Beyond disgust: impaired recognition of negative emotions prior to diagnosis in Huntington's disease
Bibliographic record
Abstract
Previous studies of emotion recognition suggest that detection of disgust relies on processing within the basal ganglia and insula. Research involving individuals with symptomatic and pre-diagnostic Huntington's disease (HD), a disease with known basal ganglia atrophy, has generally indicated a relative impairment in recognizing disgust. However, some data have suggested that recognition of other emotions (particularly fear and anger) may also be affected in HD, and a recent study found fear recognition deficits in the absence of other emotion-recognition impairments, including disgust. To further examine emotion recognition in HD, we administered a computerized facial emotion recognition task to 475 individuals with the HD CAG expansion and 57 individuals without. Logistic regression was used to examine associations of emotion recognition performance with estimated proximity to clinical diagnosis (based on CAG repeat length and current age) and striatal volumes. Recognition of anger, disgust, fear, sadness and surprise (but not happiness) was associated with estimated years to clinical diagnosis; performance was unrelated to striatal volumes. Compared to a CAG-normal control group, the CAG-expanded group demonstrated significantly less accurate recognition of all negative emotions (anger, disgust, fear, sadness). Additionally, participants with more pronounced motor signs of HD were significantly less accurate at recognizing negative emotions than were individuals with fewer motor signs. Findings indicate that recognition of all negative emotions declines early in the disease process, and poorer performance is associated with closer proximity to clinical diagnosis. In contrast to previous results, we found no evidence of relative impairments in recognizing disgust or fear, and no evidence to support a link between the striatum and disgust recognition.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".