Constrained Multivariate Functional Principal Components Analysis for Novel Outcomes in Eye-Tracking Experiments

Brian Kwan, Catherine A. Sugar, Qi Qian, Frederick Shic, Adam Naples, Scott P. Johnson, Sara J. Webb, Shafali Jeste, Susan Faja, April R. Levin, Geraldine Dawson, James C. McPartland and Damla Şentürk ()
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Brian Kwan: University of California, Los Angeles
Catherine A. Sugar: University of California, Los Angeles
Qi Qian: University of California, Los Angeles
Frederick Shic: Seattle Children’s Research Institute
Adam Naples: Yale University
Scott P. Johnson: University of California, Los Angeles
Sara J. Webb: Seattle Children’s Research Institute
Shafali Jeste: University of South California
Susan Faja: Boston Children’s Hospital, Harvard Medical School
April R. Levin: Boston Children’s Hospital and Harvard Medical School
Geraldine Dawson: Duke University
James C. McPartland: Yale University
Damla Şentürk: University of California, Los Angeles

Statistics in Biosciences, 2024, vol. 16, issue 3, No 4, 578-603

Abstract: Abstract Individuals with autism spectrum disorder (ASD) tend to experience greater difficulties with social communication and sensory information processing. Of particular interest in ASD biomarker research is the study of visual attention, effectively quantified in eye tracking (ET) experiments. Eye tracking offers a powerful, safe, and feasible platform for gaining insights into attentional processes by measuring moment-by-moment gaze patterns in response to stimuli. Even though recording is done with millisecond granularity, analyses commonly collapse data across trials into variables such as proportion time spent looking at a region of interest (ROI). In addition, looking times in different ROIs are typically analyzed separately. We propose a novel multivariate functional outcome that carries proportion looking time information from multiple regions of interest jointly as a function of trial type, along with a novel constrained multivariate functional principal components analysis procedure to capture the variation in this outcome. The method incorporates the natural constraint that the proportion looking times from the multiple regions of interest must sum up to one. Our approach is motivated by the Activity Monitoring task, a social-attentional assay within the ET battery of the Autism Biomarkers Consortium for Clinical Trials (ABC-CT). Application of our methods to the ABC-CT data yields new insights into dominant modes of variation of proportion looking times from multiple regions of interest for school-age children with ASD and their typically developing (TD) peers, as well as richer analysis of diagnostic group differences in social attention.

Keywords: Functional data analysis; Functional principal components analysis; Multivariate functional principal component analysis; Eye tracking; Autism spectrum disorder (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12561-023-09399-1

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