Gamma Deficits as a Neural Signature of Cognitive Impairment in Children Treated for Brain Tumors
Bibliographic record
Abstract
Cognitive impairment is consistently reported in children treated for brain tumors, particularly in the categories of processing speed, memory, and attention. Although tumor site, hydrocephalus, chemotherapy, and cranial radiation therapy (CRT) are all associated with poorer function, CRT predicts the greatest deficits. There is a particularly high correlation between CRT and slowed information-processing speed. Cortical gamma-band oscillations have been associated with processing behaviorally relevant information; however, their role in the maintenance of cognition in individuals with processing deficits is unclear. We examined gamma oscillations using magnetoencephalography (MEG) in children undergoing CRT to test whether gamma characteristics can be a signature of cognitive impairment in this population. We collected resting-state data as well as data from baseline and active periods during two visual-motor reaction time tasks of varying cognitive loads from 18 healthy children and 20 patients. We found that only high-gamma oscillations (60-100 Hz), and not low-gamma oscillations (30-59 Hz), showed significant group differences in absolute power levels. Overall, compared with healthy children, patients showed the following: (1) lower total high-gamma (60-100 Hz) power during the resting state, as well as during task-related baseline and performance measures; (2) no change in gamma reactivity to increases in cognitive load; and (3) slower processing speeds both inside and outside MEG. Our findings show that high-gamma oscillations are disrupted in children after treatment for a brain tumor. The temporal dynamic of the high-gamma response during information processing may index cognitive impairment in humans with neurological injury.
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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 | 0.001 |
| 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".