Assessing white matter pathway reproducibility from human whole-brain tractography clustering
Bibliographic record
Abstract
Abstract Diffusion MRI, together with tractography techniques, is a non-invasive tool to investigate the brain’s structural pathways (tracts). These tracts join together different regions of the brain and tract identification often involves the use of manual ROIs or automated techniques such as clustering. By studying these connections, current understanding of the connectome can be improved and changes due to disease in patient populations may be identified. We developed a tool to automatically identify all pathways in the human brain, including the short-range, U-shaped tracts, and map quantitative scalar metrics along the pathway trajectory for subsequent analysis. Pathways are identified via a spectral clustering technique on two datasets: Human Connectome Project (intersubject) and MyConnectome Project (intrasubject) and the reliability of the extracted tract and scalar values are evaluated. Average Euclidean distances and volumetric overlap were computed and indicated good spatial reliability. Intraclass correlations of the fractional anisotropy value mapped along the tract was calculated and exhibited good reproducibility within each dataset. Additionally, these evaluation metrics, together with the coefficient of variation of the mean streamline count is used to determine reliably identified U-shaped tracts across the datasets. The developed tract identification tool is an additional resource to studying the human connectome with increased confidence in the results. The identified reliable U-shaped tracts contributes to the identification of common structural connections across individuals and aids in advancing our understanding of the brain’s short-range pathways.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".