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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".