P.115 MRI based methodology for assessment of white matter neuroplasticity: preclinical validation using human motor training data
Bibliographic record
Abstract
Background: Disruption of white matter (WM) tracts is common in traumatic injury to the brain and spinal cord. However, imaging techniques for prognostication and monitoring of recovery are lacking. Myelin Water Imaging (MWI) is a validated MRI based method of quantifying myelin volume and represents a potential tool for application in a clinical environment. Methods: 12 healthy, right-handed participants completed a two-week visuomotor maze training program with MRI scans at baseline and endpoint. The task was designed to be difficult for the non-dominant hand and easy for the dominant, allowing for an inbuilt control. Diffusion Tensor Imaging (DTI) along with MWI data were collected at both timepoints using a 3T MRI. Results: Performance metrics confirmed task performance increased only in the non-dominant hand, and a corresponding endpoint>baseline comparison showed significant increases in the MWF (p<0.05) and DTI indices (p<0.05) in the right corticospinal tract (CST), and no significant change in the left CST. Conclusions: This preclinical validation shows MWI is capable of quantitatively tracking WM changes over the course of weeks in humans. MWI’s clinical utility lies in its ability to assess WM changes over short time periods, as monitoring changes in tissue integrity will assist in guiding treatment decisions after critical injury.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".