Graded cutaneous electrical vs thermal stimulation in humans shows different insular and cingulate cortex activation

Peripheral and central determinants of skin wetness sensing in humans

Evolutionarily, our ability to sense skin wetness and humidity (i.e., hygroreception) could have developed as a way of helping to maintain thermal homeostasis, as much as it is the case for the role of temperature sensation and thermoreception. Humans are not provided with a specific skin hygroreceptor, and recent studies have indicated that skin wetness is likely to be centrally processed as a result of the multisensory integration of peripheral inputs from skin thermoreceptors and mechanoreceptors coding the biophysical interactions between skin and moisture. The existence of a specific hygrosensation strategy for human wetness perception has been proposed and the first neurophysiologic model of skin wetness sensing has been recently developed. However, while these recent findings have shed light on some of the peripheral and central neural mechanisms underlying wetness sensing, our understanding of how the brain processes the thermal and mechanical inputs that give rise to one of our "most worn" skin sensory experiences is still far from being conclusive. Understanding these neural mechanisms is clinically relevant in the context of those neurologic conditions that are accompanied by somatosensory abnormalities. The present chapter will present the current knowledge on the peripheral and central determinants of skin wetness sensing in humans.

The biology of skin wetness perception and its implications in manual function and for reproducing complex somatosensory signals in neuroprosthetics

Our perception of skin wetness is generated readily, yet humans have no known receptor (hygroreceptor) to signal this directly. It is easy to imagine the sensation of water running over our hands or the feel of rain on our skin. The synthetic sensation of wetness is thought to be produced from a combination of specific skin thermal and tactile inputs, registered through thermoreceptors and mechanoreceptors, respectively. The present review explores how thermal and tactile afference from the periphery can generate the percept of wetness centrally. We propose that the main signals include information about skin cooling, signaled primarily by thinly myelinated thermoreceptors, and rapid changes in touch, through fast-conducting, myelinated mechanoreceptors. Potential central sites for integration of these signals, and thus the perception of skin wetness, include the primary and secondary somatosensory cortices and the insula cortex. The interactions underlying these processes can also be modeled to aid in understanding and engineering the mechanisms. Furthermore, we discuss the role that sensing wetness could play in precision grip and the dexterous manipulation of objects. We expand on these lines of inquiry to the application of the knowledge in designing and creating skin sensory feedback in prosthetics. The addition of real-time, complex sensory signals would mark a significant advance in the use and incorporation of prosthetic body parts for amputees in everyday life.NEW & NOTEWORTHY Little is known about the underlying mechanisms that generate the perception of skin wetness. Humans have no specific hygroreceptor, and thus temperature and touch information combine to produce wetness sensations. The present review covers the potential mechanisms leading to the perception of wetness, both peripherally and centrally, along with their implications for manual function. These insights are relevant to inform the design of neuroengineering interfaces, such as sensory prostheses for amputees.

A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.

Structural brain anomalies and chronic pain: a quantitative meta-analysis of gray matter volume

The diversity of chronic pain syndromes and the methods employed to study them make integrating experimental findings challenging. This study performed coordinate-based meta-analyses using voxel-based morphometry imaging results to examine gray matter volume (GMV) differences between chronic pain patients and healthy controls. There were 12 clusters where GMV was decreased in patients compared with controls, including many regions thought to be part of the "pain matrix" of regions involved in pain perception, but also including many other regions that are not commonly regarded as pain-processing areas. The right hippocampus and parahippocampal gyrus were the only regions noted to have increased GMV in patients. Functional characterizations were implemented using the BrainMap database to determine which behavioral domains were significantly represented in these regions. The most common behavioral domains associated with these regions were cognitive, affective, and perceptual domains. Because many of these regions are not classically connected with pain and because there was such significance in functionality outside of perception, it is proposed that many of these regions are related to the constellation of comorbidities of chronic pain, such as fatigue and cognitive and emotional impairments. Further research into the mechanisms of GMV changes could provide a perspective on these findings.

PERSPECTIVE

Quantitative meta-analyses revealed structural differences between brains of individuals with chronic pain and healthy controls. These differences may be related to comorbidities of chronic pain.