Sensorimotor processing in Huntington's disease : from behavior to circuit
Bibliographic record
Abstract
Voluntary movement arises from coordinated computations across cortical and subcortical circuits. Huntington’s disease (HD) perturbs these processes early, yet how cortical dynamics and striatal neurotransmission jointly change during action remains unresolved. This thesis integrates automated home-cage behavior, chronic mesoscale cortical imaging, and subcortical fiber photometry to define circuit mechanisms underlying impaired motor refinement in early-manifest zQ175DN knock-in mice (~6–8 months). In Chapter 2, I established an automated, forelimb lever-pull task that required maintaining the lever within a goal range for a required hold time that increased with performance. Wild-type (WT) and zQ175 mice showed comparable initial engagement. However, as task demands increased, zQ175 mice exhibited marked difficulty adapting to longer hold times. WT animals progressively refined their strategy, shifting from variable to precise pulls, while zQ175 mice maintained erratic performance. Ex vivo, experience-dependent plasticity in contralateral dorsolateral striatum (DLS), present in WT, was absent in zQ175, indicating early disruption of motor learning-relevant corticostriatal circuits in HD. Chapter 3 details the development and validation of a chronic, multiscale platform combining widefield Ca²⁺ imaging of dorsal cortex with striatal fiber photometry. The method yielded stable, concurrent measurements over weeks and supported repeated recordings during behavior. Using this platform, in Chapter 4 I examined motor execution in a lever-pull paradigm with home-cage pretraining and head-fixed recordings. In the home cage, WT mice improved over days, while zQ175 mice maintained lower success with minimal improvement. Under head fixation, primary sensorimotor cortex exhibited robust, lever pull-aligned activity in both genotypes, whereas secondary motor cortex (rostral forelimb area, M2/RFA) was selectively under-recruited in zQ175. In dorsal striatum, glutamate transients (fiber photometry) were time-locked with similar peak amplitude and latency across genotypes but decayed more slowly in zQ175. Collectively, these findings define an early-manifest phenotype with preserved movement initiation but impaired refinement under higher accuracy demands. Mechanistically, selective M2/RFA under-recruitment together with prolonged striatal glutamate signaling likely degrade feedback-based control and experience-dependent tuning while sparing basic motor drive. These systems-level insights identify concrete targets for mechanistic dissection and for translational strategies to stabilize network function early in HD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".