Neurobiological Basis of Dyskinetic Effects Induced by Antipsychotics: the Contribution of Animal Models
Bibliographic record
Abstract
Tardive dyskinesia (TD) is a movement disorder characterized by abnormal involuntary facial movements induced by chronic therapy with classical antipsychotic medications. Currently, there is no satisfactory pharmacotherapy for TD, which represents a major limitation to therapy with classical antipsychotics. In order to develop or optimize therapies for TD, and to develop new APDs with lower indices of motor side effects, the pathology underlying TD must first be understood. The use of animal models has been used to further this objective. Here, we review different preparations that have been used to model TD and discuss the contribution of neuroimaging studies conducted in these models. Studies in animal models have lead to several hypotheses of TD pathology, although none has yet emerged as the ultimate underlying cause of this syndrome. We discuss alterations in functional indices, neuron and synapse morphology and changes in specific neurotransmitter systems that have been described in animal models of TD, and outline how these findings have contributed to our understanding of antipsychotic-induced dyskinesias. We conclude that several non-mutually exclusive theories of TD are supported by animal studies, including increases in oxidative stress leading to structural and functional changes in specific neurotransmitter systems. Elucidating the mechanisms underlying TD neuropathology partly through the use of animal models will lead to the development of APDs with superior side effect profiles or more effective therapies for TD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".