Facial expression recognition is linked to clinical and neurofunctional differences in autism.

Researchers studied how well autistic people recognise facial expressions using three different tests. They found that about 30% of autistic participants had significant difficulties, while 70% performed similarly to non-autistic people. Those with more difficulties also had greater social communication challenges and different brain activity patterns. This suggests facial expression recognition could help identify which autistic individuals might benefit from specific types of support.

This large-scale study examined facial expression recognition abilities in 255-488 participants aged 6-30 years with autism, typical development, and/or mild intellectual disability. Using three different facial expression tasks and novel clustering methods, researchers identified two distinct subgroups within the autism population: approximately 30% showed significant difficulties (performing below 2 standard deviations on at least one test), while 70% performed within normal ranges. The low-performing subgroup demonstrated greater social communication difficulties and reduced brain activation in key regions (amygdala and fusiform gyrus) during fearful face processing. The study suggests facial expression recognition could serve as a bio-behavioural marker for stratifying autism interventions.

Facial expression recognition assessment could serve as a stratification tool to identify autistic individuals who may benefit from targeted social communication interventions. The identification of neurobiological differences suggests potential for developing brain-based biomarkers and more personalised therapeutic approaches.

Findings require independent replication, which is currently not possible as no other dataset includes all relevant measures. Study methodology and exact sample characteristics are not fully detailed in the abstract.

Difficulties in social communication are a defining clinical feature of autism. However, the underlying neurobiological heterogeneity has impeded targeted therapies and requires new approaches to identifying clinically relevant bio-behavioural subgroups. In the largest autism cohort to date, we comprehensively examined difficulties in facial expression recognition, a key process in social communication, as a bio-behavioural stratification biomarker, and validated them against clinical features and neurofunctional responses. Between 255 and 488 participants aged 6-30 years with autism, typical development and/or mild intellectual disability completed the Karolinska Directed Emotional Faces task, the Reading the Mind in the Eyes Task and/or the Films Expression Task.

We first examined mean-group differences on each test. Then, we used a novel intersection approach that compares two centroid and connectivity-based clustering methods to derive subgroups based on the combined performance across the three tasks. Measures and subgroups were then related to clinical features and neurofunctional differences measured using fMRI during a fearful face-matching task. We found significant mean-group differences on each expression recognition test.

However, cluster analyses showed that these were driven by a low-performing autistic subgroup (~ 30% of autistic individuals who performed below 2SDs of the neurotypical mean on at least one test), while a larger subgroup (~ 70%) performed within 1SD on at least 2 tests. The low-performing subgroup also had on average significantly more social communication difficulties and lower activation in the amygdala and fusiform gyrus than the high-performing subgroup. Findings of autism expression recognition subgroups and their characteristics require independent replication. This is currently not possible, as there is no other existing dataset that includes all relevant measures.

However, we demonstrated high internal robustness (91.6%) of findings between two clustering methods with fundamentally different assumptions, which is a critical pre-condition for independent replication. We identified a subgroup of autistic individuals with expression recognition difficulties and showed that this related to clinical and neurobiological characteristics. If replicated, expression recognition may serve as bio-behavioural stratification biomarker and aid in the development of targeted interventions for a subgroup of autistic individuals.