Open access dataset of task-free hemodynamic activity in 4-month-old infants during sleep using fNIRS
Bibliographic record
Abstract
Spontaneous, task-free, hemodynamic activity of the brain provides useful information about its functional organization, as it can describe how different brain regions communicate to each other. Neuroimaging studies measuring the spontaneous activity of the brain are conducted while the participants are not engaged in a particular task or receiving any external stimulation. This approach is particularly useful in developmental populations as brain activity can be measured without the need for infant compliance and the risks of data contamination due to motion artifacts. In this project we sought to i) characterize the intrinsic functional organization of the brain in 4-month-old infants and ii) investigate whether bilingualism, as a specific environmental factor, could lead to adaptations on functional brain network development at this early age. Measures of spontaneous hemodynamic activity were acquired in 4-month-old infants (n = 104) during natural sleep using functional near-infrared spectroscopy (fNIRS). Emphasis was placed on acquiring high-quality data that could lead to reproducible results and serve as a valuable resource for researchers investigating the developing functional connectome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".