Altered brain state dynamics between preterm and term-born infants
Bibliographic record
Abstract
Preterm birth alters the development of infant brain networks. However, most prior studies investigate its effects on static brain networks rather than dynamic brain states. Increasing evidence shows that brain state dynamics reflect cognitive processes beyond what is revealed by static brain networks. In the current study, we identify infant brain states and test how their dynamics are influenced by prematurity. To do so, we applied Leading Eigenvector Analysis (LEiDA) to resting-state fMRI data collected from term (n = 86) and preterm-born (n = 102) infants after term equivalent age which identified four discrete brain states across both groups. These brain states corroborate, in an independent dataset, those found in the only other large-scale study of infant brain states. Furthermore, we show that term-born infants spent more time than preterm infants in a "Transmodal State" that resembles the Default-Mode Network in adults. In contrast, preterm birth was associated with transitioning from the Transmodal state to states dominated by sensory processing or where subcortical and cortical areas were dissociated from each other. Together, these findings suggest that preterm birth alters not just static brain networks as previously shown but also brain network dynamics.
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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 | 0.003 |
| 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 | 0.001 |
| 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".