Prediction learning in adults with autism and its molecular correlates

Predictive Processing Among Individuals with Autism Spectrum Disorder During Online Language Comprehension: A Preliminary Systematic Review

The present study aims to fill the research gap by evaluating published empirical studies and answering the specific research question: Can individuals with autism spectrum disorder (ASD) predict upcoming linguistic information during real-time language comprehension? Following the PRISMA framework, an initial search via PubMed, Web of Science, SCOPUS, and Google Scholar yielded a total of 697 records. After screening the abstract and full text, 10 studies, covering 350 children and adolescents with ASD ranging from 2 to 15 years old, were included for analysis. We found that individuals with ASD may predict the upcoming linguistic information by using verb semantics but not pragmatic prosody during language comprehension. Nonetheless, 9 out of 10 studies used short spoken sentences as stimuli, which may not encompass the complexity of language comprehension. Moreover, eye-tracking in the lab setting was the primary data collection technique, which may further limit the generalizability of the research findings. Using a narrative approach to synthesize and evaluate the research findings, we found that individuals with ASD may have the ability to predict the upcoming linguistic information. However, this field of research still calls for more studies that will expand the scope of research topics, utilize more complex linguistic stimuli, and employ more diverse data collection techniques.

Unraveling neural adaptation to vocal and non-vocal sounds in autism

OBJECTIVE

Autism is linked to a strong need for sameness and difficulties in social communication, associated with atypical brain responses to voices and changes. This study aimed to characterize neural adaptation in autistic adults using a Roving paradigm and assess how vocal vs. non-vocal, as well as neutral vs. emotional sounds, influence this adaptation.

METHODS

Neural adaptation was measured in 20 autistic and 20 non-autistic adults using a Roving paradigm, where sounds were repeated 4, 8, or 14 times. Neural responses and Repetition Positivity (RP) amplitudes were analyzed as indices of adaptation.

RESULTS

RP amplitudes showed no significant differences between groups for vocal or non-vocal sounds, but adaptation dynamics varied. Non-autistic adults adapted more quickly to non-vocal (5-8 repetitions) compared to vocal sounds (12-14 repetitions). In contrast, autistic adults adapt faster to vocal than to non-vocal sounds. Moreover emotional prosodic content influenced RP amplitude in autistic adults only, suggesting heightened sensitivity to emotional cues in social contexts.

CONCLUSIONS

The study highlights how atypical neural adaptation in autism how emotional content impacts social communication deficits. These insights enhance understanding of autism-related adaptation challenges.

Fast updating of stimulus history reveals weak internal representations of faces in autism

Atypical perception has been widely reported in autism spectrum disorders, and deficits in face recognition, specifically, are argued to be closely associated with social impairment experienced by these individuals. However, it is still debated (a) whether deficits are perceptually based, and (b) what the role is of experience-based refinements of perceptual face representations in autism. We investigated the effect of short- and long-term experienced stimulus history on face processing. Autistic and non-autistic individuals performed same-different judgments in a serial discrimination task where two consecutive faces were drawn from a distribution of morphed faces. Use of stimulus statistics was measured by testing the gravitation of face representations towards, the mean of a range of morphed faces around which they were sampled (regression-to-the-mean). The results show that unlike non-autistic individuals, representations of own- and other-race faces were equally biased by stimulus statistics in autistic individuals. Moreover, autistic individuals used the most recently exposed faces without forming a strong internal representation based on the overall experienced faces, indicating a weaker internal model of the "typical" averaged face. This accumulated history of faces may underlie typical face specialization, and thus may account for the reduced specialization for own-race faces shown in autism. The results shed light on the way autistic people process and recognize faces, and on the basic mechanisms underlying atypical face perception.

