NCT04296968

Brief Summary

Pain is a highly complex and subjective phenomenon which is not only rooted in sensory information but also shaped by cognitive processes such as expectation. However, the interaction of brain activity cording sensory information and expectation in pain processing are not completely understood. Predictive coding models postulate specific hypothesis about the interplay between bottom-up sensory information and top-down expectations in terms of prediction errors and predictions, respectively. They further implicate brain oscillations at different frequencies, which play a crucial role in processing prediction errors and predictions. More specifically, recent evidence in visual and auditory modalities suggests that predictions are reflected by alpha (8-13 Hz) and beta oscillations (14-30 Hz) and prediction errors by gamma oscillations (60-100 Hz). However, for the processing of pain, these frequency-specific relationships have not been addressed so far. The current project aims to investigate brain activity which reflects predictions, prediction errors and sensory evidence in pain processing using a cueing paradigm. To this end, we will apply painful stimuli with low and high intensity to the dorsum of the left hand in 50 healthy subjects. A visual cue, preceding to each painful stimulus, will predict the intensity of the consecutive painful stimulus (low vs. high) with a probability of 75%. After each painful stimulus, participants will be asked to rate the perceived pain intensity. Electroencephalography (EEG) and skin conductance will be recorded continuously during anticipation and stimulation intervals. This paradigm enables us to compare pain-associated brain responses of validly and invalidly cued trials, i.e. the representation of the prediction error, on the one hand. On the other hand, brain activity related to predictions can be investigated in the anticipation interval preceding to the painful stimulus by comparing trials with low and high intensity cues. Further, we will compare models including predictions, prediction error and sensory evidence to ascertain the involvement of each brain response in processing sensory information and expectation. Results of the study promise to elucidate the interplay of predictions, predictions errors and sensory evidence in pain processing and how they differentially relate to neural oscillations at different frequency bands and pain-evoked responses.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
50

participants targeted

Target at P25-P50 for not_applicable

Timeline
Completed

Started Mar 2020

Shorter than P25 for not_applicable

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

March 1, 2020

Completed
2 days until next milestone

First Submitted

Initial submission to the registry

March 3, 2020

Completed
2 days until next milestone

First Posted

Study publicly available on registry

March 5, 2020

Completed
9 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 1, 2020

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

December 1, 2020

Completed
Last Updated

April 1, 2021

Status Verified

March 1, 2021

Enrollment Period

9 months

First QC Date

March 3, 2020

Last Update Submit

March 30, 2021

Conditions

Keywords

painelectroencephalographylaser-evoked potentialsneural oscillationsbrainpredictive coding

Outcome Measures

Primary Outcomes (2)

  • Verbal pain rating (NRS; 0: 'no pain' to 100: 'maximum tolerable pain')

    160 painful stimuli will be applied to the participants' left hand. Participants will be asked to verbally rate the perceived pain intensity of each stimulus on a numerical rating scale (see above).

    During 40 minutes of the experimental paradigm

  • Oscillatory and evoked brain responses pre- and post-stimulus

    EEG including 64 channels will be recorded. In offline analyses, power of oscillatory brain activity will be quantified in the alpha (8-13 Hz), beta (14-30 Hz) and gamma (60-100 Hz) frequency bands. In addition, laser-evoked potentials (LEPs) will be quantified with regard to amplitudes and latencies.

    During 40 minutes of the experimental paradigm

Secondary Outcomes (1)

  • SCRs (µS)

    During 40 minutes of the experimental paradigm

Study Arms (1)

Expectation and experimental pain in humans

EXPERIMENTAL
Device: Painful stimulation using a laser device (DEKA Stimul 1340, Calenzano, Italy)Device: Visual cueing

Interventions

In the experimental paradigm, 160 painful stimuli of two intensities (3 J, 3.5 J) will be applied to the dorsum of the left hand using the laser device listed above.

Expectation and experimental pain in humans

Preceding to each painful stimulus, visual cues (e.g., blue dot and yellow square) will be presented on a screen indicating the intensity of the subsequent stimulus (low and high intensity) with an accuracy of 75%. The contingencies of the visual cues will be explicitly stated to the participants.

