Relaxation-compensated chemical exchange saturation transfer MRI in the cervical spinal cord at 3T: An application in multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is an autoimmune disease affecting the central nervous system, characterized by demyelination. Exploring pathological changes in the spinal cord could improve our understanding of the mechanisms that cause neurological dysfunction and clinical symptoms; however, conventional MRI is not sensitive to molecular changes within the tissue. Chemical exchange saturation transfer (CEST) can probe tissue biochemistry with high resolution and sensitivity, without exogenous contrasts. However, CEST measurements in vivo are contaminated by concurrent effects including semi-solid magnetization transfer (MT), direct water saturation, and T1-relaxation, which can be altered in MS and need to be removed to accurately quantify changes. Fifty-three people with relapsing-remitting MS (pwRRMS) and 45 healthy controls (HCs) were imaged at 3 T to quantify amide and nuclear Overhauser enhancement (NOE) CEST effects in the cervical spinal cord. Using Lorentzian fitting, confounding effects were removed, and the apparent exchange-dependent relaxation (AREX) contrast was calculated. Uncorrected and corrected AREX amide and NOE contrasts were compared across groups and tissue types. In pwRRMS, AREX NOE was significantly different in lesions compared to normal-appearing white matter. Greater heterogeneity in both CEST contrasts was observed in pwRRMS compared to the HCs. In a sub-analysis of pwRRMS separated by neurological disability, AREX amide was significantly different between pwRRMS with and without disability. The correction of confounding factors in this study highlights the importance of isolating CEST effects in the cervical spinal cord for more specific characterization and to better understand changes in tissue pathology and relationship to disease severity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".