The Effect of Music and White Noise on Electroencephalographic (EEG) Functional Connectivity in Neonates in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
The purpose of this study is to investigate whether listening to music and white noise affects functional connectivity on scalp electroencephalography (EEG) in neonates in the neonatal intensive care unit. Nine neonates of ≥34 weeks’ gestational age, who were already undergoing clinical continuous EEG monitoring in the neonatal intensive care unit, listened to lullaby-like music and white noise for 1 hour each separated by a 2-hour interval of no intervention. EEG segments during periods of music, white noise, and no intervention were band-pass filtered as delta (0.5-4 Hz), theta (4-8 Hz), lower alpha (8-10 Hz), upper alpha (10-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz). Synchronization likelihood was used as a measure of connectivity between any 2 electrodes. In theta, lower alpha, and upper alpha frequency bands, the synchronization likelihood values yielded statistical significance with sound (music, white noise and no intervention) and with edge (between any 2 electrodes) factors. In theta, lower alpha, and upper alpha frequency bands, statistical significance was obtained between music and white noise ( t = 3.12, 3.32, and 3.68, respectively; P < .017), and between white noise and no intervention ( t = 4.51, 3.09, and 2.95, respectively, P < .017). However, there was no difference between music and no intervention. Although limited by a small sample size and the 1-time only auditory intervention, these preliminary results demonstrate the feasibility of EEG connectivity analyses even at bedside in neonates on continuous EEG monitoring in the neonatal intensive care unit. They also point to the possibility of detecting significant changes in functional connectivity related to the theta and alpha bands using auditory interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".