Detecting outliers of pursuit eye movements: a preliminary analysis of autism spectrum disorder

TL;DR

This study introduces an outlier analysis method for detecting individual atypical eye movement patterns in ASD, revealing high heterogeneity and potential for personalized diagnostics.

Contribution

The paper presents a novel outlier scoring approach using Mahalanobis distance and PCA to identify individual oculomotor deviations in ASD.

Findings

ASD group showed a significantly higher outlier rate (38.9%) than TD group (5.1%)

Mean outlier score was significantly higher in ASD (3.00) than TD (1.52)

Outlier analysis captured atypicalities even when mean-based methods did not

Abstract

Background: Autism spectrum disorder (ASD) is characterized by significant clinical and biological heterogeneity. Conventional group-mean analyses of eye movements often mask individual atypicalities, potentially overlooking critical pathological signatures. This study aimed to identify idiosyncratic oculomotor patterns in ASD using an "outlier analysis" of smooth pursuit eye movement (SPEM). Methods: We recorded SPEM during a slow Lissajous pursuit task in 18 adults with ASD and 39 typically developed (TD) individuals. To quantify individual deviations, we derived an "outlier score" based on the Mahalanobis distance. This score was calculated from a feature vector, optimized via Principal Component Analysis (PCA), comprising the temporal lag ($\Delta$t) and the spatial deviation ($\Delta$s). An outlier was statistically defined as a score exceeding $\sqrt{10}$ (approximately 3.16$\sigma$) relative to the TD normative distribution. Results: While the TD group exhibited a low outlier rate of 5.1%, the ASD group demonstrated a significantly higher prevalence of 38.9% (7/18) (binomial P = 0.0034). Furthermore, the mean outlier score was significantly elevated in the ASD group (3.00 $\pm$ 2.62) compared to the TD group (1.52 $\pm$ 0.80; P = 0.002). Notably, these extreme deviations were captured even when conventional mean-based comparisons showed limited sensitivity. Conclusions: Our outlier analysis successfully visualized the high degree of idiosyncratic atypicality in ASD oculomotor control. By shifting the focus from group averages to individual deviations, this approach provides a sensitive metric for capturing the inherent heterogeneity of ASD, offering a potential baseline for identifying clinical subtypes.