N-Methyl-d-aspartate (NMDA) receptor antibody in relation to autism spectrum disorder (ASD): presence and association with symptom profile

Background Several studies pointed to immune dysregulation abnormalities linked to autism spectrum disorders (ASD). Of those, several autoantibodies had been identified. Recent findings of N-methyl d-aspartate (NMDA) antibodies in autoimmune encephalitis suggested that it caused symptoms like autistic regression. Thus, the purpose of the study was to test for the presence of anti-NMDAR antibodies in the ASD disorder population and to correlate this with the clinical findings. Results Eighty-seven autistic children, 4–12 years old, were enrolled in the study and were matched with sixty typically developing children used as controls. The diagnosis of cases was confirmed by ADOS-2 and clinical evaluation. None of the control children had positive anti-NMDAR antibodies, while 26.4% (23 children) of the patients’ group were positive for serum anti-NMDA receptor antibodies (> 200 pg/ml, p = 0.0157). The positive anti-NMDAR antibody was statistically correlated with better speech stage (p = 0.017), more severe stereotyped behavior (p ≤ 0.001), and abnormal EEG findings (p = 0.025). Conclusions There is a possibility of the presence of anti-NMDAR antibodies in the autism spectrum disorder population with certain characteristics, especially the severity of the stereotyped behaviors.

Long-term stability of neural activity in the motor system

How established behaviors are retained and stably produced by a nervous system in constant flux remains a mystery. One possible solution is to fix the activity patterns of single neurons in the relevant circuits. Alternatively, activity in these circuits could change over time, provided that the network dynamics are contained within a manifold that produces stable behavior. To arbitrate between these possibilities, we recorded single unit activity in motor cortex and striatum continuously for several months as rats performed stereotyped motor behaviors - both learned and innate. We found long-term stability in behaviorally locked single neuron activity patterns across both brain regions. A small amount of neural drift observed over weeks of recording could be explained by concomitant changes in task-irrelevant behavioral output and the stochasticity of neural firing. These results suggest that stereotyped behaviors are stored and generated in stable neural circuits.

Robot enhanced interventions and repetitive and stereotyped behaviors in children with Autism Spectrum Disorder: a scoping review

Repetitive and stereotyped behaviors (RSBs) are core symptoms of Autism Spectrum Disorder (ASD), and they affect the functionality of individuals with ASD. Robot assisted therapy can be beneficial for children with ASD in various ways, but relevant research focusing specifically on robot enhanced interventions (REIs) for RSBs in children with ASD has been limited. A scoping review was conducted to explore the role of REIs on RSBs of children with ASD and to investigate the components of REIs focusing on RSBs of younger and older children with ASD. A literature search was made in the databases of Scopus, PubMed, Web of Science, EBSCO, MEDLINE, and Google Scholar, using keywords pertaining to robots, ASD, RSBs, and children. Of the 89 studies identified, 10 met the inclusion criteria. They involved 99 participants aged 3-14 years (mean 7.27 years) from six countries on three different continents. These studies varied with respect to sample size, the research design, the robot used, the length of intervention, the training and the type of measurement. Following the application of most REIs, the participants showed reduction in RSBs. Only one study reported that REI led to some increase in stereotyped behaviors in children with ASD and one detected no training-related changes in repetitive behaviors. The review findings indicate the potential of REIs for reducing RSBs in children with ASD, but the relevant studies were diverse, and controlled studies with larger samples of children and rigorous design are needed to clarify their impact.

Ionic Channels as Potential Targets for the Treatment of Autism Spectrum Disorder: A Review

: Autism spectrum disorder (ASD) is a neurological condition that directly affects brain functions and can culminate in delayed intellectual development, problems in verbal communication, difficulties in social interaction and stereotyped behaviors. Its etiology reveals a genetic basis which can be strongly influenced by socio-environmental factors. Ion channels controlled by ligand voltage- activated calcium, sodium, and potassium channels may play important roles in modulating sensory and cognitive responses, and their dysfunctions may be closely associated with neurodevelopmental disorders such as ASD. This is due to ionic flow, which is of paramount importance to maintaining physiological conditions in the central nervous system, and triggers action potentials, gene expression, and cell signaling. However, since ASD is a multifactorial disease, treatment is directed only to secondary symptoms. Therefore, this research aims to gather evidence concerning the principal pathophysiological mechanisms involving ion channels, in order to recognize their importance as therapeutic targets for treatment of central and secondary ASD symptoms.

The Dopamine Hypothesis of Autism Spectrum Disorder Revisited: Current Status and Future Prospects

Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders characterized by social deficits and stereotyped behaviors. Despite intensive research, its etiopathogenesis remains largely unclear. Although studies consistently reported dopaminergic anomalies, a coherent dopaminergic model of ASD was lacking until recently. In 2017, we provided a theoretical framework for a “dopamine hypothesis of ASD” which proposed that autistic behavior arises from a dysfunctional midbrain dopaminergic system. Namely, we hypothesized that malfunction of 2 critical circuits originating in the midbrain, that is, the mesocorticolimbic and nigrostriatal pathways, generates the core behavioral features of ASD. Moreover, we provided key predictions of our model along with testing means. Since then, a notable number of studies referenced our work and numerous others provided support for our model. To account for these developments, we review all these recent data and discuss their implications. Furthermore, in the light of these new insights, we further refine and reconceptualize our model, debating on the possibility that various etiologies of ASD converge upon a dysfunctional midbrain dopaminergic system. In addition, we discuss future prospects, providing new means of testing our hypothesis, as well as its limitations. Along these lines, we aimed to provide a model which, if confirmed, could provide a better understanding of the etiopathogenesis of ASD along with new therapeutic strategies.

