Non-invasive measurement of biochemical profiles in the healthy fetal brain
Bibliographic record
Abstract
H-MRS) of the fetal brain can be used to study emerging metabolite profiles in the developing brain. Identifying early deviations in brain metabolic profiles in high-risk fetuses may offer important adjunct clinical information to improve surveillance and management during pregnancy. OBJECTIVE: To investigate the normative trajectory of the fetal brain metabolites during the second half of gestation, and to determine the impact of using different Cramer-Rao Lower Bounds (CRLB) threshold on metabolite measurements using magnetic resonance spectroscopy. STUDY DESIGN: H-MRS studies with gestational age in the rage of 18-39 weeks with 112 of the enrolled participants scanned twice. All the spectra in this study were acquired on a GE 1.5 T scanner using long echo-time of 144 ms and analyzed in LCModel. RESULTS: H-MRS with a success rate of 93%. We observed increases in total NAA, total creatine, total choline, scyllo inositol and total NAA-to-total choline ratio with advancing GA. Our results also showed faster increases in total NAA and total NAA-to-total choline ratio during the third trimester compared to the second trimester. We also observed faster increases in total choline and total NAA in female fetuses. Increasing the Cramer-Rao lower bounds threshold progressively from 100% to 40%-20% increased the mean metabolite concentrations and decreased the number of observations available for analysis. CONCLUSION: We report serial fetal brain biochemical profiles in a large cohort of health fetuses studied twice in gestation with a high success rate in the second and third trimester of pregnancy. We present normative in-vivo fetal brain metabolite trajectories over a 21-week gestational period which can be used to non-invasively measure and monitor brain biochemistry in the healthy and high-risk fetus.
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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".