Corticospinal tract integrity measured using transcranial magnetic stimulation and magnetic resonance imaging in neuromyelitis optica and multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Both multiple sclerosis (MS) and neuromyelitis optica (NMO) can present with transverse myelitis; however, NMO symptoms are usually more severe and may present with more extensive axonal loss. Transcranial magnetic stimulation (TMS)-based input-output recruitment curves can quantitatively assess the excitability of corticospinal tract pathways and myelin water imaging can quantify the amount of myelin within this same pathway. OBJECTIVE: To compare differential effects of MS and NMO on TMS recruitment curves and myelin water imaging. METHODS: Ten healthy controls, 10 individuals with MS and 10 individuals with NMO completed clinical assessments, a TMS assessment and magnetic resonance imaging scan to measure recruitment curves and myelin water fraction in the corticospinal tract. RESULTS: Individuals with NMO had lower recruitment curve slopes (mean 13.6±6 μV/%) than MS (23.6±11 μV/%) and controls (21.9±9 μV/%, analysis of variance (ANOVA) P=0.05). Corticospinal tract myelin water fraction was lower in individuals with NMO (mean 0.17±0.02) compared to MS (0.19±0.02) and controls (0.20±0.02, ANOVA P=0.0006). CONCLUSION: Corticospinal pathway damage in individuals with NMO was evident by reduced recruitment curve slope and lower myelin water fraction. These specific measures of corticospinal function and structure may be used to obtain a better understanding and monitor brain injury caused by inflammatory central nervous system disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".