Brain responses to biological motion predict treatment outcome in young children with autism

Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation

Insufficiency of training data is a persistent issue in medical image analysis, especially for task-based functional magnetic resonance images (fMRI) with spatio-temporal imaging data acquired using specific cognitive tasks. In this paper, we propose an approach for generating synthetic fMRI sequences that can then be used to create augmented training datasets in downstream learning tasks. To synthesize high-resolution task-specific fMRI, we adapt the α-GAN structure, leveraging advantages of both GAN and variational autoencoder models, and propose different alternatives in aggregating temporal information. The synthetic images are evaluated from multiple perspectives including visualizations and an autism spectrum disorder (ASD) classification task. The results show that the synthetic task-based fMRI can provide effective data augmentation in learning the ASD classification task.

Neuroanatomical predictors of real‐time fMRI ‐based anterior insula regulation. A supervised machine learning study

Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including fronto-occipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix significantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings highlighted specific neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Clinical and demographic differences by sex in autistic Venezuelan children: A cross-sectional study

BACKGROUND

Sex differences in symptom severity and adaptive function in children with ASD have been historically inconsistent and studies are predominantly from American- and European-residing populations. Alike, there is limited information on the complex interplay between sex, intelligence, adaptive function, and autism symptom severity; this is crucial to identify given their predictive value for health outcomes in autism AIM: This study aimed to identify sex differences in autism symptom severity and adaptive function in a sample of Venezuelan children.

METHOD

One-hundred-and-three Venezuelan children ages 3-7 completed a comprehensive assessment for symptom severity, adaptive functioning, and intelligence.

RESULTS

Sex differences were not present in any autism diagnostic domain or adaptive function.Symptom severity was not a significant predictor for adaptive function, which contrasts with studies sampling American children.

CONCLUSION

This study corroborates other findings based on non-American children, where symptom severity was not a function of adaptive function. Awareness of the interplay of culture, sex-related standards, and autism symptomatology will result in better identification and diagnosis of autism regardless of sex or cultural background. What this paper adds? This paper aids the current literature on sex difference on both autism symptom severity and adaptive function. It also provides a snapshot of the relationship between symptom severity, adaptive function, and other psychological variables that influence the outcome of children with ASD.

Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity

Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.

Re-Examining Labels in Neurocognitive Research: Evidence from Bilingualism and Autism as Spectrum-Trait Cases

Despite the fact that the urge to investigate bilingualism and neurodevelopmental disorders as continuous indices rather than categorical ones has been well-voiced among researchers with respect to research methodological approaches, in the recent literature, when it comes to examining language, cognitive skills and neurodivergent characteristics, it is still the case that the most prevalent view is the categorisation of adults or children into groups. In other words, there is a categorisation of individuals, e.g., monolingual vs. bilingual children or children with typical and atypical/non-typical/non-neurotypical development. We believe that this labelling is responsible for the conflicting results that we often come across in studies. The aim of this review is to bring to the surface the importance of individual differences through the study of relevant articles conducted in bilingual children and children with autism, who are ideal for this study. We concur with researchers who already do so, and we further suggest moving away from labels and instead shift towards the view that not everything is either white or black. We provide suggestions as to how this shift could be implemented in research, while mostly aiming at starting a discourse rather than offering a definite path.

Attentional influences on neural processing of biological motion in typically developing children and those on the autism spectrum

BACKGROUND

Biological motion imparts rich information related to the movement, actions, intentions and affective state of others, which can provide foundational support for various aspects of social cognition and behavior. Given that atypical social communication and cognition are hallmark symptoms of autism spectrum disorder (ASD), many have theorized that a potential source of this deficit may lie in dysfunctional neural mechanisms of biological motion processing. Synthesis of existing literature provides some support for biological motion processing deficits in autism spectrum disorder, although high study heterogeneity and inconsistent findings complicate interpretation. Here, we attempted to reconcile some of this residual controversy by investigating a possible modulating role for attention in biological motion processing in ASD.

METHODS

We employed high-density electroencephalographic recordings while participants observed point-light displays of upright, inverted and scrambled biological motion under two task conditions to explore spatiotemporal dynamics of intentional and unintentional biological motion processing in children and adolescents with ASD (n = 27), comparing them to a control cohort of neurotypical (NT) participants (n = 35).

RESULTS

Behaviorally, ASD participants were able to discriminate biological motion with similar accuracy to NT controls. However, electrophysiologic investigation revealed reduced automatic selective processing of upright biologic versus scrambled motion stimuli in ASD relative to NT individuals, which was ameliorated when task demands required explicit attention to biological motion. Additionally, we observed distinctive patterns of covariance between visual potentials evoked by biological motion and functional social ability, such that Vineland Adaptive Behavior Scale-Socialization domain scores were differentially associated with biological motion processing in the N1 period in the ASD but not the NT group.

LIMITATIONS

The cross-sectional design of this study does not allow us to definitively answer the question of whether developmental differences in attention to biological motion cause disruption in social communication, and the sample was limited to children with average or above cognitive ability.

CONCLUSIONS

Together, these data suggest that individuals with ASD are able to discriminate, with explicit attention, biological from non-biological motion but demonstrate diminished automatic neural specificity for biological motion processing, which may have cascading implications for the development of higher-order social cognition.

Preference for biological motion is reduced in ASD: implications for clinical trials and the search for biomarkers

BACKGROUND

The neurocognitive mechanisms underlying autism spectrum disorder (ASD) remain unclear. Progress has been largely hampered by small sample sizes, variable age ranges and resulting inconsistent findings. There is a pressing need for large definitive studies to delineate the nature and extent of key case/control differences to direct research towards fruitful areas for future investigation. Here we focus on perception of biological motion, a promising index of social brain function which may be altered in ASD. In a large sample ranging from childhood to adulthood, we assess whether biological motion preference differs in ASD compared to neurotypical participants (NT), how differences are modulated by age and sex and whether they are associated with dimensional variation in concurrent or later symptomatology.

