Evidence map›Paper›PMID 28646349›Full record

ReviewNeuroscience bulletin2018

Modeling Pain Using fMRI: From Regions to Biomarkers.

Marianne C Reddan, Tor D Wager

Registry-linked trialAbstract readReview
In one paragraph

Review in Neuroscience bulletin, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06603168 (Autonomic Recalibration Approach to Myofascial Pain Elimination), which is not on this map. Cited by 64 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06603168 nacompletednot on this mapstarted 2023, after this paper: background citation

Autonomic Recalibration Approach to Myofascial Pain Elimination

TypeinterventionalSponsorEdward Via Virginia College of Osteopathic MedicineRan2023 to 2024Enrolled20ConditionsChronic Pain (Back / Neck), Autonomic DysregulationArmsAutonomic Recalibration Treatment
3 · Its place in the literature

Who cites it

64 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  13. Action-mode subnetworks for decision-making, action control, and feedback.Proceedings of the National Academy of Sciences of the United States of America · 2025
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4 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Marianne C ReddanDepartment of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80303, USA.
Tor D WagerDepartment of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80303, USA. tor.wager@colorado.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Brain MappingMagnetic Resonance ImagingAnimalsBrainHumansImage Processing, Computer-AssistedOxygenPainOxygenBiomarkersfMRIMachine learningModelsPain

Identifiers

PMID28646349
PMCPMC5799128

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.