scholarly journals Core-Symptom-Defined Cortical Gyrification Differences in Autism Spectrum Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Mingmin Ning ◽  
Cuicui Li ◽  
Lei Gao ◽  
Jingyi Fan
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Imaging ◽  
Data Exchange ◽  
Developmental Trajectories ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Cortical Folding ◽  
The Core ◽  
Core Symptom

Autism spectrum disorder (ASD) is a heterogeneous disease that is characterized by abnormalities in social communication and interaction as well as repetitive behaviors and restricted interests. Structural brain imaging has identified significant cortical folding alterations in ASD; however, relatively less known is whether the core symptoms are related to neuroanatomical differences. In this study, we aimed to explore core-symptom-anchored gyrification alterations and their developmental trajectories in ASD. We measured the cortical vertex-wise gyrification index (GI) in 321 patients with ASD (aged 7–39 years) and 350 typically developing (TD) subjects (aged 6–33 years) across 8 sites from the Autism Brain Imaging Data Exchange I (ABIDE I) repository and a longitudinal sample (14 ASD and 7 TD, aged 9–14 years in baseline and 12–18 years in follow-up) from ABIDE II. Compared with TD, the general ASD patients exhibited a mixed pattern of both hypo- and hyper- and different developmental trajectories of gyrification. By parsing the ASD patients into three subgroups based on the subscores of the Autism Diagnostic Interview—Revised (ADI-R) scale, we identified core-symptom-specific alterations in the reciprocal social interaction (RSI), communication abnormalities (CA), and restricted, repetitive, and stereotyped patterns of behavior (RRSB) subgroups. We also showed atypical gyrification patterns and developmental trajectories in the subgroups. Furthermore, we conducted a meta-analysis to locate the core-symptom-anchored brain regions (circuits). In summary, the current study shows that ASD is associated with abnormal cortical folding patterns. Core-symptom-based classification can find more subtle changes in gyrification. These results suggest that cortical folding pattern encodes changes in symptom dimensions, which promotes the understanding of neuroanatomical basis, and clinical utility in ASD.

scholarly journals CBD-enriched cannabis for autism spectrum disorder: an experience of a single center in Turkey and reviews of the literature

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Serap Bilge ◽  
Barış Ekici
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Interaction ◽  
Behavioral Problems ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Average Dose ◽  
The Core ◽  
Core Symptoms

Abstract Introduction Autism spectrum disorder is a neurodevelopmental disorder characterized by deficits in communication, social interaction, restricted interest, and repetitive behaviors. Although more cases are being diagnosed, no drugs are approved to treat the core symptoms or cognitive and behavioral problems associated with autism. Therefore, there is an urgent need to develop an effective and safe treatment. Objective In this study, we aim to share our 2-year experience with CBD-enriched cannabis treatment in autism and review the latest studies. Materials and methods The study included 33 (27 males, six females) children diagnosed with autism spectrum disorder who were followed up between January 2018 and August 2020. The mean age was 7.7 ± 5.5 years. The average daily dosage of cannabidiol (CBD) was 0.7 mg/kg/day (0.3–2 mg/kg/day). The median duration of treatment was 6.5 months (3–28 months). The preparations used in this study contained full-spectrum CBD and trace elements tetrahydrocannabinol (THC) of less than 3%. Results The outcomes were evaluated before and after treatment based on clinical interviews. At each follow-up visit, parents were asked to evaluate the effectiveness of the CBD-enriched cannabis treatment. According to the parents’ reports, no change in daily life activity was reported in 6 (19.35%) patients. The main improvements of the treatment were as follows: a decrease in behavioral problems was reported in 10 patients (32.2%), an increase in expressive language was reported in 7 patients (22.5%), improved cognition was reported in 4 patients (12,9%), an increase in social interaction was reported in 3 patients (9.6%), and a decrease in stereotypes was reported in 1 patient (3.2%). The parents reported improvement in cognition among patients who adhered to CBD-enriched cannabis treatment for over two years. The antipsychotic drug could be stopped only in one patient who showed mild ASD symptoms. No change could be made in other drug use and doses. Additionally, this study includes an extensive review of the literature regarding CBD treatment in autism spectrum disorder. According to recent studies, the average dose of CBD was 3.8±2.6 mg/kg/day. The ratio of CBD to THC in the used preparations was 20:1. The most significant improvements were seen in the behavioral problems reported in 20–70% of the patients. Conclusion Using lower doses of CBD and trace THC seems to be promising in managing behavioral problems associated with autism. In addition, this treatment could be effective in managing the core symptoms and cognitive functions. No significant side effects were seen at the low doses of CBD-enriched cannabis when compared to other studies.

scholarly journals Spatial and Temporal Gene Function Studies in Rodents: Towards Gene-Based Therapies for Autism Spectrum Disorder

