Autistic traits are associated with atypical precision-weighted integration of top-down and bottom-up neural signals

Can similarity of autistic traits promote neural synchronization?

People with similar levels of autistic traits are reported to exhibit better interactions than those with larger differences in autistic traits. However, whether this "similarity effect" exists at the neural level remains unclear. To address this gap, the present study employed functional near-infrared spectroscopy (fNIRS) hyperscanning technology to assess inter-brain synchronization (IBS) during naturalistic conversations among dyads with three types of autistic trait combinations (20 high-high, 22 high-low, and 18 low-low dyads). The results revealed that the high-high dyads exhibited significantly lower IBS in the right temporoparietal junction (rTPJ) region compared to the low-low dyads, with no significant differences observed between the high-low group and the other two groups. Moreover, though dyadic differences in conversation satisfaction were positively correlated with dyadic autistic trait differences, IBS only showed a significant negative correlation with the dyadic average autistic trait scores and no significant correlation with the dyadic difference scores of autistic traits. These findings suggest that dyads with high autistic traits may have shared feelings about conversations, but cannot produce IBS through successful mutual prediction and understanding.

Are autistic traits associated with a social-emotional memory bias?

Autistic traits are associated with differential processing of emotional and social cues. By contrast little is known about the relationship of autistic traits to socio-emotional memory, though research suggests an integral relationship between episodic memory processes and psychosocial well-being. Using an experimental paradigm, we tested if autistic traits moderate the effects of negative emotion and social cues on episodic memory (i.e. memory for past events). Young adults (N = 706) with varied levels of self-reported autistic traits (24% in clinical range) encoded images stratified by emotion (negative, neutral) and social cues (social, non-social) alongside a neutral object. After 24 h, item memory for images and associative memory for objects was tested. For item memory, after controlling for anxiety, a small effect emerged whereby a memory-enhancing effect of social cues was reduced as autistic traits increased. For associative memory, memory for pairings between neutral, but not negative, images reduced as autistic traits increased. Results suggest autistic traits are associated with reduced ability to bind neutral items together in memory, potentially impeding nuanced appraisals of past experience. This bias toward more negative, less nuanced memories of past experience may represent a cognitive vulnerability to social and mental health challenges commonly associated with autistic traits and a potential intervention target.

Measuring attentional selection of object categories using hierarchical frequency tagging

In the present study, we used Hierarchical Frequency Tagging (Gordon et al., 2017) to investigate in electroencephalography how different levels of the neural processing hierarchy interact with category-selective attention during visual object recognition. We constructed stimulus sequences of cyclic wavelet scrambled face and house stimuli at two different frequencies (f1 = 0.8 Hz and f2 = 1 Hz). For each trial, two stimulus sequences of different frequencies were superimposed and additionally augmented by a sinusoidal contrast modulation with f3 = 12.5 Hz. This allowed us to simultaneously assess higher level processing using semantic wavelet-induced frequency-tagging (SWIFT) and processing in earlier visual levels using steady-state visually evoked potentials (SSVEPs), along with their intermodulation (IM) components. To investigate the category specificity of the SWIFT signal, we manipulated the category congruence between target and distractor by superimposing two sequences containing stimuli from the same or different object categories. Participants attended to one stimulus (target) and ignored the other (distractor). Our results showed successful tagging of different levels of the cortical hierarchy. Using linear mixed-effects modeling, we detected different attentional modulation effects on lower versus higher processing levels. SWIFT and IM components were substantially increased for target versus distractor stimuli, reflecting attentional selection of the target stimuli. In addition, distractor stimuli from the same category as targets elicited stronger SWIFT signals than distractor stimuli from a different category indicating category-selective attention. In contrast, for IM components, this category-selective attention effect was largely absent, indicating that IM components probably reflect more stimulus-specific processing.

Improved emotion differentiation under reduced acoustic variability of speech in autism

BACKGROUND

Socio-emotional impairments are among the diagnostic criteria for autism spectrum disorder (ASD), but the actual knowledge has substantiated both altered and intact emotional prosodies recognition. Here, a Bayesian framework of perception is considered suggesting that the oversampling of sensory evidence would impair perception within highly variable environments. However, reliable hierarchical structures for spectral and temporal cues would foster emotion discrimination by autistics.

METHODS

Event-related spectral perturbations (ERSP) extracted from electroencephalographic (EEG) data indexed the perception of anger, disgust, fear, happiness, neutral, and sadness prosodies while listening to speech uttered by (a) human or (b) synthesized voices characterized by reduced volatility and variability of acoustic environments. The assessment of mechanisms for perception was extended to the visual domain by analyzing the behavioral accuracy within a non-social task in which dynamics of precision weighting between bottom-up evidence and top-down inferences were emphasized. Eighty children (mean 9.7 years old; standard deviation 1.8) volunteered including 40 autistics. The symptomatology was assessed at the time of the study via the Autism Diagnostic Observation Schedule, Second Edition, and parents' responses on the Autism Spectrum Rating Scales. A mixed within-between analysis of variance was conducted to assess the effects of group (autism versus typical development), voice, emotions, and interaction between factors. A Bayesian analysis was implemented to quantify the evidence in favor of the null hypothesis in case of non-significance. Post hoc comparisons were corrected for multiple testing.