Neurometabolite differences in Autism as assessed with Magnetic Resonance Spectroscopy: A systematic review and meta-analysis

1H-Magnetic Resonance Spectroscopy (MRS) is a non-invasive technique that can be used to quantify the concentrations of metabolites in the brain in vivo. MRS findings in the context of autism are inconsistent and conflicting. We performed a systematic review and meta-analysis of MRS studies measuring glutamate and gamma-aminobutyric acid (GABA), as well as brain metabolites involved in energy metabolism (glutamine, creatine), neural and glial integrity (e.g. n-acetyl aspartate (NAA), choline, myo-inositol) and oxidative stress (glutathione) in autism cohorts. Data were extracted and grouped by metabolite, brain region and several other factors before calculation of standardised effect sizes. Overall, we find significantly lower concentrations of GABA and NAA in autism, indicative of disruptions to the balance between excitation/inhibition within brain circuits, as well as neural integrity. Further analysis found these alterations are most pronounced in autistic children and in limbic brain regions relevant to autism phenotypes. Additionally, we show how study outcome varies due to demographic and methodological factors , emphasising the importance of conforming with standardised consensus study designs and transparent reporting.

GABA and Glx predict EEG responses of visual sensitivity in autism

The mechanisms underlying atypical sensory processing in autism remain to be elucidated, but research points toward a role of the glutamatergic/GABAergic balance. To investigate the potential relationships between visual sensitivity and its molecular correlates in autism, we combined data from electroencephalography (EEG) and magnetic resonance spectroscopy (MRS) studies. Twenty autistic adults and sixteen neurotypical adults (NT) participated in both an EEG study assessing visual sensitivity (Sapey-Triomphe et al., Autism Research, 2023) and in an MRS study measuring Glx and GABA+ concentrations in the occipital cortex (Sapey-Triomphe et al., Molecular Autism, 2021). These studies revealed no group differences in neural detection thresholds or in Glx/GABA levels in the occipital cortex. Neural detection thresholds for contrast and spatial frequency (SF) were determined using fast periodic visual stimulations and neural frequency tagging. In the present study, Glx/GABA+ concentrations in the occipital cortex and neural detection thresholds did not differ between groups. Interestingly, lower Glx/GABA+ ratios were associated with lower contrast detection thresholds and higher SF detection thresholds. These correlations were also significant within the neurotypical and autistic groups. This report suggests that the Glx/GABA balance regulates visual detection thresholds across individuals. In both autistic and NTs, lower Glx/GABA ratios in the occipital cortex allow for better detection of visual inputs at the neural level. This study sheds light on the neurochemical underpinnings of visual sensitivity in autism and warrants further investigation.

Is she still angry? Intact learning but no updating of facial expressions priors in autism

Autistic people exhibit atypical use of prior information when processing simple perceptual stimuli; yet, it remains unclear whether and how these difficulties in using priors extend to complex social stimuli. Here, we compared autistic people without accompanying intellectual disability and nonautistic people in their ability to acquire an "emotional prior" of a facial expression and update this prior to a different facial expression of the same identity. Participants performed a two-interval same/different discrimination task between two facial expressions. To study the acquisition of the prior, we examined how discrimination was modified by the contraction of the perceived facial expressions toward the average of presented stimuli (i.e., regression to the mean). At first, facial expressions surrounded one average emotional prior (mostly sad or angry), and then the average switched (to mostly angry or sad, accordingly). Autistic people exhibited challenges in facial discrimination, and yet acquired the first prior, demonstrating typical regression-to-the-mean effects. However, unlike nonautistic people, autistic people did not update their perception to the second prior, suggesting they are less flexible in updating an acquired prior of emotional expressions. Our findings shed light on the perception of emotional expressions, one of the most pressing challenges in autism.

Probabilistic Learning of Cue-Outcome Associations is not Influenced by Autistic Traits

According to Bayesian/predictive coding models of autism, autistic individuals may have difficulties learning probabilistic cue-outcome associations, but empirical evidence has been mixed. The target cues used in previous studies were often straightforward and might not reflect real-life learning of such associations which requires learners to infer which cue(s) among many to track. Across two experiments, we compared adult learners with varying levels of autistic traits on their ability to infer the correct cue to learn probabilistic cue-outcome associations when explicitly instructed to do so or when exposed implicitly. We found no evidence for the effect of autistic traits on probabilistic learning accuracy, contrary to the predictions of Bayesian/predictive coding models. Implications for the current Bayesian/predictive coding models are discussed.

