QUANTITATIVE EXAMINATION OF A NOVEL CLUSTERING METHOD USING MAGNETIC RESONANCE DIFFUSION TENSOR TRACTOGRAPHY
Bibliographic record
Abstract
Introduction: MR diffusion tensor imaging (DTI) is the most powerful and currentlythe only way to visualize the organization of white matter fiber tracts in vivo. As this is a relatively newimaging technique, new tools are developed for quantifying fiber tracts, andrequire evaluation. We examined scalar indices of the diffusion tensor with two different tractography methods. We compared a novel clustering approach with a multiple region of interest (MROI) approach in a healthy and disease (schizophrenia) population. Methods: DTI images were acquired in 12 participants (n=6 patients withschizophrenia: 58 ± 12 years; n=6 controls: 57 ± 21 years) on a 1.5 Tesla GE system with diffusion gradients applied in 23 non-collinear directions, repeated three times. Tractography andfiber tract creation was performed using 3D Slicer software. Interraterreliability of the clustering approach and its similarity to the MROI methodwere evaluated. Results: The clustering approach was reliable both quantitatively and spatially (k > 0.8 for all tracts). There was high spatial(voxel-based) agreement between the clustering and MROI methods. Fractionalan isotropy and trace values were highly correlated between the clustering and MROI methods (p < 0.001 for all tracts). Discussion: Our clustering method has excellent interrater reliability and thereis a high level of agreement between our clustering method and the MROI method, both quantitatively and spatially. The clustering method is less susceptible touser bias. Moreover, not limited by a priori predictions, our clustering method may be a more robust and efficient way to identify and measure fiber tracts of interest. (colour figure available in PDF version)
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 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) | 0.002 | 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".