Emotion recognition and experience in Huntington disease: a voxel-based morphometry study
Bibliographic record
Abstract
BACKGROUND: The neuroanatomic basis of affective processing deficits in Huntington disease is insufficiently understood. We investigated whether Huntington disease-related deficits in emotion recognition and experience are associated with specific changes in grey matter volume. METHOD: We assessed grey matter volume in symptomatic patients with Huntington disease and healthy controls using voxel-based morphometry, and we correlated regional grey matter volume with participants' affective ratings. RESULTS: We enrolled 18 patients with Huntington disease and 18 healthy controls in our study. Patients with Huntington disease showed normal affective experience but impaired recognition of negative emotions (disgust, anger, sadness). The patients perceived the emotions as less intense and made more classification errors than controls. These deficits were correlated with regional atrophy in emotion-relevant areas (insula, orbitofrontal cortex) and in memory-relevant areas (dorsolateral prefrontal cortex, hippocampus). LIMITATIONS: Our study was limited by the small sample size and the resulting modest statistical power relative to the number of tests. CONCLUSION: Our study sheds new light on the importance of a cognitive-affective brain circuit involved in the affect recognition impairment in patients with Huntington 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 | 0.001 |
| 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) | 0.001 | 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".