Large-Scale Neural Network Compensation Underlying Camouflaging in Trait Autism and Its Potential Mental Health Costs

Social camouflaging refers to strategies to hide or compensate for social difficulties, often at a significant mental health cost, and is particularly prevalent in autism. The large-scale neural network mechanisms underlying this adaptation remain poorly understood. This study aimed to identify these neural underpinnings and their link to other 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. Using the 300-node Seitzman atlas encompassing 13 functional networks, we examined the interaction between camouflaging and autistic traits on brain network connectivity. Results showed that among individuals with higher autistic traits, greater camouflaging was associated with hyperconnectivities between the Default Mode (DMN) and Cingulo-Opercular Networks (CON), and within the CON. Crucially, DMN-CON hyperconnectivity mediated the relationship between camouflaging and potential mental health cost (i.e., depression and anxiety scores) but only in individuals with higher autistic traits. These findings reveal a specific neuro-compensatory mechanism underlying autistic camouflaging involving 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.