During downtime, some of us daydream while others might focus on a to-do list, or get stuck in a negative loop. Psychology has traditionally defined all these thought patterns as variations of “mind-wandering.”
How long will it be before we can open doors and send emails with just our minds?
Severity of symptoms correlates with brain adaptation measures.
Carnegie Mellon University scientists have discovered a crucial difference in the way learning occurs in the brains of adults with autism spectrum disorder (ASD).
Published in NeuroImage, Sarah Schipul and Marcel Just examined how the brains of typical and ASD individuals gradually became adapted to visual patterns they were learning, without awareness of the pattern, or implicit learning.
Using functional magnetic resonance (fMRI) imaging, Schipul and Just found that the brain activation of ASD individuals was slower to become familiar with the pattern they repeatedly saw, – meaning their brains failed to register the “oldness” of the patterns to the same degree that the control participants did. The brains of the control participants kept decreasing their level of activation with repeated exposures to the patterns being learned – showing adaptation – whereas the decreases in the brain of participants with ASD were significantly smaller.
They also found that the severity of an individual’s autism symptoms correlated with the brain’s degree of adaptation to the patterns. The findings provide insight into why many real-world implicit learning situations, such as learning to interpret facial expressions, pose challenges for those with ASD.
“This finding provides a tentative explanation for why people with ASD might have difficulty with everyday social interactions, if their learning of implicit social cues has been altered,” said Just, the D.O. Hebb University Professor of Psychology in the Dietrich College of Humanities and Social Sciences.
While having their brains scanned, 16 high-functioning adults with ASD and 16 typical adults were trained to perform an implicit dot pattern-learning task. The target pattern was a random array of dots, which can gradually become familiar over multiple exposures despite minor changes in the pattern. Prior to the brain scan, both groups were familiarized with the type of task that would be used in the scanner. The ASD participants took longer than the control group to learn the task, demonstrating altered implicit learning in ASD. After equalizing the task structure learning and using the fMRI scanner, the two groups’ brain activation differed while they were learning a new dot pattern.
The imaging showed that at the beginning of the learning session, both groups’ brain activation levels were similar. By the end of the task, the typical participants showed decreased activation in the posterior regions. The ASD participants’ brain activation did not decrease later in learning. In fact, it increased in frontal and parietal regions.
“Behaviorally, the two groups looked very similar throughout the task — both the ASD and typical participants were able to learn how to correctly categorize the dot patterns with reasonable accuracy,” Just said. “But, because their activation levels differed, it tells us that there may be something qualitatively different in the way individuals with ASD learn and perform these kinds of task and reveals insights into the disorder that are not discernable from behavior alone.”
A second finding involved brain synchronization — a measure of how well coordinated the brain activation was across different regions of the brain. The implicit learning exercise was specifically designed to engage both the frontal and posterior regions of the brain, and the results showed that brain synchronization between these regions was lower in ASD. This supports Just’s 2004 influential “Frontal-Posterior Underconnectivity Theory of Autism,” which first discovered this lower synchronization. In later studies, Just showed how this theory accounted for many brain imaging and behavioral findings in tasks that required a substantial role for the frontal cortex.
“This lack of synchronization with frontal regions in ASD — an impairment in brain connectivity — may lead to symptoms of the disorder that involve processes that require brain coordination between frontal and other areas, such as language processing and social interaction,” Just explained.
The researchers also found that adaptation and synchronization were directly related to the severity of the participants’ ASD symptoms.
“Seeing that individuals with more atypical neural responses also had more severe ASD symptoms suggests that these neural characteristics underlie or contribute to the core symptoms of ASD,” Just said. “It is possible that reduced neural adaptability during learning in ASD may lead to the behavioral symptoms of the disorder. For example, the ability to learn implicit social clues may be affected in ASD, leading to impaired social processing.”
Schipul, who received her bachelor’s degree in cognitive science and Ph.D. in psychology from CMU and is now a postdoctoral fellow at the University of North Carolina at Chapel Hill, and Just believe that therapeutic approaches for ASD might benefit from making the learning of various everyday skills that people without ASD learn implicitly very clear.
