Social Cognitive Networks and Social Cognitive Performance Across Individuals With Schizophrenia Spectrum Disorders and Healthy Control Participants

Abstract

Background: Schizophrenia spectrum disorders (SSDs) feature social cognitive deficits, although their neural basis remains unclear. Social cognitive performance may relate to neural circuit activation patterns more than to diagnosis, which would have important prognostic and therapeutic implications. The current study aimed to determine how functional connectivity within and between social cognitive networks relates to social cognitive performance across individuals with SSDs and healthy control participants.

Methods: Participants with SSDs (n = 164) and healthy control participants (n = 117) completed the Empathic Accuracy task during functional magnetic resonance imaging as well as lower-level (e.g., emotion recognition) and higher-level (e.g., theory of mind) social cognitive measures outside the scanner. Functional connectivity during the Empathic Accuracy task was analyzed using background connectivity and graph theory. Data-driven social cognitive networks were identified across participants. Regression analyses were used to examine network connectivity-performance relationships across individuals. Positive and negative within- and between-network connectivity strengths were also compared in poor versus good social cognitive performers and in SSD versus control groups.

Results: Three social cognitive networks were identified: motor resonance, affect sharing, and mentalizing. Regression and group-based analyses demonstrated reduced between-network negative connectivity, or segregation, and greater within- and between-network positive connectivity in worse social cognitive performers. There were no significant effects of diagnostic group on within- or between-network connectivity.

Conclusions: These findings suggest that the neural circuitry of social cognitive performance may exist dimensionally. Across participants, better social cognitive performance was associated with greater segregation between social cognitive networks, whereas poor versus good performers may compensate via hyperconnectivity within and between social cognitive networks.

Keywords: Empathic accuracy; Functional connectivity; Graph theory; Research domain criteria; Schizophrenia spectrum disorders; Social cognition.

Figure 1.

Social cognitive networks identified using community detection and consensus clustering across individuals with schizophrenia spectrum disorders and healthy control participants.

Figure 2.

Significant dimensional predictors of lower- and higher-level social cognition factor scores. Plots display the effect of motor resonance–affect sharing negative connectivity on lower-level (A) and higher-level (B) social cognition factor scores from regression models with 95% confidence intervals.

Figure 3.

Positive and negative within- and between-network connectivity strengths by lower-level social cognitive performance group. Connectivity strengths for poor and good lower-level social cognitive performance groups are shown. Edge width corresponds to the between-group difference in connection weight. (A) Within-network positive (left) and negative (right) connectivity strengths for motor resonance, affect sharing, and mentalizing networks. Node size corresponds to the within-network connectivity strength of the node (sum of within-network connections to the node). (B) Between-network positive (left) and negative (right) connectivity strengths for motor resonance, affect sharing, and mentalizing networks. Node size corresponds to the between-network connectivity strength of the node (sum of between-network connections to the node). See Figure 1 for abbreviations.

Figure 4.

Positive and negative within- and between-network connectivity strengths by higher-level social cognitive performance group. Connectivity strengths for poor and good higher-level social cognitive performance groups are shown. Edge width corresponds to the between-group difference in connection weight. (A) Within-network positive (left) and negative (right) connectivity strengths for motor resonance, affect sharing, and mentalizing networks. Node size corresponds to the within-network connectivity strength of the node (sum of within-network connections to the node). (B) Between-network positive (left) and negative (right) connectivity strengths for motor resonance, affect sharing, and mentalizing networks. Node size corresponds to the between-network connectivity strength of the node (sum of between-network connections to the node). See Figure 1 for abbreviations

Figure 5.

Positive and negative within- and between-network connectivity strengths by diagnostic group. Connectivity strengths for schizophrenia spectrum disorder (SSD) and control groups are shown. Edge width corresponds to the between-group difference in connection weight. (A) Within-network positive (left) and negative (right) connectivity strengths for motor resonance, affect sharing, and mentalizing networks. Node size corresponds to the within-network connectivity strength of the node (sum of within-network connections to the node). (B) Between-network positive (left) and negative (right) connectivity strengths for motor resonance, affect sharing, and mentalizing networks. Node size corresponds to the between-network connectivity strength of the node (sum of between-network connections to the node). See Figure 1 for abbreviations