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 28
Author(s):  
Iris W. Riemersma ◽  
Robbert Havekes ◽  
Martien J. H. Kas
Keyword(s):  
Autism Spectrum Disorder ◽  
Gene Function ◽  
Current Knowledge ◽  
Developmental Trajectories ◽  
Autism Spectrum ◽  
Synaptic Function ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Restricted Interests ◽  
Highly Correlated

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that is characterized by differences in social interaction, repetitive behaviors, restricted interests, and sensory differences beginning early in life. Especially sensory symptoms are highly correlated with the severity of other behavioral differences. ASD is a highly heterogeneous condition on multiple levels, including clinical presentation, genetics, and developmental trajectories. Over a thousand genes have been implicated in ASD. This has facilitated the generation of more than two hundred genetic mouse models that are contributing to understanding the biological underpinnings of ASD. Since the first symptoms already arise during early life, it is especially important to identify both spatial and temporal gene functions in relation to the ASD phenotype. To further decompose the heterogeneity, ASD-related genes can be divided into different subgroups based on common functions, such as genes involved in synaptic function. Furthermore, finding common biological processes that are modulated by this subgroup of genes is essential for possible patient stratification and the development of personalized early treatments. Here, we review the current knowledge on behavioral rodent models of synaptic dysfunction by focusing on behavioral phenotypes, spatial and temporal gene function, and molecular targets that could lead to new targeted gene-based therapy.

scholarly journals The functional brain organization of an individual predicts measures of social abilities in autism spectrum disorder: Predicting symptoms in autism with brain imaging

2018 ◽  
Author(s):  
Evelyn MR Lake ◽  
Emily S Finn ◽  
Stephanie M Noble ◽  
Tamara Vanderwal ◽  
Xilin Shen ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Brain Imaging ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Organization ◽  
Functional Brain ◽  
Functional Brain Organization

ABSTRACTAutism Spectrum Disorder (ASD) is associated with multiple complex abnormalities in functional brain connectivity measured with functional magnetic resonance imaging (fMRI). Despite much research in this area, to date, neuroimaging-based models are not able to characterize individuals with ASD with sufficient sensitivity and specificity; this is likely due to the heterogeneity and complexity of this disorder. Here we apply a data-driven subject-level approach, connectome-based predictive modeling, to resting-state fMRI data from a set of individuals from the Autism Brain Imaging Data Exchange. Using leave-one-subject-out and split-half analyses, we define two functional connectivity networks that predict continuous scores on the Social Responsiveness Scale (SRS) and Autism Diagnostic Observation Schedule (ADOS) and confirm that these networks generalize to novel subjects. Notably, these networks were found to share minimal anatomical overlap. Further, our results generalize to individuals for whom SRS/ADOS scores are unavailable, predicting worse scores for ASD than typically developing individuals. In addition, predicted SRS scores for individuals with attention-deficit/hyperactivity disorder (ADHD) from the ADHD-200 Consortium are linked to ADHD symptoms, supporting the hypothesis that the functional brain organization changes relevant to ASD severity share a component associated with attention. Finally, we explore the membership of predictive connections within conventional (atlas-based) functional networks. In summary, our results suggest that an individual’s functional connectivity profile contains information that supports dimensional, non-binary classification in ASD, aligning with the goals of precision medicine and individual-level diagnosis.

scholarly journals The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8171
Author(s):  
Yaser ElNakieb ◽  
Mohamed T. Ali ◽  
Ahmed Elnakib ◽  
Ahmed Shalaby ◽  
Ahmed Soliman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Imaging ◽  
Gold Standard ◽  
Data Exchange ◽  
Diffusion Tensor ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Feature Engineering

Autism spectrum disorder (ASD) is a combination of developmental anomalies that causes social and behavioral impairments, affecting around 2% of US children. Common symptoms include difficulties in communications, interactions, and behavioral disabilities. The onset of symptoms can start in early childhood, yet repeated visits to a pediatric specialist are needed before reaching a diagnosis. Still, this diagnosis is usually subjective, and scores can vary from one specialist to another. Previous literature suggests differences in brain development, environmental, and/or genetic factors play a role in developing autism, yet scientists still do not know exactly the pathology of this disorder. Currently, the gold standard diagnosis of ASD is a set of diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS) or Autism Diagnostic Interview–Revised (ADI-R) report. These gold standard diagnostic instruments are an intensive, lengthy, and subjective process that involves a set of behavioral and communications tests and clinical history information conducted by a team of qualified clinicians. Emerging advancements in neuroimaging and machine learning techniques can provide a fast and objective alternative to conventional repetitive observational assessments. This paper provides a thorough study of implementing feature engineering tools to find discriminant insights from brain imaging of white matter connectivity and using a machine learning framework for an accurate classification of autistic individuals. This work highlights important findings of impacted brain areas that contribute to an autism diagnosis and presents promising accuracy results. We verified our proposed framework on a large publicly available DTI dataset of 225 subjects from the Autism Brain Imaging Data Exchange-II (ABIDE-II) initiative, achieving a high global balanced accuracy over the 5 sites of up to 99% with 5-fold cross validation. The data used was slightly unbalanced, including 125 autistic subjects and 100 typically developed (TD) ones. The achieved balanced accuracy of the proposed technique is the highest in the literature, which elucidates the importance of feature engineering steps involved in extracting useful knowledge and the promising potentials of adopting neuroimaging for the diagnosis of autism.

