Abstract
Post-stroke fatigue (PSF) is prevalent among stroke patients, but its mechanisms are poorly understood. Many patients with PSF experience cognitive difficulties, but studies aiming to identify cognitive correlates of PSF have been largely inconclusive. With the aim of characterizing the relationship between subjective fatigue and attentional function, we collected behavioral data using the attention network test (ANT) and self-reported fatigue scores using the fatigue severity scale (FSS) from 53 stroke patients. In order to evaluate the utility and added value of computational modeling for delineating specific underpinnings of response time (RT) distributions, we fitted a hierarchical drift diffusion model (hDDM) to the ANT data. Results revealed a relationship between fatigue and RT distributions. Specifically, there was a positive interaction between FSS score and elapsed time on RT. Group analyses suggested that patients without PSF increased speed during the course of the session, while patients with PSF did not. In line with the conventional analyses based on observed RT, the best fitting hDD model identified an interaction between elapsed time and fatigue on non-decision time, suggesting an increase in time needed for stimulus encoding and response execution rather than cognitive information processing and evidence accumulation. These novel results demonstrate the significance of considering the sustained nature of effort when defining the cognitive phenotype of PSF, intuitively indicating that the cognitive phenotype of fatigue entails an increased vulnerability to sustained effort, and suggest that the use of computational approaches offers a further characterization of specific processes underlying behavioral differences.
Highlights
Post-stroke fatigue (PSF) is a common complaint among stroke survivors, with an estimated prevalence ranging between 25% and 85% (Cumming, Packer, Kramer, & English, 2016)
Many of the studies failing to identify an association use rather general measures of cognitive function such as the Mini-Mental State Examination (MMSE; (Folstein, Folstein, & McHugh, 1975); van Eijsden, van de Port, Visser-Meily, & Kwakkel, 2012; Kutlubaev et al, 2013) and a recent review on factors associated with PSF concluded that the evidence does not support a link between general cognitive function and PSF, there may be an association between attentional functioning, processing speed and fatigue (Pihlaja, Uimonen, Mustanoja, Tatlisumak, & Poutiainen, 2014; Ponchel, Bombois, Bordet, & Hénon, 2015)
That included only trial number and not fatigue severity scale (FSS) score as predictor, the results suggested that patients without PSF demonstrated more speeded response times (RT) in the incongruent condition during the course of the experiment, while patients with PSF did not show any significant changes in RT in any condition
Summary
Post-stroke fatigue (PSF) is a common complaint among stroke survivors, with an estimated prevalence ranging between 25% and 85% (Cumming, Packer, Kramer, & English, 2016). The parameters have been validated in various experimental paradigms (Lerche & Voss, 2017; Voss, Rothermund, & Voss, 2004) Applying computational models such as the DDM in clinical research may allow for a dissection of specific cognitive processes underlying observed group and individual differences in RT patterns. Evaluating whether DDM modeling can elaborate our understanding further by characterizing the specific cognitive processes underlying observed differences in RT patterns, we performed an exploratory analysis where we fitted a hDDM to the ANT behavioral data and tested for associations between the model parameters (drift rate (v), non-decision time (t) and boundary separation (a)) and fatigue (FSS) score. In line with our first hypothesis, we hypothesized that any associations between subjective fatigue and model parameters will interact with time, with increasing associations between fatigue and model parameters with more sustained performance
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