3-F-384 - Cognitive impairments in mice after targeted perineuronal net depletion in the medial prefrontal or retrosplenial cortex.
Bibliographic record
Abstract
Authors: John Paylor¹, Ian Winship¹ ¹University of Alberta Abstract: Perineuronal nets are highly organized components of the extracellular matrix which inhibit cellular plasticity and support cellular function. Depletion of perineuronal nets in rodents via developmental models of disease or via direct pharmacological interventions (e.g. chondroitinase) can impact cognitive function. In our current study, we utilized injections of an immune-evasive dual vector system, where the chondroitinase gene is under a doxycycline regulatory control, to induce bilateral secretion of chondroitinase, with temporal control, in the medial prefrontal cortex and retrosplenial cortex. These two brain regions are known to be involved in the function of working memory. We subjected mice to 30 days of activated chondroitinase-expression to deplete their perineuronal nets, followed by 30 days without to allow for a window of recovery. Prior to treatment, after treatment, and after 30 days without doxycycline-induced chondroitinase expression, animals were tested in a cross-modal object recognition task and an oddity task, which have been shown to be sensitive to manipulations of the medial prefrontal and retrosplenial cortex. Preliminary data indicates that controlled depletion of perineuronal nets in the medial prefrontal and retrosplenial cortex impacts performance of working memory. While there is some recovery in their working memory performance after 30 days without continuing enzymatic depletion, they do not recover to baseline behavioural performance. This data suggests that perineuronal net degradation in the medial prefrontal and retrosplenial cortex impacts working memory performance, and that these changes are long-lasting.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".