Evidence map›Paper›PMID 27445681›Full record

ArticleFrontiers in neuroscience2016

Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment.

Daniele Di Lernia, Silvia Serino, Pietro Cipresso, Giuseppe Riva

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Pain in Tourette Syndrome: A Comprehensive Review.Journal of child and adolescent psychopharmacology · 2025
    Review
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Embodied Medicine: Mens Sana in Corpore Virtuale Sano.Frontiers in human neuroscience · 2017
    Article
  16. Review
  17. Review
  18. Article
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

4 authors.

Daniele Di LerniaDepartment of Psychology, Università Cattolica del Sacro Cuore Milan, Italy.
Silvia SerinoDepartment of Psychology, Università Cattolica del Sacro CuoreMilan, Italy; Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico ItalianoMilan, Italy.
Pietro CipressoApplied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano Milan, Italy.
Giuseppe RivaDepartment of Psychology, Università Cattolica del Sacro CuoreMilan, Italy; Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico ItalianoMilan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pain is a complex and multidimensional perception, embodied in our daily experiences through interoceptive appraisal processes. The article reviews the recent literature about interoception along with predictive coding theories and tries to explain a missing link between the sense of the physiological condition of the entire body and the perception of pain in chronic conditions, which are characterized by interoceptive deficits. Understanding chronic pain from an interoceptive point of view allows us to better comprehend the multidimensional nature of this specific organic information, integrating the input of several sources from Gifford's Mature Organism Model to Melzack's neuromatrix. The article proposes the concept of residual interoceptive images (ghosts), to explain the diffuse multilevel nature of chronic pain perceptions. Lastly, we introduce a treatment concept, forged upon the possibility to modify the interoceptive chronic representation of pain through external input in a process that we call interoceptive modeling, with the ultimate goal of reducing pain in chronic subjects.

Indexed as

chronic painfree energyinteroceptioninteroceptive modelingpredictive coding

Identifiers

PMID27445681
PMCPMC4927564

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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.