Association between increased anterior cingulate glutamate and psychotic-like experiences, but not autistic traits in healthy volunteers

Despite many differences, autism spectrum disorder and schizophrenia spectrum disorder share environmental risk factors, genetic predispositions as well as neuronal abnormalities, and show similar cognitive deficits in working memory, perspective taking, or response inhibition. These shared abnormalities are already present in subclinical traits of these disorders. The literature proposes that changes in the inhibitory GABAergic and the excitatory glutamatergic system could explain underlying neuronal commonalities and differences. Using magnetic resonance spectroscopy (1H-MRS), we investigated the associations between glutamate concentrations in the anterior cingulate cortex (ACC), the left/right putamen, and left/right dorsolateral prefrontal cortex and psychotic-like experiences (Schizotypal Personality Questionnaire) and autistic traits (Autism Spectrum Quotient) in 53 healthy individuals (26 women). To investigate the contributions of glutamate concentrations in different cortical regions to symptom expression and their interactions, we used linear regression analyses. We found that only glutamate concentration in the ACC predicted psychotic-like experiences, but not autistic traits. Supporting this finding, a binomial logistic regression predicting median-split high and low risk groups for psychotic-like experiences revealed ACC glutamate levels as a significant predictor for group membership. Taken together, this study provides evidence that glutamate levels in the ACC are specifically linked to the expression of psychotic-like experiences, and may be a potential candidate in identifying early risk individuals prone to developing psychotic-like experiences.

Intact predictive processing in autistic adults: evidence from statistical learning

Impairment in predictive processes gained a lot of attention in recent years as an explanation for autistic symptoms. However, empirical evidence does not always underpin this framework. Thus, it is unclear what aspects of predictive processing are affected in autism spectrum disorder. In this study, we tested autistic adults on a task in which participants acquire probability-based regularities (that is, a statistical learning task). Twenty neurotypical and 22 autistic adults learned a probabilistic, temporally distributed regularity for about 40 min. Using frequentist and Bayesian methods, we found that autistic adults performed comparably to neurotypical adults, and the dynamics of learning did not differ between groups either. Thus, our study provides evidence for intact statistical learning in autistic adults. Furthermore, we discuss potential ways this result can extend the scope of the predictive processing framework, noting that atypical processing might not always mean a deficit in performance.

Habituation, Adaptation and Prediction Processes in Neurodevelopmental Disorders: A Comprehensive Review

Habituation, the simplest form of learning preserved across species and evolution, is characterized by a response decrease as a stimulus is repeated. This adaptive function has been shown to be altered in some psychiatric and neurodevelopmental disorders such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or schizophrenia. At the brain level, habituation is characterized by a decrease in neural activity as a stimulation is repeated, referred to as neural adaptation. This phenomenon influences the ability to make predictions and to detect change, two processes altered in some neurodevelopmental and psychiatric disorders. In this comprehensive review, the objectives are to characterize habituation, neural adaptation, and prediction throughout typical development and in neurodevelopmental disorders; and to evaluate their implication in symptomatology, specifically in sensitivity to change or need for sameness. A summary of the different approaches to investigate adaptation will be proposed, in which we report the contribution of animal studies as well as electrophysiological studies in humans to understanding of underlying neuronal mechanisms.

Neural correlates of hierarchical predictive processes in autistic adults

Bayesian theories of autism spectrum disorders (ASD) suggest that atypical predictive mechanisms could underlie the autistic symptomatology, but little is known about their neural correlates. Twenty-six neurotypical (NT) and 26 autistic adults participated in an fMRI study where they performed an associative learning task in a volatile environment. By inverting a model of perceptual inference, we characterized the neural correlates of hierarchically structured predictions and prediction errors in ASD. Behaviorally, the predictive abilities of autistic adults were intact. Neurally, predictions were encoded hierarchically in both NT and ASD participants and biased their percepts. High-level predictions were following activity levels in a set of regions more closely in ASD than NT. Prediction errors yielded activation in shared regions in NT and ASD, but group differences were found in the anterior cingulate cortex and putamen. This study sheds light on the neural specificities of ASD that might underlie atypical predictive processing.

