Longitudinal network-based brain grey matter MRI measures are clinically relevant and sensitive to treatment effects in multiple sclerosis
Notice bibliographique
Résumé
Abstract In multiple sclerosis clinical trials, MRI outcome measures are typically extracted at a whole-brain level, but pathology is not homogeneous across the brain and so whole-brain measures may overlook regional treatment effects. Data-driven methods, such as independent component analysis, have shown promise in identifying regional disease effects but can only be computed at a group level and cannot be applied prospectively. The aim of this work was to develop a technique to extract longitudinal independent component analysis network-based measures of co-varying grey matter volumes, derived from T1-weighted volumetric MRI, in individual study participants, and assess their association with disability progression and treatment effects in clinical trials. We used longitudinal MRI and clinical data from 5089 participants (22 045 visits) with multiple sclerosis from eight clinical trials. We included people with relapsing–remitting, primary and secondary progressive multiple sclerosis. We used data from five negative clinical trials (2764 participants, 13 222 visits) to extract the independent component analysis-based measures. We then trained and cross-validated a least absolute shrinkage and selection operator regression model (which can be applied prospectively to previously unseen data) to predict the independent component analysis measures from the same regional MRI volume measures and applied it to data from three positive clinical trials (2325 participants, 8823 visits). We used nested mixed-effect models to determine how networks differ across multiple sclerosis phenotypes are associated with disability progression and to test sensitivity to treatment effects. We found 17 consistent patterns of co-varying regional volumes. In the training cohort, volume loss was faster in four networks in people with secondary progressive compared with relapsing–remitting multiple sclerosis and three networks with primary progressive multiple sclerosis. Volume changes were faster in secondary compared with primary progressive multiple sclerosis in four networks. In the combined positive trials cohort, eight independent component analysis networks and whole-brain grey matter volume measures showed treatment effects, and the magnitude of treatment–placebo differences in the network-based measures was consistently greater than with whole-brain grey matter volume measures. Longitudinal network-based analysis of grey matter volume changes is feasible using clinical trial data, showing differences cross-sectionally and longitudinally between multiple sclerosis phenotypes, associated with disability progression, and treatment effects. Future work is required to understand the pathological mechanisms underlying these regional changes.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,000 | 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 tête enseignante, 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 ».