Default mode network functional connectivity strength in utero and the association with fetal subcortical development
Bibliographic record
Abstract
The default mode network is essential for higher-order cognitive processes and is composed of an extensive network of functional and structural connections. Early in fetal life, the default mode network shows strong connectivity with other functional networks; however, the association with structural development is not well understood. In this study, resting-state functional magnetic resonance imaging and anatomical images were acquired in 30 pregnant women with singleton pregnancies. Participants completed 1 or 2 MR imaging sessions, on average 3 weeks apart (43 data sets), between 28- and 39-weeks postconceptional ages. Subcortical volumes were automatically segmented. Activation time courses from resting-state functional magnetic resonance imaging were extracted from the default mode network, medial temporal lobe network, and thalamocortical network. Generalized estimating equations were used to examine the association between functional connectivity strength between default mode network-medial temporal lobe, default mode network-thalamocortical network, and subcortical volumes, respectively. Increased functional connectivity strength in the default mode network-medial temporal lobe network was associated with smaller right hippocampal, left thalamic, and right caudate nucleus volumes, but larger volumes of the left caudate. Increased functional connectivity strength in the default mode network-thalamocortical network was associated with smaller left thalamic volumes. The strong associations seen among the default mode network functional connectivity networks and regionally specific subcortical volume development indicate the emergence of short-range connectivity in the third trimester.
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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 | 0.004 |
| 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".