Cortical Correlates of Executive Functions in Adolescents and Young Adults with a Congenital Heart Defect
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".