Altered functional connectivity in preterm neonates with intraventricular hemorrhage assessed using functional near-infrared spectroscopy
Bibliographic record
Abstract
Intraventricular hemorrhage (IVH) is a common neurological injury following very preterm birth. Resting-state functional connectivity (RSFC) using functional magnetic resonance imaging (fMRI) is associated with injury severity; yet, fMRI is impractical for use in intensive care settings. Functional near-infrared spectroscopy (fNIRS) measures RSFC through cerebral hemodynamics and has greater bedside accessibility than fMRI. We evaluated RSFC in preterm neonates with IVH using fNIRS and fMRI at term-equivalent age, and compared fNIRS connectivity between healthy newborns and those with IVH. Sixteen very preterm born neonates were scanned with fMRI and fNIRS. Additionally, fifteen healthy newborns were scanned with fNIRS. In preterms with IVH, fNIRS and fMRI connectivity maps were compared using Euclidean and Jaccard distances. The severity of IVH in relation to fNIRS-RSFC strength was examined using generalized linear models. fNIRS and fMRI RSFC maps showed good correspondence. Connectivity strength was significantly lower in healthy newborns (p-value = 0.023) and preterm infants with mild IVH (p-value = 0.026) compared to infants with moderate/severe IVH. fNIRS has potential to be a new bedside tool for assessing brain injury and monitoring cerebral hemodynamics, as well as a promising biomarker for IVH severity in very preterm born infants.
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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 | 0.003 |
| 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".