34.2 SOCIAL COGNITIVE NETWORK CONNECTIVITY SUPPORTS A NETWORK EFFICIENCY MODEL FOR BETTER SOCIAL COGNITIVE PERFORMANCE ACROSS SCHIZOPHRENIA SPECTRUM DISORDERS AND HEALTHY CONTROLS
Notice bibliographique
Résumé
Schizophrenia spectrum disorders (SSDs) often feature social cognitive impairments, which have been associated with functional outcome. These include lower-level processes (emotion recognition), thought to be sub-served by a frontoparietal mirroring network, and higher-level mentalizing processes (theory of mind), involving cortical midline and lateral temporal regions. Atypical functional connectivity has also been consistently observed in SSDs. However, it is unclear whether altered connectivity in putative social cognitive brain networks may underlie social deficits in SSDs. Thus, our objective was to identify the neural circuitry underlying social cognition across a large group of individuals with SSDs and healthy controls and determine how within and between network connectivity relates to social cognitive performance and functional outcome. We hypothesized that increased connectivity in the mentalizing network and decreased connectivity in the mirroring network during a complex social cognitive task would be associated with better social cognitive performance, aligning with a network efficiency model. This work is part of the ongoing NIMH-funded ‘Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS)’, a multi-center Research Domain Criteria (RDoC) study, and the largest neuroimaging and behavioral investigation of social impairment in SSDs to date. Across three sites, 178 people with SSDs and 118 healthy controls completed the Empathic Accuracy task during functional magnetic resonance imaging. The task involves rating how positive or negative someone else is feeling throughout a series of emotional autobiographical videos and is known to engage both the mentalizing and mirroring networks. Participants also completed measures of lower- and higher-level social cognition and functional outcome outside the scanner. Background connectivity analysis was used during the Empathic Accuracy task, which focuses on residual activation, to extract mentalizing, mirroring, and emotional empathy network functional connectivity. ANOVAs and correlational analyses were used to examine relationships between background connectivity and social cognitive performance. The Empathic Accuracy task was found to engage both regions of the mentalizing and mirroring networks. Background connectivity analysis followed by ANOVAs revealed a group x network interaction, characterized by lower social cognitive performers showing greater connectivity in the mirroring network in comparison to higher social cognitive performers (p < .05). Further, mirroring network connectivity was negatively associated with both lower-level (r = -.275, p < .001) and higher-level (r = -.270, p < .001) social cognitive performance outside the scanner and social functioning scores (r = -.203, p < .001) across individuals with SSDs and healthy controls. Our results suggest that greater connectivity within the mirroring network during a complex social cognitive task is associated with reduced lower- and higher-level social cognition and lower functional outcome across people with SSDs and healthy individuals. This aligns with previous findings in schizophrenia and first episode psychosis using resting state functional connectivity, suggesting that reduced compensatory connectivity within the mirroring network, or more efficient use of the mentalizing network, is associated with better social cognitive performance and social functioning.
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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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».