September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Direct Neural Read-Out of Binocular Rivalry Dynamics in Autism using EEG
Author Affiliations
  • Alina Spiegel
    McGovern Institute for Brain Research, MITSchool of Medicine, Johns Hopkins University
  • Jackson Lee
    McGovern Institute for Brain Research, MITDuke University
  • AJ Haskins
    McGovern Institute for Brain Research, MIT
  • Nancy Kanwisher
    McGovern Institute for Brain Research, MIT
  • Caroline Robertson
    McGovern Institute for Brain Research, MITHarvard Society of Fellows, Harvard University
Journal of Vision September 2018, Vol.18, 37. doi:
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      Alina Spiegel, Jackson Lee, AJ Haskins, Nancy Kanwisher, Caroline Robertson; Direct Neural Read-Out of Binocular Rivalry Dynamics in Autism using EEG. Journal of Vision 2018;18(10):37.

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      © ARVO (1962-2015); The Authors (2016-present)

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Intro: We have previously shown atypical binocular rivalry dynamics in individuals with autism, which are predictive of clinical measures of autistic symptomatology (Robertson et al., 2013) and likely reflect reduced GABAergic action in the autistic visual cortex (Robertson et al., 2016). As a simple visual assessment, rivalry could serve as an objective marker of autism. But traditional binocular rivalry paradigms have a key limitation: rivalry is a self-report measure, restricting its use to high-functioning, verbal individuals. Here, we aimed to develop a neural marker of binocular rivalry dynamics – and rivalry differences in autism – using electroencephalography (EEG). Methods: 46 participants (23 autism and 23 age- and IQ-matched controls) viewed true and simulated binocular rivalry displays (18, 30-second trials each) through a mirror stereoscope while EEG signals were recorded over occipital cortex. Signals corresponding to each eye's stimulus were independently measured. Behavioral report was collected using button-press. Results: First, we replicate our previous behavioral findings, including slower switch-rates and reduced perceptual suppression during binocular rivalry in individuals with autism (both p< 0.006). Second, these effects were directly mirrored in individuals' neural activity, as recorded from Oz: individuals with autism exhibited slower rivalry rates than controls (p=0.01). Third, using machine-learning analyses, we were able to correctly classify individuals' perceptual state (left eye, right eye, mixed) as well as diagnostic status (autistic vs. controls) with accuracies greater than 70%. Conclusions: Our results demonstrate a direct neural read-out of altered binocular rivalry dynamics in individuals with autism, predictive of diagnostic status, and provide a non-verbal method for quantifying binocular rivalry switch-rates. Down the road, this paradigm may offer an inexpensive, objective, neural marker of autism that can be used with non- and pre-verbal individuals as well as in animal models of the condition.

Meeting abstract presented at VSS 2018


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