Different decision deficits impair response inhibition in progressive supranuclear palsy and Parkinson's disease

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Abstract

Progressive supranuclear palsy and Parkinson's disease have distinct underlying neuropathology, but both diseases affect cognitive function in addition to causing a movement disorder. They impair response inhibition and may lead to impulsivity, which can occur even in the presence of profound akinesia and rigidity. The current study examined the mechanisms of cognitive impairments underlying disinhibition, using horizontal saccadic latencies that obviate the impact of limb slowness on executing response decisions. Nineteen patients with clinically diagnosed progressive supranuclear palsy (Richardson's syndrome), 24 patients with clinically diagnosed Parkinson's disease and 26 healthy control subjects completed a saccadic Go/No-Go task with a head-mounted infrared saccadometer. Participants were cued on each trial to make a pro-saccade to a horizontal target or withhold their responses. Both patient groups had impaired behavioural performance, with more commission errors than controls. Mean saccadic latencies were similar between all three groups. We analysed behavioural responses as a binary decision between Go and No-Go choices. By using Bayesian parameter estimation, we fitted a hierarchical drift-diffusion model to individual participants' single trial data. The model decomposes saccadic latencies into parameters for the decision process: decision boundary, drift rate of accumulation, decision bias, and non-decision time. In a leave-one-out three-way classification analysis, the model parameters provided better discrimination between patients and controls than raw behavioural measures. Furthermore, the model revealed disease-specific deficits in the Go/No-Go decision process. Both patient groups had slower drift rate of accumulation, and shorter non-decision time than controls. But patients with progressive supranuclear palsy were strongly biased towards a pro-saccade decision boundary compared to Parkinson's patients and controls. This indicates a prepotency of responding in combination with a reduction in further accumulation of evidence, which provides a parsimonious explanation for the apparently paradoxical combination of disinhibition and severe akinesia. The combination of the well-tolerated oculomotor paradigm and the sensitivity of the model-based analysis provides a valuable approach for interrogating decision-making processes in neurodegenerative disorders. The mechanistic differences underlying participants' poor performance were not observable from classical analysis of behavioural data, but were clearly revealed by modelling. These differences provide a rational basis on which to develop and assess new therapeutic strategies for cognition and behaviour in these disorders.

Figures

  • Figure 1 Saccadic Go/No-Go task and the drift-diffusion model. (A) Participants fixated on green and red points overlapping at the centre of the screen. One of the two points disappeared and a saccadic target was presented on the left or right of the
  • Table 1 Demographics and neuropsychological measures of participants with PSP, Parkinson’s disease and
  • Figure 2 Behavioural results. The mean proportion of errors (top) and saccadic latencies (bottom) in the Go/No-Go task. The error bars represent the standard errors across participants in each group. In all panels, asterisks denote statistical significance at *P5 0.05, **P5 0.01, or ***P5 0.001 from independent sample t-tests and n.s. denotes non-significant difference. PD = Parkinson’s disease.
  • Figure 3 Model comparison and model fits. (A) The deviance information criterion (DIC) value differences between the best fit model (Model 4) and the other three model variants, for each group separately (dash lines) and all participants combined (solid lines). (B) The graphical representation of the best fit model. The shaded node Data(g,p,i,j) indicates the observed data of each group (g), participant (p), condition (i) and trial (j). Nodes a, Ter, z, and v are parameters of the drift-diffusion model, each with a group distribution for each patient group with mean m and standard deviation . (C) Posterior predictive data distributions from the best fit model. The distribution along the positive x-axis shows the
  • Figure 4 Posterior estimates of the hierarchical drift-diffusion model parameters for each group. (A) Response bias z. (B) Nondecision time Ter. (C) Draft rates v for Go (solid lines) and No-Go (dashed lines) conditions. (D) Boundary separation a. In all panels, the asterisks denote significant difference between PSP and patients Parkinson’s disease from frequentist and Bayesian statistics, and n.s. denotes non-significant difference. (E) The schematic diagram of the altered Go/No-go decision processes in patients. PSP leads to an exaggerated response bias towards Go decisions (i.e. the upper boundary), and a reduced non-decision time, but that the further accumulation of evidence towards a response is accumulated very slowly, predisposing patients to inhibition errors but without prolonged latencies of actual responses. In contrast, Parkinson’s disease leads to a shorter non-decision time but normal initial response bias and a mild reduction in the rate of accumulation of evidence.
  • Table 2 Leave-one-out cross-validation results from three-way linear logistic regression classifiers
  • Figure 5 Receiver operating characteristic curves of each class in the leave-one-out three-way classification based on the model parameters (solid lines) and raw behavioural measures (dash lines). PD = Parkinson’s disease.

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CITATION STYLE

APA

Zhang, J., Rittman, T., Nombela, C., Fois, A., Coyle-Gilchrist, I., Barker, R. A., … Rowe, J. B. (2016). Different decision deficits impair response inhibition in progressive supranuclear palsy and Parkinson’s disease. Brain, 139(1), 161–173. https://doi.org/10.1093/brain/awv331

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