Neuroinflammatory component of gray matter pathology in multiple sclerosis
Bibliographic record
Abstract
Objective In multiple sclerosis (MS), using simultaneous magnetic resonance–positron emission tomography (MR‐PET) imaging with 11 C‐PBR28, we quantified expression of the 18kDa translocator protein (TSPO), a marker of activated microglia/macrophages, in cortex, cortical lesions, deep gray matter (GM), white matter (WM) lesions, and normal‐appearing WM (NAWM) to investigate the in vivo pathological and clinical relevance of neuroinflammation. Methods Fifteen secondary‐progressive MS (SPMS) patients, 12 relapsing–remitting MS (RRMS) patients, and 14 matched healthy controls underwent 11 C‐PBR28 MR‐PET. MS subjects underwent 7T ‐weighted imaging for cortical lesion segmentation, and neurological and cognitive evaluation. 11 C‐PBR28 binding was measured using normalized 60‐ to 90‐minute standardized uptake values and volume of distribution ratios. Results Relative to controls, MS subjects exhibited abnormally high 11 C‐PBR28 binding across the brain, the greatest increases being in cortex and cortical lesions, thalamus, hippocampus, and NAWM. MS WM lesions showed relatively modest TSPO increases. With the exception of cortical lesions, where TSPO expression was similar, 11 C‐PBR28 uptake across the brain was greater in SPMS than in RRMS. In MS, increased 11 C‐PBR28 binding in cortex, deep GM, and NAWM correlated with neurological disability and impaired cognitive performance; cortical thinning correlated with increased thalamic TSPO levels. Interpretation In MS, neuroinflammation is present in the cortex, cortical lesions, deep GM, and NAWM, is closely linked to poor clinical outcome, and is at least partly linked to neurodegeneration. Distinct inflammatory‐mediated factors may underlie accumulation of cortical and WM lesions. Quantification of TSPO levels in MS could prove to be a sensitive tool for evaluating in vivo the inflammatory component of GM pathology, particularly in cortical lesions. Ann Neurol 2016;80:776–790
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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 | 0.000 | 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) | 0.000 | 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".