Early Life Resting-State Network Functional Connectivity and Long-Term Neurodevelopmental Outcomes after Neonatal Encephalopathy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Neonatal encephalopathy (NE) is a risk factor for long-term neurodevelopmental impairments, and early measures of brain function may help understand which survivors are most at risk. This study investigates the relationship between resting-state network (RSN) functional connectivity (FC) in the first week of life and long-term neurodevelopmental outcomes after NE. MATERIALS AND METHODS: A prospective cohort of therapeutic hypothermia-treated neonates ≥36 weeks gestational age with NE had resting-state functional MRI (rs-fMRI) in the first week of life, and follow-up at 18 and 36 months. Subject-wise Pearson correlations between each pair of 27 seeds (seed-to-seed correlation matrix) that represent 6 RSNs of interest (Language, Somatomotor, Default Mode, Frontoparietal, Dorsal attention, Ventral Attention), and subject-wise seed-based mean z scores for each seed were generated. Spearman correlations assessed the associations between 6 intra-RSN FC strength values (averaged subject-wise z-transformed seed-to-seed correlations within each RSN) and 18- and 36-month neurodevelopmental outcomes. Based on primary analyses findings, Spearman correlations also assessed the associations between seed-based mean z scores for specific RSNs and neurodevelopmental outcomes at 36 months. RESULTS: Of 72 neonates, 52 (72%) had adequate quality rs-fMRI data. Based on Barkovich scoring, 3 (6%) neonates had basal ganglia, 2 (4%) had watershed, and 10 (19%) had both brain injury patterns. Of 52, 47 (90%) neonates had follow-up at 19 ± 1.7 months, and 45 (87%) at 36.8 ± 1.5 months. Correcting for multiple comparisons, intra-RSN FC strength was not correlated with 18- or 36-month outcomes. Correcting for multiple comparisons, mean z scores for the right motor, and the right and left supplementary motor area within the Somatomotor RSN were positively correlated with Bayley-III language composite score, and mean z scores for the right temporoparietal junction within the Ventral Attention RSN were positively correlated with Child Behavior Checklist Externalizing Problems composite T-score at 36 months. CONCLUSIONS: RSN FC in the first week of life is correlated with 36-month neurodevelopmental outcomes after NE. Early rs-fMRI could provide meaningful clinical insight for predicting long-term neurodevelopmental outcomes after NE.
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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 | 0.002 |
| 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".