Social cognition and functional brain network in autism spectrum disorder: Insights from EEG graph-theoretic measures

2021 ◽  
Vol 67 ◽  
pp. 102556
Author(s):  
Tanu Wadhera ◽  
Deepti Kakkar
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Cognition ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Functional Brain ◽  
Graph Theoretic ◽  
Functional Brain Network

scholarly journals State-dependent changes of connectivity patterns and functional brain network topology in autism spectrum disorder

2012 ◽  
Vol 50 (14) ◽  
pp. 3653-3662 ◽  
Author(s):  
Pablo Barttfeld ◽  
Bruno Wicker ◽  
Sebastián Cukier ◽  
Silvana Navarta ◽  
Sergio Lew ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Network Topology ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Functional Brain ◽  
State Dependent ◽  
Functional Brain Network ◽  
Connectivity Patterns

Classification of Autism Spectrum Disorder from EEG-based Functional Brain Connectivity Analysis

2021 ◽  
pp. 1-27
Author(s):  
Noura Alotaibi ◽  
Koushik Maharatna
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Connectivity ◽  
Phase Locking ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Functional Brain ◽  
Graph Theoretic ◽  
Phase Locking Value

Abstract Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.

scholarly journals Changing diagnostic practices: autism spectrum disorder

2014 ◽  
Vol 20 (1) ◽  
pp. 23-26 ◽  
Author(s):  
Marc Woodbury-Smith
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Cognition ◽  
Social Interaction ◽  
Medical Practice ◽  
Classification System ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Psychiatric Nosology ◽  
Diagnostic Practices

SummaryIn medical practice it is crucial that symptom descriptions are as precise and objective as possible, which psychiatry attempts to achieve through its psychopathological lexicon. The term ‘autism spectrum disorder’ has now entered psychiatric nosology, but the symptom definitions on which it is based are not robust, potentially making reliable and valid diagnoses a problem. This is further compounded by the spectral nature of the disorder and its lack of clear diagnostic boundaries. To overcome this, there is a need for a psychopathological lexicon of 'social cognition’ and a classification system that splits rather than lumps disorders with core difficulties in social interaction.

scholarly journals Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks

2021 ◽  
Vol 14 ◽  
Author(s):  
Jingjing Gao ◽  
Mingren Chen ◽  
Yuanyuan Li ◽  
Yachun Gao ◽  
Yanling Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Convolutional Neural Network ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Structural Covariance ◽  
Almost All ◽  
New Framework

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients’ families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.

scholarly journals Altered brain network dynamics in youths with autism spectrum disorder

2016 ◽  
Vol 234 (12) ◽  
pp. 3425-3431 ◽  
Author(s):  
Evie Malaia ◽  
Erik Bates ◽  
Benjamin Seitzman ◽  
Katherine Coppess
Keyword(s):  
Autism Spectrum Disorder ◽  
Network Dynamics ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder

Neural Targets in the Study and Treatment of Social Cognition in Autism Spectrum Disorder

2015 ◽  
pp. 309-334 ◽  
Author(s):  
Arshya Vahabzadeh ◽  
Samantha M. Landino ◽  
Beate C. Finger ◽  
William A. Carlezon ◽  
Christopher J. McDougle
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Cognition ◽  
Autism Spectrum ◽  
Spectrum Disorder
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