METHODS

Eye-tracking data were collected from 486 6-to-30-year-old autistic (N = 282) and non-autistic control (N = 204) participants whilst they viewed 28 trials pairing biological (BM) and control (non-biological, CTRL) motion. Preference for the biological motion stimulus was calculated as (1) proportion looking time difference (BM-CTRL) and (2) peak look duration difference (BM-CTRL).

RESULTS

The ASD group showed a present but weaker preference for biological motion than the NT group. The nature of the control stimulus modulated preference for biological motion in both groups. Biological motion preference did not vary with age, gender, or concurrent or prospective social communicative skill within the ASD group, although a lack of clear preference for either stimulus was associated with higher social-communicative symptoms at baseline.

LIMITATIONS

The paired visual preference we used may underestimate preference for a stimulus in younger and lower IQ individuals. Our ASD group had a lower average IQ by approximately seven points. 18% of our sample was not analysed for various technical and behavioural reasons.

CONCLUSIONS

Biological motion preference elicits small-to-medium-sized case-control effects, but individual differences do not strongly relate to core social autism associated symptomatology. We interpret this as an autistic difference (as opposed to a deficit) likely manifest in social brain regions. The extent to which this is an innate difference present from birth and central to the autistic phenotype, or the consequence of a life lived with ASD, is unclear.

Investigating Changes in Reward-Related Neural Correlates After PEERS Intervention in Adolescents With ASD: Preliminary Evidence of a "Precision Medicine" Approach

Background: The Social Motivation Hypothesis proposes that individuals with autism spectrum disorder (ASD) experience social interactions as less rewarding than their neurotypical (TD) peers, which may lead to reduced social initiation. Existing studies of the brain's reward system in individuals with ASD report varied findings for anticipation of and response to social rewards. Given discrepant findings, the anticipation of and response to social rewards should be further evaluated, particularly in the context of intervention outcome. We hypothesized that individual characteristics may help predict neural changes from pre- to post-intervention. Methods: Thirteen adolescents with ASD received the Program for the Education and Enrichment of Relational Skills (PEERS) intervention for 16 weeks; reward-related EEG was collected before and after intervention. Fourteen TD adolescents were tested at two timepoints but did not receive intervention. Event-related potentials were calculated to measure anticipation of (stimulus-preceding negativity; SPN) and response to (reward-related positivity; RewP) social and non-social rewards. Additionally, measures of social responsiveness, social skills, and intervention-engagement were collected. Group differences were analyzed as well as individual differences using prediction models. Result: Parent-reported social responsiveness and social skills improved in adolescents with ASD after participation in PEERS. ASD adolescents displayed marginally decreased anticipation of social rewards at post-intervention compared to pre-intervention. Regression models demonstrated that older adolescents and those with lower parent-reported social motivation prior to participation in PEERS displayed marginally increased social reward anticipation (more robust SPN) from pre- to post-intervention. Participants who displayed more parent-reported social motivation before intervention and were more actively engaged in the PEERS intervention evidenced increased social reward processing (more robust RewP) from pre- to post-intervention. Conclusion: Findings suggest that there may be differences in saliency between wanting/anticipating social rewards vs. liking/responding to social rewards in individuals with ASD. Our findings support the hypothesis that identification of individual differences may predict which adolescents are poised to benefit the most from particular interventions. As such, reported findings set the stage for the advancement of "precision medicine." This investigation is a critical step forward in our ability to understand and predict individual response to interventions in individuals with ASD.

Social cognitive skills groups increase medial prefrontal cortex activity in children with autism spectrum disorder

Few studies have examined the neural mechanisms of change following social skills interventions for children with autism spectrum disorder (ASD). This study examined the neural effects of social cognitive skills groups during functional MRI (fMRI) tasks of irony comprehension and eye gaze processing in school-aged children with ASD. Verbally fluent children (ages 8-11) were randomized to social cognitive skills groups or facilitated play comparison groups. Behavioral assessments and fMRI scans were obtained at baseline and endpoint (12 weeks). During fMRI, children completed two separate tasks to engage social cognition circuitry: comprehension of potentially ironic scenarios (n = 34) and viewing emotionally expressive faces with direct or averted gaze (n = 24). Whole-brain analyses were conducted to examine neural changes following treatment. Regression analyses were also conducted to explore the relationship between neural and behavioral changes. When comparing the two groups directly, the social cognitive skills group showed greater increases in activity in the medial prefrontal cortex (mPFC), implicated in theory of mind, relative to the comparison group for both irony comprehension and gaze processing tasks. Increased mPFC activity during the irony task was associated with improvement in social functioning on the Social Responsiveness Scale across both groups. Findings indicate that social cognitive skills interventions may increase activity in regions associated with social cognition and mentalizing abilities. LAY SUMMARY: Social skills groups are a common intervention for school-aged children with ASD. However, few studies have examined the neural response to social skills groups in school-aged children with ASD. Here, we report on a study evaluating neural outcomes from an empirically supported social cognitive skills training curriculum using fMRI. This study seeks to understand the effects of targeting emotion recognition and theory of mind on the brain circuitry involved in social cognition in verbally fluent children ages 8-11. Results indicate increased neural activity in the mPFC, a region considered to be a central hub of the "social brain," in children randomized to social cognitive skills groups relative to a comparison group that received a high-quality, child-directed play approach. In addition, increased activation in the mPFC during an irony comprehension task was associated with gains in social functioning across both groups from pre- to post-treatment. This is the first fMRI study of social skills treatment outcomes following a randomized trial with an active treatment condition in school-aged children with ASD.

