Insight into in vivo magnetization exchange in human white matter regions
Bibliographic record
Abstract
Water exchange can play an important role in interpreting compartment-specific magnetic resonance imaging data in brain. For example, an MR method of myelin measurement, known as myelin water fraction imaging, assumes that water exchange processes are slow compared with the measurement time scale. In this article, we examined whether water exchange processes have an effect on myelin water fraction values. A previously established four pool model of white matter was used to simulate the interactions between two aqueous compartments (myelin water and intra/extracellular water) and nonaqueous compartments (myelin and nonmyelin tissues). To extract the water exchange cross relaxation times, the Bloch equations were solved analytically. As the water exchange time scales are dependent on the spin-lattice T(1) relaxation of each of these four pools and due to the current uncertainties regarding the T(1) associated with each pool, exchange cross relaxation times for three different T(1) scenarios were calculated. The corrections that need to be considered in order for myelin water fraction to be an accurate marker for myelin were found to be less than 15%. This work indicates that regional variations in white matter myelin water fraction values are most likely due to variations in myelin content rather than regional differences in exchange rates.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".