Large-scale neural network compensation associated with camouflaging in trait autism and its potential mental health costs

BACKGROUND: Social camouflaging refers to strategies to hide or compensate for social difficulties, often at significant mental health costs, and is particularly prevalent in autism. The large-scale neural network associated with this adaptation remains poorly understood. This study aimed to identify these neural network patterns and their link to potential mental health issues. METHODS: Using a dimensional approach, we recruited 110 healthy young adults who completed self-report questionnaires measuring autistic traits and camouflaging as well as depression and anxiety, and underwent resting-state fMRI scans. The interaction between camouflaging and autistic traits on brain network connectivity was examined using the 300-node Seitzman atlas, encompassing 13 functional networks. RESULTS: Among individuals with higher autistic traits, greater camouflaging was associated with increased connectivity between the Default Mode Network (DMN) and the Cingulo-Opercular Network (CON), as well as within the CON. Crucially, DMN-CON hyperconnectivity statistically mediated the relationship between camouflaging and potential mental health costs (i.e., depression and anxiety scores) but only in individuals with higher autistic traits. Limitations: Our study was limited by its predominantly non-clinical sample, the cross-sectional design, and the use of resting-state rather than task-based fMRI. CONCLUSIONS: These findings reveal specific compensatory neural network patterns associated with camouflaging in those high in autistic traits, involving interoception, self-referential, and executive control systems, and provide a neurobiological explanation for its potential mental health burden, highlighting the need for societal changes that reduce the pressure for such adaptations.