Inflammation and Mitochondrial Dysfunction in Autism Spectrum Disorder

Autism Spectrum Disorders (ASD) is a severe childhood psychiatric condition with an array of cognitive, language and social impairments that can significantly impact family life. ASD is classically characterized by reduced communication skills and social interactions, with limitations imposed by repetitive patterns of behavior, interests, and activities. The pathophysiology of ASD is thought to arise from complex interactions between environmental and genetic factors within the context of individual development. A growing body of research has raised the possibility of identifying the aetiological causes of the disorder. This review highlights the roles of immune-inflammatory pathways, nitro-oxidative stress and mitochondrial dysfunctions in ASD pathogenesis and symptom severity. The role of NK-cells, T helper, T regulatory and B-cells, coupled with increased inflammatory cytokines, lowered levels of immune-regulatory cytokines, and increased autoantibodies and microglial activation is elucidated. It is proposed that alterations in mitochondrial activity and nitrooxidative stress are intimately associated with activated immune-inflammatory pathways. Future research should determine as to whether the mitochondria, immune-inflammatory activity and nitrooxidative stress changes in ASD affect the development of amygdala-frontal cortex interactions. A number of treatment implications may arise, including prevention-orientated prenatal interventions, treatment of pregnant women with vitamin D, and sodium butyrate. Treatments of ASD children and adults with probiotics, sodium butyrate and butyrate-inducing diets, antipurinergic therapy with suramin, melatonin, oxytocin and taurine are also discussed.

Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing

It has been challenging to elucidate the differences in brain structure that underlie behavioral features of autism. Prior studies have begun to identify patterns of changes in autism across multiple structural indices, including cortical thickness, local gyrification, and sulcal depth. However, common approaches to local gyrification indexing used in prior studies have been limited by low spatial resolution relative to functional brain topography. In this study, we analyze the aforementioned structural indices, utilizing a new method of local gyrification indexing that quantifies this index adaptively in relation to specific sulci/gyri, improving interpretation with respect to functional organization. Our sample included n = 115 autistic and n = 254 neurotypical participants aged 5-54, and we investigated structural patterns by group, age, and autism-related behaviors. Differing structural patterns by group emerged in many regions, with age moderating group differences particularly in frontal and limbic regions. There were also several regions, particularly in sensory areas, in which one or more of the structural indices of interest either positively or negatively covaried with autism-related behaviors. Given the advantages of this approach, future studies may benefit from its application in hypothesis-driven examinations of specific brain regions and/or longitudinal studies to assess brain development in autism.

Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine

INTRODUCTION

Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM.

METHODS

PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019.

RESULTS

From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease.

CONCLUSION

This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.

Diffusion kurtosis imaging of gray matter in young adults with autism spectrum disorder

Prior ex vivo histological postmortem studies of autism spectrum disorder (ASD) have shown gray matter microstructural abnormalities, however, in vivo examination of gray matter microstructure in ASD has remained scarce due to the relative lack of non-invasive methods to assess it. The aim of this work was to evaluate the feasibility of employing diffusional kurtosis imaging (DKI) to describe gray matter abnormalities in ASD in vivo. DKI data were examined for 16 male participants with a diagnosis of ASD and IQ>80 and 17 age- and IQ-matched male typically developing (TD) young adults 18-25 years old. Mean (MK), axial (AK), radial (RK) kurtosis and mean diffusivity (MD) metrics were calculated for lobar and sub-lobar regions of interest. Significantly decreased MK, RK, and MD were found in ASD compared to TD participants in the frontal and temporal lobes and several sub-lobar regions previously associated with ASD pathology. In ASD participants, decreased kurtosis in gray matter ROIs correlated with increased repetitive and restricted behaviors and poor social interaction symptoms. Decreased kurtosis in ASD may reflect a pathology associated with a less restrictive microstructural environment such as decreased neuronal density and size, atypically sized cortical columns, or limited dendritic arborizations.

Examining the value of body gestures in social reward contexts

Brain regions associated with the processing of tangible rewards (such as money, food, or sex) are also involved in anticipating social rewards and avoiding social punishment. To date, studies investigating the neural underpinnings of social reward have presented feedback via static or dynamic displays of faces to participants. However, research demonstrates that participants find another type of social stimulus, namely, biological motion, rewarding as well, and exert effort to engage with this type of stimulus. Here we examine whether feedback presented via body gestures in the absence of facial cues also acts as a rewarding stimulus and recruits reward-related brain regions. To achieve this, we investigated the neural underpinnings of anticipating social reward and avoiding social disapproval presented via gestures alone, using a social incentive delay task. As predicted, the anticipation of social reward and avoidance of social disapproval engaged reward-related brain regions, including the nucleus accumbens, in a manner similar to previous studies' reports of feedback presented via faces and money. This study provides the first evidence that human body motion alone engages brain regions associated with reward processing in a similar manner to other social (i.e. faces) and non-social (i.e. money) rewards. The findings advance our understanding of social motivation in human perception and behavior.

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient regions is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, e.g. brain networks constructed by functional magnetic resonance imaging (fMRI). We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders. Specifically, we design novel regularized pooling layers that highlight salient regions of interests (ROIs) so that we can infer which ROIs are important to identify a certain disease based on the node pooling scores calculated by the pooling layers. Our proposed framework, Pooling Regularized-GNN (PR-GNN), encourages reasonable ROI-selection and provides flexibility to preserve either individual- or group-level patterns. We apply the PR-GNN framework on a Biopoint Autism Spectral Disorder (ASD) fMRI dataset. We investigate different choices of the hyperparameters and show that PR-GNN outperforms baseline methods in terms of classification accuracy. The salient ROI detection results show high correspondence with the previous neuroimaging-derived biomarkers for ASD.

Potential role of primed microglia during obesity on the mesocorticolimbic circuit in autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disease which involves functional and structural defects in selective central nervous system (CNS) regions that harm function and individual ability to process and respond to external stimuli. Individuals with ASD spend less time engaging in social interaction compared to non-affected subjects. Studies employing structural and functional magnetic resonance imaging reported morphological and functional abnormalities in the connectivity of the mesocorticolimbic reward pathway between the nucleus accumbens and the ventral tegmental area (VTA) in response to social stimuli, as well as diminished medial prefrontal cortex in response to visual cues, whereas stronger reward system responses for the non-social realm (e.g., video games) than social rewards (e.g., approval), associated with caudate nucleus responsiveness in ASD children. Defects in the mesocorticolimbic reward pathway have been modulated in transgenic murine models using D2 dopamine receptor heterozygous (D2+/-) or dopamine transporter knockout mice, which exhibit sociability deficits and repetitive behaviors observed in ASD phenotypes. Notably, the mesocorticolimbic reward pathway is modulated by systemic and central inflammation, such as primed microglia, which occurs during obesity or maternal overnutrition. Therefore, we propose that a positive energy balance during obesity/maternal overnutrition coordinates a systemic and central inflammatory crosstalk that modulates the dopaminergic neurotransmission in selective brain areas of the mesocorticolimbic reward pathway. Here, we will describe how obesity/maternal overnutrition may prime microglia, causing abnormalities in dopamine neurotransmission of the mesocorticolimbic reward pathway, postulating a possible immune role in the development of ASD.

Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements

An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.

Increased Neural Reward Responsivity in Adolescents with ASD after Social Skills Intervention

The reward system has been implicated as a potential neural mechanism underlying social-communication deficits in individuals with autism spectrum disorder (ASD). However, it remains unclear whether the neural reward system in ASD is sensitive to behavioral interventions. The current study measured the reward positivity (RewP) in response to social and nonsocial stimuli in seven adolescents with ASD before and after participation in the Program for the Education and Enrichment of Relational Skills (PEERS^®) intervention. This study also included seven neurotypical adolescents who were tested at two time points but did not receive intervention. We examined the RewP across the course of a task by comparing brain activity during the first versus second half of trials to understand patterns of responsivity over time. Improvements in social skills and decreased social-communication impairments for teens with ASD were observed after PEERS^®. Event-related potential (ERP) results suggested increased reward sensitivity during the first half of trials in the ASD group after intervention. Adolescents with ASD who exhibited less reward-related brain activity before intervention demonstrated the greatest behavioral benefits from the intervention. These findings have implications for how neuroscience can be used as an objective outcome measure before and after intervention in ASD.

Perceptual mechanisms of social affiliation in zebrafish

Social living animals need to recognize the presence of conspecifics in the environment in order to engage in adaptive social interactions. Social cues can be detected through different sensory modalities, including vision. Two main visual features can convey information about the presence of conspecifics: body form and biological motion (BM). Given the role that oxytocin plays in social behavior regulation across vertebrates, particularly in the salience and reward values of social stimuli, we hypothesized that it may also be involved in the modulation of perceptual mechanisms for conspecific detection. Here, using videoplaybacks, we assessed the role of conspecific form and BM in zebrafish social affiliation, and how oxytocin regulates the perception of these cues. We demonstrated that while each visual cue is important for social attraction, BM promotes a higher fish engagement than the static conspecific form alone. Moreover, using a mutant line for one of the two oxytocin receptors, we show that oxytocin signaling is involved in the regulation of BM detection but not conspecific form recognition. In summary, our results indicate that, apart from oxytocin role in the regulation of social behaviors through its effect on higher-order cognitive mechanisms, it may regulate social behavior by modulating very basic perceptual mechanisms underlying the detection of socially-relevant cues.

Study protocol of the multi-centre, randomised controlled trial of the Frankfurt Early Intervention Programme A-FFIP versus early intervention as usual for toddlers and preschool children with Autism Spectrum Disorder (A-FFIP study)

BACKGROUND

Naturalistic developmental behavioural interventions (NDBI) have been shown to improve autism-specific symptoms in young children with Autism Spectrum Disorder (ASD). NDBI approaches, such as the ASD-specific Frankfurt Early Intervention Programme for ASD (A-FFIP), are based on ASD-specific developmental and learning aspects. A-FFIP is a low-intensity intervention which can easily be implemented in the local health care/social welfare system. The aim of the present study is to establish 1-year efficacy of the manualised early intervention programme A-FFIP in toddlers and preschool children with ASD. It is hypothesised that A-FFIP will result in improved ASD-specific symptoms compared to early intervention as usual (EIAU). Child- and family-specific secondary outcomes, as well as moderators and mediators of outcome, will be explored.

METHODS/DESIGN

A prospective, multi-centre, parallel-group, randomised controlled, phase-III trial comparing A-FFIP versus EIAU. A total of 134 children (A-FFIP: 67, EIAU: 67) aged 24-66 months at baseline assessment meeting the criteria for ASD (DSM-5) will be included. The primary outcome is the absolute change of the total score of the Brief Observation of Social Communication Change (BOSCC-AT) between baseline (T2) and 1-year follow-up (T6). The treatment effect will be tested, adjusted for relevant covariates applying a mixed model for repeated measures. Secondary outcomes are BOSCC social communication and repetitive-behaviour scores, single ASD symptoms, language, cognition, psychopathology, parental well-being and family quality of life. Predictors, moderators and mediating mechanisms will be explored.

DISCUSSION

If efficacy of the manualised A-FFIP early intervention is established, the current study has the potential to change clinical practice strongly towards the implementation of a low-intensity, evidence-based, natural early intervention in ASD. Early intervention in ASD requires specialist training, which subsequently needs to be developed or included into current training curricula.

TRIAL REGISTRATION

German Registry for Clinical Trials (Deutscher Register Klinischer Studien, DRKS); ID: 00016330. Retrospectively registered on 4 January 2019. URL: https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00016330.

Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection

Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality and low signal-to-noise ratio of fMRI, embedding informative and robust brain regional fMRI representations for both graph-level classification and region-level functional difference detection tasks between ASD and healthy control (HC) groups is difficult. Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data. We investigate the potential of including mutual information (MI) loss (Infomax), which is an unsupervised term encouraging large MI of each nodal representation and its corresponding graph-level summarized representation to learn a better graph embedding. Specifically, this work developed a pipeline including a GNN encoder, a classifier and a discriminator, which forces the encoded nodal representations to both benefit classification and reveal the common nodal patterns in a graph. We simultaneously optimize graph-level classification loss and Infomax. We demonstrated that Infomax graph embedding improves classification performance as a regularization term. Furthermore, we found separable nodal representations of ASD and HC groups in prefrontal cortex, cingulate cortex, visual regions, and other social, emotional and execution related brain regions. In contrast with GNN with classification loss only, the proposed pipeline can facilitate training more robust ASD classification models. Moreover, the separable nodal representations can detect the functional differences between the two groups and contribute to revealing new ASD biomarkers.

