<p>Dysfunction in Automatic Processing of Emotional Facial Expressions in Patients with Obstructive Sleep Apnea Syndrome: An Event-Related Potential Study</p>
Bibliographic record
Abstract
AIM: Obstructive sleep apnea syndrome (OSAS) is a prevalent chronic disease characterized by sleep fragmentation and intermittent hypoxemia. Several studies suggested that electrophysiological changes and neurocognitive abnormalities occurred in OSAS patients. In this study, we compared automatic processing of emotional facial expressions schematic in OSAS patients and matched healthy controls via assessing expression-related mismatch negativity (EMMN). METHODS: Twenty-two OSAS patients (mean age 44.59 years) and twenty-one healthy controls (mean age 42.71 years) were enrolled in this study. All participants underwent Montreal Cognitive Assessment (MoCA) scale test and polysomnographic recording. An expression-related oddball paradigm was used to elicit EMMN and the electroencephalogram was recorded and analyzed. Furthermore, Pearson's correlations were calculated to discuss the correlation between neuropsychological test scores, clinical variables and electrophysiological data. RESULTS: Compared with healthy controls, OSAS sufferers demonstrated significantly reduced EMMN mean amplitudes within corresponding time intervals, regardless of happy or sad conditions. Meanwhile, we observed that amplitude of sad EMMN was larger (more negative) than happy EMNN in healthy controls, while not in patients. Moderate correlations were found between MoCA test scores, sleep parameters and EMMN amplitudes. CONCLUSION: Our findings suggested pre-attentive dysfunction of processing emotional facial expressions in patients with OSAS, without the existence of negative bias effect. Moreover, correlation analysis showed that clinical characteristics of OSAS patients could affect EMMN amplitudes. Further studies on the advantages of EMMN as clinical and electrophysiological indicators of OSAS are warranted.
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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".