MRI-based cerebrovascular reactivity using transfer function analysis reveals temporal group differences between patients with sickle cell disease and healthy controls
Notice bibliographique
Résumé
OBJECTIVES: Cerebrovascular reactivity (CVR) measures the ability of cerebral blood vessels to change their diameter and, hence, their capacity to regulate regional blood flow in the brain. High resolution quantitative maps of CVR can be produced using blood-oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) in combination with a carbon dioxide stimulus, and these maps have become a useful tool in the clinical evaluation of cerebrovascular disorders. However, conventional CVR analysis does not fully characterize the BOLD response to a stimulus as certain regions of the brain are slower to react to the stimulus than others, especially in disease. Transfer function analysis (TFA) is an alternative technique that can account for dynamic temporal relations between signals and has recently been adapted for CVR computation. We investigated the application of TFA in data on children with sickle cell disease (SCD) and healthy controls, and compared them to results derived from conventional CVR analysis. MATERIALS AND METHODS: Data from 62 pediatric patients with SCD and 34 age-matched healthy controls were processed using conventional CVR analysis and TFA. BOLD data were acquired on a 3 Tesla MRI scanner while a carbon dioxide stimulus was quantified by sampling the end-tidal partial pressures of each exhaled breath. In addition, T1 weighted structural imaging was performed to identify grey and white matter regions for analysis. The TFA method generated maps representing both the relative magnitude change of the BOLD signal in response to the stimulus (Gain), as well as the BOLD signal speed of response (Phase) for each subject. These were compared to CVR maps calculated from conventional analysis. The effect of applying TFA on data from SCD patients versus controls was also examined. RESULTS: The Gain measures derived from TFA were significantly higher than CVR values based on conventional analysis in both SCD patients and healthy controls, but the difference was greater in the SCD data. Moreover, while these differences were uniform across the grey and white matter regions of controls, they were greater in white matter than grey matter in the SCD group. Phase was also shown to be significantly correlated with the amount that TFA increases CVR estimates in both the grey and white matter. CONCLUSIONS: We demonstrated that conventional CVR analysis underestimates vessel reactivity and this effect is more prominent in patients with SCD. By using TFA, the resulting Gain and Phase measures more accurately characterize the BOLD response as it accounts for the temporal dynamics responsible for the CVR underestimation. We suggest that the additional information offered through TFA can provide insight into the mechanisms underlying CVR compromise in cerebrovascular diseases.
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 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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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) | 0,000 | 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 ».