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

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. 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. 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. 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.