Modeling Pain Using fMRI: From Regions to Biomarkers.
10.1007/s12264-017-0150-1
- Author:
Marianne C REDDAN
1
;
Tor D WAGER
2
Author Information
1. Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80303, USA.
2. Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80303, USA. tor.wager@colorado.edu.
- Publication Type:Journal Article
- Keywords:
Biomarkers;
Machine learning;
Models;
Pain;
fMRI
- MeSH:
Animals;
Brain;
diagnostic imaging;
Brain Mapping;
Humans;
Image Processing, Computer-Assisted;
Magnetic Resonance Imaging;
Oxygen;
blood;
Pain;
diagnostic imaging
- From:
Neuroscience Bulletin
2018;34(1):208-215
- CountryChina
- Language:English
-
Abstract:
Pain is a subjective and complex phenomenon. Its complexity is related to its heterogeneity: multiple component processes, including sensation, affect, and cognition, contribute to pain experience and reporting. These components are likely to be encoded in distributed brain networks that interact to create pain experience and pain-related decision-making. Therefore, to understand pain, we must identify these networks and build models of these interactions that yield testable predictions about pain-related outcomes. We have developed several such models or 'signatures' of pain, by (1) integrating activity across multiple systems, and (2) using pattern-recognition to identify processes related to pain experience. One model, the Neurologic Pain Signature, is sensitive and specific to pain in individuals, involves brain regions that receive nociceptive afferents, and shows little effect of expectation or self-regulation in tests to date. Another, the 'Stimulus Intensity-Independent Pain Signature', explains substantial additional variation in trial-to-trial pain reports. It involves many brain regions that do not show increased activity in proportion to noxious stimulus intensity, including medial and lateral prefrontal cortex, nucleus accumbens, and hippocampus. Responses in this system mediate expectancy and perceived control effects in several studies. Overall, this approach provides a pathway to understanding pain by identifying multiple systems that track different aspects of pain. Such componential models can be combined in unique ways on a subject-by-subject basis to explain an individual's pain experience.
