Long-range temporal correlation development in resting-state fMRI signal in preterm infants: Scanned shortly after birth and at term-equivalent age
Bibliographic record
Abstract
While the newborn’s brain is functionally organised early on—with similar resting state networks as those of adults present at birth—these networks continue to develop at different rates and in complex ways over time. While most in vivo functional imaging studies examine the spatial characteristics of resting state networks (RSNs), such as their size or the degree of connectivity, the temporal characteristics of these networks are less well characterised. We set out to examine the long-range temporal correlation (LRTC) of the fMRI blood oxygen level-dependent (BOLD) signal using the Hurst exponent in various RSNs in infants born very preterm shortly after birth (< 32 weeks gestational age; n = 64) and again later at term equivalent age (TEA) (n = 69). The Hurst exponent in grey matter and white matter was 0.69 at preterm age and increased significantly to 0.80 at TEA, with a greater difference between the tissues at TEA. The Hurst exponent in RSNs similarly was found to be approximately 0.68 for most networks at preterm age but increased significantly at different rates by TEA: 0.77 and 0.76 in the cerebellum and frontal networks respectively, and 0.84 and 0.83 in the motor and visual networks respectively. This pattern is partly consistent with findings from previous functional connectivity fMRI studies that the general growth and maturation of RSNs occur first and develop more quickly in sensory and motor networks, but later in associative networks like frontal ones. Importantly, this is the first time that this pattern has been shown in the LRTC of the fMRI BOLD signal itself, an area of study that may provide greater insight into functional brain development.
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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 | 0.000 |
| 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) | 0.000 | 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".