Assessing cognitive dysfunction in Parkinson's disease: An online tool to detect visuo-perceptual deficits

Abstract

Background: People with Parkinson's disease (PD) who develop visuo-perceptual deficits are at higher risk of dementia, but we lack tests that detect subtle visuo-perceptual deficits and can be performed by untrained personnel. Hallucinations are associated with cognitive impairment and typically involve perception of complex objects. Changes in object perception may therefore be a sensitive marker of visuo-perceptual deficits in PD.

Objective: We developed an online platform to test visuo-perceptual function. We hypothesised that (1) visuo-perceptual deficits in PD could be detected using online tests, (2) object perception would be preferentially affected, and (3) these deficits would be caused by changes in perception rather than response bias.

Methods: We assessed 91 people with PD and 275 controls. Performance was compared using classical frequentist statistics. We then fitted a hierarchical Bayesian signal detection theory model to a subset of tasks.

Results: People with PD were worse than controls at object recognition, showing no deficits in other visuo-perceptual tests. Specifically, they were worse at identifying skewed images (P < .0001); at detecting hidden objects (P = .0039); at identifying objects in peripheral vision (P < .0001); and at detecting biological motion (P = .0065). In contrast, people with PD were not worse at mental rotation or subjective size perception. Using signal detection modelling, we found this effect was driven by change in perceptual sensitivity rather than response bias.

Conclusions: Online tests can detect visuo-perceptual deficits in people with PD, with object recognition particularly affected. Ultimately, visuo-perceptual tests may be developed to identify at-risk patients for clinical trials to slow PD dementia. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Keywords: Parkinson's disease; hallucinations; perception; signal detection theory; vision.

Figure 1

(A) Patient recruitment and inclusion in the study, showing the number of patients and controls in each online task. (B) Online visuo‐perceptual tasks: Object invariance. Example skewed image. A dog is shown here. On each trial, an image of a cat or dog was shown for 280 milliseconds. Participants were then shown a choice screen, where they reported whether they had seen a cat or a dog using the computer mouse (3000 milliseconds response time, 24 trials). Hidden figures. The image contains 22 horses, 7 of which are not hidden, the rest are formed within the background features. Participants used their computer mouse to click on all the horses they could find. A maximum of 6 minutes and 75 clicks was allowed to reduce “blind” clicking across the entire image. Image produced by Steven M. Gardner and used here with permission. Peripheral object detection. Two images of animals were shown: one at fixation the other in the periphery. Presentation time was 280 milliseconds, followed by a choice screen where participants indicated whether the 2 presented animals were the same or different in identity. The image at fixation was smaller to enforce central fixation (24 trials). Biological motion. Participants were shown a moving point‐light walker, either with dots at the position of the major joints of a person moving, or with the position of the dots scrambled so that no percept of a person is formed. Participants indicated whether they had seen a person or scrambled moving dots (3000 milliseconds response time, 24 trials). Subjective size perception. In the classical form of the Ebbinghaus illusion, 2 identical circles are surrounded by smaller or larger inducers. This causes a perceived difference in the size of the central circles. Here, we modified this illusion by surrounding the larger inducing circles by 8 test circles that were each surrounded by 12 inducers. One of the test circles matched the central target circle in diameter. The others differed by a pseudorandom amount, drawn from a normal distribution around the diameter of the reference circle. The position of the identical test circle differed on each trial. Participants selected the test circle that matched the central circle in size using the computer mouse (15 trials). Mental rotation. Participants selected the grid in the lower row that matched the grid in the top row, but rotated (24 trials).

Figure 2

Results. (A) Object invariance. Performance in the object invariance test (identifying skewed animals) at 3 levels of skew for controls (light gray) and people with PD (dark gray). Main effect Parkinson's disease group and difficulty, both P < .0001. Error bars are standard error of the mean in all panels. ** Significant after Bonferroni correction in all panels. (B) Hidden figures. Number of horses found by controls (light gray) and people with PD (dark gray). (C) Peripheral object detection. Performance in matching animals presented at fixation and in peripheral visual field for controls (light gray) and people with PD (dark gray). (D) Biological motion. Performance in detecting biological motion at 3 levels of difficulty (additional moving dots) for controls (light gray) and people with PD (dark gray). Main effect Parkinson's disease group and difficulty, both P < .0001.

Figure 3

Posterior densities of people with PD (dark gray in each plot) and age‐matched controls (light gray in each plot) for perceptual sensitivity (d‐prime, d') in object invariance (A), peripheral object detection (B), and biological motion (C). Increased separation of the density plots reflects confidence in difference in perceptual sensitivity between the 2 participant groups.