Multi-Site Structural Magnetic Resonance Imaging of Myelin
Bibliographic record
Abstract
Multi-site MRI studies collect large amounts of data in a short time frame. Large sample sizes are desirable to address power and replicability issues that have been problematic for scientists in the past. Although multi-site MRI solves the sample size problem, it brings with it a new set of challenges. Scanning the same person at different sites might result in differences in MRI derived measurements. In this thesis we compared three approaches to facilitate the analysis of multi-site MRI data: quantitative R1 mapping, adding site as a covariate in a linear model, and using the ComBat method. We also investigated the relationship between two common MRI measurements: signal and volume. We collected data from 64 healthy participants across 3 GE scanners and 1 Siemens scanner at 3T. We found that signal intensity was different between vendors whereas volume was not. Our R1 method resulted in values that were different across vendor and significantly lower than those reported in the literature. B1+ maps used to calculate R1 were different across sites. Using a scale factor, we were able to compensate for mistakes in R1 mapping. We also found that adding site as a covariate corrected mean differences in signal intensity across sites, but not differences in variance. The ComBat method gave best similarity between sites. However, since different people were scanned at each site, we couldn’t evaluate the effectiveness of each method as variation in the data could have been due to site effects or heterogeneity in participants. White matter volume and signal intensity in the white matter were correlated in males but not in females. We found that this low correlation was caused by outliers in our female sample. The correlation between white matter volume and signal in males suggests that both metrics are measuring myelin and can be used as converging evidence to detect changes in brain myelination.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".