A Touchscreen Motivation Assessment Evaluated in Huntington's Disease Patients and R6/1 Model Mice
Bibliographic record
Abstract
Apathy is pervasive across many neuropsychiatric disorders but is poorly characterised mechanistically, so targeted therapeutic interventions remain elusive. A key impediment has been the lack of validated assessment tools to facilitate translation of promising findings between preclinical disease models and patients. Apathy is a common symptom in Huntington’s disease. Due to its established genetic basis and the availability of defined animal models, this disease offers a robust translational framework for linking motivated behaviour with underlying neurobiology and an ideal context in which to evaluate a quantitative, translational apathy assessment method. In this study we therefore aimed to demonstrate the validity of using touchscreen-delivered progressive ratio tasks to mirror apathy assessment in Huntington’s disease patients and a representative mouse model. To do this we evaluated Huntington’s disease patients (n=23) and age-matched healthy controls (n=20), and male R6/1 mice (n=23) and wildtype controls (n=29) for apathy-like behaviour using touchscreen-delivered progressive ratio tasks. The primary outcome measure of the assessment was breakpoint, defined as the highest number of touchscreen responses emitted before task engagement ceased. Patients and R6/1 mice were both found to exhibit significantly reduced breakpoints relative to their respective control groups, consistent with apathy-like behaviour. This performance was also not associated with motoric differences in either species. These data demonstrate the utility of touchscreen-delivered progressive ratio tasks in detecting clinically relevant motivational deficits in Huntington’s disease. This approach may offer a platform from which clinically relevant mechanistic insights concerning motivation symptoms can be derived and provide an effective route for translation of promising preclinical findings into viable therapeutic interventions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".