Eye movements of patients with schizophrenia in a natural environment

Abstract

Alterations of eye movements in schizophrenia patients have been widely described for laboratory settings. For example, gain during smooth tracking is reduced, and fixation patterns differ between patients and healthy controls. The question remains, whether such results are related to the specifics of the experimental environment, or whether they transfer to natural settings. Twenty ICD-10 diagnosed schizophrenia patients and 20 healthy age-matched controls participated in the study, each performing four different oculomotor tasks corresponding to natural everyday behavior in an indoor environment: (I) fixating stationary targets, (II) sitting in a hallway with free gaze, (III) walking down the hallway, and (IV) visually tracking a target on the floor while walking straight-ahead. In all conditions, eye movements were continuously recorded binocularly by a mobile lightweight eye tracker (EyeSeeCam). When patients looked at predefined targets, they showed more fixations with reduced durations than controls. The opposite was true when participants were sitting in a hallway with free gaze. During visual tracking, patients showed a significantly greater root-mean-square error (representing the mean deviation from optimal) of retinal target velocity. Different from previous results on smooth-pursuit eye movements obtained in laboratory settings, no such difference was found for velocity gain. Taken together, we have identified significant differences in fundamental oculomotor parameters between schizophrenia patients and healthy controls during natural behavior in a real environment. Moreover, our data provide evidence that in natural settings, patients overcome some impairments, which might be present only in laboratory studies, by as of now unknown compensatory mechanisms or strategies.

Prediction in goal-directed action

“…For example, several influential neurophysiological studies have related a network of frontal and parietal brain areas to predictive ocular pursuit (Badler & Heinen, 2006;Heinen et al, 2005;Kim, Badler, & Heinen, 2005;Missal & Heinen, 2004;Yang & Heinen, 2014), or proposed Bayesian integration of sensory information with cognitive experience to optimize gaze control (Darlington et al, 2017;Darlington, Beck, & Lisberger, 2018). Whereas these studies have inspired behavioral studies that have provided many valuable results, some results obtained with smooth pursuit eye movements in highly simplified circumstances, for example, do not hold under real world conditions (Dowiasch et al, 2016;Dowiasch, Marx, Einhäuser, & Bremmer, 2015). These results emphasize the importance of also exploring prediction in natural behavior.…”

Morphological and functional alterations in patients with schizophrenia spectrum disorders

“…Dowiasch et al [5] recorded eye movements in four different oculomotor tasks corresponding to natural everyday behavior in schizophrenia patients compared to healthy controls. Patients looking at predefined targets showed more fixations with reduced durations and had a greater root-mean-square error of retinal target velocity during visual tracking.…”

Characterizing and automatically detecting smooth pursuit in a large-scale ground-truth data set of dynamic natural scenes

“…Segmentation of eye movements into discrete events is an important part of eye movement research and has been investigated for decades. Although we discuss the definitions of the particular eye movement types later in the paper, reliably separating gaze events from one another enables a large number of analyses of eye tracking data sets in order to search for group differences or similarities (Dowiasch et al, 2016;Silberg et al, 2019), find the differences in viewing behavior for different stimulus types (Vig, Dorr, Martinetz, & Barth, 2011), and many other research applications, including media summarisation (Salehin & Paul, 2017).…”

“…Traditionally, automatic analysis performed based on the subjects' eye movements relied either on detecting fixations and saccades (Williams, Loughland, Gordon, & Davidson, 1999), or on analyzing the recordings that correspond to synthetic stimuli (Spering, , where targets for smooth pursuit, for example, are limited and have well-defined properties. Recent works show a tendency towards naturalistic stimuli, however, which include dynamic content as well (Dowiasch et al, 2016;Silberg et al, 2019). For these, even a seemingly simple analysis that is limited to fixations and saccades may be prone to errors because of the accidental inclusion of pursuit samples (Dorr et al, 2010).…”

“…In natural scenes, and in the absence of the detailed information about all the moving targets throughout the video, such matching is practically impossible. Dowiasch et al (2016) computed optical flow of the video instead, using it as a substitute for gaze target speed, but during manual annotation of our data set we noticed that gaze samples were often offset relative to the targets they were following, likely due to tracking inaccuracy.…”