Impaired Cognitive Function after Perineuronal Net Degradation in the Medial Prefrontal Cortex
Bibliographic record
Abstract
Perineuronal nets (PNNs) are highly organized components of the extracellular matrix that surround a subset of mature neurons in the CNS. These structures play a critical role in regulating neuronal plasticity, particularly during neurodevelopment. Consistent with this role, their presence is associated with functional and structural stability of the neurons they ensheath. A loss of PNNs in the prefrontal cortex (PFC) has been suggested to contribute to cognitive impairment in disorders such as schizophrenia. However, the direct consequences of PNN loss in medial PFC (mPFC) on cognition has not been demonstrated. Here, we examined behavior after disruption of PNNs in mPFC of Long-Evans rats following injection of the enzyme chondroitinase ABC (ChABC). Our data show that ChABC-treated animals were impaired on tests of object oddity perception. Performance in the cross-modal object recognition (CMOR) task was not significantly different for ChABC-treated rats, although ChABC-treated rats were not able to perform above chance levels whereas control rats were. ChABC-treated animals were not significantly different from controls on tests of prepulse inhibition (PPI), set-shifting (SS), reversal learning, or tactile and visual object recognition memory. Posthumous immunohistochemistry confirmed significantly reduced PNNs in mPFC due to ChABC treatment. Moreover, PNN density in the mPFC predicted performance on the oddity task, where higher PNN density was associated with better performance. These findings suggest that PNN loss within the mPFC impairs some aspects of object oddity perception and recognition and that PNNs contribute to cognitive function in young adulthood.
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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) | 0.001 | 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".