Probing the cerebellar contribution to aging using chemogenetics
Bibliographic record
Abstract
Declines in motor coordination are common in aging and limit a person's quality of life. The cerebellum is critically involved in motor coordination. Cerebellar Purkinje cells fire spontaneous action potentials at high frequencies, which is disrupted in several animal models of ataxia. Rescuing Purkinje cell firing rate deficits in mouse models of ataxia has been shown to improve motor coordination, suggesting that high frequency firing of Purkinje cells is important for normal cerebellar function. We wondered whether cerebellar alterations contribute to aging-related motor decline. To address this, we measured motor coordination in healthy C57Bl/6J mice across their adult lifespan, from young to old adult, and observed a progressive age-related decline. We then performed loose cell-attached recordings from Purkinje cells to measure spontaneous action potential firing in acute cerebellar slices. We observed an age-dependent reduction in Purkinje cell firing rates, suggesting that Purkinje cell firing might contribute to the decline in motor coordination we observed. To determine whether Purkinje cell firing alterations directly contribute to motor dysfunction in aging, we used viral delivery of chemogenetic receptors to modulate Purkinje cell action potential activity. We found that chemogenetically reducing Purkinje cell firing rates led to a decrease in motor coordination in young mice, suggesting that Purkinje cell firing output directly modulates motor coordination. Our data suggest that aging-related Purkinje cell firing deficits contribute to declining motor coordination observed in aging individuals.
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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) | 0.000 | 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 | 0.000 |
| 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".