Six is enough? Comparison of diffusion parameters measured using six or more diffusion‐encoding gradient directions with deterministic tractography
Bibliographic record
Abstract
Diffusion tensor imaging tractography is commonly used to quantify white matter tracts in the human brain via parameters such as fractional anisotropy and mean diffusivity. Simulation studies recommend the use of more than six directions for robust parameter estimates; however, no study has examined the impact of the number of gradient directions on deterministic tractography-derived diffusion parameters in human brain. Here, for 10 major white matter tracts in 11 healthy volunteers at 1.5 T, six-direction diffusion tensor imaging data were compared to 30- or 60-direction data, keeping scan time and number of b = 0 images constant within each test. Mean diffusivity was systematically lower for six-direction protocols (20/40 comparisons); six-direction data had higher fractional anisotropy in the superior longitudinal fasciculus and smaller tract volume for the genu of the corpus callosum. In general, parameter differences due to the number of directions were smaller than those from intersubject variation or signal-to-noise ratio. Despite some absolute differences, standard deviations were significantly different for only one of 160 comparisons. Thus, six-direction data provide diffusion measures with comparable robustness to 30- or 60-direction data and yield appropriate parameter values for most white matter tracts, although there are clear advantages in acquiring higher angular resolution data.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".