Reproducibility of T<sub>2</sub>* mapping in the human cerebral cortex in vivo at 7 tesla MRI
Bibliographic record
Abstract
BACKGROUND: To assess the test-retest reproducibility of cortical mapping of T2 * relaxation rates at 7 Tesla (T) MRI. T2 * maps have been used for studying cortical myelo-architecture patterns in vivo and for characterizing conditions associated with changes in iron and/or myelin concentration. METHODS: T2 * maps were calculated from 7T multi-echo T2 *-weighted images acquired during separate scanning sessions on 8 healthy subjects. The reproducibility of surface-based cortical T2 * mapping was assessed at different depths of the cortex; from pial surface (0% depth) towards gray/white matter boundary (100% depth), across cortical regions and hemispheres, using coefficients of variation (COVs = SD/mean) between each couple (scan-rescan) of average T2 * measurements. RESULTS: Average cortical T2 * was significantly different among 25%, 50%, and 75% depths (analysis of variance, P < 0.001). Coefficient of variations were very low within cortical regions, and whole cortex (average COV = 0.83-1.79%), indicating a high degree of reproducibility in T2 * measures. CONCLUSION: Surface-based mapping of T2 * relaxation rates as a function of cortical depth is reproducible and could prove useful for studying the laminar architecture of the cerebral cortex in vivo, and for investigating physiological and pathological states associated with changes in iron and/or myelin concentration.
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 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 | 0.004 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".