Neural Processing and Production of Gesture in Children and Adolescents With Autism Spectrum Disorder

Individuals with autism spectrum disorder (ASD) demonstrate impairments in non-verbal communication, including gesturing and imitation deficits. Reduced sensitivity to biological motion (BM) in ASD may impair processing of dynamic social cues like gestures, which in turn may impede encoding and subsequent performance of these actions. Using both an fMRI task involving observation of action gestures and a charade style paradigm assessing gesture performance, this study examined the brain-behavior relationships between neural activity during gesture processing, gesturing abilities and social symptomology in a group of children and adolescents with and without ASD. Compared to typically developing (TD) controls, participants with ASD showed atypical sensitivity to movement in right posterior superior temporal sulcus (pSTS), a region implicated in action processing, and had poorer overall gesture performance with specific deficits in hand posture. The TD group showed associations between neural activity, gesture performance and social skills, that were weak or non-significant in the ASD group. These findings suggest that those with ASD demonstrate abnormalities in both processing and production of gestures and may reflect dysfunction in the mechanism underlying perception-action coupling resulting in atypical development of social and communicative skills.

Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy

Current diagnosis of autism spectrum disorder (ASD) is based on assessment of behavioral symptoms, although there is strong evidence that ASD affects multiple organ systems including the gastrointestinal (GI) tract. This study used Fisher discriminant analysis (FDA) to evaluate plasma metabolites from 18 children with ASD and chronic GI problems (ASD + GI cohort) and 20 typically developing (TD) children without GI problems (TD − GI cohort). Using three plasma metabolites that may represent three general groups of metabolic abnormalities, it was possible to distinguish the ASD + GI cohort from the TD − GI cohort with 94% sensitivity and 100% specificity after leave-one-out cross-validation. After the ASD + GI participants underwent Microbiota Transfer Therapy with significant improvement in GI and ASD-related symptoms, their metabolic profiles shifted significantly to become more similar to the TD − GI group, indicating potential utility of this combination of plasma metabolites as a biomarker for treatment efficacy. Two of the metabolites, sarcosine and inosine 5′-monophosphate, improved greatly after treatment. The third metabolite, tyramine O-sulfate, showed no change in median value, suggesting it and correlated metabolites to be a possible target for future therapies. Since it is unclear whether the observed differences are due to metabolic abnormalities associated with ASD or with GI symptoms (or contributions from both), future studies aiming to classify ASD should feature TD participants with GI symptoms and have larger sample sizes to improve confidence in the results.

Graph Neural Network for Interpreting Task-fMRI Biomarkers

Finding the biomarkers associated with ASD is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A promising approach to identify biomarkers is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, i.e. brain networks constructed by fMRI. One way to interpret important features is through looking at how the classification probability changes if the features are occluded or replaced. The major limitation of this approach is that replacing values may change the distribution of the data and lead to serious errors. Therefore, we develop a 2-stage pipeline to eliminate the need to replace features for reliable biomarker interpretation. Specifically, we propose an inductive GNN to embed the graphs containing different properties of task-fMRI for identifying ASD and then discover the brain regions/sub-graphs used as evidence for the GNN classifier. We first show GNN can achieve high accuracy in identifying ASD. Next, we calculate the feature importance scores using GNN and compare the interpretation ability with Random Forest. Finally, we run with different atlases and parameters, proving the robustness of the proposed method. The detected biomarkers reveal their association with social behaviors and are consistent with those reported in the literature. We also show the potential of discovering new informative biomarkers. Our pipeline can be generalized to other graph feature importance interpretation problems.

Big data approaches to decomposing heterogeneity across the autism spectrum

Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e., large sample size) and 'deep' (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.

A Pivotal Response Treatment Package for Children With Autism Spectrum Disorder: An RCT

OBJECTIVES

Our aim was to conduct a randomized controlled trial to evaluate a pivotal response treatment package (PRT-P) consisting of parent training and clinician-delivered in-home intervention on the communication skills of children with autism spectrum disorder.

METHODS

Forty-eight children with autism spectrum disorder and significant language delay between 2 and 5 years old were randomly assigned to PRT-P (n = 24) or the delayed treatment group (n = 24) for 24 weeks. The effect of treatment on child communication skills was assessed via behavioral coding of parent-child interactions, standardized parent-report measures, and blinded clinician ratings.

RESULTS

Analysis of child utterances during the structured laboratory observation revealed that, compared with the delayed treatment group, children in PRT-P demonstrated greater improvement in frequency of functional utterances (F1,41 = 6.07; P = .026; d = 0.61). The majority of parents in the PRT-P group (91%) were able to implement pivotal response treatment (PRT) with fidelity within 24 weeks. Children receiving PRT-P also demonstrated greater improvement on the Brief Observation of Social Communication Change, on the Clinical Global Impressions Improvement subscale, and in number of words used on a parent-report questionnaire.

CONCLUSIONS

This is the first 24-week randomized controlled trial in which community treatment is compared with the combination of parent training and clinician-delivered PRT. PRT-P was effective for improving child social communication skills and for teaching parents to implement PRT. Additional research will be needed to understand the optimal combination of treatment settings, intensity, and duration, and to identify child and parent characteristics associated with treatment response.

Biological motion perception is differentially predicted by Autistic trait domains

We tested the relationship between biological motion perception and the Autism-Spectrum Quotient. In three experiments, we indexed observers' performance on a classic left-right discrimination task in which participants were asked to report the facing direction of walkers containing solely structural or kinematics information, a motion discrimination task in which participants were asked to indicate the apparent motion of a (non-biological) random-dot stimulus, and a novel naturalness discrimination task. In the naturalness discrimination task, we systematically manipulated the degree of natural acceleration contained in the stimulus by parametrically morphing between a fully veridical stimulus and one where acceleration was removed. Participants were asked to discriminate the more natural stimulus (i.e., acceleration-containing stimulus) from the constant velocity stimulus. Although we found no reliable associations between overall AQ scores nor subdomain scores with performance on the direction-related tasks, we found a robust association between performance on the biological motion naturalness task and attention switching domain scores. Our findings suggest that understanding the relationship between the Autism Spectrum and perception is a far more intricate problem than previously suggested. While it has been shown that the AQ can be used as a proxy to tap into perceptual endophenotypes in Autism, the eventual diagnostic value of the perceptual task depends on the task's consideration of biological content and demands.

