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

Publication ,  Journal Article
Kwan, B; Sugar, CA; Qian, Q; Shic, F; Naples, A; Johnson, SP; Webb, SJ; Jeste, S; Faja, S; Levin, AR; Dawson, G; McPartland, JC; Şentürk, D
Published in: Stat Biosci
December 2024

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.

Duke Scholars

Published In

Stat Biosci

DOI

ISSN

1867-1764

Publication Date

December 2024

Volume

16

Issue

3

Start / End Page

578 / 603

Location

United States

Related Subject Headings

  • 4905 Statistics
  • 3102 Bioinformatics and computational biology
 

Citation

APA
Chicago
ICMJE
MLA
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Kwan, B., Sugar, C. A., Qian, Q., Shic, F., Naples, A., Johnson, S. P., … Şentürk, D. (2024). Constrained Multivariate Functional Principal Components Analysis for Novel Outcomes in Eye-Tracking Experiments. Stat Biosci, 16(3), 578–603. https://doi.org/10.1007/s12561-023-09399-1
Kwan, Brian, Catherine A. Sugar, Qi Qian, Frederick Shic, Adam Naples, Scott P. Johnson, Sara J. Webb, et al. “Constrained Multivariate Functional Principal Components Analysis for Novel Outcomes in Eye-Tracking Experiments.Stat Biosci 16, no. 3 (December 2024): 578–603. https://doi.org/10.1007/s12561-023-09399-1.
Kwan B, Sugar CA, Qian Q, Shic F, Naples A, Johnson SP, et al. Constrained Multivariate Functional Principal Components Analysis for Novel Outcomes in Eye-Tracking Experiments. Stat Biosci. 2024 Dec;16(3):578–603.
Kwan, Brian, et al. “Constrained Multivariate Functional Principal Components Analysis for Novel Outcomes in Eye-Tracking Experiments.Stat Biosci, vol. 16, no. 3, Dec. 2024, pp. 578–603. Pubmed, doi:10.1007/s12561-023-09399-1.
Kwan B, Sugar CA, Qian Q, Shic F, Naples A, Johnson SP, Webb SJ, Jeste S, Faja S, Levin AR, Dawson G, McPartland JC, Şentürk D. Constrained Multivariate Functional Principal Components Analysis for Novel Outcomes in Eye-Tracking Experiments. Stat Biosci. 2024 Dec;16(3):578–603.
Journal cover image

Published In

Stat Biosci

DOI

ISSN

1867-1764

Publication Date

December 2024

Volume

16

Issue

3

Start / End Page

578 / 603

Location

United States

Related Subject Headings

  • 4905 Statistics
  • 3102 Bioinformatics and computational biology