34.2 SOCIAL COGNITIVE NETWORK CONNECTIVITY SUPPORTS A NETWORK EFFICIENCY MODEL FOR BETTER SOCIAL COGNITIVE PERFORMANCE ACROSS SCHIZOPHRENIA SPECTRUM DISORDERS AND HEALTHY CONTROLS
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".