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Record W3090275975 · doi:10.1002/mrm.28543

Myelin water imaging depends on white matter fiber orientation in the human brain

2020· article· en· W3090275975 on OpenAlexafffund
Christoph Birkl, J. Ronald Doucette, Michael Fan, Enedino Hernández‐Torres, Alexander Rauscher

Bibliographic record

VenueMagnetic Resonance in Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersCanadian Institutes of Health ResearchAustrian Science FundNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Multiple Sclerosis Society
KeywordsWhite matterMyelinVoxelNuclear magnetic resonanceOrientation (vector space)Fiber tractChemistryMagnetic resonance imagingBiologyPhysicsNeuroscienceMedicineMathematicsCentral nervous systemRadiologyGeometry

Abstract

fetched live from OpenAlex

PURPOSE: components related to intracellular and extracellular water. In this study, we investigated to what degree the apparent myelin water fraction (MWF) depends on the angle between white matter fibers and the main magnetic field. METHODS: Maps of the apparent MWF were acquired using multi-echo Carr-Purcell-Meiboom-Gill and gradient-echo spin-echo sequences. The Carr-Purcell-Meiboom-Gill sequence was acquired with a TR of 1073 ms, 1500 ms, and 2000 ms. The fiber orientation was mapped with DTI. By angle-wise pooling the voxels across the brain's white matter, orientation-dependent apparent MWF curves were generated. RESULTS: We found that the apparent MWF varied between 25% and 35% across different fiber orientations. Furthermore, the selection of the TR influences the apparent MWF. CONCLUSION: White matter fiber orientation induces a strong systematic bias on the estimation of the apparent MWF. This finding has implications for future research and the interpretation of MWI results in previously published studies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.443
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.044
GPT teacher head0.349
Teacher spread0.305 · 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 teacher head, not a consensus.

Study designObservational
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".

Quick stats

Citations79
Published2020
Admission routes2
Has abstractyes

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