scholarly journals Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6001
Author(s):  
Zarina Rakhimberdina ◽  
Xin Liu ◽  
Tsuyoshi Murata
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Imaging ◽  
Network Architecture ◽  
Data Exchange ◽  
Imaging Techniques ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Model Ensemble

With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

Emotion Dysregulation in Autism Spectrum Disorder

2018 ◽  
pp. 282-298
Author(s):  
Emily Neuhaus
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Educational Outcomes ◽  
Emotion Dysregulation ◽  
Research Priorities ◽  
Theoretical Models ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder

Autism spectrum disorder (ASD) is defined by deficits in social communication and interaction, and restricted and repetitive behaviors and interests. Although current diagnostic conceptualizations of ASD do not include emotional difficulties as core deficits, the disorder is associated with emotion dysregulation across the lifespan, with considerable implications for long-term psychological, social, and educational outcomes. The overarching goal of this chapter is to integrate existing knowledge of emotion dysregulation in ASD and identify areas for further investigation. The chapter reviews the prevalence and expressions of emotion dysregulation in ASD, discusses emerging theoretical models that frame emotion dysregulation as an inherent (rather than associated) feature of ASD, presents neurobiological findings and mechanisms related to emotion dysregulation in ASD, and identifies continuing controversies and resulting research priorities.

scholarly journals The Impact of COVID-19 on the Adaptive Functioning, Behavioral Problems, and Repetitive Behaviors of Italian Children with Autism Spectrum Disorder: An Observational Study

Children ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 96
Author(s):  
Martina Siracusano ◽  
Eugenia Segatori ◽  
Assia Riccioni ◽  
Leonardo Emberti Gialloreti ◽  
Paolo Curatolo ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Behavioral Problems ◽  
Adaptive Functioning ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Assessment System ◽  
Spectrum Disorder ◽  
Adaptive Skills ◽  
The Impact

Children with autism spectrum disorder (ASD) and their families have represented a fragile population on which the extreme circumstances of the COVID-19 outbreak may have doubly impaired. Interruption of therapeutical interventions delivered in-person and routine disruption constituted some of the main challenges they had to face. This study investigated the impact of the COVID-19 lockdown on adaptive functioning, behavioral problems, and repetitive behaviors of children with ASD. In a sample of 85 Italian ASD children (mean age 7 years old; 68 males, 17 females), through a comparison with a baseline evaluation performed during the months preceding COVID-19, we evaluated whether after the compulsory home confinement any improvement or worsening was reported by parents of ASD individuals using standardized instruments (Adaptive Behavior Assessment System (Second Edition), Achenbach Child Behavior Checklist, Repetitive Behavior Scale-Revised). No significant worsening in the adaptive functioning, problematic, and repetitive behaviors emerged after the compulsory home confinement. Within the schooler children, clinical stability was found in reference to both adaptive skills and behavioral aspects, whereas within preschoolers, a significant improvement in adaptive skills emerged and was related to the subsistence of web-delivered intervention, parental work continuance, and online support during the lockdown.

scholarly journals Local Protein Translation and RNA Processing of Synaptic Proteins in Autism Spectrum Disorder

2021 ◽  
Vol 22 (6) ◽  
pp. 2811
Author(s):  
Yuyoung Joo ◽  
David R. Benavides
Keyword(s):  
Autism Spectrum Disorder ◽  
Fragile X ◽  
Single Gene ◽  
Protein Translation ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Synaptic Proteins ◽  
Local Translation ◽  
Translation Control

Autism spectrum disorder (ASD) is a heritable neurodevelopmental condition associated with impairments in social interaction, communication and repetitive behaviors. While the underlying disease mechanisms remain to be fully elucidated, dysfunction of neuronal plasticity and local translation control have emerged as key points of interest. Translation of mRNAs for critical synaptic proteins are negatively regulated by Fragile X mental retardation protein (FMRP), which is lost in the most common single-gene disorder associated with ASD. Numerous studies have shown that mRNA transport, RNA metabolism, and translation of synaptic proteins are important for neuronal health, synaptic plasticity, and learning and memory. Accordingly, dysfunction of these mechanisms may contribute to the abnormal brain function observed in individuals with autism spectrum disorder (ASD). In this review, we summarize recent studies about local translation and mRNA processing of synaptic proteins and discuss how perturbations of these processes may be related to the pathophysiology of ASD.

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