Quantifying performance of machine learning methods for neuroimaging data

Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques - embedded feature selection and bootstrap aggregation (bagging) - to model performance was also quantified. Five machine learning regression methods - Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size.

A pilot investigation of neuroimaging predictors for the benefits from pivotal response treatment for children with autism

Children with autism spectrum disorder (ASD) frequently exhibit language delays and functional communication deficits. Pivotal response treatment (PRT) is an effective intervention for targeting these skills; however, similar to other behavioral interventions, response to PRT is variable across individuals. Thus, objective markers capable of predicting treatment response are critically-needed to identify which children are most likely to benefit from this intervention. In this pilot study, we investigated whether structural neuroimaging measures from language regions in the brain are associated with response to PRT. Children with ASD (n = 18) who were receiving PRT to target their language deficits were assessed with MRI at baseline. T1-weighted images were segmented with FreeSurfer and morphometric measures of the primary language regions (inferior frontal (IFG) and superior temporal (STG) gyri) were evaluated. Children with ASD and language deficits did not exhibit the anticipated relationships between baseline structural measures of language regions and baseline language abilities, as assessed by the number of utterances displayed during a structured laboratory observation (SLO). Interestingly, the level of improvement on the SLO was correlated with baseline asymmetry of the IFG, and the size of the left STG at baseline was correlated with the level of improvement on standardized parental questionnaires. Although very preliminary, the observed associations between baseline structural properties of language regions and improvement in language abilities following PRT suggest that neuroimaging measures may be able to help identify which children are most likely to benefit from specific language treatments, which could help improve precision medicine for children with ASD.

The Use of Eye Tracking as a Biomarker of Treatment Outcome in a Pilot Randomized Clinical Trial for Young Children with Autism

There is a pressing need for objective, quantifiable outcome measures in intervention trials for children with autism spectrum disorder (ASD). The current study investigated the use of eye tracking as a biomarker of treatment response in the context of a pilot randomized clinical trial of treatment for young children with ASD. Participants included 28 children with ASD, aged 18-48 months, who were randomized to one of two conditions: Pivotal Response Intervention for Social Motivation (PRISM) or community treatment as usual (TAU). Eye-tracking and behavioral assessment of developmental functioning were administered at Time 1 (prior to randomization) and at Time 2 (after 6 months of intervention). Two well-established eye-tracking paradigms were used to measure social attention: social preference and face scanning. As a context for understanding relationships between social attention and developmental ability, we first examined how scanning patterns at Time 1 were associated with concurrent developmental functioning and compared to those of 23 age-matched typically developing (TD) children. Changes in scanning patterns from Time 1 to Time 2 were then compared between PRISM and TAU groups and associated with behavioral change over time. Results showed that the social preference paradigm differentiated children with ASD from TD children. In addition, attention during face scanning was associated with language and adaptive communication skills at Time 1 and change in language skills from Time 1 to Time 2. These findings highlight the importance of examining targeted biomarkers that measure unique aspects of child functioning and that are well-matched to proposed mechanisms of change. Autism Research 2019, 12: 779-793. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Biomarkers have the potential to provide important information about how and why early interventions effect positive change for young children with ASD. The current study suggests that eye-tracking measures of social attention can be used to track change in specific areas of development, such as language, and points to the need for targeted eye-tracking paradigms designed to measure specific behavioral changes. Such biomarkers could inform the development of optimal, individualized, and adaptive interventions for young children with ASD.

Structural and effective brain connectivity underlying biological motion detection

The perception of actions underwrites a wide range of socio-cognitive functions. Previous neuroimaging and lesion studies identified several components of the brain network for visual biological motion (BM) processing, but interactions among these components and their relationship to behavior remain little understood. Here, using a recently developed integrative analysis of structural and effective connectivity derived from high angular resolution diffusion imaging (HARDI) and functional magnetic resonance imaging (fMRI), we assess the cerebro-cerebellar network for processing of camouflaged point-light BM. Dynamic causal modeling (DCM) informed by probabilistic tractography indicates that the right superior temporal sulcus (STS) serves as an integrator within the temporal module. However, the STS does not appear to be a "gatekeeper" in the functional integration of the occipito-temporal and frontal regions: The fusiform gyrus (FFG) and middle temporal cortex (MTC) are also connected to the right inferior frontal gyrus (IFG) and insula, indicating multiple parallel pathways. BM-specific loops of effective connectivity are seen between the left lateral cerebellar lobule Crus I and right STS, as well as between the left Crus I and right insula. The prevalence of a structural pathway between the FFG and STS is associated with better BM detection. Moreover, a canonical variate analysis shows that the visual sensitivity to BM is best predicted by BM-specific effective connectivity from the FFG to STS and from the IFG, insula, and STS to the early visual cortex. Overall, the study characterizes the architecture of the cerebro-cerebellar network for BM processing and offers prospects for assessing the social brain.

Time-evolving dynamics in brain networks forecast responses to health messaging

Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? To address this question, we scanned 45 adult smokers by using functional magnetic resonance imaging while they viewed anti-smoking images. Participants reported their smoking behavior and intentions to quit smoking before the scan and 1 month later. We focused on regions within four atlas-defined networks and examined whether they formed consistent network communities during this task (measured as allegiance). Smokers who showed reduced allegiance among regions within the default mode and fronto-parietal networks also demonstrated larger increases in their intentions to quit smoking 1 month later. We further examined dynamics of the ventromedial prefrontal cortex (vmPFC), as activation in this region has been frequently related to behavior change. The degree to which vmPFC changed its community assignment over time (measured as flexibility) was positively associated with smoking reduction. These data highlight the value in considering brain network dynamics for understanding message effectiveness and social processes more broadly.

