Preclinical Huntington's disease: Compensatory brain responses during learning
Bibliographic record
Abstract
Motor sequence learning is abnormal in presymptomatic Huntington's disease (p-HD). The neural substrates underlying this early manifestation of HD are poorly understood. To study the mechanism of this cognitive abnormality in p-HD, we used positron emission tomography to record brain activity during motor sequence learning in these subjects. Eleven p-HD subjects (age, 45.8 +/- 11.0 years; CAG repeat length, 41.6 +/- 1.8) and 11 age-matched control subjects (age, 45.3 +/- 13.4 years) underwent H(2) (15)O positron emission tomography while performing a set of kinematically controlled motor sequence learning and execution tasks. Differences in regional brain activation responses between groups and conditions were assessed. In addition, we identified discrete regions in which learning-related activity correlated with performance. We found that sequence learning was impaired in p-HD subjects despite normal motor performance. In p-HD, activation responses during learning were abnormally increased in the left mediodorsal thalamus and orbitofrontal cortex (OFC; BA 11/47). Impaired learning performance in these subjects was associated with increased activation responses in the precuneus (BA 18/31). These data suggest that enhanced activation of thalamocortical pathways during motor learning can compensate for caudate degeneration in p-HD. Nonetheless, this mechanism may not be sufficient to sustain a normal level of task performance, even during the presymptomatic stage of the disease.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".