scholarly journals Integration and segregation in Autism Spectrum Disorders modulated by age, disease, and interaction: A graph theoretic study of intrinsic functional connectivity

2018 ◽  
Author(s):  
Vatika Harlalka ◽  
Shruti Naik ◽  
Raju S. Bapi ◽  
P.K. Vinod ◽  
Dipanjan Roy
Keyword(s):  
Data Exchange ◽  
Diffusion Tensor ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Developmental Trajectory ◽  
Clustering Coefficient ◽  
Functional Networks ◽  
Graph Theoretic ◽  
Network Measures ◽  
Effect Of Age

AbstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder affecting 1 in 50 children between the ages of 6 and 17 years. Brain connectivity and graph theoretic methods have been particularly very useful in shedding light on the differences between high functioning autistic children compared to typically developing (TD) ones. However, very recent developments in network measures raise a cautionary note by highlighting gross under- and over-connectivity in ASD may be an oversimplified hypothesis. Thus the primary aim of our study is to investigate these notions in functional connectomics of ASD versus TD by subjecting the data to reproducibility experiments using two independent datasets.Further, we tested the hypothesis of alteration in network segregation and integration in the ASD subjects. We have analyzed the resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) data from the University of California Los Angeles (UCLA) multimodal connectivity database (n=42 ASD, n=37 TD) and rs-fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) (n=187 ASD, n=176 TD) dataset. We assessed the differences in connection strength between TD and ASD subjects. We also performed graph theoretical analysis to analyze the effect of disease on various network measures. Further, using the larger ABIDE dataset, we performed two-factor ANOVA test, to study the effect of age, disease and their interaction by classifying the TD and ASD participants into two cohorts: children (9-12 years, n=73 TD and n=87 ASD) and adolescents (13-16 years, n=103 TD and n=100 ASD). In ASD, we show the existence of atypical connectivity within and between functional networks as compared to TD. We also found in ASD both hypo-and hyper-connectivity within functional networks such as the default mode network (DMN). Further, graph theoretic analysis showed that there is significant effect of age and disease on modularity, clustering coefficient, and local efficiency. We also identified specific areas within the DMN, sensorimotor, visual and attention networks that are affected by age, disease and their interaction. Overall, our findings suggest that maturation, disease and their interaction are critical for unraveling the biological basis and developmental trajectory in ASD and other neuropsychiatric disorders.

The gap between parents’ knowledge and causal beliefs about etiology of autism: A key variable to understand parents’ anxiety

2014 ◽  
Vol 29 (S3) ◽  
pp. 598-599 ◽  
Author(s):  
C. Derguy ◽  
M. Bouvard ◽  
G. Michel ◽  
K. M’Bailara
Keyword(s):  
Cultural Values ◽  
Medical Information ◽  
Causal Attribution ◽  
Neurodevelopmental Disorder ◽  
Depression Scale ◽  
Parental Beliefs ◽  
Autism Spectrum ◽  
Developmental Trajectory ◽  
Knowledge And Beliefs ◽  
The Impact

Autism Spectrum Disorders (ASD) are associated with higher levels of anxiety for parents [1]. Provide medical information about autism etiology is the first step to help parents to understand the child disorder and to cope with it. The medical current community accepts that autism is a neurodevelopmental disorder in which genes play a role but that environmental factors likely contribute as well [2]. This conception can meet parent's beliefs constructed on their cultural values and personal experiences. In line with causal attribution theory [3], it is important to consider to parental beliefs because it can impact the treatment choices and the child developmental trajectory [4]. The Main purpose is to evaluate the consistency between parental knowledge and beliefs about ASD etiology. The second purpose is to explore the impact of consistency on parents’ anxiety. We interviewed through open-ended questions 89 parents of ASD children aged between 3 to 10 years about their knowledge and their beliefs about ASD etiology. A content analysis was performed using the Nvivo10 software. Anxiety is evaluated with the subscale of the Hospital Anxiety and Depression Scale (HADS). In agreement with previous work four categories of causes have been identified: biological (BIO), psychological (PSY), multifactorial etiology (BIO + PSY), others (OT). A percentage of 55.1% of parents is consistent between their knowledge and beliefs about ASD etiology while 43.8% are inconsistent. Parent anxiety is significantly higher (T (71.91) = 2.34; P < 0.05) when knowledge and beliefs are inconsistent than when they are consistent. This study demonstrates the deleterious impact of inconsistency between knowledge and beliefs about ASD etiology, on parental anxiety. In order to provide relevant support for parents, information delivered after diagnosis must consider pre-existing parental beliefs. A systematic assessment of parental beliefs would adjust the information provided after the diagnosis.

