Preservation of diffusion tensor properties during spatial normalization by use of tensor imaging and fibre tracking on a normal brain database
Bibliographic record
Abstract
White matter connectivity in the human brain can be mapped by diffusion tensor magnetic resonance imaging (DTI). After reconstruction, the diffusion tensors, the diffusion amplitude and the diffusion direction can be displayed on a morphological background. Consequently, diffusion tensor fibre tracking can be applied as a non-invasive in vivo technique for the delineation and quantification of specific white matter pathways. The aim of this study was to show that normalization to the Montreal Neurological Institute (MNI) stereotaxic standard space preserves specific diffusion features. Therefore, techniques for tensor imaging and fibre tracking were applied to the normalized brains as well as to the group averaged brain data. A normalization step of individual data was included by registration to a scanner- and sequence-specific DTI template data set which was created from a normal database transformed to MNI space. The algorithms were tested and validated for a group of 13 healthy controls.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".