Longitudinal stability of brain and spinal cordquantitative MRI measures
Bibliographic record
Abstract
Quantitative MRI (qMRI) promises better specificity, accuracy, and stability relative to its clinically-used qualitative MRI counterpart.Longitudinal stability is particularly important in qMRI.The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment.In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years.This first release includes three years of data collection and up to ten sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion).Coefficient of variations (COV) over this timeframe ranged between 0.6% to 2.3% (intrasubject) and 0.4% to 3.5% (intersubject) for T1/MTR/MTsat in whole-brain white matter (WM), and between 0.6% to 1.3% (intrasubject) and 3.0% to 10.3% (intersubject) for diffusion FA/MD/RD in the three corpus callosum regions.In the spine, COVs ranged between 2.3% and 4.5% (intrasubject) and 5.1% to 9.7% (intersubject) for measured spine WM cross-sectional area (CSA) across the C2 and C3 vertebral levels, and between 3.9% to 9.5% (intrasubject) and 4.0% to 8.4% (intersubject) in WM across the C2 and C5 vertebral levels for all qMRI metrics (T1, MTR, MTsat, FA, MD, RD).Results from this work show the level of stability that can be expected from qMRI protocols in the brain and spinal cord, and could help in the design of future longitudinal clinical studies.
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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 | 0.030 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".