“Ependymal‐in” Gradient of Thalamic Damage in Progressive Multiple Sclerosis
Bibliographic record
Abstract
Leptomeningeal and perivenular infiltrates are important contributors to cortical grey matter damage and disease progression in multiple sclerosis (MS). Whereas perivenular inflammation induces vasculocentric lesions, leptomeningeal involvement follows a subpial “surface‐in” gradient. To determine whether similar gradient of damage occurs in deep grey matter nuclei, we examined the dorsomedial thalamic nuclei and cerebrospinal fluid (CSF) samples from 41 postmortem secondary progressive MS cases compared with 5 non‐neurological controls and 12 controls with other neurological diseases. CSF/ependyma‐oriented gradient of reduction in NeuN + neuron density was present in MS thalamic lesions compared to controls, greatest (26%) in subventricular locations at the ependyma/CSF boundary and least with increasing distance (12% at 10 mm). Concomitant graded reduction in SMI31 + axon density was observed, greatest (38%) at 2 mm from the ependyma/CSF boundary and least at 10 mm (13%). Conversely, gradient of major histocompatibility complex (MHC)‐II + microglia density increased by over 50% at 2 mm at the ependyma/CSF boundary and only by 15% at 10 mm and this gradient inversely correlated with the neuronal ( R = −0.91, p < 0.0001) and axonal ( R = −0.79, p < 0.0001) thalamic changes. Observed gradients were also detected in normal‐appearing thalamus and were associated with rapid/severe disease progression; presence of leptomeningeal tertiary lymphoid‐like structures; large subependymal infiltrates, enriched in CD20 + B cells and occasionally containing CXCL13 + CD35 + follicular dendritic cells; and high CSF protein expression of a complex pattern of soluble inflammatory/neurodegeneration factors, including chitinase‐3‐like‐1, TNFR1, parvalbumin, neurofilament‐light‐chains and TNF. Substantial “ependymal‐in” gradient of pathological cell alterations, accompanied by presence of intrathecal inflammation, compartmentalized either in subependymal lymphoid perivascular infiltrates or in CSF, may play a key role in MS progression. Summary for Social Media Imaging and neuropathological evidences demonstrated the unique feature of “surface‐in” gradient of damage in multiple sclerosis (MS) since early pediatric stages, often associated with more severe brain atrophy and disease progression. In particular, increased inflammation in the cerebral meninges has been shown to be strictly associated with an MS‐specific gradient of neuronal, astrocyte, and oligodendrocyte loss accompanied by microglial activation in subpial cortical layers, which is not directly related to demyelination. To determine whether a similar gradient of damage occurs in deep grey matter nuclei, we examined the potential neuronal and microglia alterations in the dorsomedial thalamic nuclei from postmortem secondary progressive MS cases in combination with detailed neuropathological characterization of the inflammatory features and protein profiling of paired CSF samples. We observed a substantial “subependymal‐in” gradient of neuro‐axonal loss and microglia activation in active thalamic lesions of progressive MS cases, in particular in the presence of increased leptomeningeal and cerebrospinal fluid (CSF) inflammation. This altered graded pathology was found associated with more severe and rapid progressive MS and increased inflammatory degree either in large perivascular subependymal infiltrates, enriched in B cells, or within the paired CSF, in particular with elevated levels of a complex pattern of soluble inflammatory and neurodegeneration factors, including chitinase 3‐like‐1, TNFR1, parvalbumin, neurofilament light‐chains and TNF. These data support a key role for chronic, intrathecally compartmentalized inflammation in specific disease endophenotypes. CSF biomarkers, together with advance imaging tools, may therefore help to improve not only the disease diagnosis but also the early identification of specific MS subgroups that would benefit of more personalized treatments. ANN NEUROL 2022;92:670–685
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".