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Modeling Pain Using fMRI: From Regions to Biomarkers
Marianne C Reddan1, Tor D Wager2
1Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80303, USA.
Neuroscience Bulletin
|June 25, 2017
Summary
Understanding pain requires identifying brain networks. New models, or
Area of Science:
- Neuroscience
- Pain Research
- Cognitive Science
Background:
- Pain is a complex, subjective experience influenced by sensory, affective, and cognitive factors.
- These components are encoded in distributed, interacting brain networks.
- Understanding pain necessitates identifying these networks and modeling their interactions.
Purpose of the Study:
- To develop models ('signatures') of pain by integrating multi-system brain activity.
- To use pattern recognition to identify processes related to pain experience.
- To create predictive models of pain-related outcomes.
Main Methods:
- Integration of activity across multiple brain systems.
- Pattern-recognition analysis to identify pain-related processes.
- Development of specific pain signatures, including the Neurologic Pain Signature and Stimulus Intensity-Independent Pain Signature.
Main Results:
- The Neurologic Pain Signature is sensitive and specific to individual pain, involving nociceptive pathways with minimal modulation by expectation or self-regulation.
- The Stimulus Intensity-Independent Pain Signature explains additional variance in pain reports, involving prefrontal cortex, nucleus accumbens, and hippocampus.
- These brain regions mediate expectancy and perceived control effects.
Conclusions:
- A componential approach to modeling pain is effective.
- Identifying distinct neural systems tracking different pain aspects provides a pathway to understanding pain.
- Individualized pain experiences can be explained by combining these componential models.