RESULTS

Autistic children presented impaired emotion differentiation while listening to speech uttered by human voices, which was improved when the acoustic volatility and variability of voices were reduced. Divergent neural patterns were observed from neurotypicals to autistics, emphasizing different mechanisms for perception. Accordingly, behavioral measurements on the visual task were consistent with the over-precision ascribed to the environmental variability (sensory processing) that weakened performance. Unlike autistic children, neurotypicals could differentiate emotions induced by all voices.

CONCLUSIONS

This study outlines behavioral and neurophysiological mechanisms that underpin responses to sensory variability. Neurobiological insights into the processing of emotional prosodies emphasized the potential of acoustically modified emotional prosodies to improve emotion differentiation by autistics.

TRIAL REGISTRATION

BioMed Central ISRCTN Registry, ISRCTN18117434. Registered on September 20, 2020.

A novel model of divergent predictive perception

Predictive processing theories state that our subjective experience of reality is shaped by a balance of expectations based on previous knowledge about the world (i.e. priors) and confidence in sensory input from the environment. Divergent experiences (e.g. hallucinations and synaesthesia) are likely to occur when there is an imbalance between one's reliance on priors and sensory input. In a novel theoretical model, inspired by both predictive processing and psychological principles, we propose that predictable divergent experiences are associated with natural or environmentally induced prior/sensory imbalances: inappropriately strong or inflexible (i.e. maladaptive) high-level priors (beliefs) combined with low sensory confidence can result in reality discrimination issues, a characteristic of psychosis; maladaptive low-level priors (sensory expectations) combined with high sensory confidence can result in atypical sensory sensitivities and persistent divergent percepts, a characteristic of synaesthesia. Crucially, we propose that whether different divergent experiences manifest with dominantly sensory (e.g. hallucinations) or nonsensory characteristics (e.g. delusions) depends on mental imagery ability, which is a spectrum from aphantasia (absent or weak imagery) to hyperphantasia (extremely vivid imagery). We theorize that imagery is critically involved in shaping the sensory richness of divergent perceptual experience. In sum, to predict a range of divergent perceptual experiences in both clinical and general populations, three factors must be accounted for: a maladaptive use of priors, individual level of confidence in sensory input, and mental imagery ability. These ideas can be expressed formally using nonparametric regression modeling. We provide evidence for our theory from previous work and deliver predictions for future research.

Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic models

Vigorous research has been conducted to accumulate biological and theoretical knowledge about neurodevelopmental disorders, including molecular, neural, computational, and behavioral characteristics; however, these findings remain fragmentary and do not elucidate integrated mechanisms. An obstacle is the heterogeneity of developmental pathways causing clinical phenotypes. Additionally, in symptom formations, the primary causes and consequences of developmental learning processes are often indistinguishable. Herein, we review developmental neurorobotic experiments tackling problems related to the dynamic and complex properties of neurodevelopmental disorders. Specifically, we focus on neurorobotic models under predictive processing lens for the study of developmental disorders. By constructing neurorobotic models with predictive processing mechanisms of learning, perception, and action, we can simulate formations of integrated causal relationships among neurodynamical, computational, and behavioral characteristics in the robot agents while considering developmental learning processes. This framework has the potential to bind neurobiological hypotheses (excitation-inhibition imbalance and functional disconnection), computational accounts (unusual encoding of uncertainty), and clinical symptoms. Developmental neurorobotic approaches may serve as a complementary research framework for integrating fragmented knowledge and overcoming the heterogeneity of neurodevelopmental disorders.

Pre-attentive representation of prediction certainty in autism: A mismatch negativity (MMN) study

According to predictive processing theories of perception, the brain generates predictions to prepare for sensory input, and calibrates certainty of predictions based on their likelihood. When an input doesn't match the prediction, an error signal leads to updating of the predictive model. Prior research suggests altered prediction certainty in autism, but predictive processing occurs across the cortical hierarchy, and the stage(s) of processing where prediction certainty breaks down is unknown. We therefore tested the integrity of prediction certainty in autism at pre-attentive and relatively automatic processing stages using the pre-attentive Mismatch Negativity (MMN) brain response. The MMN occurs in response to a "deviant" presented in a stream of "standards" and is measured while the participant performs an orthogonal task. Most critically, MMN amplitude typically varies with the level of certainty associated with the prediction. We recorded high-density EEG while presenting adolescents and young adults with and without autism with repetitive tones every half second (the standard) interspersed with infrequent pitch and inter-stimulus-interval (ISI) deviants. Pitch and ISI deviant probabilities were manipulated at 4, 8, or 16% within a block of trials to test whether MMN amplitude varied in a typical manner with respect to probability. For both groups, Pitch-MMN amplitude increased as the probability of deviance decreased. Unexpectedly, ISI-MMN amplitude did not reliably vary by probability in either group. Our Pitch-MMN findings suggest intact neural representation of pre-attentive prediction certainty in autism, addressing a critical knowledge gap in autism research. The implications of these findings are considered.

