Reply: A lack of consistent brain grey matter alterations in migraine
Notice bibliographique
Résumé
Sir, We thank Sheng et al. (2020) for their interest in our article (Burke et al., 2020). Their letter critiques the VBM (voxel-based morphometry) meta-analysis previously conducted by Jia and Yu (2017), which was used for input coordinates for our migraine network mapping analysis. We obviously did not conduct the 2017 meta-analysis, and thus will avoid defending this paper or engaging in a debate about best practices for VBM meta-analyses. In fact, we intentionally chose an existing meta-analysis rather than conduct our own to avoid this debate, as there are many different meta-analysis techniques and no clear consensus on the ‘best’ approach (Radua and Mataix-Cols, 2012). Choosing an existing meta-analysis also has the benefit of helping us avoid any potential bias in study selection. We selected the Jia and Yu (2017) meta-analysis because it was the most recently published. Using a different VBM meta-analysis approach, Sheng and colleagues suggest a lack of consistent VBM findings in migraine. Had their meta-analysis been published at the time of our search, we would likely have used their data for our network-mapping algorithm rather than Jia and Yu (2017), as their study would have been the most recent. However, we would have modified our network mapping approach slightly to account for the lack of significant meta-analytic coordinates. When there are no significant meta-analytic findings, we perform network-mapping at the individual study level (Darby et al., 2018; Weil et al., 2019). Specifically, we use the coordinates from each individual study as an input, rather than the final coordinates from the meta-analysis. For example, we previously found that coordinates of neuroimaging abnormalities in Alzheimer’s disease map to a common brain network, even though no significant findings were identified using a conventional meta-analysis (Darby et al., 2018). We used a similar approach to study cognitive impairment and visual hallucinations in Parkinson’s disease (Weil et al., 2019). When significant meta-analytic coordinates are reported, as in Jia and Yu (2017), we can test whether the reported coordinates map to a common brain network. When there are no significant coordinates, as in the study by Sheng and colleagues, we can test whether the coordinates from each individual study map to a common brain network. Future work is needed to determine whether network mapping results using coordinates from individual studies align with network mapping results using coordinates from a significant meta-analysis. Given that the meta-analytic coordinates are derived from the individual study coordinates, we suspect the two network mapping approaches will align. Data sharing is not applicable to this article as no new data were created or analysed in this study. M.J.B. has nothing to disclose. M.D.F. has intellectual property on using connectivity imaging to guide brain stimulation but receives no royalties.
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 | 0,013 | 0,122 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,006 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,029 | 0,038 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,008 |
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 ».