Multiple studies indicate atypical face processing in autism spectrum disorder (ASD), but these difficulties are not universal and are subject to individual variability. This as-yet unexplained heterogeneity could be accounted for by introduction of subgroups within the diverse category of individuals presenting with ASD. One potential grouping factor is alexithymia, a disorder characterized by difficulties in the recognition and identification of emotion. Recent research demonstrates that emotion recognition impairments in ASD are predicted by its comorbidity with alexithymia (Bird et al., 2010). No research to-date has examined the relative influence of autistic and alexithymic traits on social perception in terms of brain-behavior relationships.
Objectives:
To examine the relative influence of alexithymic and autistic traits on neural circuits subserving face and action perception and associated behavior.
Methods:
Participants consisted of 28 typically developing (TD) adults screened for levels of autistic traits (using the Autism Quotient) and alexithymia (using the Toronto Alexithymia Questionnaire and the Bermond-Vorst Alexithymia Questionnaire). Behavioral measures were administered to assess social behavior (Social Responsiveness Scale), face recognition (Benton Facial Recognition Test), and theory of mind (Reading the Mind in the Eyes Test). EEG was recorded with a Hydrocel Geodesic Sensor net while participants viewed static and dynamic faces presented in three conditions encompassing both affective and neutral content (fearful, puffed cheeks, biologically impossible movement). Event related potentials (ERPs) were extracted to initial static faces, indexing basic visual processing (P100), face structural encoding (N170) and emotional arousal (late positive potential; LPP). Evoked oscillatory activity was calculated during dynamic movement of faces, indicating activity in the action-perception system (mu desynchronization).
Results:
Level of autistic traits predicted P1 amplitude and mu suppression, such that higher levels of autistic traits correlated with reduced P1 amplitude (p=0.043) and mu suppression (p=0.025). Additionally, P1 amplitude was found to predict N170 amplitude (p=0.005). A repeated-measures analysis of variance revealed a main effect of emotion on the mu rhythm attenuation (F=3.86, p<.05), such that fear expressions elicited reduced mu suppression compared to puffed or impossible expressions. Analyses in progress examine relationships among behavioral measures and ERP/EEG markers of social perception at each stage of processing. Preliminary regression analysis indicates unique contributions of alexithymic traits in influencing P1 and N170 amplitude and latency and mu attenuation.
Conclusions:
Results reveal distinct contributions of the level of autistic traits and alexithymia at different stages of the face perceptual process, spanning from basic visual perception to mirror neuron system activation. Understanding the relationship between ASD and alexithymia has important implications for parsing heterogeneity in autism and applying individually tailored intervention techniques.