Investigating Task-Free Functional Connectivity Patterns in Newborns Using functional Near-Infrared Spectroscopy
Bibliographic record
Abstract
Abstract Significance Resting-state networks (RSN), particularly the sensorimotor network, begin to develop in the third trimester of pregnancy and mature extensively by term age. The integrity and structure of these networks have been linked to neurological health outcomes in neonates, highlighting the significance of monitoring RSN development. To this end, functional near-infrared spectroscopy (fNIRS) has emerged as a neuroimaging technique that utilizes near-infrared light to indirectly measure neural activity by detecting changes in oxygenated (HbO) and deoxygenated (HbR) hemoglobin concentrations. Compared to other imaging methods, fNIRS is non-invasive and allows for naturalistic monitoring of neural activity at the bedside, particularly in awake infants. Aim Use fNIRS to expand on previous findings regarding the development of functional networks in awake neonates. Approach fNIRS was acquired in 41 term-born neonates (17 females, gestational age range=36+0 to 42+1 weeks) within the first 48 hours after birth. Results Group level analysis of functional connectivity showed strong positive connectivity in most channel-pairs over the sensorimotor network, especially the left hemisphere (q < 0.05). Next, we examined the relationship between functional connectivity, gestational age and postnatal age, while controlling for sex and subject effects. Both gestational and postnatal age were found to be positively associated with an increase in functional connectivity in the sensorimotor RSN, especially in channels covering the posterior portion. Conclusions Our findings emphasize the importance of considering developmental changes in functional networks in awake infants. Moreover, our study demonstrates the potential of fNIRS as a valuable tool for studying neural activity in naturalistic settings in 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".