Exploring subcortical pathology and processing speed in neuromyelitis optica spectrum disorder with myelin water imaging
Bibliographic record
Abstract
Abstract Background and Purpose Neuromyelitis optica spectrum disorder (NMOSD) affects the optic nerves and spinal cord but can also cause focal brain inflammation. Subcortical pathology may contribute to the etiology of cognitive deficits in NMOSD. Using myelin water imaging, we investigated cerebral normal‐appearing white matter (NAWM) and thalamic metrics and their association with cognition in NMOSD participants compared to healthy controls (HC). Methods Seventeen NMOSD participants and 21 HC were scanned on a 3.0‐Tesla MRI scanner using a multicomponent driven‐equilibrium single‐pulse observation of T 1 and T 2 protocol. Tissue compartment and thalamic volumes (normalized to intracranial volume), T 1 relaxation time, and myelin water fraction (MWF) were reported. Eleven NMOSD participants underwent the Symbol Digit Modalities Test (SDMT) for cognitive evaluation. Group comparisons were performed using Student's t ‐test. The association between thalamic metrics and SDMT score was assessed using multiple regression analysis with age as a covariate. Results Compared to HC, NMOSD participants had reduced white matter volume (−14.2%, p < .0001), increased T 1 relaxation time (+2.29%, p = .022), and lower MWF (−3.64%, p = .024) in NAWM. NMOSD group had a trend for smaller thalamic volumes than HC (−5.52%, p = .082) and no differences in thalamic MWF ( p = .258) or T 1 ( p = .714). Thalamic T 1 predicted SDMT score (adjusted R 2 = .51, p = .04) when controlling for age. Conclusions NAWM in NMOSD demonstrates diffuse abnormalities with increased water content and demyelination, suggesting a diffuse disease process overlooked by focal inflammation measures. Increased water content, as a biomarker for diffuse thalamic pathology, may partially explain cognitive impairment in NMOSD.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".