title: Tracking of nociceptive thresholds using adaptive psychophysical methods.
abstract: Psychophysical thresholds reflect the state of the underlying nociceptive mechanisms. For example, noxious events can activate endogenous analgesic mechanisms that increase the nociceptive threshold. Therefore, tracking thresholds over time facilitates the investigation of the dynamics of these underlying mechanisms. Threshold tracking techniques should use efficient methods for stimulus selection and threshold estimation. This study compares, in simulation and in human psychophysical experiments, the performance of different combinations of adaptive stimulus selection procedures and threshold estimation methods. Monte Carlo simulations were first performed to compare the bias and precision of threshold estimates produced by three different stimulus selection procedures (simple staircase, random staircase, and minimum entropy procedure) and two estimation methods (logistic regression and Bayesian estimation). Logistic regression and Bayesian estimations resulted in similar precision only when the prior probability distributions (PDs) were chosen appropriately. The minimum entropy and simple staircase procedures achieved the highest precision, while the random staircase procedure was the least sensitive to different procedure-specific settings. Next, the simple staircase and random staircase procedures, in combination with logistic regression, were compared in a human subject study (n = 30). Electrocutaneous stimulation was used to track the nociceptive perception threshold before, during, and after a cold pressor task, which served as the conditioning stimulus. With both procedures, habituation was detected, as well as changes induced by the conditioning stimulus. However, the random staircase procedure achieved a higher precision. We recommend using the random staircase over the simple staircase procedure, in combination with logistic regression, for nonstationary threshold tracking experiments.
authors.affiliation: Biomedical Signals and Systems, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Drienerlolaan 5, PO Box 217, Enschede, The Netherlands, r.j.doll@utwente.nl.
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Doll
authors.forename: Jan R
authors.initials: JR
authors.lastname: Buitenweg
authors.forename: Hil G E
authors.initials: HG
authors.lastname: Meijer
authors.forename: Peter H
authors.initials: PH
authors.lastname: Veltink
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D015982:Bias; D004056:Differential Threshold / Q000502:physiology; D004867:Equipment Design; D005260:Female; D006225:Hand; D006801:Humans; D007101:Immersion; D016015:Logistic Models; D008297:Male; D008960:Models, Psychological; D015233:Models, Statistical; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods*; D009010:Monte Carlo Method; D009619:Nociceptors / Q000502:physiology*; D011601:Psychophysics / Q000379:methods*; D011897:Random Allocation; D012016:Reference Values; D012684:Sensory Thresholds / Q000502:physiology*; D012984:Software; D004561:Transcutaneous Electric Nerve Stimulation / Q000295:instrumentation*; D055815:Young Adult