MRI graph parameters are longitudinal markers of neuronal integrity in multiple sclerosis
Bibliographic record
Abstract
We sought to determine if structural network parameters add to traditional markers of MS treatment response following immunoablation and autologous haemopoietic stem cell transplantation (IAHSCT). The post-IAHSCT paradigm afforded us the opportunity to study MS patients after relapsing biology had been effectively suppressed, enabling us to study the cortical substrate of progressive MS in a less confounded manner. In this analysis of data from a phase 2 prospective study, associations between magnetic resonance graph parameters, N-acetylaspartate to creatine ratio (NAA/Cr), and serum neurofilament light chain (sNfL), among other markers, were assessed at 3 months pre-and 12 months post-IAHSCT. Correlations between graph parameter score changes and markers of brain health were calculated. Predictive factors of NAA/Cr or sNfL levels were calculated, adjusting for reference models. Model improvements were evaluated using the G2 likelihood-ratio test. 24 patients (aged 18-38) were evaluated. Post-IAHSCT, high NAA/Cr and low sNfL (both measures of neuronal injury) were respectively associated with more favorable degree, density, clustering and path lengths, and degree, γ, and path length. Post-IAHSCT, absolute change in degree, path length and γ were associated with NAA/Cr and sNfL. Multivariate analysis demonstrated that the relative change in network parameters after IAHSCT accounted for 14% and 35% more variance in NAA/Cr and sNfL levels respectively than the reference model alone. Cross-sectionally and longitudinally, network parameters demonstrate added utility as markers of disease severity in MS. These measures have the potential to capture cortical changes relevant to progressive non-relapsing biology in MS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".