SU85. The Behavioral and Neural Correlates of Social Cognition in Youth With Mental Illness

Abstract

Background: Schizophrenia, autism spectrum disorders, and bipolar disorder share vulnerability and genetic underpinnings, yet there is considerable heterogeneity in social cognitive performance and social function within and across these disorders The objective of this work is to identify subgroups of patients across these disease-based groups that share similar impairments in social cognition and brain structure in order to identify therapeutic targets. Methods: Structural and diffusion weighted magnetic resonance images were obtained for youth aged 16–35 years with schizophrenia spectrum disorders (SSD) (n = 32), verbal autism spectrum disorder (ASD) without intellectual disability (n = 20), bipolar disorder (BD-euthymic phase) (n = 17), and healthy controls (n = 41). Social cognition and neurocognition were assessed. Similarity Network Fusion (Wang et al, 2014) was used to integrate demographic, neurocognitive, social cognitive, structural, and white matter microstructural data. Results: The fused network revealed 5 subgroups with a number of significant differences between groups in social cognitive performance and in fractional anisotropy (FA) of white matter tracts central to social emotional processing such as the uncinate fasciculus (UF) and genu of the corpus callosum. A young, male-dominant group (N = 21) comprised mostly of youth with ASD and SSD had the most impaired performance on all social cognitive tests; an effect which is particularly significant for the most complex subtest (Welch’s F(4,38) = 10.91, P < .001). A young female-dominant group (N = 33) comprised mostly of healthy controls and youth with BD had the highest performance on all social cognitive tasks. When comparing the FA of the UF there is a significant difference between groups in the left (P < .001) and right hemispheres (P = .001) whereby the lowest performing groups (ASD and SSD group and an older SSD dominant group) had significantly lower FA compared to the highest performing group. Interestingly, a mixed-sex cluster comprised mostly of youth with ASD had average to high performance on the social cognitive and memory subtests but were the worst performers for the processing speed domain (Welch’s F(4,44) = 7.399, P = .001). Conclusion: Using both transdiagnostic and data integration approaches, we can successfully identify biologically informed subtypes that allow us to better understand the heterogeneity in traditional disease-based classifications. These findings may help facilitate the development of new hypotheses regarding the design of studies interested in identifying the genetic underpinnings of social cognitive impairment, as well as the design of therapeutic targets for social cognitive deficits.