Poster - Thurs Eve-28: New brain diffusion analysis method: White matter grey matter dissasociation
Bibliographic record
Abstract
Diffusion MR studies are often used to investigate the physical properties of brain tissues (1, 2). It is known that a full characterisation of the diffusion decay for brain could give valuable information about the structural organisation of cerebral tissue. The significance of the present diffusion decay study lies in the combination of three novel procedures to provide a better characterization of the diffusion decay: i) the acquisition of a large number of b-values (96 b-values up to 10,000 s/mm2), ii) the application of a noise correction technique (3) to the acquired data, and iii) the use of a Non Negative Least Squares (NNLS) fitting algorithm to evaluate the diffusion coefficients. The presence of noise in magnitude MR images can affect the calculation of the diffusion parameters (4) and therefore a noise correction technique (3) is applied. The NNLS algorithm is used to fit the corrected data instead of the more commonly used Levenberg-Marquardt algorithm since the NNLS algorithm does not require the number of components to be specified, nor does it need initial estimates of the fitting parameters as input; thus giving it more versatility as a fitting tool for the diffusion decay. The results indicate that the diffusion decays in grey and white matter have one and two components, respectively. Consequently, the short diffusion component in white matter (Fig. 1.c) can be used as a tool in the disassociation of white and grey matter tissues.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".