scholarly journals An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism

Children ◽  
2020 ◽  
Vol 7 (10) ◽  
pp. 182
Author(s):  
Harshini Sewani ◽  
Rasha Kashef
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Data Exchange ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Imaging Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Unsupervised Neural Network

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a lack of social communication and social interaction. Autism is a mental disorder investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning models to enhance clinicians’ ability to provide robust diagnosis and prognosis of autism. However, with dynamic changes in autism behaviour patterns, these models’ quality and accuracy have become a great challenge for clinical practitioners. We applied a deep neural network learning on a large brain image dataset obtained from ABIDE (autism brain imaging data exchange) to provide an efficient diagnosis of ASD, especially for children. Our deep learning model combines unsupervised neural network learning, an autoencoder, and supervised deep learning using convolutional neural networks. Our proposed algorithm outperforms individual-based classifiers measured by various validations and assessment measures. Experimental results indicate that the autoencoder combined with the convolution neural networks provides the best performance by achieving 84.05% accuracy and Area under the Curve (AUC) value of 0.78.

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 Structure–Function Connectomics Reveals Aberrant Developmental Trajectory Occurring at Preadolescence in the Autistic Brain

2020 ◽  
Vol 30 (9) ◽  
pp. 5028-5037
Author(s):  
Changchun He ◽  
Huafu Chen ◽  
Lucina Q Uddin ◽  
Asier Erramuzpe ◽  
Paolo Bonifazi ◽  
...  
Keyword(s):  
Structure Function ◽  
Diffusion Tensor ◽  
Critical Role ◽  
Autism Spectrum ◽  
Developmental Trajectory ◽  
Brain Maturation ◽  
Support Vector ◽  
Imaging Data ◽  
Brain Connectome ◽  
Brain Connectomics

Abstract Accumulating neuroimaging evidence shows that age estimation obtained from brain connectomics reflects the level of brain maturation along with neural development. It is well known that autism spectrum disorder (ASD) alters neurodevelopmental trajectories of brain connectomics, but the precise relationship between chronological age (ChA) and brain connectome age (BCA) during development in ASD has not been addressed. This study uses neuroimaging data collected from 50 individuals with ASD and 47 age- and gender-matched typically developing controls (TDCs; age range: 5–18 years). Both functional and structural connectomics were assessed using resting-state functional magnetic resonance imaging and diffusion tensor imaging data from the Autism Brain Imaging Data Exchange repository. For each participant, BCA was estimated from structure–function connectomics through linear support vector regression. We found that BCA matched well with ChA in TDC children and adolescents, but not in ASD. In particular, our findings revealed that individuals with ASD exhibited accelerated brain maturation in youth, followed by a delay of brain development starting at preadolescence. Our results highlight the critical role of BCA in understanding aberrant developmental trajectories in ASD and provide the new insights into the pathophysiological mechanisms of this disorder.

scholarly journals Estimating the impact of structural directionality: How reliable are undirected connectomes?

2018 ◽  
Vol 2 (2) ◽  
pp. 259-284 ◽  
Author(s):  
Penelope Kale ◽  
Andrew Zalesky ◽  
Leonardo L. Gollo
Keyword(s):  
Imaging Techniques ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Graph Theoretic ◽  
C Elegans ◽  
Network Measures ◽  
Reciprocal Connections ◽  
Neuronal Connections ◽  
The Impact

Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Because of the limitations of noninvasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse, C. elegans, and three macaque networks), we estimate the inaccuracies in network measures (degree, betweenness, clustering coefficient, path length, global efficiency, participation index, and small-worldness) associated with the removal of the directionality of connections. We employ three different methods to render directed brain networks undirected: (a) remove unidirectional connections, (b) add reciprocal connections, and (c) combine equal numbers of removed and added unidirectional connections. We quantify the extent of inaccuracy in network measures introduced through neglecting connection directionality for individual nodes and across the network. We find that the coarse division between core and peripheral nodes remains accurate for undirected networks. However, hub nodes differ considerably when directionality is neglected. Comparing the different methods to generate undirected networks from directed ones, we generally find that the addition of reciprocal connections ( false positives) causes larger errors in graph-theoretic measures than the removal of the same number of directed connections ( false negatives). These findings suggest that directionality plays an essential role in shaping brain networks and highlight some limitations of undirected connectomes.

