Pillet, I. | bioRxiv Science

bioRxiv2019

Social touch observation in adults with autism: intact neural representations of affective meaning but lack of embodied resonance

Humans can easily grasp the affective meaning of touch when observing social interactions. Several neural systems support this ability, including theory of mind (ToM) and somatosensory resonance systems, but it is unclear how these systems are affected in autism spectrum disorder (ASD). Individuals with ASD are characterized by impairments in social interaction and the use of (non)verbal communication such as social and reciprocal touch. The present study applies an ecologically valid stimulus set and multivoxel pattern fMRI neuroimaging to pinpoint atypicalities in the neural circuitry underlying socio-affective touch observation in adults with ASD as compared to matched neurotypical controls. The MVPA results reveal that the affective meaning of touch is well represented in the temporoparietal junction, a core ToM mentalizing area, in both groups. Conversely, only the neurotypical group hosts affective touch representations in the somatosensory cortex, not the ASD group, yielding a significant group difference. Lastly, individuals with a more positive attitude towards receiving, witnessing, and providing social touch and with a higher score on social responsivity, show more differentiated representations of the affective meaning of touch in these somatosensory areas. Together, our findings imply that individuals with ASD are able to cognitively represent the affective meaning of touch, but they lack the spontaneous embodied somatosensory resonance when observing social touch communications. Individual differences in this diminished resonance appear to be related to social touch avoidance and quantitative autism traits.\n\nSignificance StatementAutism is characterized by socio-communicative impairments, including abnormal processing of interpersonal touch. Little is known about the neural basis of atypicalities in social touch processing in autism. Here, adults with and without autism watched video clips displaying social touch interactions and judged the affective valence of the touch. Subsequently, they underwent functional magnetic resonance imaging while watching the same videos. Brain activity patterns demonstrate that adults with autism show intact cognitive understanding (i.e. \"knowing\") of observed socio-affective touch experiences but lack of embodied emotional resonance (i.e. \"feeling\"). This lack of emotional resonance is linked to social touch avoidance and quantitative autism traits. These findings highlight that the depth of experiencing the state of others is shallower in people with autism.

neuroscience↗

bioRxiv2018

The (dis)similarities between neural networks based upon functional connectivity, representational similarity, and univariate analyses

In fMRI research, univariate analysis (UNIVAR), representational similarity analysis (RSA, following multi-voxel pattern analysis (MVPA)), and functional connectivity analysis (FCA) are the most commonly used methods by cognitive neuroscientists investigating the functional organization of the human brain. Despite their popularity, few studies have examined the relationship between the network structures as identified through these different methods. Thus, the current study aims to evaluate the similarities between neural networks derived from UNIVAR, RSA, and FCA, and to clarify how these methods relate to each other. To achieve this goal, we analyzed the data of a previously published study with the three methods and compared the results by performing (partial) correlation and multiple regression analysis. Our findings reveal that neural networks resulting from UNIVAR, RSA, and FCA methods are highly similar to each other even after ruling out the effect of anatomical proximity between the network nodes. Nevertheless, the neural network from each method shows idiosyncratic structure that cannot be explained by any of the other methods. Thus, we conclude that the UNIVAR, RSA, and FCA methods provide similar but not identical information on how brain regions are organized in functional networks.

neuroscience↗