Cortical Correlates of Executive Functions in Adolescents and Young Adults with a Congenital Heart Defect
Notice bibliographique
Résumé
Introduction: Adolescents and young adults born with a complex congenital heart defect (CHD) are at risk for executive function (EF) impairments which contribute to the psychological and everyday burden of CHD. Cortical dysmaturation has been well described in fetuses and neonates with CHD and early evidence suggests that cortical alterations in thickness, surface area, and gyrification index are non-transient and can be observed in adolescents with CHD. However, cortical alterations have yet to be investigated as possible correlates for the EF deficits in youth with CHD. This study aims to use a data-driven approach to identify the cortical correlates of EF deficits in adolescents and young adults with CHD. Methods: A total of 56 youth with CHD who underwent cardiopulmonary bypass surgery within the first two years of life and 56 age- and sex-matched healthy controls from datasets acquired at the McGill University Health Centre and University Children’s Hospital Zurich were included in our analyses. For each participant, a high-resolution T1-weighted magnetic resonance image, an EF assessment using the Behaviour Rating Inventory of Executive Function – Adult Scale (BRIEF-A), and their clinical and demographic characteristics were available. Corticometric Iterative Vertex-Based Estimation of Thickness (CIVET) was used to extract cortical thickness (CT), surface area (SA), and gyrification index (GI) measures. Using orthogonal projective non-negative matrix factorization (OPNMF), we identified non-overlapping spatial components that integrate CT, SA, and GI and capture structural covariance within these features. Behavioural partial least squares (bPLS) analysis was then used to compute correlations between the individual variability in the NMF covariance patterns and EF outcomes for each subject. Results: OPNMF identified 12 cortex-wide components summarizing the inter-subject variability in CT, SA, and GI. Two significant latent variables (LV) were identified, each describing distinct patterns between brain and cognitive data. LV1 summarized a pattern of belonging to the CHD group, worse scores on most BRIEF-A scales, younger age, and female sex. This pattern was associated with increased CT, GI, and decreased SA in several NMF components. The second latent variable described a covariance pattern between younger age and female sex and higher CT, and lower SA and GI. Finally, we observed relationships between LV brain-behaviour patterns and clinical variables in the CHD group. Conclusion: In this study, we identify novel relationships between EF and cortical alterations in adolescents and young adults with CHD using a data-driven approach. These results support the need for further research into the impact of cortical alterations and perioperative variables on EF outcomes in the CHD population to help identify individuals who are especially vulnerable to EF deficits in this population
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 | 0,000 |
| 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 ».