RTG 2175 Perception in Context and its neural Basis
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New TP Ruscheweyh/Flanagin "Supraspinal correlates of learned activation of descending pain inhibition in humans "

Background and Goals

Humans have powerful endogenous systems for inhibiting pain. One of them is the descending pain inhibitory pathway, originating in the brainstem and descending towards the spinal cord, where it inhibits pain transmission, thereby reducing the amount of nociceptive input that reaches the brain [2]. This system can be modulated by cognitive and emotional processes. E.g. distraction activates the descending pain inhibition while negative emotions deactivate the system [1,11,12] (Fig. 1). A remapping of the relationship between cortical pathways and descending pain inhibition likely is an important factor in chronic pain [5,6].
In our previous work, we developed a paradigm in which persons are trained to deliberately activate their descending pain inhibitory system [4,7,8]. Subjects learn to use cognitive-emotional strategies to activate their descending pain inhibition via online feedback on the state of their descending pain inhibition pathway. The nociceptive flexor (RIII) reflex, a measure of spinal nociception in humans, is used as feedback parameter, and a reduction in RIII reflex size indicates successful activation of the descending inhibition. Three RIII feedback training sessions are sufficient for subjects to learn to activate their descending pain inhibition system and to reduce clinical pain in chronic back pain patients [4,7,8].
In the present study, we plan to use functional magnetic resonance imaging (fMRI) to investigate which brain regions are used by RIII-feedback trained subjects to activate their descending pain inhibition and reduce their spinal nociception (Fig. 2,3). In short, the study will serve to identify which brain regions functionally activate the descending pain inhibitory pathways. This may also provide target regions for future real-time fMRI neurofeedback studies aiming at pain modulation.

Ruscheweyh

Methodology and Work Program

The potential PhD candidate will have the opportunity to learn our established RIII reflex recordings and use its feedback to train a group of healthy subjects to activate their descending pain inhibition as described before [4,7]. The candidate will gain hands on experience in both electrophysiological recordings of the RIII reflex, somatosensory evoked potentials (Fig. 2) and human whole-brain imaging with MRI. Once the established paradigm is understood, the student will be able to move to the MRI environment where they will adapt the RIII reflex recordings to the MRI and then plan, perform and analyze a longitudinal fMRI experiment. Participants will use the RIII feedback training to suppress painful stimuli during functional MRI. Our MRI facilities have state of the art fMRI sequences tailored to detect both cortical and brainstem activity [10]. In addition, resting state activity and high quality structural images will be acquired to determine if baseline resting state activity or regional grey matter volume is able to predict individual training success in activation of the descending pain inhibition (see [3] and [9] for similar approaches).
Single trial RIII reflex recordings and pain ratings will be obtained during the fMRI sessions to quantify activation of descending pain inhibitory systems. By comparing active vs. passive instruction before the actual electrical stimulus, we can identify brain regions recruited during active descending pain inhibition. In addition, we will compare fMRI activity immediately following the electrical stimulus to investigate the influence of descending inhibition on the processing of nociceptive information in the brain. This paradigm gives us an enormous potential for analysis, both at the individual level (by correlating trial-by-trial performance with the fMRI signal) and at the group level (by correlating individual average performance with the fMRI signal).

ruscheweyh fig 2

          ruscheweyh fig 3

Supervision

• The RIII training, electrophysiological and pain measurement parts of the project will be supervised by R. Ruscheweyh.
• The fMRI parts of the project will be supervised by V. Flanagin and E. Schulz.

If you are interested in this position, you should:
- Have a genuine interest in the processes involved in acute and chronic pain perception, underlying neuronal systems, and their modulation by psychological processes.
- Like to work with human subjects.
- Have a desire to bring in your own ideas for establishing electrophysiological recordings in the MRI environment.
- Enjoy working both independently and in a group.
- Have an interest in programming; experience with Matlab and MR analysis is always welcome.
- Apply to and successfully complete the GSN-LMU’s 2019 selection procedure (see http://www.gsn.uni-muenchen.de/).

More information

The 3-year PhD position starts in October 2019. It is part of a dedicated doctoral research training group (RTG 2175, http://www.rtg2175.bio.lmu.de/index.html) at the Graduate School of Systemic Neurosciences of LMU Munich.
For more information, please contact: ruth.ruscheweyh@med.uni-muenchen.de

Reference List

  • [1] Bingel U, Tracey I. Imaging CNS modulation of pain in humans. Physiology 2008;23:371-380.
  • [2] Fields HL, Basbaum AI. Central nervous system mechanisms of pain modulation. In: McMahon SB, Koltzenburg M, editors. Textbook of Pain. London: Churchill Livingstone, 2006. pp. 125-142.
  • [3] Jensen MP, Sherlin LH, Fregni F, Gianas A, Howe JD, Hakimian S. Baseline brain activity predicts response to neuromodulatory pain treatment. Pain Med 2014;15:2055-2063.
  • [4] Krafft S, Göhmann HD, Sommer J, Straube A, Ruscheweyh R. Learned control over spinal nociception in patients with chronic back pain. Eur J Pain 2017;doi: 10.1002/ejp.1055.
  • [5] Kwon M, Altin M, Duenas H, Alev L. The role of descending inhibitory pathways on chronic pain modulation and clinical implications. Pain Pract 2014;14:656-667.
  • [6] Ossipov MH, Morimura K, Porreca F. Descending pain modulation and chronification of pain. Curr Opin Support Palliat Care 2014;8:143-151.
  • [7] Ruscheweyh R, Bäumler M, Feller M, Krafft S, Sommer J, Straube A. Learned control over spinal nociception reduces supraspinal nociception as quantified by late somatosensory evoked potentials. Pain 2015;156:2505-2513.
  • [8] Ruscheweyh R, Weinges F, Schiffer M, Bäumler M, Feller M, Krafft S, Straube A, Sommer J, Marziniak M. Control over spinal nociception as quantified by the nociceptive flexor reflex (RIII reflex) can be achieved under feedback of the RIII reflex. Eur J Pain 2015;19:480-489.
  • [9] Ruscheweyh R, Wersching H, Kugel H, Sundermann B, Teuber A. Gray matter correlates of pressure pain thresholds and self-rated pain sensitivity: a voxel-based morphometry study. Pain 2018;159:1359-1365.
  • [10] Stirnberg R, Huijbers W, Brenner D, Poser BA, Breteler M, Stocker T. Rapid whole-brain resting-state fMRI at 3 T: Efficiency-optimized three-dimensional EPI versus repetition time-matched simultaneous-multi-slice EPI. Neuroimage 2017;163:81-92.
  • [11] Tracey I, Mantyh PW. The cerebral signature for pain perception and its modulation. Neuron 2007;55:377-391.
  • [12] Wiech K, Tracey I. The influence of negative emotions on pain: behavioral effects and neural mechanisms. Neuroimage 2009;47:987-994.