Surface-based analysis reveals regions of reduced cortical magnetization transfer ratio in patients with multiple sclerosis: A proposed method for imaging subpial demyelination
Bibliographic record
Abstract
The in vivo detection of subpial cortical gray matter lesions in multiple sclerosis is challenging. We quantified the spatial extent of subpial decreases in the magnetization transfer ratio (MTR) of cortical gray matter in subjects with multiple sclerosis, as such reductions may indicate regions of cortical demyelination. We exploited the unique geometry of cortical lesions by using two-dimensional parametric surface models of the cortex instead of traditional three-dimensional voxel-wise analyses. MTR images were mapped onto intermediate surfaces between the pial and white matter surfaces and were used to compute differences between secondary-progressive MS (n = 12), relapsing-remitting MS (n = 12), and normal control (n = 12) groups as well as between each individual patient and the normal controls. We identified large regions of significantly reduced cortical MTR in secondary-progressive patients when compared with normal controls. We also identified large regions of reduced cortical MTR in 11 individual patients (8 secondary-progressive, 3 relapsing-remitting). The secondary-progressive patients showed larger areas of abnormally low MTR compared with relapsing-remitting patients both at the group level and on an individual basis. The spatial distributions of abnormal MTR preferentially involved cingulate cortex, insula, and the depths of sulci, in agreement with pathological descriptions of subpial gray matter lesion distribution. These findings suggest that our method is a plausible in vivo imaging technique for quantifying subpial cortical demyelinating lesions in patients with multiple sclerosis and, furthermore, can be applied at the typical clinical field strength of 1.5 T.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".