To calibrate or not to calibrate? A methodological dilemma in experimental pain research

To calibrate or not to calibrate? This question is raised by almost everyone designing an experimental pain study with supra-threshold stimulation. The dilemma is whether to individualize stimulus intensity to the pain threshold / supra-threshold pain level of each participant or whether to provide the noxious stimulus at a fixed intensity so that everyone receives the identical input. Each approach has unique pros and cons which need to be considered to i) accurately design an experiment, ii) enhance statistical inference in the given data and, iii) reduce bias and the influence of confounding factors in the individual study e.g., body composition, differences in energy absorption and previous experience. Individualization requires calibration, a procedure already irritating the nociceptive system but allowing to match the pain level across individuals. It leads to a higher variability of the stimulus intensity, thereby influencing the encoding of noxiousness by the central nervous system. Results might be less influenced by statistical phenomena such as ceiling/floor effects and the approach does not seem to rise ethical concerns. On the other hand, applying a fixed (standardized) intensity reduces the problem of intensity encoding leading to a large between-subjects variability in pain responses. Fixed stimulation intensities do not require pre-exposure. It can be proposed that one method is not preferable over another, however the choice depends on the study aim and the desired level of external validity. This paper discusses considerations for choosing the optimal approach for experimental pain studies and provides recommendations for different study designs. PERSPECTIVE: To calibrate pain or not? This dilemma is related to almost every experimental pain research. The decision is a trade-off between statistical power and greater control of stimulus encoding. The article decomposes both approaches and presents the pros and cons of either approach supported by data and simulation experiment

Is the Measurement of Resistance during Passive Movements a Reliable Assessment Tool? : Systematic Review

Passive manuelle Bewegungen von Gelenk- und Neuralstrukturen werden häufig mit einem ersten (R 1 = erster fühlbarer Widerstand innerhalb einer passiven Bewegung) und zweiten Widerstand (R2 = fühlbarer Widerstand am Bewegungsende) gemessen. Die vorliegende Arbeit gibt einen Überblick über die Zuverlässigkeit der Messung von R1 und R2. In den Datenbanken PubMed (Medline), The Cochrane Database, CINAHL und Web of Science wurde in einer systematischen Literaturrecherche nach Diagnostikstudien zu R1 und R2 gesucht und die eingeschlossenen Studien mit dem Quality Appraisal of Reliability Studies Tool (QAREL) bewertet. Die Ergebnisse zeigten widersprüchliche Reliabilität bei neurodynamischen Tests. Passive Außenrotationen bei Schulterpathologien erzielten eine gute, passive akzessorische intervertebrale Bewegungen (PAIVM) eher eine geringere Reliabilität. R2 scheint eine höhere Reliabilität als R1 aufzuweisen, und die Intrarater-Reliabilität bei neurodynamischen Tests besser zu sein als die Interrater-Reliabilität. Für die geringe Reliabilität werden verschiedene Ursachen diskutiert. Trotz der methodischen Mängel in einigen Studien scheinen Tests mit größerem Hebel eine höhere Reliabilität zu erreichen als PAIVM.Passive manual movements of articular and neural structures are often assessed using a first (R1 = first perceptible resistance during a passive movement) and second resistance (R2 = perceptible end of range resistance). This article reviews the reliability of the measurement of R1 and R2. A systematic literature research in the databases PubMed (medline), The Cochrane Database, CINAHL and Web of Science searched for diagnostic studies on R1 and R2. The included studies were evaluated using the Quality Appraisal of Reliability Studies Tool (QAREL). The results showed inconclusive reliability of neurodynamic tests. Passive external rotation in shoulder pathologies produced good, passive accessory intervertebral movements (PAIVM) rather low reliability. R2 seems to have a higher reliability than R1, and the intrarater reliability in neurodynamic tests seems to be better than the interrater reliability. Different sources of the poor reliability are discussed. In spite of the methodological deficits in some studies the tests with a larger lever seem to attain better reliability than PAIVM

Increased mechanosensivity of the greater occipital nerve in subjects with side-dominant head and neck pain : a diagnostic case-control study

Objectives To investigate differences in pressure pain thresholds (PPTs) and longitudinal mechanosensitivity of the greater occipital nerve (GON) between patients with side-dominant head and neck pain (SDHNP) and healthy controls. Evaluation of neural sensitivity is not a standard procedure in the physical examination of headache patients but may influence treatment decisions. Methods Two blinded investigators evaluated PPTs on two different locations bilaterally over the GON as well as the occipitalis longsitting-slump (OLSS) in subjects with SDHNP (n = 38)) and healthy controls (n = 38). Results Pressure pain sensitivity of the GON was lower at the occiput in patients compared to controls (p = 0.001). Differences in pressure sensitivity of the GON at the nucheal line, or between the dominant headache side and the non-dominant side were not found (p > 0.05). The OLSS showed significant higher pain intensity in SDHNP (p < 0.001). In comparison to the non-dominant side, the dominant side was significantly more sensitive (p = 0.004). Discussion Palpation of the GON at the occiput and the OLSS may be potentially relevant tests in SDHNP. One explanation for an increased bilateral sensitivity may be sensitization mechanisms. Future research should investigate the efficacy of neurodynamic techniques directed at the GON

Psychological mechanisms of offset analgesia: The effect of expectancy manipulation.

A frequently used paradigm to quantify endogenous pain modulation is offset analgesia, which is defined as a disproportionate large reduction in pain following a small decrease in a heat stimulus. The aim of this study was to determine whether suggestion influences the magnitude of offset analgesia in healthy participants. A total of 97 participants were randomized into three groups (hypoalgesic group, hyperalgesic group, control group). All participants received four heat stimuli (two constant trials and two offset trials) to the ventral, non-dominant forearm while they were asked to rate their perceived pain using a computerized visual analogue scale. In addition, electrodermal activity was measured during each heat stimulus. Participants in both intervention groups were given a visual and verbal suggestion about the expected pain response in an hypoalgesic and hyperalgesic manner. The control group received no suggestion. In all groups, significant offset analgesia was provoked, indicated by reduced pain ratings (p 0.05). The results of this study indicate that suggestion can be effective to reduce but not increase endogenous pain modulation quantified by offset analgesia in healthy participants

Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition

Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been proposed to replace the previous gold standard with an automated and objective assessment. While the accuracy of such models could be increased incrementally, the understandability and transparency of these systems have not been the main focus of the research community thus far. Thus, in this work, several outcomes and insights of explainable artificial intelligence applied to the electrodermal activity sensor data of the PainMonit and BioVid Heat Pain Database are presented. For this purpose, the importance of hand-crafted features is evaluated using recursive feature elimination based on impurity scores in Random Forest (RF) models. Additionally, Gradient-weighted class activation mapping is applied to highlight the most impactful features learned by deep learning models. Our studies highlight the following insights: (1) Very simple hand-crafted features can yield comparative performances to deep learning models for pain recognition, especially when properly selected with recursive feature elimination. Thus, the use of complex neural networks should be questioned in pain recognition, especially considering their computational costs; and (2) both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data to make their decision in the context of automated pain recognition