Assessment of resting state structural-functional relationships in perisylvian region during the early weeks after birth
Bibliographic record
Abstract
Abstract This study investigates the structural-functional (S-F) relationships of the perisylvian region during the first weeks after birth in the resting state. Previous joint S-F studies of perisylvian development were mainly conducted on individual structural and functional metrics. By employing a weighted combination of metrics, joint S-F studies can enhance the understanding of perisylvian development in neonates, thereby providing valuable features for the prediction of neurodevelopmental disorders. To this end, we employed both structural and functional metrics derived from 16 perisylvian sub-regions (PSRs). Structural metrics included morphological and myelination measures, while functional metrics encompassed functional connectivity (FC) values between a designated seed PSR and the remaining PSRs. In addition, fractional amplitude of low-frequency fluctuations (fALFF) for all PSRs was computed and used as a distinct group of functional metrics. During statistical procedure, based on sparse canonical correlation analysis (CCA), the structural metrics were correlated with each respective group of functional metrics. Then, CCA was employed to delineate regional interdependencies and derive combined structural-functional features. The findings revealed that combined features outperform individual metrics for characterizing the normal development of the PSRs in term neonates. The myelination is prominently related to both fALFF and FCs, while morphological metrics of the PSRs have very limited contribution in combined structural features. Among the designated PSRs, the FCs of both the right insula and Heschl’s gyrus with other PSRs demonstrated a more robust correlation with the combined structural features.
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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 | 0.000 |
| 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".