Facilitation of biological motion processing by group-based autism specific social skills training

Abnormalities in neurophysiological correlates of social perception are a well-known feature of autism spectrum disorder (ASD). However, little is known if and how ASD specific behavioral interventions may affect neural processing in ASD. The aim of the current study was to investigate for the first time, whether the group-based social skills training SOSTA-FRA would elicit changes in neurophysiological correlates of social perception in high-functioning ASD individuals aged 8-17 years. Event-related potentials (ERPs) of a facial emotion recognition (FER) and a biological motion perception task were examined. ERPs were compared between a randomized intervention and a treatment as usual group at three time points (baseline, post-intervention, and at 3 months follow-up). A reduction of P100 amplitude in the right hemisphere and a trend toward reduced N200 latency in the biological motion task were found after the training only in the intervention group, whereas behavioral performance remained stable. Change in N200 latencies and parent-rated social responsiveness showed small but statistically nonsignificant correlations. No changes were observed regarding FER. Results indicate that the intervention changed neural correlates of social perception in ASD. Especially neural correlates of biological motion perception, which is an important prerequisite for successful social interaction, were sensitive to change. ERPs of social perception tasks that are impaired in ASD can well be used to objectively measure neural processing improvement by behavioral intervention. Autism Res 2018, 11: 1376-1387. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: It is well known that people with autism spectrum disorder (ASD) process social information differently than other people and that these differences can also be seen in their brain activity. We also know that behavioral therapies, such as group-based social skills trainings can help people with ASD improve their behavior. But it is unclear how therapy changes social processing in the brain. The aim of our study was therefore to examine how neural processing of social stimuli changed after behavioral intervention. Comparing a group of children and adolescents that received the group-based social skills training SOSTA-FRA to a control group we found that the neural processing of human motion became faster and involved less brain resources after the intervention, while behavioral performance remained stable. No changes were seen for the processing of emotional facial expressions. We recommend that future studies should also analyze changes in brain function as well as behavioral changes as a secondary therapy outcome parameter.

Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis

Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not effective for all patients and the factors predicting a good outcome remain poorly understood. Machine learning is a powerful approach that allows new predictors to be identified in a data-driven way, which can inform understanding of the mechanisms underlying therapeutic interventions, and ultimately make predictions about symptom improvement at the individual patient level. Thirty-eight patients with a diagnosis of schizophrenia completed a social affect task during functional MRI. Multivariate pattern analysis assessed whether treatment response in those receiving CBTp (n = 22) could be predicted by pre-therapy neural responses to facial affect that was either threat-related (ambiguous ‘neutral’ faces perceived as threatening in psychosis, in addition to angry and fearful faces) or prosocial (happy faces). The models predicted improvement in psychotic (r = 0.63, p = 0.003) and affective (r = 0.31, p = 0.05) symptoms following CBTp, but not in the treatment-as-usual group (n = 16). Psychotic symptom improvement was predicted by neural responses to threat-related affect across sensorimotor and frontal-limbic regions, whereas affective symptom improvement was predicted by neural responses to fearful faces only as well as prosocial affect across sensorimotor and frontal regions. These findings suggest that CBTp most likely improves psychotic and affective symptoms in those endorsing more threatening appraisals and mood-congruent processing biases, respectively, which are explored and reframed as part of the therapy. This study improves our understanding of the neurobiology of treatment response and provides a foundation that will hopefully lead to greater precision and tailoring of the interventions offered to patients.

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Machine learning models using neuroimaging data can predict response to CBTp.

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Neural responses to social threat predicted improvement in psychotic symptoms.

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Activation related to different social stimuli predicted distinct symptom domains.

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Predictors included activity in the hippocampus, frontal, and sensorimotor regions.

Back-translating behavioral intervention for autism spectrum disorders to mice with blunted reward restores social abilities

The mu opioid receptor (MOR) plays a critical role in modulating social behavior in humans and animals. Accordingly, MOR null mice display severe alterations in their social repertoire as well as multiple other behavioral deficits, recapitulating core and secondary symptoms of autism spectrum disorder (ASD). Such behavioral profile suggests that MOR dysfunction, and beyond this, altered reward processes may contribute to ASD etiopathology. Interestingly, the only treatments that proved efficacy in relieving core symptoms of ASD, early behavioral intervention programs, rely principally on positive reinforcement to ameliorate behavior. The neurobiological underpinnings of their beneficial effects, however, remain poorly understood. Here we back-translated applied behavior analysis (ABA)-based behavioral interventions to mice lacking the MOR (Oprm1^-/-), as a model of autism with blunted reward processing. By associating a positive reinforcement, palatable food reward, to daily encounter with a wild-type congener, we were able to rescue durably social interaction and preference in Oprm1^-/- mice. Along with behavioral improvements, the expression of marker genes of neuronal activity and plasticity as well as genes of the oxytocin/vasopressin system were remarkably normalized in the reward/social circuitry. Our study provides further evidence for a critical involvement of reward processes in driving social behavior and opens new perspectives regarding therapeutic intervention in ASD.

PREDICTION OF PIVOTAL RESPONSE TREATMENT OUTCOME WITH TASK FMRI USING RANDOM FOREST AND VARIABLE SELECTION

Behavior intervention has shown promise for treatment for young children with autism spectrum disorder (ASD). However, current therapeutic decisions are based on trial and error, often leading to suboptimal outcomes. We propose an approach that employs task-based fMRI for early outcome prediction. Our strategy is based on the general linear model (GLM) and a random forest, combined with feature selection techniques. GLM analysis is performed on each voxel to get t-statistic of contrast between two tasks. Due to the high dimensionality of predictor variables, feature selection is crucial for accurate prediction. Thus we propose a two-step feature selection method: a "shadow" method to select all-relevant variables, followed by a stepwise method to select minimal-optimal set of variables for prediction. A few columns of random noise are generated and added as shadow variables. Regression based on the random forest is performed, and permutation importance of each variable is estimated. Candidate voxels with higher importance than the shadow are kept. Surviving voxels are fed into stepwise variable selection methods. We test both forward and backward stepwise selection. Our method was validated on a dataset of 20 children with ASD using leave-one-out cross-validation, and compared to other standard regression methods. The proposed pipeline generated highest accuracy.

