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Record W2163754224 · doi:10.1177/070674370605100601

Characterization of the Facial Expression of Emotions in Schizophrenia Patients: Preliminary Findings with a New Electromyography Method

2006· article· en· W2163754224 on OpenAlexvenueno aff
Karsten D. Wolf, Reinhard Maß, Falk Kiefer, Klaus Wiedemann, Dieter Naber

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2006
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychopathologyFacial electromyographyFacial expressionPsychologyPositive and Negative Syndrome ScaleSchizophrenia (object-oriented programming)Facial musclesElectromyographyAudiologyPsychosisClinical psychologyPsychiatryMedicineNeuroscienceCommunication

Abstract

fetched live from OpenAlex

Objective: We investigated facial expression of emotions (FEE) in schizophrenia patients, using an improved and highly selective facial electromyography (EMG) method, and we examined the correlation between FEE and psychopathology. Method: We compared unmedicated patients with schizophrenia ( n = 32) with healthy subjects ( n = 21) with regard to the activity of 3 joy-relevant facial muscles (the M.zygomaticus, the M.orbicularis oculi, and the M.levator labii). Emotions were induced by pictures from the International Affective Picture System. We measured previsible muscle activity with a new, highly selective facial EMG. We used the Positive and Negative Syndrome Scale to evaluate psychopathology. Results: Patients with schizophrenia showed fewer joy or smile reactions than did control subjects and displayed decreased activity of the M.orbicularis oculi and M.zygomaticus under presentation of positive pictures. Reduced activity of these muscles can be caused by depression. Increased activity of the M.levator labii correlates with positive symptoms. Conclusions: Our findings indicate that psychopathological syndromes correlate with schizophrenic mimic disturbances. These results can be used to compare various antipsychotics with regard to their influence on mimic disturbances. Objectif: Nous avons étudié l'expression émotionnelle du visage (EEV) chez des patients souffrant de schizophrénie, à l'aide d'une technique d'électromyographie (EMG) faciale améliorée et très sélective, et nous avons examiné la corrélation entre l'EEV et la psychopathologie. Méthode: Nous avons comparé des patients non médicamentés souffrant de schizophrénie ( n = 32) avec des sujets en santé ( n = 21) en ce qui concerne l'activité de 3 muscles faciaux liés à la joie (le muscle grand zygomatique, le muscle orbiculaire et le muscle releveur de la lèvre). Les émotions ont été provoquées par des images du système international d'images affectives (IAPS). Nous avons mesuré l'activité musculaire prévisible au moyen d'une nouvelle EMG faciale très sélective. Nous avons utilisé l'échelle de syndrome positif et négatif (PANSS) pour évaluer la psychopathologie. Résultats: Les patients souffrant de schizophrénie ont eu moins de réactions joyeuses ou souriantes que les sujets témoins et ont démontré une activité moindre du muscle orbiculaire et du muscle grand zygomatique devant les images positives présentées. L'activité réduite de ces muscles peut être causée par la dépression. L'activité accrue du muscle releveur de la lèvre est en corrélation avec les symptômes positifs. Conclusions: Nos observations indiquent que les syndromes psychopathologiques sont en corrélation avec les anomalies mimiques schizophrènes. Ces résultats peuvent servir à comparer divers antipsychotiques relativement à leur influence sur les anomalies mimiques.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.241
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations38
Published2006
Admission routes1
Has abstractyes

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