Decision-making in autism: A narrative review

LAY SUMMARY

Many autistic people report difficulties with real-life decision-making. However, when doing decision-making tests in laboratory experiments, autistic people often perform as well or better than non-autistic people. We review previously published studies on autistic people's decision-making, across different types of tests, to understand what type of decision-making is more challenging. To do this, we searched four databases of research papers. We found 104 studies that tested, in total, 2712 autistic and 3189 comparison participants on different decision-making tasks. We found that there were four categories of decision-making tests that were used in these experiments: perceptual (e.g. deciding which image has the most dots); reward learning (e.g. learning which deck of cards gives the best reward); metacognition (e.g. knowing how well you perform or what you want); and value-based (e.g. making a decision based on a choice between two outcomes that differ in value to you). Overall, these studies suggest that autistic and comparison participants tend to perform similarly well at perceptual and reward-learning decisions. However, autistic participants tended to decide differently from comparison participants on metacognition and value-based paradigms. This suggests that autistic people might differ from typically developing controls in how they evaluate their own performance and in how they make decisions based on weighing up the subjective value of two different options. We suggest these reflect more general differences in metacognition, thinking about thinking, in autism.

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 Cerebellum Gets Social: Evidence from an Exploratory Study of Cerebellar, Neurodevelopmental, and Psychiatric Disorders

Social prediction is a key feature of social cognition (SC), a function in which the modulating role of the cerebellum is recognized. Accordingly, cerebellar alterations are reported in cerebellar pathologies, neurodevelopmental disorders, and psychiatric conditions that show SC deficits. Nevertheless, to date, no study has directly compared populations representative of these three conditions with respect to SC and cerebellar alterations. Therefore, the present exploratory study aimed to compare the SC profiles of individuals with cerebellar neurodegenerative disorders (CB), autism (ASD), bipolar disorder type 2 (BD2), or healthy subjects (HS) using a battery of social tests requiring different degrees of prediction processing. The patterns of cerebellar gray matter (GM) alterations were compared among the groups using voxel-based morphometry. Compared to HS, the clinical groups showed common SC deficits in tasks involving a moderate to high level of prediction. The behavioral results of the clinical groups are consistent with the presence of overlapping GM reduction in cerebellar right Crus II, an area notably involved in complex social processing and prediction. Although exploratory and preliminary, these results deepen the cerebellar role in social prediction and highlight the transdiagnostic value of the cerebellum in social functioning and prediction in pathologies of different aetiologies, forecasting novel possibilities for shared interventions.

Increased prefrontal GABA concentrations in adults with autism spectrum disorders

The excitatory-inhibitory imbalance hypothesis postulates dysregulation of the gamma-aminobutyric acid (GABA) and glutamate (Glu) neurotransmitter systems as a common underlying deficit in individuals with autism spectrum disorders (ASD). Previous studies suggest an important role of these systems in the pathophysiology of ASD, including a study of our group reporting decreased glutamate concentrations in the pregenual anterior cingulate cortex (ACC) of adults with ASD. The aim of this study was to replicate our previous findings of impaired glutamate metabolism in ASD in a new sample and to additionally quantify GABA in the ACC and dorsolateral prefrontal cortex (dlPFC). Concentrations of GABA and glutamate-glutamine (Glx; combined glutamate and glutamine signal) were quantified in the ACC and dlPFC of 43 adults with ASD and 43 neurotypical controls (NTC) by magnetic resonance spectroscopy (MRS). The ASD group showed increased absolute GABA concentrations and elevated GABA/creatine ratios in the left dlPFC compared to NTC, while no group differences were detected in the pregenual and dorsal ACC. Previous findings of altered Glx concentration in the pregenual ACC of the ASD group could not be replicated. Regarding Glx concentrations and Glx/creatine ratios, there were no significant differences in the dlPFC and ACC either. The study supports the hypothesis of an altered GABA and glutamate equilibrium, indicating an imbalance between excitatory and inhibitory metabolism in ASD patients. However, inconsistent results across studies and brain regions suggest a complex underlying phenomenon. LAY SUMMARY: Adults of the autism spectrum exhibit elevated levels of the inhibitory neurotransmitter GABA in the left dorsolateral prefrontal cortex. This finding supports the hypothesis of an imbalance between excitatory and inhibitory equilibrium in patients with autism spectrum disorders.