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

Hannah Meyer-Lindenberg, Carolin Moessnang, Bethany Oakley, Jumana Ahmad, Luke Mason, Emily J.H. Jones, Hannah L. Hayward, Jennifer Cooke, Daisy Crawley, Rosemary Holt, Julian Tillmann, Tony Charman, Simon Baron-Cohen, Tobias Banaschewski, Christian Beckmann, Heike Tost, Andreas Meyer-Lindenberg, Jan K. Buitelaar, Declan G. Murphy, Michael J. BrammerEva Loth*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Background: 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. Methods: 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. Results: 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. Limitations: 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. Conclusions: 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.

Original languageEnglish
Article number43
JournalMolecular Autism
Volume13
Issue number1
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Autism
  • Autism spectrum disorder
  • Clustering analysis
  • Development
  • Facial expression recognition
  • fMRI
  • Multi-site
  • Social brain
  • Stratification biomarkers

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