Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,046 | 0,013 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».