Mesoscale Imaging of Cortical Sensorimotor Integration in Huntington’s Disease Mice During Reward-Guided Behaviour
Bibliographic record
Abstract
Abstract Huntington’s disease (HD) is a neurodegenerative disorder that affects numerous brain functions, yet how altered sensory processing contributes to behavioral and learning deficits remains poorly understood. Previous wide-field mesoscale imaging and electrophysiological recording from anesthetized HD mice revealed that sensory stimulation induced exaggerated, prolonged cortical activity across more brain regions compared to wildtype (WT) littermates. This suggests differences in sensory processing; as such, this study aimed to investigate the cortical activity in a cue-based sensory-guided learning task in a custom-built Raspberry Pi-controlled two-alternative forced choice (2AFC) rig. The rig was designed to enable head-fixed zQ175 knock-in HD mice and WT controls crossed with Thy1-GCaMP6s mice, to perform a cue-based visual discrimination task while mesoscale calcium imaging recorded activity across layer 2/3 of the cortex. Mice that successfully licked the reward spout displayed decreased global cortical activity before the reward presentation, with WT mice showing more spatially localized suppression than HD mice. HD mice exhibited exaggerated cortical responses to visual stimuli and prolonged cortical motor-related activity during licking. While this is an exploratory pilot study with limited sample size, preventing definitive genotype-based comparisons, the custom behavioral system lays the ground-work for future studies into how sensory processing deficits contribute to cognitive impairments in HD. This work provides an important step toward understanding the interplay between cortical circuit dysfunction and behavioral outcomes in HD, offering a novel platform to investigate early sensorimotor integration learning impairments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".