A dynamic neural model of localization of brief successive stimuli in saltation

Somatosensory saltation is an illusion robustly generated using short tactile stimuli [1,2]. There is a perceived displacement of a first stimulus if followed by a subsequent nearby stimulus with a short stimulus onset asynchrony (SOA). Experimental reports suggest that this illusion results from spatiotemporal integration in early processing stages, but the exact neural mechanism is unknown. The neuronal mechanism involved is probably quite generic as similar phenomena occur in other modalities, audition for example [3]

Multiple threshold tracking methods for improved observation of nociceptive function

Estimating momentary perception thresholds cannot reveal dynamic properties of underlying mechanisms. However, continuously estimating multiple thresholds can. This talk focussed on the possibility of tracking multiple thresholds over time. A cold pressor model was used to activate descending nociceptive pathways, and a capsaicin defunctionalization model was used to induce nociceptive peripheral changes

Identification of the nociceptive forward pathway using amplitude-response pairs

Malfunctioning of the nociceptive forward pathway plays a key role in the development of chronic pain, which reduces the quality of lives of the patients. To quantitatively characterize the nociceptive forward pathway, four neurophysiological parameters can be estimated by integrating computational models and multiple perception thresholds. This model-based approach could reveal the state of the nociceptive malfunctioning for understanding the development of pain, e.g. central sensitization. With suitable psychophysical procedures, one can obtain amplitude-response pairs around a perception threshold. Combining these techniques, one can first perform logistic regression to obtain a threshold from amplitude-response pairs and use that for parameter estimation. In this work, we directly estimate parameters using the amplitude-response pairs without intermediate transformations. We study how the number of trials included influences the estimation and compare with the earlier approach. Furthermore, considering the clinical aspect, whether the pairs using fewer combinations of the temporal settings can still enough to estimate the parameters will be addressed. This work will only consider the simulated dataset to estimate the parameters, which is an essential step to further investigations with real datasets. The estimate of the system parameters using amplitude-response pairs directly converges faster than the estimate based on the perception threshold. Such improvement of estimation could provide more reliable information for further interpretations of the state of the nociceptive system

Simulation of psychophysical stimulus selection procedures for dynamic threshold tracking

Stimulus selection procedures are of importance for adequate psychophysical nociceptive threshold estimation. Various stimulus selection procedures were analyzed by means of simulations. Precision, bias, efficiency, and time constants of the various stimulus selection procedures were determined in a simulation model wherein a threshold is tracked. A new adaptive stimulus selection procedure based on stochastic stimulus selection is proposed as a nociceptive threshold tracking procedure

Estimation and Identifiability of Model Parameters in Human Nociceptive Processing Using Yes-No Detection Responses to Electrocutaneous Stimulation

Healthy or pathological states of nociceptive subsystems determine different stimulus-response relations measured from quantitative sensory testing. In turn, stimulus-responses measurements may be used to assess these states. In a recently developed computational model, six model parameters characterize activation of nerve endings and spinal neurons. However, both model nonlinearity and limited information in yes-no detection responses to electrocutaneous stimuli challenge to estimate model parameters. Here, we address the question whether and how one can overcome these difficulties for reliable parameter estimation. First, we fit the computational model to experimental stimulus-response pairs by maximizing the likelihood. To evaluate the balance between model fit and complexity, we evaluate the Bayesian Information Criterion. We find that the computational model is better than a conventional logistic model regarding the balance. Second, our theoretical analysis suggests to vary the pulse width among applied stimuli as a necessary condition to prevent structural non-identifiability. In addition, the numerically implemented profile likelihood approach reveals structural and practical non-identifiability. Our model-based approach with integration of psychophysical measurements can be useful for a reliable assessment of states of the nociceptive system

Simultaneous tracking of multiple nociceptive thresholds: a simulation and human subject study

Monte Carlo simulations were performed to compare bias, precision and bandwidth of thresh-old estimates while varying the number of simultaneous tracked thresholds (1-7). An adaptive random staircase procedure was used as stimulus selection procedure while logistic regression was used to obtain threshold estimates. We found that the bias was similar in all simulations. Moreover, precision and bandwidth lowered with more simultaneous tracked thresholds. Three different numbers of simultaneous tracked thresholds were compared in a human sub-ject study. 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

RESPOND – A patient-centred programme to prevent secondary falls in older people presenting to the emergency department with a fall: Protocol for a mixed methods programme evaluation.

Background Programme evaluations conducted alongside randomised controlled trials (RCTs) have potential to enhance understanding of trial outcomes. This paper describes a multi-level programme evaluation to be conducted alongside an RCT of a falls prevention programme (RESPOND). Objectives 1) To conduct a process evaluation in order to identify the degree of implementation fidelity and associated barriers and facilitators. 2) To evaluate the primary intended impact of the programme: participation in fall prevention strategies, and the factors influencing participation. 3) To identify the factors influencing RESPOND RCT outcomes: falls, fall injuries and ED re-presentations. Methods/ Design Five hundred and twenty eight community-dwelling adults aged 60–90 years presenting to two EDs with a fall will be recruited and randomly assigned to the intervention or standard care group. All RESPOND participants and RESPOND clinicians will be included in the evaluation. A mixed methods design will be used and a programme logic model will frame the evaluation. Data will be sourced from interviews, focus groups, questionnaires, clinician case notes, recruitment records, participant-completed calendars, hospital administrative datasets, and audio-recordings of intervention contacts. Quantitative data will be analysed via descriptive and inferential statistics and qualitative data will be interpreted using thematic analysis. Discussion The RESPOND programme evaluation will provide information about contextual and influencing factors related to the RCT outcomes. The results will assist researchers, clinicians, and policy makers to make decisions about future falls prevention interventions. Insights gained are likely to be transferable to preventive health programmes for a range of chronic conditions