LAY SUMMARY

Our brains are always trying to predict what will happen next. For example, when you open your utensil drawer, it would be surprising to see books because your brain expected to see utensils. In our study, we looked at whether the brains of autistic individuals automatically and accurately recognize when something unexpected happens. Results showed similar brain patterns in individuals with and without autism, suggesting that responses to prediction violations are generated in a typical manner during early cortical information processing.

10 years of Bayesian theories of autism: A comprehensive review

Ten years ago, Pellicano and Burr published one of the most influential articles in the study of autism spectrum disorders, linking them to aberrant Bayesian inference processes in the brain. In particular, they proposed that autistic individuals are less influenced by their brains' prior beliefs about the environment. In this systematic review, we investigate if this theory is supported by the experimental evidence. To that end, we collect all studies which included comparisons across diagnostic groups or autistic traits and categorise them based on the investigated priors. Our results are highly mixed, with a slight majority of studies finding no difference in the integration of Bayesian priors. We find that priors developed during the experiments exhibited reduced influences more frequently than priors acquired previously, with various studies providing evidence for learning differences between participant groups. Finally, we focus on the methodological and computational aspects of the included studies, showing low statistical power and often inconsistent approaches. Based on our findings, we propose guidelines for future research.

The sense of agency for brain disorders: A comprehensive review and proposed framework

Sense of Agency (SoA) refers to the feeling of control over voluntary actions and the outcomes of those actions. Several brain disorders are characterized by an abnormal SoA. To date, there is no robust treatment for aberrant agency across disorders; this is, in large part, due to gaps in our understanding of the cognitive mechanisms and neural correlates of the SoA. This apparent gap stems from a lack of synthesis in established findings. As such, the current review reconciles previously established findings into a novel neurocognitive framework for future investigations of the SoA in brain disorders, which we term the Agency in Brain Disorders Framework (ABDF). In doing so, we highlight key top-down and bottom-up cues that contribute to agency prospectively (i.e., prior to action execution) and retrospectively (i.e., after action execution). We then examine brain disorders, including schizophrenia, autism spectrum disorders (ASD), obsessive-compulsive disorders (OCD), and cortico-basal syndrome (CBS), within the ABDF, to demonstrate its potential utility in investigating neurocognitive mechanisms underlying phenotypically variable presentations of the SoA in brain disorders.

Alterations of Prefrontal-Posterior Information Processing Patterns in Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a heterogeneous disorder characterized by different levels of repetitive and stereotypic behavior as well as deficits in social interaction and communication. In this current study, we explored the changes in cerebral neural activities in ASD. The purpose of this study is to investigate whether there exists a dysfunction of interactive information processing between the prefrontal cortex and posterior brain regions in ASD. We investigated the atypical connectivity and information flow between the prefrontal cortex and posterior brain regions in ASD utilizing the entropy connectivity (a kind of directional connectivity) method. Eighty-nine patients with ASD and 94 typical developing (TD) teenagers participated in this study. Two-sample t-tests revealed weakened interactive entropy connectivity between the prefrontal cortex and posterior brain regions. This result indicates that there exists interactive prefrontal-posterior underconnectivity in ASD, and this disorder might lead to less prior knowledge being used and updated. Our proposals highlighted that aforementioned atypical change might accelerate the deoptimization of brain networks in ASD.

Representational 'touch' and modulatory 'retouch'-two necessary neurobiological processes in thalamocortical interaction for conscious experience

Theories of consciousness using neurobiological data or being influenced by these data have been focused either on states of consciousness or contents of consciousness. These theories have occasionally used evidence from psychophysical phenomena where conscious experience is a dependent experimental variable. However, systematic catalog of many such relevant phenomena has not been offered in terms of these theories. In the perceptual retouch theory of thalamocortical interaction, recently developed to become a blend with the dendritic integration theory, consciousness states and contents of consciousness are explained by the same mechanism. This general-purpose mechanism has modulation of the cortical layer-5 pyramidal neurons that represent contents of consciousness as its core. As a surplus, many experimental psychophysical phenomena of conscious perception can be explained by the workings of this mechanism. Historical origins and current views inherent in this theory are presented and reviewed.

Connecting brain and behavior in clinical neuroscience: A network approach

In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior.

Autistic Cognition: Charting Routes to Anxiety

Autism Spectrum Conditions are typified by a divergence in cognitive style from that of the non-autistic population. Cognitive differences in autism may underlie significant strengths, but also increase vulnerability to psychopathology such as anxiety, which is a major problem for many autistic people. Many autistic people also do not respond to typical psychotherapeutic interventions, suggesting that autism-specific models and interventions are needed. We advance a theoretical model explaining how three constructs, attenuated predictions, intolerance of uncertainty, and 'black and white thinking', may interact to lead to anxiety in autism. We hope to start a dialogue surrounding how we can best address specific autistic cognitive differences that may lead to distress by developing appropriate models, measurements, and psychotherapeutic interventions.