Is the Relationship between Cortical and White Matter Pathologic Changes in Multiple Sclerosis Spatially Specific? A Multimodal 7-T and 3-T MR Imaging Study with Surface and Tract-based Analysis
Bibliographic record
Abstract
PURPOSE: To investigate in vivo the spatial specificity of the interdependence between intracortical and white matter (WM) pathologic changes as function of cortical depth and distance from the cortex in multiple sclerosis (MS), and their independent contribution to physical and cognitive disability. MATERIALS AND METHODS: This study was institutional review board-approved and participants gave written informed consent. In 34 MS patients and 17 age-matched control participants, 7-T quantitative T2* maps, 3-T T1-weighted anatomic images for cortical surface reconstruction, and 3-T diffusion tensor images (DTI) were obtained. Cortical quantitative T2* maps were sampled at 25%, 50%, 75% depth from pial surface. Tracts of interest were reconstructed by using probabilistic tractography. The relationship between DTI metrics voxelwise of the tracts and cortical integrity in the projection cortex was tested by using multilinear regression models. RESULTS: In MS, DTI abnormal findings along tracts correlated with quantitative T2* changes (suggestive of iron and myelin loss) at each depth of the cortical projection area (P < .01, corrected). This association, however, was not spatially specific because abnormal findings in WM tracts also related to cortical pathologic changes outside of the projection cortex of the tract (P < .001). Expanded Disability Status Scale pyramidal score was predicted by axial diffusivity along the corticospinal tract (β = 4.6 × 10(3); P < .001), Symbol Digit Modalities Test score by radial diffusivity along the cingulum (β = -4.3 × 10(4); P < .01), and T2* in the cingulum cortical projection at 25% depth (β = -1.7; P < .05). CONCLUSION: Intracortical and WM injury are concomitant pathologic processes in MS, which are not uniquely distributed according to a tract-cortex-specific pattern; their association may reflect a common stage-dependent mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".