Expectation and experimental pain in humans

Eligibility Criteria

Age18 Years - 65 Years
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • Age 18-65 years
  • Right-handedness
  • Written informed consent

You may not qualify if:

  • Pregnancy
  • Neurological or psychiatric diseases (e.g. epilepsy, stroke, depression, anxiety disorders)
  • Severe general illnesses (e.g. tumors, diabetes)
  • Skin diseases (e.g. dermatitis, psoriasis or eczema)
  • Current or recurrent pain
  • Regular intake of medication
  • Surgical procedures involving the head or spinal cord
  • Metal (except titanium) or electronic implants
  • Side-effects following previous thermal stimulation

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Department of Neurology, Klinikum rechts der Isar, Technische Universität München

Munich, Bavaria, 81675, Germany

Location

Related Publications (9)

  • Bastos AM, Usrey WM, Adams RA, Mangun GR, Fries P, Friston KJ. Canonical microcircuits for predictive coding. Neuron. 2012 Nov 21;76(4):695-711. doi: 10.1016/j.neuron.2012.10.038.

    PMID: 23177956BACKGROUND
  • Bastos AM, Vezoli J, Bosman CA, Schoffelen JM, Oostenveld R, Dowdall JR, De Weerd P, Kennedy H, Fries P. Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron. 2015 Jan 21;85(2):390-401. doi: 10.1016/j.neuron.2014.12.018. Epub 2014 Dec 31.

    PMID: 25556836BACKGROUND
  • Buchel C, Geuter S, Sprenger C, Eippert F. Placebo analgesia: a predictive coding perspective. Neuron. 2014 Mar 19;81(6):1223-1239. doi: 10.1016/j.neuron.2014.02.042.

    PMID: 24656247BACKGROUND
  • de Lange FP, Heilbron M, Kok P. How Do Expectations Shape Perception? Trends Cogn Sci. 2018 Sep;22(9):764-779. doi: 10.1016/j.tics.2018.06.002. Epub 2018 Jun 29.

    PMID: 30122170BACKGROUND
  • Egner T, Monti JM, Summerfield C. Expectation and surprise determine neural population responses in the ventral visual stream. J Neurosci. 2010 Dec 8;30(49):16601-8. doi: 10.1523/JNEUROSCI.2770-10.2010.

    PMID: 21147999BACKGROUND
  • Fazeli S, Buchel C. Pain-Related Expectation and Prediction Error Signals in the Anterior Insula Are Not Related to Aversiveness. J Neurosci. 2018 Jul 18;38(29):6461-6474. doi: 10.1523/JNEUROSCI.0671-18.2018. Epub 2018 Jun 22.

    PMID: 29934355BACKGROUND
  • Geuter S, Boll S, Eippert F, Buchel C. Functional dissociation of stimulus intensity encoding and predictive coding of pain in the insula. Elife. 2017 May 19;6:e24770. doi: 10.7554/eLife.24770.

    PMID: 28524817BACKGROUND
  • Todorovic A, de Lange FP. Repetition suppression and expectation suppression are dissociable in time in early auditory evoked fields. J Neurosci. 2012 Sep 26;32(39):13389-95. doi: 10.1523/JNEUROSCI.2227-12.2012.

    PMID: 23015429BACKGROUND
  • Todorovic A, van Ede F, Maris E, de Lange FP. Prior expectation mediates neural adaptation to repeated sounds in the auditory cortex: an MEG study. J Neurosci. 2011 Jun 22;31(25):9118-23. doi: 10.1523/JNEUROSCI.1425-11.2011.

    PMID: 21697363BACKGROUND

MeSH Terms

Conditions

Pain

Condition Hierarchy (Ancestors)

Neurologic ManifestationsSigns and SymptomsPathological Conditions, Signs and Symptoms

Study Officials

  • Markus Ploner, Prof Dr med

    Department of Neurology, Klinikum rechts der Isar, TUM

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Masking Details
Participants will not be informed about the objective intensities of the painful stimuli as being binary.
Purpose
BASIC SCIENCE
Intervention Model
SINGLE GROUP
Model Details: Each participant will participate in one experimental session with a duration of 40 minutes. The experimental paradigm consists of 160 painful stimuli of two intensities (low and high), which will be applied using a laser stimulation device (DEKA Stimul 1340, Calenzano, Italy). Preceding to each painful stimulus, visual cues will be presented indicating the intensity of the subsequent stimulus with an accuracy of 75%. After each painful stimulus, participants will be prompted to verbally rate the pain intensity on a scale ranging from 0 ('no pain') to 100 ('maximum tolerable pain'). Brain activity and autonomic activity will be recorded simultaneously using EEG and skin conductance responses (SCRs), respectively.
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Professor of Human Pain Research

Study Record Dates

First Submitted

March 3, 2020

First Posted

March 5, 2020

Study Start

March 1, 2020

Primary Completion

December 1, 2020

Study Completion

December 1, 2020

Last Updated

April 1, 2021

Record last verified: 2021-03

Data Sharing

IPD Sharing
Will share

Pseudonymized individual participant data sets will be made available at the OSF online repository \[https://osf.io/\] upon publication.

More information

Locations