White Matter Integrity and Core Cognitive Function in Children Diagnosed with Sickle Cell Disease.
Notice bibliographique
Résumé
Abstract Abstract 2589 Poster Board II-565 Introduction: Cerebral damage to white matter by overt or silent stroke presents as regions of high intensity on a diffusion-weighted (DW) magnetic resonance (MR) image. Evidence is mounting that such damage is directly linked to decreased cognitive function in children diagnosed with Sickle Cell (SC) Disease. While insult caused by infarct is visible on a DW MR scan, the degree to which normal-appearing white matter is compromised in SC patients remains unclear. Furthermore, the extent of correlation between damage in normal-appearing white matter and cognitive function has yet to be investigated. Patients and Methods: 16 children diagnosed with SC and 10 control patients were included in this retrospective study. DW MR scans were clinically acquired from all participants. Post-processing of DW images yielded measures of relative water diffusion within the brain, represented by voxels of varying intensity on apparent diffusion coefficient (ADC) maps. A template of anatomically divided white matter was registered to each ADC map to collect regional measures of diffusion. Specifically, increased diffusion (measured as increased ADC relative to controls) suggested white matter damage. Within 6 months of the scan, children from each cohort underwent a battery of neuropsychological tests. Processing speed and working memory were assessed by administering the Wechsler Intelligence Scale for Children (WISC) and sustained visual attention was assessed by administering Conners' Continuous Performance Test (CPT). Measures of regional ADC were correlated with neuropsychological test scores. Results: Approximately half of the SC patients presented with at least one lesion embedded within normal-appearing white matter. Average ADC in the frontal, parietal, temporal and cerebellar lobes was significantly higher in children with SC Disease than in control subjects (p < 0.05) when examining ADC across regions carrying both normal-appearing and infarct-containing white matter. For example, average ADC in the left frontal lobes was 1014.67 × 10−6 mm2/s in SC patients and 895.18 × 10−6 mm2/s in control subjects. Findings to date show that excluding the lesions (measuring only diffusion in normal-appearing white matter) does not substantially change average ADC. Moreover, scores from Letter/Number Sequencing and Symbol Search tests (derived from the WISC) were significantly lower (p < 0.05) in SC patients as compared to control scores. For example, while controls obtained a mean scaled score of 12.8 on the Symbol Search task, SC patients obtained a mean scaled score of 7.1. After performing multiple correlations, a significant negative correlation (p < 0.05) was detected between ADC of the frontal, parietal and cerebellar lobes, and tests of processing speed. Interestingly, SC patients showed a significantly higher standard error for Reaction Time (p < 0.05) during the CPT than did the control children. Conclusions: In this study, we present an imaging approach to identify compromised white matter earlier in SC patients. We show that while some damage is visible as focal lesions on a DW image, changes to tissue architecture exist in what otherwise appears as normal white matter. We also show that children diagnosed with SC exhibit deficits in working memory and processing speed, and are less consistent with respect to tests requiring sustained visual attention than are control children. Furthermore, as damage to normal-appearing white matter increases, proficiency in processing speed decreases. This approach can be used to detect compromised white matter prior to the appearance of lesions, and in turn, will help to pinpoint and address potential cognitive impairments in this population sooner. Disclosures: No relevant conflicts of interest to declare.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,002 | 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 ».