Daily Pain Is Associated with Alterations in Functional Connectivity of the Brain on fMRI in Adults with Sickle Cell Disease
Notice bibliographique
Résumé
Abstract Background:One-third of adults with sickle cell disease (SCD) have daily, chronic pain. Despite the high prevalence of chronic pain in adults with SCD, the mechanism of is not well defined. In other chronic pain disorders, functional MRI (fMRI) demonstrates a re-organization of the brain's connectivity, which may be maladaptive and contribute to the development of a chronic pain syndrome. We performed fMRI in adults with SCD as well as age-matched controls in order to test two hypotheses: 1) functional connectivity is different between adults with SCD and controls, and 2) differences in functional connectivity among adults with SCD are associated with a more severe pain phenotype. Methods:We performed resting-state fMRI in adults with SCD and age-matched controls. Functional connectivity was calculated using two approaches: 1) a seed-voxel approach with the seed being periaqueductal gray (PAG), an area of the brain known to inhibit pain sensation, and 2) an inter-network functional connectivity strength (FCS) analysis, in which seven brain functional networks were selected based on previous brain modularity analysis findings. To calculate the inter-network FCS between networks A and B, the summation of all functional connectivities between two networks are used. Thereafter, the networks that were significantly different in FCS between SCD and controls were used to determine the association between altered functional connectivity and pain phenotype within SCD subjects. Pain phenotype measurements in SCD subjects included a day-of-study pain score, a 15-day diary to document daily pain and opioid use, McGill pain and Pain DETECT questionnaires, and quantitative sensory testing in response to mechanical, cold, and heat stimuli. Statistical analyses were performed using FSL and Matlab software. Results: A total of 27 adults were examined, including 13 with SCD (9 HbSS, 4 HbSC) and 14 age-matched controls. Seed-based functional connectivity analyses revealed significantly decreased connectivity in SCD as compared to controls between PAG and the regions involved in pain, sensation, salience, emotion, learning, and memory (temporal gyrus, anterior/posterior insula, parahippocampal gyrus, fusiform gyrus, precunes, posterior cingulate gyrus, anterior cingulate, subcallosal gyrus, paracentral gyrus, inferior/superior parietal lobe, inferior frontal gyrus and superior temporal gyrus) (P<0.001, t-test with AlphaSim correction). Furthermore, inter-network analyses show significantly decreased FCS in SCD as compared to controls among networks involved in salience, emotion, learning, and memory (between the salience network and the striatum network, between the salience network and the temporal network, and within both the salience network and the hippocampus network) (P<0.001, t-test). When these inter-network differences in FCS between SCD subjects and controls were examined within SCD subjects to determine the association with clinical phenotype, significant associations were found with age (rs=0.63, P<0.024, Spearman correlation analysis), SCD genotype (SS vs SC) (r2=0.43, P<0.016, linear regression analysis), and number of diary days with pain score >5 (r2=0.5, P<0.011, linear regression analysis). Conclusions: In adults with SCD compared to controls, there were differences in inter-network FCS, including the salience, striatum, temporal, and hippocampus networks, which are crucial networks for salience, emotion, learning, and memory. When these inter-network FCS differences were examined within adults with SCD, significant associations were found with age, SCD genotype and number of pain days. Taken together, these data suggest that altered connectivity in the brain of adults with SCD contributes to the development of a chronic pain syndrome. These changes in functional connectivity on fMRI could be used as a biomarker to determine the efficacy of interventions targeted to decrease chronic pain. Disclosures Field: NKT Therapeutics: Research Funding; Astellas Pharmaceuticals: Research Funding.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».