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

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 ($Δ$t) and the spatial deviation ($Δ$s). An outlier was statistically defined as a score exceeding $\sqrt{10}$ (approximately 3.16$σ$) 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.

Figures (2)

• Figure 1: Analytical pipeline for the calculation of the outlier score. This flowchart illustrates the step-by-step process used to quantify individual atypicality in smooth pursuit eye movement (SPEM). Input Data: High-resolution gaze and target trajectories $(x, y, t)$ were recorded. Polar Coordinate Transformation: Raw coordinates were converted into a polar coordinate system relative to the display center. Temporal and Spatial Axis Analysis: Temporal axis ($\Delta t$): calculated as the time difference for the gaze to reach the same radial position as the target; features include the mean and standard deviation of $\Delta t$. Spatial axis ($\Delta s$): calculated as the difference in radial distance, normalized by the instantaneous target radius; only the standard deviation was utilized. Outlier Score Calculation: The three parameters were integrated via PCA and Mahalanobis distance to derive the final "outlier score," representing the degree of departure from the TD normative pattern.
• Figure 2: Outlier scores of typically developed (TD) individuals and individuals with autism spectrum disorder (ASD). (Left) Scatter plot of Outlier Scores: each dot represents the outlier score for an individual participant, calculated as the Mahalanobis distance derived from PCA of temporal and spatial deviation features. The dashed red line indicates the statistical threshold for outliers ($\sqrt{10} \approx 3.16$). (Right) Statistical summary: the ASD group exhibited significantly higher mean outlier scores ($3.00 \pm 2.62$) compared to the TD group ($1.52 \pm 0.80$; $P = 0.002$). The TD group showed a low outlier rate of 5.1% (2/39), whereas the ASD group demonstrated a markedly higher prevalence of 38.9% (7/18). A binomial parameter analysis confirmed that the proportion of outliers in the ASD group was significantly greater than that in the TD group ($P = 0.0034$).