Neural Modelling of Antisaccade Performance of Healthy Controls and Early Huntington’s Disease Patients

Cutsuridis, Vassilis, Jiang, Shouyong, Dunn, Matt , Brawn, James, Rosser, Anne and Erichsen, Jonathan (2021) Neural Modelling of Antisaccade Performance of Healthy Controls and Early Huntington’s Disease Patients. Chaos: An Interdisciplinary Journal of Nonlinear Science . ISSN 1054-1500

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Neural Modelling of Antisaccade Performance of Healthy Controls and Early Huntington’s Disease Patients
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Abstract

Huntington’s disease (HD), a genetically determined neurodegenerative disease, is positively correlated with eye movement abnormalities in decision making. The antisaccade conflict paradigm has been widely used to study response inhibition in eye movements and reliable performance deficits in HD subjects have been observed including greater number and timing of direction errors. We recorded the error rates and response latencies of early HD patients and healthy age-matched controls performing the mirror antisaccade task. HD participants displayed slower and more variable antisaccade latencies and increased error rates relative to healthy controls. A competitive accumulator-to-threshold neural model was then employed to quantitatively simulate the controls’ and patients’ reaction latencies and error rates and uncover the mechanisms giving rise to the observed HD antisaccade deficits. Our simulations showed: 1) a more gradual and noisy rate of accumulation of evidence by HD patients is responsible for the observed prolonged and more variable antisaccade latencies in early HD; 2) the confidence level of early HD patients making a decision is unaffected by the disease; and 3) the antisaccade performance of healthy controls and early HD patients is the end product of a neural lateral competition (inhibition) between a correct and an erroneous decision process, and not the end product of a third top-down stop signal suppressing the erroneous decision process as many have speculated.

Keywords:Neural model; Antisaccade task; Evidence accumulation; Reaction time; Error rate; Competition
Subjects:G Mathematical and Computer Sciences > G750 Cognitive Modelling
B Subjects allied to Medicine > B140 Neuroscience
G Mathematical and Computer Sciences > G730 Neural Computing
C Biological Sciences > C860 Neuropsychology
Divisions:College of Science > School of Computer Science
ID Code:43363
Deposited On:15 Dec 2020 11:27

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