Applied Behavior Analysis Intervention Can Improved Behaviors and Social Skills among Children with Autism

Background: Children with Autism Spectrum Disorders (ASD) are at particular risk of impairment in social interaction, repetitive and stereotyped behaviors, and a poor quality of life. We tested whether applied behavior analysis intervention could, compared to a control condition, reduce repetitive and stereotyped behaviors, and increase social skills and thus enhance the quality of life among children with ASD. Methods: Thirty children with ASD (mean age: 6.5 years) were randomly assigned either to applied behavior analysis intervention or to a control condition. All participants completed Gareth test on stereotypical behaviors, social communication, and interaction skills at the following time points: baseline, and six months later at completion of the intervention. Findings: Stereotypical behaviors, social communication, and interaction skills improved over time from baseline to intervention completion, but more so in the applied behavior analysis intervention than in the control group. Compared to the control group, the applied behavior analysis intervention group reported had more positive scores. Conclusion: The findings suggest that among children with ASD and compared to a control condition, applied behavior analysis intervention has the potential to improve symptoms of autism, thus contributing to their quality of life.

A large-scale neural network training framework for generalized estimation of single-trial population dynamics

Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics. However, the sheer volume of data and its dynamical complexity are critical barriers to uncovering and interpreting these dynamics. Deep learning methods are a promising approach due to their ability to uncover meaningful relationships from large, complex, and noisy datasets. When applied to high-D spiking data from motor cortex (M1) during stereotyped behaviors, they offer improvements in the ability to uncover dynamics and their relation to subjects’ behaviors on a millisecond timescale. However, applying such methods to less-structured behaviors, or in brain areas that are not well-modeled by autonomous dynamics, is far more challenging, because deep learning methods often require careful hand-tuning of complex model hyperparameters (HPs). Here we demonstrate AutoLFADS, a large-scale, automated model-tuning framework that can characterize dynamics in diverse brain areas without regard to behavior. AutoLFADS uses distributed computing to train dozens of models simultaneously while using evolutionary algorithms to tune HPs in a completely unsupervised way. This enables accurate inference of dynamics out-of-the-box on a variety of datasets, including data from M1 during stereotyped and free-paced reaching, somatosensory cortex during reaching with perturbations, and frontal cortex during cognitive timing tasks. We present a cloud software package and comprehensive tutorials that enable new users to apply the method without needing dedicated computing resources.

Impacts of using a social robot to teach music to children with low-functioning autism

This article endeavors to present the impact of conducting robot-assisted music-based intervention sessions for children with low-functioning (LF) autism. To this end, a drum/xylophone playing robot is used to teach basic concepts of how to play the instruments to four participants with LF autism during nine educational sessions. The main findings of this study are compared to similar studies conducted with children with high-functioning autism. Our main findings indicated that the stereotyped behaviors of all the subjects decreased during the course of the program with an approximate large Cohen’sdeffect size. Moreover, the children showed some improvement in imitation, joint attention, and social skills from the Pre-Test to Post-Test. In addition, regarding music education, we indicated that while the children could not pass a test on the music notes or reading music phrases items because of their cognitive deficits, they showed acceptable improvements (with a large Cohen’sdeffect size) in the Stambak Rhythm Reproduction Test, which means that some rhythm learning occurred for the LF participants. In addition, we indicated that parenting stress levels decreased during the program. This study presents some potential possibilities of performing robot-assisted interventions for children with LF autism.

The Urine Metabolome of Young Autistic Children Correlates with Their Clinical Profile Severity

Autism diagnosis is moving from the identification of common inherited genetic variants to a systems biology approach. The aims of the study were to explore metabolic perturbations in autism, to investigate whether the severity of autism core symptoms may be associated with specific metabolic signatures; and to examine whether the urine metabolome discriminates severe from mild-to-moderate restricted, repetitive, and stereotyped behaviors. We enrolled 57 children aged 2–11 years; thirty-one with idiopathic autism and twenty-six neurotypical (NT), matched for age and ethnicity. The urine metabolome was investigated by gas chromatography-mass spectrometry (GC-MS). The urinary metabolome of autistic children was largely distinguishable from that of NT children; food selectivity induced further significant metabolic differences. Severe autism spectrum disorder core deficits were marked by high levels of metabolites resulting from diet, gut dysbiosis, oxidative stress, tryptophan metabolism, mitochondrial dysfunction. The hierarchical clustering algorithm generated two metabolic clusters in autistic children: 85–90% of children with mild-to-moderate abnormal behaviors fell in cluster II. Our results open up new perspectives for the more general understanding of the correlation between the clinical phenotype of autistic children and their urine metabolome. Adipic acid, palmitic acid, and 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid can be proposed as candidate biomarkers of autism severity.