– REMASS

Deep pressure touch can be felt as pleasant and calming, although sometimes intense. It is often experienced in interactions with others, such as a hug, and is used in manual therapy, including massage, to relive musculoskeletal pain. We aim to investigate how such deep touch is encoded by peripheral receptors in the skin and deeper sub-cutaneous tissue and to elucidate how single mechanoreceptor subtypes respond to such manipulation, as well as how they work in harmony as a population, to produce sensations of comfort and relaxation. Deep touch activates many different types of mechanoreceptors, including those in the skin, muscles, joints, and fascia. Further, the responses are often strong, leading to clear tactile sensations of deep tissue movement. Not only does it initiate responses in many different types of low-threshold receptors, i.e. those activated by gentle touch, it also stimulates nociceptors that encode into the painful touch range. We will do this by recording directly from mechanically-sensitive receptors in nerves from the leg, using microneurography in humans, and use computational modeling to analyze how these respond and under what conditions, by monitoring skin displacement to a variety of touch stimulation. We will compare baseline touch measures of force activation thresholds and vibration with naturalistic massaging touch, inducing complex deformations and skin stretch. We will also look at how longer periods of massage can change tissue and neuronal properties, as well as comparing this to transcutaneous electrical nerve stimulation (TENS), which is also often used to treat musculoskeletal pain, yet by-passes the mechanical encoding receptor mechanism. We will explore modeling approaches spanning black box to biophysical, which is needed to capitalize on the richness of human-to-human touch. Machine learning will identify temporal patterns of skin contact which correlate with evoked spike firing over the course of tissue manipulation. Finally, we will use information from the single receptor models to help computationally model responses on a population level, as well as linking this to how the person feels during massage, their perception, and how their body reacts, from the autonomic nervous system. This will provide us with a holistic view of how deep touch makes us feel and its effects on the body system. Investigating such deep touch will help us understand better why this intense touch is comforting and why it is of benefit in musculoskeletal pain. We have put together an international team with a highly complementary and interdisciplinary approach, to fuse engineering and computational neural modeling with physiology and perception in humans. The insights we gain will impact how non-pharmaceutical approaches may help treat musculoskeletal pain, as well as advancing our fundamental understanding of the human somatosensory system, including the distinct mechanisms underlying human-to-human touch interactions.