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

Brian Kwan1, Catherine A Sugar1,2, Qi Qian1

  • 1Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.

Statistics in Biosciences
|December 20, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using eye tracking (ET) to analyze visual attention in autism spectrum disorder (ASD). The approach provides deeper insights into how children with ASD process social information compared to typically developing peers.

Keywords:
Autism spectrum disorderEye trackingFunctional data analysisFunctional principal components analysisMultivariate functional principal component analysis

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Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Biomarker Research

Background:

  • Autism spectrum disorder (ASD) is characterized by challenges in social communication and sensory processing.
  • Visual attention is a key area of interest for ASD biomarker research, measurable through eye tracking (ET).
  • Current ET analyses often simplify complex gaze data, analyzing regions of interest (ROIs) separately.

Purpose of the Study:

  • To develop a novel multivariate functional outcome for analyzing joint looking times across multiple ROIs in ET experiments.
  • To introduce a constrained multivariate functional principal components analysis (FPCA) to capture variations in this outcome.
  • To apply these methods to understand social attention differences in children with ASD.

Main Methods:

  • Proposed a novel multivariate functional outcome integrating proportion looking time across multiple ROIs.
  • Developed a constrained multivariate FPCA procedure accounting for the sum-to-one constraint of proportions.
  • Applied the method to eye tracking data from the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) Activity Monitoring task.

Main Results:

  • Identified dominant modes of variation in proportion looking times across multiple ROIs for children with ASD and typically developing (TD) peers.
  • Revealed richer analyses of diagnostic group differences in social attention patterns.
  • Demonstrated the utility of the novel functional outcome and FPCA method in capturing complex gaze behaviors.

Conclusions:

  • The novel multivariate functional approach offers a more comprehensive analysis of visual attention in ASD.
  • This method enhances the understanding of social attention deficits and variations in autism.
  • The findings contribute to the development of more sensitive biomarkers for ASD using eye tracking data.