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Background Although non-suicidal self-injury (NSSI) disorder is highly prevalent in adolescents, its relationship with pain system function and suicidality is still controversial. The present study was designed to assess the function of the nociceptive afferent pathways and the endogenous pain modulation in adolescent patients with NSSI and to longitudinally register any suicide attempt, describe its frequency and find a possible association between suicide, neurophysiological measures, and psychological measures. Methods We enrolled 30 adolescents suffering from NSSI and 20 age- and gender-matched healthy controls. Patients underwent a comprehensive psychological evaluation. Each participant underwent thermal-pain thresholds of the quantitative sensory testing, laser evoked potential recording to study the ascending nociceptive pathway and the conditioned pain modulation testing to test the endogenous pain modulation. Results We found that patients with NSSI had a reduced amplitude of the N2 component of laser evoked potentials and an abnormal conditioned pain modulation. The amplitude of the N2 was associated with suicidal risk. Conclusions The deficit of the endogenous pain modulation likely depends on a saturation due to continuous pain solicitation. The strong association of a reduced amplitude of the N2 component with suicide suggests it may serve as a possible biomarker in self-harming adolescents. Significance The present study, for the first time, identifies the N2 component of laser evoked potentials as a possible neurophysiological biomarker of suicidal risk in patients with non-suicidal self-injury, therefore raising the possibility for a non-invasive test to identify subjects at higher risk of suicide among self-harming patients.

Objectives We aimed to investigate the conduction velocity of the cold spinal pathway in healthy humans. Methods Using a cold stimulator consisting of micro-Peltier elements that was able to produce steep cooling ramps up to -300°C/s, we recorded cold-evoked potentials after stimulation of the dorsal midline at C5, T2, T6 and T10 vertebral levels and calculated the conduction velocity of the cold spinal pathway. In all participants, we used laser stimulation to deliver painful heat (Aδ-fibres-mediated) and warm (C-fibres-mediated) stimuli to the same sites in order to compare the conduction velocity of the cold spinal pathway with that of the nociceptive and warm spinal pathways. Results Cold stimulation evoked large-amplitude vertex potentials from all stimulation sites. The mean conduction velocity of the cold spinal pathway was 12.0 m/s, which did not differ from that of the nociceptive spinal pathway (10.5 m/s). The mean conduction velocity of the warm spinal pathway was 2.0 m/s. Discussion This study provides previously unreported findings regarding cold spinal pathway conduction velocity in humans that may be useful in the assessment of spinal cord lesions as well as in intraoperative monitoring during spinal surgery. Significance This neurophysiological study provides previously unreported findings on cold spinal pathway conduction velocity in healthy humans. Cold-evoked potentials may represent an alternative to laser-evoked potential recording, useful to assess spinothalamic tract in patients with spinal cord lesions and monitor patients during spinal surgery.

We propose the use of probability models for ranked data as a useful alternative to a quantitative data analysis to investigate the outcome of bioassay experiments, when the preliminary choice of an appropriate normalization method for the raw numerical responses is difficult or subject to criticism. We review standard distance-based and multistage ranking models and in this last context we propose an original generalization of the Plackett-Luce model to account for the order of the ranking elicitation process. The usefulness of the novel model is illustrated with its maximum likelihood estimation for a real data set. Specifically, we address the heterogeneous nature of experimental units via model-based clustering and detail the necessary steps for a successful likelihood maximization through a hybrid version of the Expectation-Maximization algorithm. The performance of the mixture model using the new distribution as mixture components is compared with those relative to alternative mixture models for random rankings. A discussion on the interpretation of the identified clusters and a comparison with more standard quantitative approaches are finally provided., (revised to properly include references)