Data-driven analysis of gaze patterns in face perception: Methodological and clinical contributions

Paolo Masulli,Martyna Galazka,David Eberhard,Jakob Åsberg Johnels,Christopher Gillberg,Eva Billstedt,Nouchine Hadjikhani,Tobias S Andersen

doi:10.1016/j.cortex.2021.11.011

Paolo Masulli, Martyna Galazka + Show 6 more

Open Access

https://doi.org/10.1016/j.cortex.2021.11.011

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Abstract

Gaze patterns during face perception have been shown to relate to psychiatric symptoms. Standard analysis of gaze behavior includes calculating fixations within arbitrarily predetermined areas of interest. In contrast to this approach, we present an objective, data-driven method for the analysis of gaze patterns and their relation to diagnostic test scores. This method was applied to data acquired in an adult sample (N = 111) of psychiatry outpatients while they freely looked at images of human faces. Dimensional symptom scores of autism, attention deficit, and depression were collected. A linear regression model based on Principal Component Analysis coefficients computed for each participant was used to model symptom scores. We found that specific components of gaze patterns predicted autistic traits as well as depression symptoms. Gaze patterns shifted away from the eyes with increasing autism traits, a well-known effect. Additionally, the model revealed a lateralization component, with a reduction of the left visual field bias increasing with both autistic traits and depression symptoms independently. Taken together, our model provides a data-driven alternative for gaze data analysis, which can be applied to dimensionally-, rather than categorically-defined clinical subgroups within a variety of contexts. Methodological and clinical contribution of this approach are discussed.

Highlights

The tracking of eye gaze in response to social stimuli has been studied in experimental research for more than half a century (Yarbus, 1967)
With the increasing accessibility and convenience of eye tracking, research on face perception has extended into psychiatric populations with a focus on patient groups with difficulties in the social domain, including autism (Pelphrey et al, 2002), schizophrenia (Phillips & David, 1997) and social phobia (Horley, Williams, Gonsalvez, & Gordon, 2004), as well as patients diagnosed with mood disorders such as depression (Duque & Vazquez, 2015) and attention deficit disorders including ADD/ADHD (Muszkat et al, 2015)
A tendency for atypical face scanning patterns has been shown in children diagnosed with ADHD (Muszkat et al, 2015) and importantly, since signs and symptoms of autism, depression and ADHD often overlap in the same individual (Astle, Holmes, Kievit, & Gathercole, 2021; Coghill & SonugaBarke, 2012; Gillberg, 2010) the need for a multivariate approach is needed to unravel the specificity of these associations

Summary

Introduction

The tracking of eye gaze in response to social stimuli has been studied in experimental research for more than half a century (Yarbus, 1967). As recently highlighted by Falck-Ytter and colleagues, “eye tracking has a huge translational potential [including] risk assessment [and] monitoring treatment outcome in clinical trials” (Falck-Ytter et al, 2020). Among these conditions, autism spectrum disorder (ASD) has been examined in particular, due to a its association with deficits in social communication, and its characteristical atypical eye contact (American Psychiatric Association, 2013). A tendency for atypical face scanning patterns has been shown in children diagnosed with ADHD (Muszkat et al, 2015) and importantly, since (clinical or subclinical) signs and symptoms of autism, depression and ADHD often overlap in the same individual (Astle, Holmes, Kievit, & Gathercole, 2021; Coghill & SonugaBarke, 2012; Gillberg, 2010) the need for a multivariate approach is needed to unravel the specificity of these associations

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