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Record W4306176742 · doi:10.1002/jmri.28482

Feasibility of <scp>MRI</scp> Quantification of Myelin Water Fraction in the Fetal Guinea Pig Brain

2022· article· en· W4306176742 on OpenAlexafffund
Simran Sethi, Lanette J. Friesen‐Waldner, Trevor Wade, Marc Courchesne, Karen Nygard, Ousseynou Sarr, Brian G. Sutherland, Timothy R.H. Regnault, Charles A. McKenzie

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2022
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsChildren’s Health Research InstituteLawson Health Research InstituteWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFetusMyelinMyelin basic proteinMedicineWhite matterCorpus callosumInternal medicineCoefficient of variationEndocrinologyPregnancyBiologyPathologyMagnetic resonance imagingCentral nervous systemChemistryRadiology

Abstract

fetched live from OpenAlex

Background Fetal myelination assessment is important for understanding neurodevelopment and neurodegeneration. Myelin water imaging (MWI) quantifies myelin water fraction (MWF), a validated marker for myelin content, and has been used to assess brain myelin in children and neonates. Purpose To demonstrate that MWI can quantify MWF in fetal guinea pigs (GPs). Study Type Animal model. Animal Model Nine pregnant, Dunkin‐Hartley GPs with 31 fetuses (mean ± standard deviation = 60 ± 1.5 days gestation). Field Strength/Sequence 3D spoiled gradient echo and balanced steady‐state free precession sequences at 3.0 T. Assessment MWF maps were reconstructed for maternal and fetal GP brains using the multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) approach. Myelin basic protein (MBP) stain provided histological validation of the MWF. Regions of interest were placed in the maternal corpus callosum (CC), maternal fornix (FOR), fetal CC, and fetal FOR in MWF maps and MBP stains. Statistical Tests Linear regression between MWF and MBP stain intensity (SI) of all four regions (coefficient of determination, R2). A paired t‐test compared the MWF of maternal and mean fetal CC, MBP SI of maternal and mean fetal CC, MWF of maternal and mean fetal FOR, MBP SI of maternal and mean fetal FOR. A paired t‐test with a linear mixed model compared the MWF of fetal CC and fetal FOR, and MBP SI of fetal CC and fetal FOR. A P value < 0.0083 was considered statistically significant. Results The mean MWF of the analyzed regions are as follows (mean ± standard deviation): 0.338 + 0.016 (maternal CC), 0.340 ± 0.017 (maternal FOR), 0.214 ± 0.016 (fetal CC), and 0.305 ± 0.025 (fetal FOR). MWF correlated with MBP SI in all regions (R2 = 0.81). Significant differences were found between MWF and MBP SI of maternal and fetal CC, and MWF and MBP SI of fetal CC and fetal FOR. Data Conclusion This study demonstrated the feasibility of MWI in assessing fetal brain myelin content. Evidence Level: 2 Technical Efficacy: Stage 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.054
GPT teacher head0.348
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations3
Published2022
Admission routes2
Has abstractyes

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