slower in the autistic brain
• Potential marker of E/I balance in visual cortex
• Predicts clinical symptoms and classifies diagnostic status (autism versus controls)
• Non-verbal measure, which may be suitable for infant and cross-species research
Autism has traditionally been regarded as a disorder of the social brain. Recent reports of differences in visual perception have challenged this notion, but little evidence for altered visual processing in the autistic brain exists. We have previously observed slower behaviorally reported rates of a basic visual phenomenon, binocular rivalry, in autism. During rivalry, two images—one presented to each eye—vie for awareness, alternating back and forth in perception. This competition is modeled to rely, in part, on the balance of excitation and inhibition in visual cortex, which may be altered in autism. Yet direct neural evidence for this potential marker of excitation/inhibition (E/I) balance in autism is lacking. Here, we report a striking alteration in the neural dynamics of binocular rivalry in individuals with autism. Participants viewed true and simulated frequency-tagged binocular rivalry displays while steady-state visually evoked potentials (SSVEPs) were measured over occipital cortex using electroencephalography (EEG). First, we replicate our prior behavioral findings of slower rivalry and reduced perceptual suppression in individuals with autism compared with controls. Second, we provide direct neural evidence for slower rivalry in autism compared with controls, which strongly predicted individuals’ behavioral switch rates. Finally, using neural data alone, we were able to predict autism symptom severity (ADOS) and correctly classify individuals’ diagnostic status (autistic versus control; 87% accuracy). These findings clearly implicate atypical visual processing in the neurobiology of autism. Down the road, this paradigm may serve as a non-verbal marker of autism for developmental and cross-species research.