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. Brammer; Eva Loth

doi:10.1186/s13229-022-00520-7

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 journal › Article › peer-review

2 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 language	English
Article number	43
Journal	Molecular Autism
Volume	13
Issue number	1
DOIs	https://doi.org/10.1186/s13229-022-00520-7
Publication status	Published - Dec 2022

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1186/s13229-022-00520-7

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Meyer-Lindenberg, H., Moessnang, C., Oakley, B., Ahmad, J., Mason, L., Jones, E. J. H., Hayward, H. L., Cooke, J., Crawley, D., Holt, R., Tillmann, J., Charman, T., Baron-Cohen, S., Banaschewski, T., Beckmann, C., Tost, H., Meyer-Lindenberg, A., Buitelaar, J. K., Murphy, D. G., ... Loth, E. (2022). Facial expression recognition is linked to clinical and neurofunctional differences in autism. Molecular Autism, 13(1), [43]. https://doi.org/10.1186/s13229-022-00520-7

@article{d85a4189b2054f68a9c82d09a59ffe63,

title = "Facial expression recognition is linked to clinical and neurofunctional differences in autism",

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.",

keywords = "Autism, Autism spectrum disorder, Clustering analysis, Development, Facial expression recognition, fMRI, Multi-site, Social brain, Stratification biomarkers",

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

note = "Funding Information: This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under Grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. Additional funding was received by the European Community{\textquoteright}s Seventh Framework Programme under the Grant agreement no. 602805 (Project EU-AGGRESSOTYPE) and no. 602450 (Project EU-IMAGEMEND), and by the German Federal Ministry of Education and Research under the Grant no. 01ZX1314GM (Project IntegraMent) and Grant no. 01GQ1102. C.M. is a recipient of an Olympia-Morata grant of the University Heidelberg. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1186/s13229-022-00520-7",

language = "English",

volume = "13",

journal = "Molecular Autism",

issn = "2040-2392",

publisher = "BioMed Central",

number = "1",

}

Meyer-Lindenberg, H, Moessnang, C, Oakley, B , Ahmad, J, Mason, L, Jones, EJH, Hayward, HL, Cooke, J , Crawley, D, Holt, R, Tillmann, J , Charman, T, Baron-Cohen, S, Banaschewski, T, Beckmann, C, Tost, H, Meyer-Lindenberg, A, Buitelaar, JK, Murphy, DG, Brammer, MJ & Loth, E 2022, 'Facial expression recognition is linked to clinical and neurofunctional differences in autism', Molecular Autism, vol. 13, no. 1, 43. https://doi.org/10.1186/s13229-022-00520-7

TY - JOUR

T1 - Facial expression recognition is linked to clinical and neurofunctional differences in autism

AU - Meyer-Lindenberg, Hannah

AU - Moessnang, Carolin

AU - Oakley, Bethany

AU - Ahmad, Jumana

AU - Mason, Luke

AU - Jones, Emily J.H.

AU - Hayward, Hannah L.

AU - Cooke, Jennifer

AU - Crawley, Daisy

AU - Holt, Rosemary

AU - Tillmann, Julian

AU - Charman, Tony

AU - Baron-Cohen, Simon

AU - Banaschewski, Tobias

AU - Beckmann, Christian

AU - Tost, Heike

AU - Meyer-Lindenberg, Andreas

AU - Buitelaar, Jan K.

AU - Murphy, Declan G.

AU - Brammer, Michael J.

AU - Loth, Eva

N1 - Funding Information: This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under Grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. Additional funding was received by the European Community’s Seventh Framework Programme under the Grant agreement no. 602805 (Project EU-AGGRESSOTYPE) and no. 602450 (Project EU-IMAGEMEND), and by the German Federal Ministry of Education and Research under the Grant no. 01ZX1314GM (Project IntegraMent) and Grant no. 01GQ1102. C.M. is a recipient of an Olympia-Morata grant of the University Heidelberg. Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

KW - Autism

KW - Autism spectrum disorder

KW - Clustering analysis

KW - Development

KW - Facial expression recognition

KW - fMRI

KW - Multi-site

KW - Social brain

KW - Stratification biomarkers

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U2 - 10.1186/s13229-022-00520-7

DO - 10.1186/s13229-022-00520-7

M3 - Article

C2 - 36357905

AN - SCOPUS:85141604832

SN - 2040-2392

VL - 13

JO - Molecular Autism

JF - Molecular Autism

IS - 1

M1 - 43

ER -

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

Abstract

Keywords

UN SDGs

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