FACIAL EMOTION EXPRESSIVENESS AND FACIAL EMOTION RECOGNITION IN PARKINSON'S DISEASE: HOW MUCH DOES ALEXITHYMIA COUNT?
Bibliographic record
Abstract
Objective Background and aims: It is recognized that emotional deficits are part of the non-motor features of Parkinson's disease but scant attention has been paid to specific aspects such as emotional facial expression, subjective emotional experience (alexithymia) and recognition of facial emotion expressions. This study aimed to investigate the relationship between alexithymia, emotion facial recognition, and emotion facial expression in PD patients. Method Forty-one PD patients and seventeen healthy controls, matched for demographical characteristics, were enrolled in the study. Alexithymia was assessed by means of Toronto Alexithymia Scale (TAS-20), emotion facial recognition was tested by means of the Ekman 60 Faces Test, emotion facial expression was investigated with a video protocol encompassing of a static expression recording (subject watching the camera in silence for 30 seconds), a dynamic expression recording (subject recorded while talking for 30 seconds) and emotion expression (subject was asked to express with his/her face the six main human expressions: happiness, sadness, anger, fear, surprise, disgust). Six blind raters evaluated the patients' video recordings. Results No difference in alexithymia was detected between PD patients and HC. PD patients performed significantly worse than HC in recognizing Surprize (p=0.03) and showed significant poorer global facial expression than HC (in static, dynamic and emotion facial expression). There was a significant negative correlation between the factor F3 of TAS (externally orientated thoughts) and the patient's capability to express disgust (−0.447, p=0.007). Elkman total score positively correlates with the patient's capability to express disgust with his face (0.325, p=0.006). Conclusion These results suggest that PD patients have difficulties with emotional recognition and expression in a contest of a unimpaired subjective emotional experience. These deficits need to be targeted in clinical practise for rehabilitation purposes.
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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 | 0.002 |
| 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 | 0.001 | 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".