Towards repeatable and converging methods in diffusion MRI: Evidence from a longitudinal chronic pain cohort
Bibliographic record
Abstract
Abstract Introduction Studies of white matter (WM) alterations in chronic low back pain (CLBP) using diffusion MRI (dMRI) have yielded inconsistent results, highlighting a need for more reproducible methods. This study introduces a longitudinal analysis pipeline designed to identify stable and repeatable WM microstructural differences between CLBP patients and healthy controls. Methods Diffusion MRI was acquired from 27 CLBP patients and 25 control participants at three separate visits with two-month intervals. A customized Tract-Based Spatial Statistics (TBSS) analysis was performed on fractional anisotropy (FA) maps that were averaged across visits for each participant. Resulting clusters showing group differences were subsequently filtered based on cluster size (>10 voxels) and a high repeatability threshold (image intra-class correlation coefficient [I2C2] > 0.75) to ensure findings were stable across all three imaging sessions. Results were compared against standard single-visit analyses and an alternative tractometry analysis. Results The repeatability analysis identified 11 clusters with stable and significant FA differences. Seven clusters, located primarily in the occipital, parietal, and frontal lobes, showed higher FA in controls. Four clusters, located in the frontal and temporal lobes, showed higher FA in the CLBP group. In contrast, single-visit analyses identified a much larger number of clusters (21 to 36), the majority of which were not spatially consistent across time and did not overlap with the final repeatable clusters. The repeatable TBSS findings demonstrated a strong spatial correspondence with group differences found using a tractometry analysis. Discussion By incorporating a longitudinal design and explicit repeatability filtering, our method effectively reduces spurious findings common in single-visit dMRI studies. This approach successfully isolated reliable WM regions with altered dMRI metrics in CLBP, demonstrating its value in improving the robustness of neuroimaging research in chronic pain.
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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 | 0.038 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".