Capturing trial-by-trial variability in behaviour: people with Parkinson's disease exhibit a greater rate of short-term fluctuations in response times.

Average response time is frequently used to reflect executive function. Less often studied is intra-individual variability in response times (IIVRT) which reflects within-person consistency. Higher IIVRT in Parkinson's disease (PD) has been associated with poor executive function but almost exclusively studied using standard deviation (SD). SD provides a standardised measure of inconsistency in RTs but is necessarily calculated as an average measure, precluding any trial-level investigation. Such linear measures cannot capture rapid and spontaneous changes in biological systems such as dopaminergic bursting activity. Therefore, nonlinear measures provide important complementary insights into dopamine-related neurocognition. The nonlinear method of graph theory is one viable approach to capture the complex biological changes in PD and their effect on behaviour. Our primary aim was to increase the understanding of RT fluctuations in PD beyond the use of SD by investigating nonlinear IIVRT measures using graph theory, constituting the first use of this approach on RT data. As hypothesized, PD was associated with a greater rate of trial-by-trial IIVRT compared to healthy older adults. The difference between groups could not be explained simply by worse overall RT performance, as average RT was comparable between groups. Instead, the IIVRT findings reflected impaired consistency in performance for people with PD and specifically a greater rate of short-term fluctuations in behaviour. These novel results indicate that a similarity graph algorithm may be a sensitive tool to capture the rapidly varying changes in behaviour that result from dysfunctional dopamine bursting activity in PD.