Sensory Evidence and Expectations in Pain Processing

NCT04296968 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 50

Last updated 2021-04-01

No results posted yet for this study

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.

Conditions

  • Experimental Pain in Healthy Human Participants

Interventions

DEVICE

Painful stimulation using a laser device (DEKA Stimul 1340, Calenzano, Italy)

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.

DEVICE

Visual cueing

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.

Sponsors & Collaborators

  • German Research Foundation

    collaborator OTHER
  • Technical University of Munich

    lead OTHER

Principal Investigators

  • Markus Ploner, Prof Dr med · Department of Neurology, Klinikum rechts der Isar, TUM

Study Design

Allocation
NA
Purpose
BASIC_SCIENCE
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2020-03-01
Primary Completion
2020-12-01
Completion
2020-12-01

Countries

  • Germany

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04296968 on ClinicalTrials.gov