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Intention attribution in children and adolescents with autism spectrum disorder: an EEG study
(2021)
The ability to infer intentions from observed behavior and predict actions based on this inference, known as intention attribution (IA), has been hypothesized to be impaired in individuals with autism spectrum disorder (ASD). The underlying neural processes, however, have not been conclusively determined. The aim of this study was to examine the neural signature of IA in children and adolescents with ASD, and to elucidate potential links to contextual updating processes using electroencephalography. Results did not indicate that IA or early contextual updating was impaired in ASD. However, there was evidence of aberrant processing of expectation violations in ASD, particularly if the expectation was based on IA. Results are discussed within the context of impaired predictive coding in ASD.
Two-person neuroscience (2 PN) is a recently introduced conceptual and methodological framework used to investigate the neural basis of human social interaction from simultaneous neuroimaging of two or more subjects (hyperscanning). In this study, we adopted a 2 PN approach and a multiple-brain connectivity model to investigate the neural basis of a form of cooperation called joint action. We hypothesized different intra-brain and inter-brain connectivity patterns when comparing the interpersonal properties of joint action with non-interpersonal conditions, with a focus on co-representation, a core ability at the basis of cooperation. 32 subjects were enrolled in dual-EEG recordings during a computerized joint action task including three conditions: one in which the dyad jointly acted to pursue a common goal (joint), one in which each subject interacted with the PC (PC), and one in which each subject performed the task individually (Solo).
A combination of multiple-brain connectivity estimation and specific indices derived from graph theory allowed to compare interpersonal with non-interpersonal conditions in four different frequency bands. Our results indicate that all the indices were modulated by the interaction, and returned a significantly stronger integration of multiple-subject networks in the joint vs. PC and Solo conditions. A subsequent classification analysis showed that features based on multiple-brain indices led to a better discrimination between social and non-social conditions with respect to single-subject indices. Taken together, our results suggest that multiple-brain connectivity can provide a deeper insight into the understanding of the neural basis of cooperation in humans.