Structural Neuroimaging Findings in Autism Spectrum Disorder: A Systematic Review

2021 ◽  
pp. 2341-2347
Author(s):  
P. Yugander ◽  
M. Jagannath
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Volume ◽  
Diffusion Tensor ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Future Research ◽  
Human Beings ◽  
Total Brain Volume ◽  
Total Brain

Autism Spectrum Disorder (ASD) is widely developing neurodevelopmental disorder. The ASD is a lifelong neurodevelopmental disorder that effects the social interaction and behavior of human beings. In this review, we presented structural magnetic resonance imaging (sMRI) studies that were examined in structural brain abnormalities of ASD patients. To date sMRI results were distinct, due to the diversity of the ASD itself. The accelerated brain volume is the uniform finding of ASD. However, the recent investigation reports have started to interpret the structural abnormalities of ASD patient’s brain. The most common abnormalities found in total brain volume, cerebellum, amygdala, hippocampal, basal ganglia, insula, gray and white matter. Limited sMRI research has been done on less than 2 years ASD children. Future research should include autistic children less than 2 years along with functional MRI and diffusion tensor imaging.

Development of Neural Structure and Function in Autism Spectrum Disorder: Potential Implications for Learning Language

2020 ◽  
Vol 29 (4) ◽  
pp. 1783-1797
Author(s):  
Kelly L. Coburn ◽  
Diane L. Williams
Keyword(s):  
Autism Spectrum Disorder ◽  
Language Development ◽  
Diffusion Tensor ◽  
Autism Spectrum ◽  
Language Intervention ◽  
Spectrum Disorder ◽  
Autistic Children ◽  
Neural Structure ◽  
Complex Information ◽  
And Function

Purpose Neurodevelopmental processes that begin during gestation and continue throughout childhood typically support language development. Understanding these processes can help us to understand the disruptions to language that occur in neurodevelopmental conditions, such as autism spectrum disorder (ASD). Method For this tutorial, we conducted a focused literature review on typical postnatal brain development and structural and functional magnetic resonance imaging, diffusion tensor imaging, magnetoencephalography, and electroencephalography studies of the neurodevelopmental differences that occur in ASD. We then integrated this knowledge with the literature on evidence-based speech-language intervention practices for autistic children. Results In ASD, structural differences include altered patterns of cortical growth and myelination. Functional differences occur at all brain levels, from lateralization of cortical functions to the rhythmic activations of single neurons. Neuronal oscillations, in particular, could help explain disrupted language development by elucidating the timing differences that contribute to altered functional connectivity, complex information processing, and speech parsing. Findings related to implicit statistical learning, explicit task learning, multisensory integration, and reinforcement in ASD are also discussed. Conclusions Consideration of the neural differences in autistic children provides additional scientific support for current recommended language intervention practices. Recommendations consistent with these neurological findings include the use of short, simple utterances; repetition of syntactic structures using varied vocabulary; pause time; visual supports; and individualized sensory modifications.

Specialized Diet Therapies: Exploration for Improving Behavior in Autism Spectrum Disorder (ASD)

2020 ◽  
Vol 27 (40) ◽  
pp. 6771-6786
Author(s):  
Geir Bjørklund ◽  
Nagwa Abdel Meguid ◽  
Maryam Dadar ◽  
Lyudmila Pivina ◽  
Joanna Kałużna-Czaplińska ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Neurodevelopmental Disorder ◽  
Caloric Intake ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Nutritional Deficiencies ◽  
Childhood And Adolescence ◽  
Restricted Interests ◽  
Balanced Diet ◽  
The Individual

