Atypical gaze patterns in autistic adults are heterogeneous across but reliable within individuals.


Journal

Molecular autism
ISSN: 2040-2392
Titre abrégé: Mol Autism
Pays: England
ID NLM: 101534222

Informations de publication

Date de publication:
24 09 2022
Historique:
received: 27 05 2022
accepted: 16 09 2022
entrez: 24 9 2022
pubmed: 25 9 2022
medline: 28 9 2022
Statut: epublish

Résumé

Across behavioral studies, autistic individuals show greater variability than typically developing individuals. However, it remains unknown to what extent this variability arises from heterogeneity across individuals, or from unreliability within individuals. Here, we focus on eye tracking, which provides rich dependent measures that have been used extensively in studies of autism. Autistic individuals have an atypical gaze onto both static visual images and dynamic videos that could be leveraged for diagnostic purposes if the above open question could be addressed. We tested three competing hypotheses: (1) that gaze patterns of autistic individuals are less reliable or noisier than those of controls, (2) that atypical gaze patterns are individually reliable but heterogeneous across autistic individuals, or (3) that atypical gaze patterns are individually reliable and also homogeneous among autistic individuals. We collected desktop-based eye tracking data from two different full-length television sitcom episodes, at two independent sites (Caltech and Indiana University), in a total of over 150 adult participants (N = 48 autistic individuals with IQ in the normal range, 105 controls) and quantified gaze onto features of the videos using automated computer vision-based feature extraction. We found support for the second of these hypotheses. Autistic people and controls showed equivalently reliable gaze onto specific features of videos, such as faces, so much so that individuals could be identified significantly above chance using a fingerprinting approach from video epochs as short as 2 min. However, classification of participants into diagnostic groups based on their eye tracking data failed to produce clear group classifications, due to heterogeneity in the autistic group. Three limitations are the relatively small sample size, assessment across only two videos (from the same television series), and the absence of other dependent measures (e.g., neuroimaging or genetics) that might have revealed individual-level variability that was not evident with eye tracking. Future studies should expand to larger samples across longer longitudinal epochs, an aim that is now becoming feasible with Internet- and phone-based eye tracking. These findings pave the way for the investigation of autism subtypes, and for elucidating the specific visual features that best discriminate gaze patterns-directions that will also combine with and inform neuroimaging and genetic studies of this complex disorder.

Sections du résumé

BACKGROUND
Across behavioral studies, autistic individuals show greater variability than typically developing individuals. However, it remains unknown to what extent this variability arises from heterogeneity across individuals, or from unreliability within individuals. Here, we focus on eye tracking, which provides rich dependent measures that have been used extensively in studies of autism. Autistic individuals have an atypical gaze onto both static visual images and dynamic videos that could be leveraged for diagnostic purposes if the above open question could be addressed.
METHODS
We tested three competing hypotheses: (1) that gaze patterns of autistic individuals are less reliable or noisier than those of controls, (2) that atypical gaze patterns are individually reliable but heterogeneous across autistic individuals, or (3) that atypical gaze patterns are individually reliable and also homogeneous among autistic individuals. We collected desktop-based eye tracking data from two different full-length television sitcom episodes, at two independent sites (Caltech and Indiana University), in a total of over 150 adult participants (N = 48 autistic individuals with IQ in the normal range, 105 controls) and quantified gaze onto features of the videos using automated computer vision-based feature extraction.
RESULTS
We found support for the second of these hypotheses. Autistic people and controls showed equivalently reliable gaze onto specific features of videos, such as faces, so much so that individuals could be identified significantly above chance using a fingerprinting approach from video epochs as short as 2 min. However, classification of participants into diagnostic groups based on their eye tracking data failed to produce clear group classifications, due to heterogeneity in the autistic group.
LIMITATIONS
Three limitations are the relatively small sample size, assessment across only two videos (from the same television series), and the absence of other dependent measures (e.g., neuroimaging or genetics) that might have revealed individual-level variability that was not evident with eye tracking. Future studies should expand to larger samples across longer longitudinal epochs, an aim that is now becoming feasible with Internet- and phone-based eye tracking.
CONCLUSIONS
These findings pave the way for the investigation of autism subtypes, and for elucidating the specific visual features that best discriminate gaze patterns-directions that will also combine with and inform neuroimaging and genetic studies of this complex disorder.

Identifiants

pubmed: 36153629
doi: 10.1186/s13229-022-00517-2
pii: 10.1186/s13229-022-00517-2
pmc: PMC9508778
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

39

Subventions

Organisme : NICHD NIH HHS
ID : P50 HD103556
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH110630
Pays : United States

Informations de copyright

© 2022. The Author(s).

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Auteurs

Umit Keles (U)

Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, USA. ukeles@caltech.edu.

Dorit Kliemann (D)

Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, USA.
Department of Psychological and Brain Sciences, The University of Iowa, Iowa City, USA.

Lisa Byrge (L)

Department of Psychology, University of North Florida, Jacksonville, USA.

Heini Saarimäki (H)

Faculty of Social Sciences, Tampere University, Tampere, Finland.

Lynn K Paul (LK)

Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, USA.

Daniel P Kennedy (DP)

Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA.

Ralph Adolphs (R)

Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, USA.
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, USA.
Chen Neuroscience Institute, California Institute of Technology, Pasadena, USA.

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