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Targeted Subcutaneous Vibration With Single-Neuron Electrophysiology As a Novel Method for Understanding the Central Effects of Peripheral Vibrational Therapy in a Rodent Model.

Kyle B BillsTravis ClarkeGeorge H MajorCecil B JacobsonJonathan D BlotterJeffrey Brent FelandScott C Steffensen
Published in: Dose-response : a publication of International Hormesis Society (2019)
Very little is known about the effects of whole body vibration on the supraspinal central nervous system. Though much clinical outcome data and mechanistic data about peripheral neural and musculoskeletal mechanisms have been explored, the lack of central understanding is a barrier to evidence-based, best practice guidelines in the use of vibrational therapy. Disparate methods of administration render study to study comparisons difficult. To address this lack of uniformity, we present the use of targeted subcutaneous vibration combined with simultaneous in vivo electrophysiological recordings as a method of exploring the central effects of peripheral vibration therapy. We used implanted motors driven by both Grass stimulators and programmed microcontrollers to vary frequency and location of stimulation in an anesthetized in vivo rat model while simultaneously recording firing rate from gamma-aminobutyric acid (GABA) neurons in the ventral tegmental area. We show that peripheral vibration can alter GABA neuron firing rate in a location- and frequency-dependent manner. We include detailed schematics and code to aid others in the replication of this technique. This method allows for control of previous weaknesses in the literature including variability in body position, vibrational intensity, node and anti-node interactions with areas of differing mechanoreceptor densities, and prefrontal cortex influence.
Keyphrases
  • high frequency
  • prefrontal cortex
  • density functional theory
  • lymph node
  • molecular dynamics simulations
  • healthcare
  • spinal cord
  • big data
  • cancer therapy
  • spinal cord injury
  • high intensity
  • cell therapy
  • deep learning