Origins of <i>R</i> <sub>2</sub> <sup>∗</sup> orientation dependence in gray and white matter
Bibliographic record
Abstract
Estimates of the apparent transverse relaxation rate (R2*) can be used to quantify important properties of biological tissue. Surprisingly, the mechanism of R2* dependence on tissue orientation is not well understood. The primary goal of this paper was to characterize orientation dependence of R2* in gray and white matter and relate it to independent measurements of two other susceptibility based parameters: the local Larmor frequency shift (fL) and quantitative volume magnetic susceptibility (Δχ). Through this comparative analysis we calculated scaling relations quantifying R2' (reversible contribution to the transverse relaxation rate from local field inhomogeneities) in a voxel given measurements of the local Larmor frequency shift. R2' is a measure of both perturber geometry and density and is related to tissue microstructure. Additionally, two methods (the Generalized Lorentzian model and iterative dipole inversion) for calculating Δχ were compared in gray and white matter. The value of Δχ derived from fitting the Generalized Lorentzian model was then connected to the observed R2* orientation dependence using image-registered optical density measurements from histochemical staining. Our results demonstrate that the R2* and fL of white and cortical gray matter are well described by a sinusoidal dependence on the orientation of the tissue and a linear dependence on the volume fraction of myelin in the tissue. In deep brain gray matter structures, where there is no obvious symmetry axis, R2* and fL have no orientation dependence but retain a linear dependence on tissue iron concentration and hence Δχ.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".