A new study reveals that the wide variety of sensory experiences reported by autistic individuals corresponds to distinct patterns of information exchange in the brain. Researchers found that specific functional connections between cortical and subcortical brain regions can predict an autistic child’s sensory profile. The findings were published in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.
Differences in sensory processing represent a defining feature for up to 87 percent of autistic individuals. These differences mean that sights, sounds, textures, smells, or tastes might be perceived with unusually high or low intensity. Some individuals actively seek out certain types of sensory input, while others go to great lengths to avoid everyday stimuli. These variations affect how people navigate their environments and can influence cognitive development and daily functioning.
While these sensory variations can seem highly individualized, previous research has shown that they tend to cluster into specific behavioral categories. In a prior large-scale study, scientists identified five distinct sensory phenotypes among autistic youth. The first phenotype is “sensory adaptive,” describing children who show minimal sensory differences from neurotypical peers. The second is “generalized sensory differences,” which involves severe sensitivities across nearly all sensory domains.
The remaining three phenotypes involve more specialized sensory responses. One group is characterized primarily by selective sensitivities to taste and smell. Another group features children who are generally underresponsive to their environment but actively seek out intense sensations. The final group is defined by movement difficulties combined with low physical energy. Until now, it was unknown exactly how these five behavioral profiles mapped onto the brain’s internal wiring.
Western University researcher Matthew Kolisnyk and senior author Ryan Stevenson led a team to investigate whether these sensory profiles arise from specific patterns of brain activity. They designed an analysis to determine if different sensory behaviors correspond to unique functional connectivity configurations in the brain. Uncovering these neural foundations could help explain the immense biological diversity seen across the autism spectrum.
To test this idea, the research team analyzed data from 146 autistic children and adolescents between the ages of three and twenty-one. The participants’ caregivers filled out a standardized clinical questionnaire detailing the children’s daily responses to various sensory stimuli. Based on the scores from these questionnaires, the researchers sorted each participant into one of the five previously established sensory phenotypes.
The participants then underwent resting-state functional magnetic resonance imaging. This brain scanning technique tracks blood oxygen levels to measure brain activity while a person rests quietly without performing a specific task. By observing which areas of the brain activate and deactivate in sync over time, researchers can map out the brain’s functional connectivity. This connectivity represents how different neural regions communicate and share information.
The researchers divided the brain scans into 200 distinct sections of the cerebral cortex and 32 subcortical regions located deep within the brain. They then applied a mathematical framework known as graph theory to quantify how information moves through this sprawling biological network. Graph theory provides specific metrics, such as how strongly regions are connected to one another and how efficiently they pass information to local neighbors.
The team also used graph theory to calculate betweenness centrality, a metric that identifies whether certain brain regions act as central hubs for long-distance communication. Another metric, called the clustering coefficient, measured how highly interconnected a region’s immediate neighbors are with one another. Together, these mathematical calculations generated hundreds of individual data points describing each participant’s unique brain network.
Finally, the team fed all these graph theory measurements into a machine learning algorithm. Machine learning involves training a computer model to recognize hidden patterns within massive datasets. The researchers tested whether the model could learn the connectivity data well enough to correctly identify which sensory phenotype a child belonged to, running comparisons between all ten possible pairs of the five groups.
The algorithm successfully differentiated between seven of the ten pairs of sensory phenotypes based purely on their brain connectivity metrics. The model performed best when comparing the two most extreme behavioral profiles. It most accurately distinguished the sensory adaptive group from the group with generalized sensory differences.
Specific brain networks emerged as strong predictors of a child’s sensory profile. Children in the sensory adaptive group showed stronger global connections in the prefrontal cortex, a region primarily involved in decision making and executive function. They also showed high connectivity in the orbitofrontal cortex, an area just above the eyes that helps integrate sensory information with emotional rewards.
Conversely, children with generalized sensory differences exhibited stronger local connections within the somatomotor network, which directly handles touch and voluntary physical movement. Their primary sensory processing regions appeared highly interconnected on a local level but lacked strong communication links to the prefrontal cortex. This pattern points to a potential difference in how higher-level executive brain regions modulate incoming sensory signals from the outside world.
The analysis also identified distinct connectivity signatures for the more specialized sensory profiles. Children characterized by isolated taste and smell sensitivities consistently showed altered hub activity in the posterior parietal cortex, a region important for spatial reasoning and directing attention. Differences in broad attentional networks and the default mode network, a system active during internal thought and daydreaming, also helped the algorithm separate several phenotypes.
Subcortical regions proved highly relevant for distinguishing specific sensory groups. These deep brain structures, which include areas like the thalamus and the nucleus accumbens, often act as the initial gatekeepers for sensory information before it reaches the cerebral cortex. The study found that subcortical connectivity patterns were particularly distinct in the generalized sensory differences group and the taste and smell sensitivity group.
The study carries several caveats regarding its interpretation. The machine learning model failed to produce statistically significant distinctions between three of the phenotype pairs, such as the underresponsive group and the movement difficulty group. This lack of differentiation suggests that some sensory profiles might share overlapping neural mechanisms, or that a larger sample size is needed to detect subtler brain differences.
The analysis relied heavily on caregiver reports to categorize the children’s sensory behaviors. While these questionnaires are standard clinical tools, they introduce a degree of subjectivity that might not perfectly capture the internal experiences of the autistic participants. The study also did not account for variations in overall autism symptom severity, intelligence quotient, or co-occurring conditions, which might independently influence brain connectivity patterns.
Small head movements during brain scans can artificially alter functional connectivity measurements. While the researchers removed data with excessive motion and applied mathematical corrections, systematic differences in how well children from different sensory groups tolerate the scanner environment remain a potential factor. Future research will need to test these predictive models on separate groups of autistic individuals to verify that the patterns hold true across broader populations.
The study, “Decoding the Neural Basis of Sensory Phenotypes in Autism,” was authored by Matthew Kolisnyk, Kathleen Lyons, Eun Jung Choi, Marlee M. Vandewouw, Bobby Stojanoski, Evdokia Anagnostou, Azadeh Kushki, Rob Nicolson, Elizabeth Kelley, Stelios Georgiades, Jason Lerch, Jennifer Crosbie, Russell Schachar, Muhammad Ayub, Jessica Jones, Paul Arnold, Xudong Liu, and Ryan Stevenson.