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Comparison of adaptive psychophysical methods for nociceptive threshold tracking : a simulation and human subject study

Doll, R.J. (2012) Comparison of adaptive psychophysical methods for nociceptive threshold tracking : a simulation and human subject study.

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Abstract:Tracking psychophysical thresholds over time is useful for the investigation of dynamic behavior of underlying mechanisms. For example, noxious events can activate endogenous analgesic mechanisms reflected in an increasing nociceptive threshold. Adequate psychophysical methods for threshold tracking should include an efficient stimulus selection procedure and threshold estimation method. This study compares the performance of adaptive stimulus selection procedures and estimation methods in both a simulation and a human subject study. Monte Carlo simulations were performed to compare bias and precision of threshold estimates of three stimulus selection procedures (simple staircase, random staircase and a minimum entropy procedure) and two threshold estimation methods (logistic regression and Bayesian estimation). Logistic regression was found to result in more precise estimates than Bayesian estimates in all simulations. Moreover, estimates were more precise with the simple staircase procedure than the other procedures. However, the random staircase procedure is less sensitive to different procedure specific settings (e.g. step‐size) than the other procedures. The simple staircase and random staircase procedures, both using logistic regression, were compared in a human subject study (N=30). A cold pressor was applied as nociceptive conditioning stimulus. Electrocutaneous stimulation was used for nociceptive detection threshold tracking before, during and after the conditioning stimulus. Both procedures were able to detect habituation as well as changes induced by the conditioning stimulus, but highest precision was obtained with the random staircase procedure. Based on these results, we recommend the random staircase procedure in combination with logistic regression for threshold tracking experiments.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:44 medicine
Programme:Biomedical Engineering MSc (66226)
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