QOL-40. THE IMPACT OF TASK COMPLEXITY ON INFORMATION PROCESSING SPEED AND NEURAL COMMUNICATION IN PAEDIATRIC BRAIN TUMOUR SURVIVORS
Bibliographic record
Abstract
Abstract Paediatric brain tumour survivors (PBTS) experience slower information processing speed (IPS) that contributes to difficulty performing tasks of minimal (MC) and greater complexity (GC), and is related to aberrant neural communication. It is still unknown whether deficient IPS exists during increasing complexity. We aim to determine if PBTS experience deficient IPS and neural communication relative to typically developing children (TDC) during an increasingly complex visual-motor reaction time (RT) task. During magnetoencephalography recording, participants (n=58, 12.69 ±3.24 years) pressed a button with their left or right thumb after an arrow pointing in the corresponding direction appeared on a screen. During two MC conditions, the arrow pointed in a single direction. During a GC condition, the arrow alternated direction randomly. Mean RT >3SD and signal artifacts were removed prior to analyses. The phase lag index (PLI) estimated neural communication between 90 cortical sources. Linear regression and Network Based Statistics assessed group differences in mean RT and the PLI. PBTS demonstrated increased RT relative to TDC during the GC condition (p=0.04, MPBTS=354.00s, MTDC=326.00s). Group differences in mean RT during MC conditions and the PLI during all conditions were not detected (p>0.05). These results suggest PBTS experience slower IPS during GC. Reduced IPS is thought to contribute to difficulty recruiting cognitive resources needed to perform more complex tasks. Subtle deficits in neural communication may underlie slower IPS. The weighted PLI is superior to the PLI when estimating small differences in neural communication. We will now use the weighted PLI to assess task-related neural communication.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".