# CRCNS: Computational modeling to predict afferent firing to deep pressure touch

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2024 · $155,748

## Abstract

Deep pressure touch can be pleasant and calming, although sometimes intense. It is often experienced in 
interpersonal touch, such as a hug, and is commonly used in manual therapy and massage to relieve 
musculoskeletal pain. Understanding the neuronal mechanisms underlying chronic musculoskeletal pain 
is a key health concern. Physical therapists commonly use deep pressure touch in their patients with 
musculoskeletal pain. Deep pressure touch activates different types of mechanoreceptors in skin, 
muscles, joints, and fascia. This project will investigate how deep pressure touch is encoded by peripheral 
neural afferents - to elucidate how individual subtypes respond to such manipulation, as well as how they 
work together as a population. The longer-term goal is to uncover correspondences with the encoding of 
tissue stiffness and tension, giving rise to musculoskeletal discomfort, including myofascial pain. The main 
objective of this project is to develop computational stimulus-response models of firing in 
mechanosensitive afferent subtypes to deep pressure touch in humans. In comparing model predictions to 
afferent firing, obtained via microneurography before and after soft tissue manipulation, or massage, this 
effort will seek to attribute changes to tissue mechanics and/or neuronal mechanisms. The proposed 
methods combine microneurography recordings from human peripheral nerves, bodily physiological 
responses, and perceptual judgments, with computational models of time-series relationships between 
tissue deformation and neural firing, as well as computer vision tracking of tissue deformation. 
Microneurography is highly informative about human peripheral nerve activity, but limited by the problem 
of sparse sampling, thus a computational approach is vital to fill in gaps at individual afferent subtype and 
population levels. This project focuses on recording from the peroneal nerve, which projects widely 
throughout the lower leg, in particular the front and lateral sides of the calf, and the upper foot, which are 
sites highly relevant to musculoskeletal pain. From a clinical standpoint, the anterior of the lower leg is 
implicated in shin splits, the lateral posterior of the lower leg is tied to sprained ankles, and the posterior of 
the foot and calf are tied to calf strain and Achilles tendinosis. These conditions are frequently addressed 
via soft tissue manipulation. Overall, the results of this work seek to uncover novel, fundamental insights 
into human somatosensation, and impact non-pharmaceutical treatment of musculoskeletal pain.

## Key facts

- **NIH application ID:** 11081830
- **Project number:** 1R01AT013186-01
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Gregory John Gerling
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $155,748
- **Award type:** 1
- **Project period:** 2024-09-06 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11081830

## Citation

> US National Institutes of Health, RePORTER application 11081830, CRCNS: Computational modeling to predict afferent firing to deep pressure touch (1R01AT013186-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11081830. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*