As a major neurodevelopmental disorder, Autism Spectrum Disorder (ASD) encompasses deficits in communication and repetitive and restricted interests or behaviors in childhood and adolescence. Its etiology may come from either a genetic, epigenetic, neurological, hormonal, or an environmental cause, generating pathways that often altogether play a synergistic role in the development of ASD pathogenesis. Furthermore, the metabolic origin of ASD should be important as well. A balanced diet consisting of the essential and special nutrients, alongside the recommended caloric intake, is highly recommended to promote growth and development that withstand the physiologic and behavioral challenges experienced by ASD children. In this review paper, we evaluated many studies that show a relationship between ASD and diet to develop a better understanding of the specific effects of the overall diet and the individual nutrients required for this population. This review will add a comprehensive update of knowledge in the field and shed light on the possible nutritional deficiencies, metabolic impairments (particularly in the gut microbiome), and malnutrition in individuals with ASD, which should be recognized in order to maintain the improved socio-behavioral habit and physical health.

Correlation Between DTI Findings and Volume of Corpus Callosum in Children with AUTISM

2019 ◽  
Vol 15 (9) ◽  
pp. 895-899 ◽  
Author(s):  
Hafize Otcu Temur ◽  
Ismail Yurtsever ◽  
Gozde Yesil ◽  
Rasul Sharifov ◽  
Fatih Temel Yilmaz ◽  
...  
Keyword(s):  
Corpus Callosum ◽  
Cortical Neurons ◽  
Diffusion Tensor ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Children With Asd ◽  
Volume Measurements ◽  
Adc Values ◽  
Axonal Density ◽  
Area And Volume

Background: Autism Spectrum Disorder (ASD) is a complex developmental disorder in which neurological basis is largely unknown. The Corpus Callosum (CC) is the main commissure that connects the cerebral hemispheres. Previous evidence suggests the involvement of the CC in the pathophysiology of autism. Aim: The aim of our study is to assess whether there were any changes in Corpus Callosum (CC) area and volume and to reveal the relationship between Diffusion Tensor Imaging (DTI) features in genu and splenium of corpus callosum in children with ASD. Methods: Eighteen patient and 15 controls were recruited. The volumetric sagittal TI images were used to provide measurements of midsagittal corpus callosum surface area while FA, MD, RD, and ADC values were extracted from genu and splenium of corpus callosum after which the correlation in the area and volume in ASD children was examined. Results: CC area and volume in children with ASD were decreased than controls. FA values obtained from the genu and splenum of CC were significantly lower and RD values were significantly higher. A positive correlation was observed between the FA of the genu and splenium and area and volume of the CC. There was a negative correlation between ADC, MD and RD of CC and area and volume measurements. Conclusion: The conclusions in the interrelations of morphometric and DTI data may demonstrate a likelihood of damages in the axons and cortical neurons. The results showed that there existed microstructural damages from the DTI findings. Furthermore, the decrease in FA could be a representation of the reduction in the myelination in nerve pathways, impaired integrity, reduced axonal density, and organization. Indeed, the changes in volumetric and microstructural of CC could be useful in evaluating underlying pathophysiology in children with autism.

Attention Deficit Hyperactivity Disorder in Autism Spectrum Disorder

2020 ◽  
pp. 158-176
Author(s):  
Karen Bearss ◽  
Aaron J. Kaat
Keyword(s):  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Autism Spectrum ◽  
Developmental Trajectory ◽  
Spectrum Disorder ◽  
Adhd Symptoms ◽  
Functional Impairments ◽  
Hyperactivity Disorder ◽  
Delayed Recognition

This chapter will review the available evidence on individuals with co-occurring diagnoses of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). This chapter contends that children diagnosed with both disorders (ASD+ADHD) are a subset of the ASD population that is at risk for delayed recognition of their ASD diagnosis, poor treatment response, and poorer functional outcomes compared to those with ASD without ADHD. Specifically, the chapter highlights the best estimates of the prevalence of the comorbidity, the developmental trajectory of people with co-occurring ASD and ADHD, how ADHD symptoms change across development, overlapping genetic and neurobiological risk factors, psychometrics of ADHD diagnostic instruments in an ASD population, neuropsychological and functional impairments associated with co-occurring ASD and ADHD, and the current state of evidence-based treatment for both ASD and ADHD symptoms. Finally, the chapter discusses fruitful avenues of research for improving understanding of this high-risk comorbidity so that mechanism-to-treatment pathways for ADHD in children with ASD can be better developed.

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