Inflexible Updating of the Self-Other Divide During a Social Context in Autism: Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

Abstract

Background: Autism spectrum disorder (ASD) affects many aspects of life, from social interactions to (multi)sensory processing. Similarly, the condition expresses at a variety of levels of description, from genetics to neural circuits and interpersonal behavior. We attempt to bridge between domains and levels of description by detailing the behavioral, electrophysiological, and putative neural network basis of peripersonal space (PPS) updating in ASD during a social context, given that the encoding of this space relies on appropriate multisensory integration, is malleable by social context, and is thought to delineate the boundary between the self and others.

Methods: Fifty (20 male/30 female) young adults, either diagnosed with ASD or age- and sex-matched individuals, took part in a visuotactile reaction time task indexing PPS, while high-density electroencephalography was continuously recorded. Neural network modeling was performed in silico.

Results: Multisensory psychophysics demonstrates that while PPS in neurotypical individuals shrinks in the presence of others-as to "give space"-this does not occur in ASD. Likewise, electroencephalography recordings suggest that multisensory integration is altered by social context in neurotypical individuals but not in individuals with ASD. Finally, a biologically plausible neural network model shows, as a proof of principle, that PPS updating may be inflexible in ASD owing to the altered excitatory/inhibitory balance that characterizes neural circuits in animal models of ASD.

Conclusions: Findings are conceptually in line with recent statistical inference accounts, suggesting diminished flexibility in ASD, and further these observations by suggesting within an example relevant for social cognition that such inflexibility may be due to excitatory/inhibitory imbalances.

Keywords: Bodily self-consciousness; Computational psychiatry; Neural networks; Sensory processing; Social cognition; Statistical inference.

Figure 1.. Methods and Behavioral Results.

A) Experimental Setup. Participants responded as fast as possible to tactile stimuli (orange), which could be paired with visual stimuli (red, example show at third distance) at different distances (D1-D5, 13.2 – 52.8cm). In different blocks, an experimenter would sit facing the participants with a neutral expression (social blocks). In the non-social blocks there was nobody else in the experimental room. B) Visuo-tactile reaction times as a function of visuo-tactile distance. Both for neurotypical control (left) and ASD (right) participants, reaction times were further facilitated when visual stimuli were near the body, demonstrating a PPS effect. The facilitation was well expressed in the majority of participants by a sigmoidal function in both the non-social (black) and social (red) blocks. The fit shown is to the average RTs across participants, and not the average fit. C) Extracted Central Point. PPS become smaller in neurotypical control (left) but not ASD (right) participants during the social blocks. D) Extracted Slope Parameter. The gradient separating the space where visual presentation facilitated vs. not tactile reaction times did not change in either group as a function of social context, and instead was characterized by a marked inter-participant variability.

Figure 2.. Electroencephalography Results.

A) Global Field Power as a function of sensory modality. Results show clear evoked responses for tactile (leftmost), visual (2^nd panel), and visuo-tactile (black, 3^rd panel) stimuli presentations. More importantly, the visuo-tactile response shows true multisensory integration, being stronger than the artificially summed visual+tactile response (magenta). Rightmost panel shows the difference wave between the paired and summed response. B) Topography of responses and difference wave (rightmost). The topography of responses (nose at the front) is indicative of tactile (leftmost), visual (2^nd panel), and visuotactile (3^rd panel). The rightmost panel shows that the supra-additive multisensory effect is driven by electrodes in centro-occipital areas, between unisensory visual and somatosensory areas. C) Voltages during supra-additive period within the cluster driving the GFP effect. The difference in voltage between the paired (VT) and summed (V+T) conditions show a clear multisensory integration effect, and one that is dependent on spatial disparity between V and T. Most interestingly, social context (black = non-social, red = social) modified the degree of multisensory integration in controls (left) but not ASD (right) individuals.

Figure 3.. Neural Network Model.

A) Neural Architecture. The neural network is composed of a tactile area coding for the hand, a visual area coding for near and far space, and a multisensory neuron receiving projections from the unisensory areas, and reciprocally sending feedback projections back to unisensory areas. The output of each neuron is dependent on input-output functions, and the inset to the right shows examples of different gain functions for the multisensory neuron. Within unisensory areas, neurons are laterally connected by a “Mexican-Hat” pattern, with near excitation and far inhibition (see inset to the top). B) Model Fits to Control Participants. As a first step we fit multisensory facilitation (y-axis) in reaction time as a function of distance from the body (x-axis) in the neurotypical control and non-social condition (black). Seven distances generated from a sigmoidal with parameters equal to the median experimental parameters were used as observed data. The model is well able to account for observations. Then, we try to explain the impact of the social manipulation by either a change in neural gain at the level of the multisensory neuron (red) or the strength of feedback projects (green). The former approach accounted best for observed data. C) Model Fits to individuals with ASD. The strength of excitatory lateral connections were allowed to vary from the non-social control and the non-social ASD model, and this manipulation was well able to account for idiosyncrasies in the shape of PPS in ASD (see main text and simulations in supplementary note). D) Impact of Multisensory Gain under the Control E/I Regime. Increasing gain of the multisensory neuron (from light gray to black) increased the size of PPS. E) Impact of Multisensory Gain under the ASD E/I Regime. Increasing gain of the multisensory neuron (from light gray to black) did not impact the size of PPS.