This is among several brain research breakthroughs at Carnegie Mellon. CMU is the birthplace of artificial intelligence and cognitive psychology and has been a leader in the study of brain and behavior for more than 50 years. The university has created some of the first cognitive tutors, helped to develop the Jeopardy-winning Watson, founded a groundbreaking doctoral program in neural computation, and completed cutting-edge work in understanding the genetics of autism. Building on its strengths in biology, computer science, psychology, statistics and engineering, CMU launched BrainHub, an initiative that focuses on how the structure and activity of the brain give rise to complex behaviors.
Adapted by MNT from original media release
The findings are published in the April 9 online issue of the journal Neuron.
A major challenge of ASD diagnosis and treatment is that the neurological condition — which affects 1 in 68 children in the United States, mostly boys — is considerably heterogeneous. Early symptoms differ between each ASD toddler, as does progression of the condition. No uniform clinical phenotype exists, in part because the underlying causes for different subtypes of autism are diverse and not well-understood.
“There is no better example than early language development,” said senior author Eric Courchesne, PhD, professor of neurosciences and co-director of the Autism Center of Excellence at UC San Diego. “Some individuals are minimally verbal throughout life. They display high levels of symptom severity and may have poor clinical outcomes. Others display delayed early language development, but then progressively acquire language skills and have relatively more positive clinical outcomes.”
In other words, said Courchesne, in some children with ASD language improves substantially with age; but in some it may progress too slowly or even diminish. The neurodevelopmental bases for this variability are unknown, he said. Differences in treatment quantity do not fully account for it. But numerous studies have shown that early, accurate diagnoses of ASD can improve treatment benefits in many affected children.
“It’s important to develop more and new biological ways to identify and stratify the ASD population into clinical sub-types so that we can create better, more individualized treatments,” said co-author Karen Pierce, PhD, associate professor of neurosciences and co-director of the Autism Center of Excellence.
In the Neuron paper, Courchesne, first author Michael V. Lombardo, PhD, a senior researcher at the University of Cambridge and assistant professor at the University of Cyprus, Pierce and colleagues describe the first effort to create a process capable of detecting different brain subtypes within ASD that underlie and help explain varying development language trajectories and outcomes. “We wanted to see if patterns of brain activity in response to language can explain and predict how well language skills would develop in a toddler with ASD before that toddler actually began talking,” said Courchesne.
The researchers combined prospective fMRI measurements of neural systems’ response to speech in children at the earliest ages at which risk of ASD can be clinically detected in a general pediatric population (at approximately ages 1-2 years) with comprehensive longitudinal diagnostic and clinical assessments of language skills at 3-4 years of age.
They found that pre-diagnosis fMRI response to speech in ASD toddlers with relatively good language outcomes was highly similar to non-ASD comparison groups with robust responses to language in superior temporal cortices, a region of the brain responsible for processing sounds so that they can be understood as language.
In contrast, ASD toddlers with poor language outcomes had superior temporal cortices that showed diminished or abnormal inactivity to speech.
In sum, the study found entirely different neural substrates at initial clinical detection that precede and underlie later good versus poor language outcome in autism. These findings, said researchers, will open new avenues of progress towards identifying the causes and best treatment for these two very different types of autism.
“For the first time, our study shows a strong relationship between irregularities in speech-activation in the language-critical superior temporal cortex and actual, real-world language ability in ASD toddlers,” said Lombardo.
The scientists said fMRI imaging also showed that the brains of ASD toddlers with poor language development processed speech differently, including how neural regions governing emotion, memory and motor skills were involved.
“Our work represents one of the first attempts using fMRI to define a neurofunctional biomarker of a subtype in very young ASD toddlers,” said Pierce. “Such subtypes help us understand the differences between persons with ASD. More importantly, they can help us determine how and why treatments are effective for some, but not all, on the autism spectrum.”
The above story is based on materials provided by University of California, San Diego Health Sciences. The original article was written by Scott LaFee. Note: Materials may be edited for content and length.
- Michael V. Lombardo, Karen Pierce, Lisa T. Eyler, Cindy Carter Barnes, Clelia Ahrens-Barbeau, Stephanie Solso, Kathleen Campbell, Eric Courchesne. Different Functional Neural Substrates for Good and Poor Language Outcome in Autism. Neuron, 2015; DOI: 10.1016/j.neuron.2015.03.023