Neural mechanisms of behavioral change in young adults with high-functioning autism receiving virtual reality social cognition training: A pilot study

Measuring treatment efficacy in individuals with Autism Spectrum Disorder (ASD) relies primarily on behaviors, with limited evidence as to the neural mechanisms underlying these behavioral gains. This pilot study addresses this void by investigating neural and behavioral changes in a Phase I trial in young adults with high-functioning ASD who received an evidence-based behavioral intervention, Virtual Reality-Social Cognition Training over 5 weeks for a total of 10 hr. The participants were tested pre- and post-training with a validated biological/social versus scrambled/nonsocial motion neuroimaging task, previously shown to activate regions within the social brain networks. Three significant brain-behavior changes were identified. First, the right posterior superior temporal sulcus, a hub for socio-cognitive processing, showed increased brain activation to social versus nonsocial stimuli in individuals with greater gains on a theory-of-mind measure. Second, the left inferior frontal gyrus, a region for socio-emotional processing, tracked individual gains in emotion recognition with decreased activation to social versus nonsocial stimuli. Finally, the left superior parietal lobule, a region for visual attention, showed significantly decreased activation to nonsocial versus social stimuli across all participants, where heightened attention to nonsocial contingencies has been considered a disabling aspect of ASD. This study provides, albeit preliminary, some of the first evidence of the harnessable neuroplasticity in adults with ASD through an age-appropriate intervention in brain regions tightly linked to social abilities. This pilot trial motivates future efforts to develop and test social interventions to improve behaviors and supporting brain networks in adults with ASD. Autism Res 2018, 11: 713-725. © 2018 The Authors Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc.

LAY SUMMARY

This study addresses how the behavioral changes after treatment for ASD reflect underlying brain changes. Before and after receiving VR-SCT, young adults with high-functioning ASD passively viewed biological motion stimuli in a MRI scanner, tapping changes in the social brain network. The results reveal neuroplasticity in this age population, extending the window of opportunity for interventions to impact social competency in adults with ASD.

Brief report: Reduced anxiety following Pivotal Response Treatment in young children with Autism Spectrum Disorder

Up to 40% of children with Autism Spectrum Disorder (ASD) exhibit co-occurring anxiety symptoms. Despite recent success in mitigating anxiety symptoms in school-aged children with ASD (mean age >9 years) using adapted versions of Cognitive Behavioural Therapy, little is known about potential treatment outcomes for younger children. To address the gap in the literature, this open-label study evaluated change in anxiety following a 16-week open-label trial of Pivotal Response Treatment (PRT) in children with ASD aged 4-8 years. PRT is a behavioural treatment based on the principles of Applied Behaviour Analysis and has a primary aim of increasing social communication skills in children with ASD through natural reinforcements. To minimise conflation of anxiety and other co-occurring symptoms such as disruptive behaviour and attention-deficit hyperactivity disorder, we measured anxiety using the autism anxiety subscale of the Child and Adolescent Symptom Inventory (CASI) devised by Sukhodolsky et al. (2008). We observed significant anxiety reduction over 16-weeks of PRT. Furthermore, anxiety reduction was independent of changes in autism symptom severity. This study shows promising results for PRT as an intervention for reducing anxiety in young children with ASD.

Pivotal response treatment for autism spectrum disorder: current perspectives

Pivotal response treatment (PRT) is an evidence-based behavioral intervention based on applied behavior analysis principles aimed to improve social communication skills in individuals with autism spectrum disorder (ASD). PRT adopts a more naturalistic approach and focuses on using a number of strategies to help increase children's motivation during intervention. Since its conceptualization, PRT has received much empirical support for eliciting therapeutic gains in greater use of functional social communication skills in individuals with ASD. Building upon the empirical evidence supporting PRT, recent advancements have increasingly turned to using interdisciplinary research integrating neuroimaging techniques and behavioral measures to help identify objective biomarkers of treatment, which have two primary purposes. First, neuroimaging results can help characterize how PRT may elicit change, and facilitate partitioning of the heterogeneous profiles of neural mechanisms underlying similar profile of behavioral changes observed over PRT. Second, neuroimaging provides an objective means to both map and track how biomarkers may serve as reliable and sensitive predictors of responder profiles to PRT, assisting clinicians to identify who will most likely benefit from PRT. Together, a better understanding of both mechanisms of change and predictors of responder profile will help PRT to serve as a more precise and targeted intervention for individuals with ASD, thus moving towards the goal of precision medicine and improving quality of care. This review focuses on the recent emerging neuroimaging evidences supporting PRT, offering current perspectives on the importance of interdisciplinary research to help clinicians better understand how PRT works and predict who will respond to PRT.

Brain responses to biological motion predict treatment outcome in young adults with autism receiving Virtual Reality Social Cognition Training: Preliminary findings

Autism Spectrum Disorder (ASD) is characterized by remarkable heterogeneity in social, communication, and behavioral deficits, creating a major barrier in identifying effective treatments for a given individual with ASD. To facilitate precision medicine in ASD, we utilized a well-validated biological motion neuroimaging task to identify pretreatment biomarkers that can accurately forecast the response to an evidence-based behavioral treatment, Virtual Reality-Social Cognition Training (VR-SCT). In a preliminary sample of 17 young adults with high-functioning ASD, we identified neural predictors of change in emotion recognition after VR-SCT. The predictors were characterized by the pretreatment brain activations to biological vs. scrambled motion in the neural circuits that support (a) language comprehension and interpretation of incongruent auditory emotions and prosody, and (b) processing socio-emotional experience and interpersonal affective information, as well as emotional regulation. The predictive value of the findings for individual adults with ASD was supported by regression-based multivariate pattern analyses with cross validation. To our knowledge, this is the first pilot study that shows neuroimaging-based predictive biomarkers for treatment effectiveness in adults with ASD. The findings have potentially far-reaching implications for developing more precise and effective treatments for ASD.