M49. BEHAVIOURAL SOCIAL COGNITION IN SCHIZOPHRENIA SPECTRUM DISORDERS IN COMPARISON TO AUTISM SPECTRUM DISORDER: A SYSTEMATIC REVIEW AND META-ANALYSIS
Notice bibliographique
Résumé
Abstract Background Schizophrenia spectrum disorders (SSDs) and autism spectrum disorder (ASD) both feature social cognitive deficits, which are highly debilitating. These include lower-level processes (e.g. emotion recognition), thought to be subserved by a frontoparietal mirroring network, and higher-level mentalizing processes (e.g. theory of mind), involving cortical midline and lateral temporal brain regions. Across both disorders, impairments in social cognition persist over time, drive disability, and predict functional outcome. Overlapping symptoms in SSDs and ASD have long been recognized, particularly in the realm of social deficits. However, despite some studies including both individuals with SSDs and ASD showing similar levels of social cognitive impairment, including lower-level and higher-level deficits, results are mixed. Thus, our objective was to determine based on the extant literature how deficits in social cognition diverge or overlap between individuals with SSDs and ASD by conducting a systematic review and meta-analysis of studies directly comparing these groups on behavioural social cognitive measures. Methods Literature searches were conducted in MEDLINE, Embase, PsycINFO, and Web of Science to identify articles that utilized behavioural measures to assess social cognition in both SSD and ASD samples. Of 3682 articles identified, 28 met all inclusion criteria. Across the accepted articles, lower-level (e.g. facial and/or context-embedded emotion recognition) and higher-level (e.g. intention understanding, perspective taking) social cognitive measures were identified, and random-effects meta-analyses were conducted for each category. A separate meta-analysis was also conducted for the Reading the Mind in the Eyes test given that it was the most commonly used social cognitive metric. Effect sizes were estimated using Hedges’ g. Homogeneity of effects and publication bias were also assessed for each meta-analysis. Results A significant difference in lower-level social cognitive performance was found between individuals with SSDs and ASD, with the SSD group performing better than the ASD group (Hedges’ g = 0.30, 95% CI [0.05, 0.56], p = .018). In contrast, there was no significant difference in higher-level social cognitive performance between SSD and ASD groups (Hedges’ g = -0.14, 95% CI [-0.52, 0.24], p = .46). Similarly, the Reading the Mind in the Eyes test meta-analysis revealed no significant difference in effect sizes between disorders (Hedges’ g = 0.24, 95% CI [-0.07, 0.55], p = .14). Effect size distributions were significantly heterogeneous in all three cases (all p < .001). Discussion Based on meta-analyses of the extant literature, both shared and differential social cognitive deficits may be present between individuals with SSDs and ASD. Though no differences were detected between SSD and ASD groups on higher-level social cognitive tasks or the Reading the Mind in the Eyes test, lower-level social cognitive deficits were found to be more severe in individuals with ASD than SSDs. Notably, the majority of studies included in the meta-analyses had small sample sizes, and heterogeneity of effect sizes was apparent. Thus, studies including larger sample sizes and validated measures of social cognition in conjunction with other methodologies are needed to substantiate these results, and better understand the shared and unique behavioural underpinnings and associated neural circuit abnormalities underlying social cognitive deficits in SSDs and ASD.
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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 | 0,010 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,018 | 0,036 |
| Bibliométrie | 0,010 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».