Excessive drinking and checking in the rat model of Schedule-Induced Polydipsia reveal impaired bi-directional plasticity at BNST GABA synapses
Bibliographic record
Abstract
Abstract Compulsions, defined by debilitating repetitive actions, permeate many mental illnesses and are challenging to treat partly because of a limited understanding of their neurobiological underpinnings. Accumulating evidence suggests the rodent model of Schedule-Induced Polydipsia (SIP) as a promising pre-clinical assay to elucidate the neurobiological and behavioural manifestations of compulsivity. In the rodent SIP paradigm, susceptible rats develop adjunctive excessive drinking when they are chronically food restricted and presented with food pellets according to a fixed-time schedule. We found that normally, bi-directional plasticity of GABA synapses in the oval bed nucleus of the stria terminalis (ovBNST) tightly followed the rats’ satiety state where low-frequency stimulation-induced potentiation (LTP GABA ) prevailed in sated rats whilst food restriction uncovered long-term depression (LTD GABA ). In rats that developed excessive drinking during SIP, removing the caloric restriction failed at reverting LTD GABA to LTP GABA whereas bi-directional plasticity at ovBNST GABA synapses was unaltered in low-drinking SIP-trained rats. Excessive drinking ceased in polydipsic rats removed from their caloric restriction; however, these rats retained a form of compulsive schedule-induced checking (SIC) and impaired plasticity at ovBNST GABA synapses for several days following termination of the caloric restriction. We conclude that altered bi-directional plasticity at ovBNST GABA synapses is a neurophysiological trace of compulsivity in susceptible rats in the SIP model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".