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.
| Original language | English |
|---|---|
| Pages (from-to) | 1202-1214 |
| Number of pages | 13 |
| Journal | Biological Psychiatry: Cognitive Neuroscience and Neuroimaging |
| Volume | 6 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2021 |
| Externally published | Yes |
Bibliographical note
Funding Information:The results from our dimensional analyses also directly align with those from the higher-level social cognition group comparisons, with good versus poor performers showing greater motor resonance–affect sharing negative connectivity. Additional higher-level social cognition group-based findings support the suggestion that better social cognitive performers may exhibit network efficiency during the EA task, including reduced affect sharing network involvement. Specifically, increased positive connectivity within the affect sharing and mentalizing networks, and between the motor resonance and affect sharing networks, was seen in poor versus good higher-level social cognitive performers. Aligning with this, greater activation during the EA task in affect sharing regions has been associated with lower empathic accuracy in adolescents (37). Increased affect sharing network positive connectivity may be compensatory or may reflect greater emotional resonance (72). Similarly, hyperconnectivity between mentalizing regions, using resting-state and dynamic connectivity during naturalistic fearful clips, has been correlated with greater symptom severity and lower theory of mind performance in SSDs (22,25). Greater positive mentalizing connectivity may be driven by overcompensation in poorer mentalizers or hypermentalizing (overattribution of intentionality), which has been posited and demonstrated in schizophrenia (1,73,74). Greater affect sharing network connectivity and reduced motor resonance–affect sharing functional segregation in worse performers, coinciding with our dimensional results, further emphasizes the importance of differential engagement of these networks during the EA task, and in network segregation more generally, for optimal social cognitive performance.This work was supported by the National Institute of Mental Health (Grant Nos. 1/3R01MH102324-01 [to ANV], 2/3R01MH102313-01 [to AKM], and 3/3R01MH102318-01 [to RWB]). We thank all participants for their contribution to this work and the research staff who performed data collection and management. Results from the manuscript were presented in part at the 74th Annual Meeting of the Society of Biological Psychiatry, Chicago, IL (May 2019), the Annual Meeting of the Social & Affective Neuroscience Society, Miami, FL (May 2019), and the Congress of the Schizophrenia International Research Society, Orlando, FL (April 2019). The authors report no biomedical financial interests or potential conflicts of interest.
Funding Information:
This work was supported by the National Institute of Mental Health (Grant Nos. 1/3R01MH102324-01 [to ANV] , 2/3R01MH102313-01 [to AKM] , and 3/3R01MH102318-01 [to RWB] ).
Publisher Copyright:
© 2020 Society of Biological Psychiatry
Keywords
- Empathic accuracy
- Functional connectivity
- Graph theory
- Research domain criteria
- Schizophrenia spectrum disorders
- Social cognition