Mapping simulated visual field defects with movie-viewing pupil perimetry

Abstract

Purpose: Assessing the quality of the visual field is important for the diagnosis of ophthalmic and neurological diseases and, consequently, for rehabilitation. Visual field defects (VFDs) are typically assessed using standard automated perimetry (SAP). However, SAP requires participants to understand instructions, maintain fixation and sustained attention, and provide overt responses. These aspects make SAP less suitable for very young or cognitively impaired populations. Here we investigate the feasibility of a new and less demanding form of perimetry. This method assesses visual sensitivity based on pupil responses while performing the perhaps simplest task imaginable: watching movies.

Method: We analyzed an existing dataset, with healthy participants (n = 70) freely watching movies with or without gaze-contingent simulated VFDs, either hemianopia (left- or right-sided) or glaucoma (large nasal arc, small nasal arc, and tunnel vision). Meanwhile, their gaze and pupil size were recorded. Using a recently published toolbox (Open-DPSM), we modeled the relative contribution of visual events to the pupil responses to indicate relative visual sensitivity across the visual field and to dissociate between conditions with and without simulated VFDs.

Result: Conditions with and without simulated VFDs could be dissociated, with an AUC ranging from 0.85 to 0.97, depending on the specific simulated VFD condition. In addition, the dissociation was better when including more movies in the modeling but the model with as few movies as 10 movies was sufficient for a good classification (AUC ranging from 0.84 to 0.96).

Conclusion: Movie-viewing pupil perimetry is promising in providing complementary information for the diagnosis of VFDs, especially for those who are unable to perform conventional perimetry.

Keywords: Glaucoma; Hemianopia; Modeling pupil size; Pupil perimetry; Simulated visual field defects.

Fig. 1

Visual field regions overlaid on a movie frame and different simulated visual field defects (sVFDs). A The visual field was divided into separate regions in the form of five eccentricities (marked with black lines) for Experiment 1 (simulated hemianopia), which was based on standards in the field of pupil perimetry (e.g. [7, 45, 49, 58]). B Same plot as in A, but for Experiment 2 (simulated glaucoma) during which movies were displayed on a larger monitor. This example, where the visual field is shifted towards the left of the movie, illustrates the gaze-contingent adaptation of the visual field that moved along with the gaze position. C-D Simulated hemianopia on the left and right side. The dark grey regions represent the masked areas, the white regions the unmasked areas, and the blue dashed lines the outlines of the mask; E–G Same as panel C-D, but corresponding to simulated scotomas: large nasal arc (E), small nasal arc (F), and tunnel vision (G). Some regions were partially masked. Regions were classified as masked instead of unmasked when the mask exceeded 50% of the region

Fig. 2

Modelled regional weights (red = high; blue = low) for A one exemplary participant per simulated visual field defect (sVFD) condition; B Mean weights of all participants per sVFD condition after subtracting weights of the control condition; C Distribution of weights (z-standardized) in unmasked regions (in red) and masked regions (in blue) per sVFD condition. ROC curves are shown as subplots; D Distribution of the average weight difference between unmasked and masked regions (z-standardized) per sVFD condition (orange), compared with the control condition (grey). Each dot indicates one participant. Positive scores on the x-axis mean that masked regions evoked weaker pupil responses than unmasked regions