Combined application of voxel-based morphometry and magnetization transfer ratio for group analysis of magnetic resonance images
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is conventionally used for macroscopic qualitative observations. However, increasingly there is a need for quantitative MRI measures, which may lead to enhanced detection sensitivity. Two quantitative techniques that may be used to make neuroanatomical inferences about a population or between different populations are magnetization transfer ratio (MTR) and voxel-based morphometry (VBM). VBM involves the statistical analysis of smoothed segmented white or gray matter maps to reflect increases or decreases in the probability of classifying a voxel as either white or gray matter. MTR provides a measure of the interaction of water and semi-solids within tissue, and thus is indicative of its macromolecular density and microstructural integrity. An MTR group analysis may detect variations of these semi-solid tissue characteristics within or between populations. This thesis investigates the relationship between information attained from VBM and MTR population studies carried out in the context of the Saguenay Youth Study. Additionally, through this study, the effects of age and gender on brain neuroanatomy are explored using the above techniques. The observed age and gender VBM and MTR effects were consistent with existing literature, but also offered new findings. Overall, applying MTR in conjunction with VBM allows for further insight into the origins of specific anatomical changes, and the discovery of areas that undergo within-tissue development without corresponding white or gray matter volume changes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.002 |
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".