Loss of Synaptic Tagging in the Anterior Cingulate Cortex after Tail Amputation in Adult Mice
Bibliographic record
Abstract
Anterior cingulate cortex (ACC) is known to play important roles in key brain functions such as pain perception, cognition, and emotion. Different forms of homosynaptic plasticity such as long-term potentiation (LTP) and long-term depression have been studied in ACC synapses. However, heterosynaptic plasticity such as synaptic tagging has not been reported. Here, we demonstrate synaptic tagging in the ACC of adult male mice by using a 64-channel multielectrode array recording system. Weak theta burst stimulation (TBS), normally inducing early-phase LTP or No-LTP in most of the activated channels, produced late phase-LTP (L-LTP) in a majority of channels when a strong TBS was applied earlier to a separate input within a certain time window. Similar to hippocampus, synaptic tagging in the ACC depends on the synthesis of new proteins. Tail amputation-induced peripheral injury caused a loss of this heterosynaptic L-LTP and occluded strong TBS-evoked L-LTP as well. Together, we provide the first report of the synaptic tagging-like phenomenon in the ACC of adult mice, and the loss of synaptic tagging to amputation may contribute to injury-related cognitive changes and phantom limb sensation and pain. SIGNIFICANCE STATEMENT ACC is an important cortical region involved in many brain functions. Previous studies have dissected the molecular mechanism of multiple types of homosynaptic plasticity of ACC synapses. Here, we report a novel form of heterosynaptic plasticity occurring in the ACC. This newly identified, protein synthesis-dependent neocortical synaptic tagging is sensitive to peripheral tail amputation injury and may provide basic mechanisms for synaptic pathophysiology of phantom pain and related cognitive changes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".