Interpersonal touch is a fundamental component of social interactions because it can mitigate physical and psychological distress. To reproduce the psychological and physiological effects associated with interpersonal touch, interest is growing in introducing tactile sensations to communication devices. However, it remains unknown whether physical contact with such devices can produce objectively measurable endocrine effects like real interpersonal touching can. We directly tested this possibility by examining changes in stress hormone cortisol before and after a conversation with a huggable communication device. Participants had 15-minute conversations with a remote partner that was carried out either with a huggable human-shaped device or with a mobile phone. Our experiment revealed significant reduction in the cortisol levels for those who had conversations with the huggable device. Our approach to evaluate communication media with biological markers suggests new design directions for interpersonal communication media to improve social support systems in modern highly networked societies.

Hypnosis is regarded as an effective treatment for psychological and physical ailments. However, its efficacy as a strategy for managing chronic pain has not been assessed through meta-analytical methods. The objective of the current study was to conduct a meta-analysis to assess the efficacy of hypnosis for managing chronic pain. When compared with standard care, hypnosis provided moderate treatment benefit. Hypnosis also showed a moderate superior effect as compared to other psychological interventions for a nonheadache group. The results suggest that hypnosis is efficacious for managing chronic pain. Given that large heterogeneity among the included studies was identified, the nature of hypnosis treatment is further discussed.

The Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) recently included sensory processing abnormalities in the diagnostic criteria for individuals with autism spectrum disorder (ASD). However, there is no standard method for evaluating sensory abnormalities in individuals with ASD. Fifteen individuals with ASD and 15 age- and sex-matched controls were enrolled in this study. We compared objective pain sensitivity by measuring the pain detection threshold and pain tolerance to three different stimuli (electricity, heat, and cold). Then, we compared both subjective pain sensitivity, assessed by the visual analog scale (VAS), and quality of pain, assessed by the short-form McGill Pain Questionnaire (SF-MPQ), to determine the maximum tolerable pain intensities of each stimulation. The pain detection threshold and pain tolerance of individuals with ASD were not impaired, indicating that there were no differences in the somatic perception of pain between groups. However, individuals with ASD were hyposensitive to subjective pain intensity compared to controls (VAS; electrical: p = 0.044, cold: p = 0.011, heat: p = 0.042) and hyposensitive to affective aspects of pain sensitivity (SF-MPQ; electrical: p = 0.0071, cold: p = 0.042). Our results suggest that the cognitive pathways for pain processing are impaired in ASD and, furthermore, that our methodology can be used to assess pain sensitivity in individuals with ASD. Further investigations into sensory abnormalities in individuals with ASD are needed to clarify the pathophysiologic processes that may alter sensory processing in this disorder.

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.