O1.5. CLUSTERING-BASED BEHAVIOURAL PHENOTYPES OF INTRINSIC MOTIVATION IN SCHIZOPHRENIA DETERMINED USING OBJECTIVELY QUANTIFIED REAL-WORLD PERFORMANCE
Notice bibliographique
Résumé
Abstract Background Intrinsic motivation (IM) deficits are a prominent feature of schizophrenia that substantially impacts functional outcome. Previous investigations have primarily assessed IM as a general construct or towards specific activities, but multifaceted examination of IM using objective and ecologically valid means has been lacking. The current study aimed to determine whether IM-driven real-world behaviours of individuals with and without schizophrenia reveal distinct behavioural phenotypes that predict differential clinical and functional profiles. Methods Forty-five stable adult outpatients with schizophrenia or schizoaffective disorder (SZ) and 47 healthy controls (HC), group-matched for age and sex, completed the study. Wireless motion capture was used to quantify participants’ behaviour (during two previously validated 15-minute open-field tasks) aligning with distinct aspects of IM: exploratory behaviour (in an unfamiliar setting that contained common and uncommon objects) and unincentivized activity engagement (under provision of explicit choice between active versus passive engagement options). Sparse k-means clustering was applied to task-derived indices of locomotion and object exploration, and activity duration, intensity, and persistence to identify behaviourally differentiable subgroups. Clusters were compared across standard clinical measures of general amotivation, cognition, and community functioning. Diagnostic group by cluster assignment interaction effects were evaluated to identify potential discrepancies in within-cluster SZ versus HC differences across clusters. Results Three clusters emerged: medium activity and low exploration (20 SZ and 19 HC), low activity and medium exploration (15 SZ and 8 HC), and high activity and exploration (10 SZ and 20 HC). Cluster assignment did not significantly predict diagnostic group (Fisher’s exact p=0.067) but indicated increased odds of diminished exploration or activity in SZ (odds ratio=2.57, p=0.047). General amotivation differed across clusters (F(2,89)=6.31, p=0.003) due to significantly elevated scores in the low exploration and low activity clusters versus the high performance cluster. Evaluation of the diagnostic group by cluster assignment interaction effect for amotivation (F(2,86)=3.12, p=0.062) revealed significantly higher scores in SZ versus HC within the low exploration and high performance clusters, but not the low activity cluster. Evaluation of the interaction effect for community functioning (F(2,86)=4.86, p=0.007) revealed significantly lower scores in SZ versus HC within all clusters, with the discrepancy increasing from high performance to low activity to low exploration. Discussion Multidimensional characterization of IM stands to provide valuable insights into motivation and functioning deficits in schizophrenia that behaviourally manifest differently across individuals. Impairment in IM-driven behaviour may be substantially detrimental particularly for individuals with schizophrenia, as SZ participants demonstrated clinically observable deficits even compared to within-cluster HC counterparts. The current lack of objective standards to measure IM underscores the benefit of behaviour-based characterization. Further, as IM has been deemed a valuable psychosocial intervention target in schizophrenia, multidimensional behavioural phenotyping may help guide personalized therapy by differentiating between IM impairments demanding amelioration versus unimpaired tendencies that may be leveraged to facilitate remediation.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».