Combined loss of brevican, neurocan, tenascin-C and tenascin-R leads to impaired fear retrieval due to perineuronal net loss
Bibliographic record
Abstract
Abstract In conditions such as neurodegenerative diseases, posttraumatic stress disorder (PTSD), addiction and spinal cord injuries, restricted synaptic plasticity hinders the formation of new neuronal connections, preventing the compensation and treatment of adverse behaviors. Perineuronal nets (PNNs) significantly restrict synaptic plasticity by inhibiting synapse formation. The digestion of PNNs has been associated with short-term cognitive improvements and reduced long-term memory, offering potential therapeutic benefits in PTSD. This study investigates the correlation between PNNs and fear memory processes in extracellular matrix (ECM) mutant mice, particularly focusing on the amygdala-medial prefrontal cortex (mPFC) circuit, which is crucial for fear memory generation and maintenance. Fear conditioning was conducted on mice lacking four key ECM-molecules: brevican, neurocan, tenascin-C and tenascin-R (4x KO). These mice exhibited severe impairments in memory consolidation, as evident by their inability to retrieve previously learned fear memories, coupled with reduced PNN density and disturbed synaptic integrity along their PNNs. Additionally, changes in neural activity in the basolateral amygdala (BL) and reductions in VGAT + synaptic puncta in the amygdala-mPFC circuit were observed. In contrast, tenascin single KOs showed intact fear behavior and memory compared to their control groups. Impaired fear memory consolidation can be advantageous in certain conditions, such as PTSD, making the 4x KO mice an intriguing model for future fear conditioning studies and highlighting brevican, neurocan, Tnc, and Tnr as compelling targets for further investigation. This study underscores the significance of ECM regulation for synaptic organization and the potential of PNN modulation as a therapeutic target for fear memory-related conditions.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".