Functional connectivity in preterm infants with intraventricular hemorrhage using fNIRS
Bibliographic record
Abstract
Abstract Introduction Intraventricular hemorrhage (IVH) is a common neurological complication 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. Sensitive bedside neuroimaging biomarkers are needed to characterize injury patterns. Functional near-infrared spectroscopy (fNIRS) measures RSFC through cerebral hemodynamics and has greater accessibility. We aimed to determine comparability of RSFC in preterm infants with IVH using fNIRS and fMRI at term equivalent age (TEA), then examine fNIRS connectivity with the severity of IVH. Methods Very preterm born infants with IVH were scanned with both modalities at rest at TEA (postmenstrual age=37±0.92 weeks). Connectivity maps of IVH infants were compared between fNIRS and fMRI with the Euclidean and Jaccard distances. The severity of IVH in relation to fNIRS RSFC strength was examined using generalized linear models. Results fNIRS and fMRI RSFC maps showed good correspondence. At TEA, 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. Conclusion fNIRS has potential to be a new tool for assessing brain injury and monitoring cerebral hemodynamics and a promising marker for IVH severity in very preterm born infants. Highlights There is no previous study combining fNIRS and fMRI focused on preterm neonates with IVH. This is the first study associating functional connectivity strength yielded from fNIRS with severity of IVH. Good correspondence of functional connectivity was shown between fNIRS and fMRI. Preterm neonates showed increased functional connectivity strength compared to healthy term born neonates. This can potentially be a marker for clinical assessment of severity of IVH. fNIRS was demonstrated to have potential as a new bedside neuromonitoring tool for assessing early brain injuries of neonates.
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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".