Tumour necrosis factor-mediated homeostatic synaptic plasticity in behavioural models: testing a role in maternal immune activation
Bibliographic record
Abstract
The proinflammatory cytokine tumour necrosis factor-alpha (TNFα) has long been characterized for its role in the innate immune system, but more recently has been found to have a distinct role in the nervous system that does not overlap with other proinflammatory cytokines. Through regulation of neuronal glutamate and GABA receptor trafficking, TNF mediates a homeostatic form of synaptic plasticity, but plays no direct role in Hebbian forms of plasticity. As yet, there is no evidence to suggest that this adaptive plasticity plays a significant role in normal development, but it does maintain neuronal circuit function in the face of several types of disruption. This includes developmental plasticity in primary sensory cortices, as well as modulating the response to antidepressants, chronic antipsychotics and drugs of abuse. TNF is also a prominent component of the neuroinflammation occurring in most neuropathologies, but the role of TNF-mediated synaptic plasticity in this context remains to be determined. We tested this in a maternal immune activation (MIA) model of neurodevelopmental disorders. Using TNF −/− mice, we observed that TNF is not required for the expression of abnormal social or anxious behaviour in this model. This indicates that TNF does not uniquely contribute to the development of neuronal dysfunction in this model, and suggests that during neuroinflammatory events, compensation between the various proinflammatory cytokines is the norm. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".