S59. A CLUSTER-ANALYTIC APPROACH TO EXAMINING MOTIVATION SYSTEM IMPAIRMENTS IN SCHIZOPHRENIA AND MAJOR DEPRESSIVE DISORDER
Notice bibliographique
Résumé
Motivation deficits have been linked to poor functional outcomes in schizophrenia (SZ) and major depressive disorder (MDD) and represent an unmet therapeutic need. Recent conceptualizations of the motivation system have outlined five inter-related reward processes, whereby (1) reward responsiveness (i.e. “liking”) and (2) reward prediction (i.e. “wanting”, established through appropriate reward learning) converge to inform both (3) reward valuation and (4) effort valuation which is associated with a cost-benefit analysis, followed by (5) the development and execution of an action plan to achieve the desired outcome. The inclusion of approach motivation within the RDoC Positive Valence System further underscores its importance as a pervasive symptom that cuts across traditional diagnostic boundaries. Previous investigations, however, have typically only focused on isolated reward processes, often within single diagnostic groups. Thus, in line with emerging dimensional approaches to examining psychopathology, the present study sought to utilize a cluster-analytic approach to objectively evaluate the multiple facets of the motivation system concurrently across SZ, MDD, and healthy control (HC) participants. The study sample consisted of 39 SZ, 38 MDD, and 39 HC participants. Participants were administered a series of assessments to evaluate symptom severity and cognitive functioning. Discrete facets of the motivation system were measured using an extensive battery of objective computerized tasks. Variables of interest were extracted from each task and entered into a principal components analysis in order to explore the factor structure of the motivation framework. Factor scores were subsequently applied to K-means cluster analysis to identify subgroups of individuals with similar motivation profiles. Principal components analysis revealed five distinct motivation factors: hedonic capacity, reward expectancy and learning, cost-benefit decision-making, goal-directed decision-making, and effort expenditure. K-means clustering identified two distinct subgroups of individuals based on their motivation task performance. The first cluster demonstrated impaired hedonic capacity (t(114)=-3.7, p<.001), whereas the second cluster was characterized by impairments in cost-benefit decision-making (t(114)=5.9, p<.001), goal-directed decision-making (t(114)=7.3, p<.001), and effort expenditure (t(114)=6.3, p<.001). Although clusters did not differ in symptom severity, the second cluster was associated with significantly greater cognitive impairments (t(114)=6.4, p<.001). Importantly, all diagnostic groups were well represented in each cluster, though with significantly different distributions. Our dimensional investigation revealed a multi-faceted motivation framework comprised of five distinct components. The emergence of two unique motivation performance profiles highlights the extensive heterogeneity of clinical amotivation and its dimensionality across disorders. Further, the pattern of motivation impairments within these profiles raises the possibility of distinct underlying neural substrates, with implications for specific therapeutic targets.
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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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 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) | 0,004 | 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 ».