Eye Movements and White Matter are Associated with Emotional Control in Children Treated for Brain Tumors
Bibliographic record
Abstract
OBJECTIVE: Children treated for brain tumors often experience social and emotional difficulties, including challenges with emotion regulation; our goal was to investigate the attention-related component processes of emotion regulation, using a novel eye-tracking measure, and to evaluate its relations with emotional functioning and white matter (WM) organization. METHOD: Fifty-four children participated in this study; 36 children treated for posterior fossa tumors, and 18 typically developing children. Participants completed two versions of an emotion regulation eye-tracking task, designed to differentiate between implicit (i.e., automatic) and explicit (i.e., voluntary) subprocesses. The Emotional Control scale from the Behavior Rating Inventory of Executive Function was used to evaluate emotional control in daily life, and WM organization was assessed with diffusion tensor imaging. RESULTS: We found that emotional faces captured attention across all groups (F(1,51) = 32.18, p < .001, η2p = .39). However, unlike typically developing children, patients were unable to override the attentional capture of emotional faces when instructed to (emotional face-by-group interaction: F(2,51) = 5.58, p = .006, η2p = .18). Across all children, our eye-tracking measure of emotion regulation was modestly associated with the parent-report emotional control score (r = .29, p = .045), and in patients it was associated with WM microstructure in the body and splenium of the corpus callosum (all t > 3.03, all p < .05). CONCLUSIONS: Our findings suggest that an attention-related component process of emotion regulation is disrupted in children treated for brain tumors, and that it may relate to their emotional difficulties and WM organization. This work provides a foundation for future theoretical and mechanistic investigations of emotional difficulties in brain tumor survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".