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Record W69392114 · doi:10.1007/s11724-006-0020-5

Neuro-imagerie de la douleur

2006· article· fr· W69392114 on OpenAlexaff
Catherine M. Bushnell

Bibliographic record

VenueDouleur et Analgésie · 2006
Typearticle
Languagefr
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsNeurosciencePsychologyNeuroimagingSomatosensory systemAnterior cingulate cortexThalamusChronic painNociceptionMedicineBrain activity and meditationCingulate cortexInsulaElectroencephalographyCognitionCentral nervous system

Abstract

fetched live from OpenAlex

Many people want to know if we can use brain imaging as a surrogate measure of pain. Insurance companies want to know if brain imaging can tell them whether a person on disability really has low back pain or is he just malingering. Pharmaceutical companies want to know if brain imaging can provide objective evidence of their manipulation's effectiveness. Moreover, can it provide a more sensitive and reliable measure than self report in patients? This talk will consider how MRI and PET may be used not only to understand pain processing but also to measure the pain experience. Our major means of assessing this in humans is to communicate with language. This talk will provide an overview of how brain imaging has been used to in humans and animals to measure nociceptive processes, pain perception, abnormal pain processing, nociceptive and modulatory brain circuitry and connectivity, neuro-anatomical changes related to chronic pain, and neuropharmacological processes related to pain and analgesia. Brain imaging has been used to measure the neural basis of experimental and clinical pain perception (Apkarian et al. 2005). A core network has been identified that includes the thalamus, primary and secondary somatosensory cortices (S1 and S2), and parts of the limbic and peri-limbic system, including anterior cingulate cortex (ACC) and insular cortex (IC). Frontal cortical regions and periaqueducal grey matter (PAG) have been found to be associated with pain modulation. Imaging studies have been particularly important for studying the neural basis of psychological modulation of pain. Human studies examining the effects of attention and distraction show modulation of pain-evoked activity in thalamus and in several cortical regions, including S1, ACC and IC [see (Villemure and Bushnell 2002) for review]. Brain imaging has also been used to examine the neural underpinnings of various chronic pain conditions, including neuropathic pain, fibromyalgia, irritable bowel syndrome, vulvovestibulis, and headache (Apkarian et al. 2005). Many of these studies show that when a normally non-painful stimulus is perceived by the patient as being painful (allodynia), pain networks in the brain are activated (Silverman et al. 1997; Gracely et al. 2002; Pukall et al. 2005; Hofbauer et al. 2006). Anatomical brain imaging now shows that some chronic pain syndromes, such as low back pain, are associated with structural changes in the brain, including loss of cortical grey matter (Apkarian et al. 2004). Human brain imaging has the potential for revealing much about normal and abnormal nociceptive processes. It can be used as a tool to examine sites of action of new pharmaceutical agents, as well as an overview of brain areas ultimately affected by an analgesic manipulation. Nevertheless, the spatial and temporal resolution is still limited, and the statistical nature of the data analysis makes the interpretation of negative results problematic. A lack of an observed effect with either fMRI or PET technologies does not mean that a clinically important effect does not exist. The sensitivity for detecting what could be a physiologically important signal is limited. However, the technology is advancing at a great rate, so that that the spatial and temporal resolution, as well as the sensitivity, of the techniques will continue to improve.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.278
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2006
Admission routes1
Has abstractyes

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