Journal article
Facial Emotions Are Accurately Encoded in the Neural Signal of Those With Autism Spectrum Disorder: A Deep Learning Approach
Biological psychiatry : cognitive neuroscience and neuroimaging, v 7(7), pp 688-695
01 Jul 2022
PMID: 33862256
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Individuals with autism spectrum disorder (ASD) exhibit frequent behavioral deficits in facial emotion recognition (FER). It remains unknown whether these deficits arise because facial emotion information is not encoded in their neural signal or because it is encodes but fails to translate to FER behavior (deployment). This distinction has functional implications, including constraining when differences in social information processing occur in ASD, and guiding interventions (i.e., developing prosthetic FER vs. reinforcing existing skills).
We utilized a discriminative and contemporary machine learning approach-deep convolutional neural networks-to classify facial emotions viewed by individuals with and without ASD (N = 88) from concurrently recorded electroencephalography signals.
The convolutional neural network classified facial emotions with high accuracy for both ASD and non-ASD groups, even though individuals with ASD performed more poorly on the concurrent FER task. In fact, convolutional neural network accuracy was greater in the ASD group and was not related to behavioral performance. This pattern of results replicated across three independent participant samples. Moreover, feature importance analyses suggested that a late temporal window of neural activity (1000-1500 ms) may be uniquely important in facial emotion classification for individuals with ASD.
Our results reveal for the first time that facial emotion information is encoded in the neural signal of individuals with (and without) ASD. Thus, observed difficulties in behavioral FER associated with ASD likely arise from difficulties in decoding or deployment of facial emotion information within the neural signal. Interventions should focus on capitalizing on this intact encoding rather than promoting compensation or FER prostheses.
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Details
- Title
- Facial Emotions Are Accurately Encoded in the Neural Signal of Those With Autism Spectrum Disorder: A Deep Learning Approach
- Creators
- Juan Manuel Mayor Torres - University of TrentoTessa Clarkson - Temple UniversityKathryn M Hauschild - Stony Brook UniversityChristian C Luhmann - Stony Brook UniversityMatthew D Lerner - Stony Brook UniversityGiuseppe Riccardi - University of Trento
- Publication Details
- Biological psychiatry : cognitive neuroscience and neuroimaging, v 7(7), pp 688-695
- Publisher
- Elsevier
- Grant note
- F31 MH122091 / NIMH NIH HHS R01 MH110585 / NIMH NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- A.J. Drexel Autism Institute
- Web of Science ID
- WOS:000829543400008
- Scopus ID
- 2-s2.0-85108551819
- Other Identifier
- 991021861859404721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Neurosciences
- Psychiatry