Spectral slowing is associated with working memory performance in children born very preterm
Bibliographic record
Abstract
Children born very preterm (VPT) often demonstrate selective difficulties in working memory (WM), which may underlie academic difficulties observed in this population. Despite this, few studies have investigated the functional networks underlying WM in young children born VPT, a period when cognitive deficits become apparent. Using magnetoencephalography, we examined the networks underlying the maintenance of visual information in 6-year-old VPT (n = 15) and full-term (FT; n = 20) children. Although task performance was similar, VPT children engaged different oscillatory mechanisms during WM maintenance. Within the FT group, we observed higher mean whole-brain connectivity in the alpha-band during the retention (i.e. maintenance) interval associated with correct compared to incorrect responses. VPT children showed reduced whole-brain alpha synchrony, and a different network organization with fewer connections. In the theta-band, VPT children demonstrated a slight increase in whole-brain connectivity during WM maintenance, and engaged similar network hubs as FT children in the alpha-band, including the left dorsolateral prefrontal cortex and superior temporal gyrus. These findings suggest that VPT children rely on the theta-band to support similar task performance. Altered oscillatory mechanisms may reflect a less mature pattern of functional recruitment underlying WM in VPT children, which may affect the processing in complex ecological situations.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".