Potentials and Pitfalls of Cross-Translational Models of Cognitive Impairment
Bibliographic record
Abstract
A number of clinical disorders that are either neurodevelopmental or neurodegenerative exhibit significant cognitive impairments that require some form of intervention. However, the current paucity of pro-cognitive treatments that are available, due to the lack of knowledge of biological targets and symptomologies, impedes the treatment of individuals with cognitive impairments. In this review, we explore three critical steps that need to be established in order to lead to the development of effective and appropriate treatments for cognitive impairments. The first step specifically involves the ability to efficiently reproduce and standardize current animal models of disease. The second step involves establishing well-controlled and standardized animal models across different species, such as rodents and monkeys, that link to human disease conditions. The third step involves building these animal models from both a translational and a reverse translational perspective in order to gain critical insight into the aetiologies of specific cognitive impairments and the development of their early physiological and behavioral biomarkers. This bidirectional translational approach is important to improve the investigation of disease biomarkers, the underlying mechanisms of novel therapeutics on cognition, and to validate preclinical findings of drug discovery. Overall, even though animal models play an important role in investigating the pathophysiological processes and mechanisms associated with typical and atypical behavior, we discuss the ongoing challenges associated with these three critical steps of cross-translational research that has led to the current lack of success of developing effective new compounds for potential treatments and suggest approaches to stimulate advances in the field.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".