Neural mechanisms of negative reinforcement in children and adolescents with autism spectrum disorders

Abstract Background Previous research has found accumulating evidence for atypical reward processing in autism spectrum disorders (ASD), particularly in the context of social rewards. Yet, this line of research has focused largely on positive social reinforcement, while little is known about the processing of negative reinforcement in individuals with ASD. Methods The present study examined neural responses to social negative reinforcement (a face displaying negative affect) and non-social negative reinforcement (monetary loss) in children with ASD relative to typically developing children, using functional magnetic resonance imaging (fMRI). Results We found that children with ASD demonstrated hypoactivation of the right caudate nucleus while anticipating non-social negative reinforcement and hypoactivation of a network of frontostriatal regions (including the nucleus accumbens, caudate nucleus, and putamen) while anticipating social negative reinforcement. In addition, activation of the right caudate nucleus during non-social negative reinforcement was associated with individual differences in social motivation. Conclusions These results suggest that atypical responding to negative reinforcement in children with ASD may contribute to social motivational deficits in this population

Association between the oxytocin receptor (OXTR) gene and mesolimbic responses to rewards

Abstract Background There has been significant progress in identifying genes that confer risk for autism spectrum disorders (ASDs). However, the heterogeneity of symptom presentation in ASDs impedes the detection of ASD risk genes. One approach to understanding genetic influences on ASD symptom expression is to evaluate relations between variants of ASD candidate genes and neural endophenotypes in unaffected samples. Allelic variations in the oxytocin receptor (OXTR) gene confer small but significant risk for ASDs for which the underlying mechanisms may involve associations between variability in oxytocin signaling pathways and neural response to rewards. The purpose of this preliminary study was to investigate the influence of allelic variability in the OXTR gene on neural responses to monetary rewards in healthy adults using functional magnetic resonance imaging (fMRI). Methods The moderating effects of three single nucleotide polymorphisms (SNPs) (rs1042778, rs2268493 and rs237887) of the OXTR gene on mesolimbic responses to rewards were evaluated using a monetary incentive delay fMRI task. Results T homozygotes of the rs2268493 SNP demonstrated relatively decreased activation in mesolimbic reward circuitry (including the nucleus accumbens, amygdala, insula, thalamus and prefrontal cortical regions) during the anticipation of rewards but not during the outcome phase of the task. Allelic variation of the rs1042778 and rs237887 SNPs did not moderate mesolimbic activation during either reward anticipation or outcomes. Conclusions This preliminary study suggests that the OXTR SNP rs2268493, which has been previously identified as an ASD risk gene, moderates mesolimbic responses during reward anticipation. Given previous findings of decreased mesolimbic activation during reward anticipation in ASD, the present results suggest that OXTR may confer ASD risk via influences on the neural systems that support reward anticipation

Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder that results in altered behavior, social development, and communication patterns. In recent years, autism prevalence has tripled, with 1 in 44 children now affected. Given that traditional diagnosis is a lengthy, labor-intensive process that requires the work of trained physicians, significant attention has been given to developing systems that automatically detect autism. We work toward this goal by analyzing audio data, as prosody abnormalities are a signal of autism, with affected children displaying speech idiosyncrasies such as echolalia, monotonous intonation, atypical pitch, and irregular linguistic stress patterns. ObjectiveWe aimed to test the ability for machine learning approaches to aid in detection of autism in self-recorded speech audio captured from children with ASD and neurotypical (NT) children in their home environments. MethodsWe considered three methods to detect autism in child speech: (1) random forests trained on extracted audio features (including Mel-frequency cepstral coefficients); (2) convolutional neural networks trained on spectrograms; and (3) fine-tuned wav2vec 2.0—a state-of-the-art transformer-based speech recognition model. We trained our classifiers on our novel data set of cellphone-recorded child speech audio curated from the Guess What? mobile game, an app designed to crowdsource videos of children with ASD and NT children in a natural home environment. ResultsThe random forest classifier achieved 70% accuracy, the fine-tuned wav2vec 2.0 model achieved 77% accuracy, and the convolutional neural network achieved 79% accuracy when classifying children’s audio as either ASD or NT. We used 5-fold cross-validation to evaluate model performance. ConclusionsOur models were able to predict autism status when trained on a varied selection of home audio clips with inconsistent recording qualities, which may be more representative of real-world conditions. The results demonstrate that machine learning methods offer promise in detecting autism automatically from speech without specialized equipment

Precision telemedicine through crowdsourced machine learning: testing variability of crowd workers for video-based autism feature recognition

International audienceMobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers-defined as vetted members of popular crowdsourcing platforms-to aid in the task of diagnosing autism. We evaluate workers when crowdsourcing the task of providing categorical ordinal behavioral ratings to unstructured public YouTube videos of children with autism and neurotypical controls. To evaluate emerging patterns that are consistent across independent crowds, we target workers from distinct geographic loci on two crowdsourcing platforms: an international group of workers on Amazon Mechanical Turk (MTurk) (N = 15) and Microworkers from Bangladesh (N = 56), Kenya (N = 23), and the Philippines (N = 25). We feed worker responses as input to a validated diagnostic machine learning classifier trained on clinician-filled electronic health records. We find that regardless of crowd platform or targeted country, workers vary in the average confidence of the correct diagnosis predicted by the classifier. The best worker responses produce a mean probability of the correct class above 80% and over one standard deviation above 50%, accuracy and variability on par with experts according to prior studies. There is a weak correlation between mean time spent on task and mean performance (r = 0.358, p = 0.005). These results demonstrate that while the crowd can produce accurate diagnoses, there are intrinsic differences in crowdworker ability to rate behavioral features. We propose a novel strategy for recruitment of crowdsourced workers to ensure high quality diagnostic evaluations of autism, and potentially many other pediatric behavioral health conditions. Our approach represents a viable step in the direction of crowd-based approaches for more scalable and affordable precision medicine

Towards a Modular Brain-Machine Interface for Intelligent Vehicle Systems Control - A CARLA Demonstration

Objective: Individuals with paralysis often have mobility and dexterity impairments that limit their ability to operate motor vehicle controls. Integrating brain-machine interface (BMI) neurotechnology with vehicle control systems (VCS) provides a novel solution to this problem. In this proof-of-concept study, we show that an intracortical BMI developed to restore voluntary grasp can be repurposed to decode motor intention for vehicle velocity and steering control. Methods: The BMI-VCS consists of four components: 1) implanted motor cortex microelectrode array and NeuroPort data acquisition system, 2) machine learning workstation, 3) Python interface to generate control signals, and 4) vehicle control system. Results: Direct cortical steering and velocity control were achieved through accurate decoding of movement intention (supination, pronation, hand open, hand close) from the participant's motor cortex, translating intention into vehicle commands (turn right, turn left, accelerate, decelerate, respectively), and dynamically switching between commands to turn corners, start and stop, shift from forward to reverse, and parallel park. Conclusion: By translating BMI decoder outputs into high-level vehicle commands, a participant with tetraparesis from C5 ASIA A spinal cord injury successfully navigated CARLA driving simulator courses in real time. These decoder outputs could also be used offline for shared control of a scale model car. Significance: High-level, shared vehicle control with BMI-VCS offers an innovative way to return independent driving abilities to those with disability. BMI systems that can control multiple end-effectors may be particularly useful to those with paralysis

Stratified Whole Genome Linkage Analysis of Chiari Type I Malformation Implicates Known Klippel-Feil Syndrome Genes as Putative Disease Candidates

<div><p>Chiari Type I Malformation (CMI) is characterized by displacement of the cerebellar tonsils below the base of the skull, resulting in significant neurologic morbidity. Although multiple lines of evidence support a genetic contribution to disease, no genes have been identified. We therefore conducted the largest whole genome linkage screen to date using 367 individuals from 66 families with at least two individuals presenting with nonsyndromic CMI with or without syringomyelia. Initial findings across all 66 families showed minimal evidence for linkage due to suspected genetic heterogeneity. In order to improve power to localize susceptibility genes, stratified linkage analyses were performed using clinical criteria to differentiate families based on etiologic factors. Families were stratified on the presence or absence of clinical features associated with connective tissue disorders (CTDs) since CMI and CTDs frequently co-occur and it has been proposed that CMI patients with CTDs represent a distinct class of patients with a different underlying disease mechanism. Stratified linkage analyses resulted in a marked increase in evidence of linkage to multiple genomic regions consistent with reduced genetic heterogeneity. Of particular interest were two regions (Chr8, Max LOD = 3.04; Chr12, Max LOD = 2.09) identified within the subset of “CTD-negative” families, both of which harbor growth differentiation factors (GDF6, GDF3) implicated in the development of Klippel-Feil syndrome (KFS). Interestingly, roughly 3–5% of CMI patients are diagnosed with KFS. In order to investigate the possibility that CMI and KFS are allelic, GDF3 and GDF6 were sequenced leading to the identification of a previously known KFS missense mutation and potential regulatory variants in GDF6. This study has demonstrated the value of reducing genetic heterogeneity by clinical stratification implicating several convincing biological candidates and further supporting the hypothesis that multiple, distinct mechanisms are responsible for CMI.</p></div

Population characteristics.

a<p>Only considered genotyped individuals after exclusions were applied (See Methods section for details).</p>b<p>Mean +/− standard deviation [range].</p>c<p>Only considered affected individuals.</p><p>Abbreviations: CMI: Chiari Malformation Type I; No.: number.</p

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

BackgroundAutomated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. ObjectiveWe designed a strategy to gamify the collection and labeling of child emotion–enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches. MethodsWe leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort, called HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion–centric database (>30 times larger than existing public pediatric emotion data sets), we trained a convolutional neural network (CNN) computer vision classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions evoked by children. ResultsThe classifier achieved a 66.9% balanced accuracy and 67.4% F1-score on the entirety of the Child Affective Facial Expression (CAFE) as well as a 79.1% balanced accuracy and 78% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously developed classifiers evaluated against CAFE, the best of which reached a 56% balanced accuracy even when combining “anger” and “disgust” into a single class. ConclusionsThis work validates that mobile games designed for pediatric therapies can generate high volumes of domain-relevant data sets to train state-of-the-art classifiers to perform tasks helpful to precision health efforts