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.

Recognition and management of bladder bowel dysfunction in children with autism spectrum disorder

2021 ◽  
Vol 14 (6) ◽  
pp. e242646
Author(s):  
Shilpee Raturi ◽  
Fay Xiangzhen Li ◽  
Chui Mae Wong
Keyword(s):  
Autism Spectrum Disorder ◽  
Parental Education ◽  
Brain Connectivity ◽  
Children With Autism ◽  
Bowel Dysfunction ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bladder Function ◽  
Children With Asd ◽  
Bladder Bowel Dysfunction

Children with autism spectrum disorder (ASD) with rigidities, anxiety or sensory preferences may establish a pattern of holding urine and stool, which places them at high risk of developing bladder bowel dysfunction (BBD). BBD, despite being common, is often unrecognised in children with ASD. With this case report of a 7-year-old girl with ASD presenting with acute retention of urine, we attempt to understand the underlying factors which may contribute to the association between BBD and ASD. Literature review indicates a complex interplay of factors such as brain connectivity changes, maturational delay of bladder function, cognitive rigidities and psychosocial stressors in children with ASD may possibly trigger events which predispose some of them to develop BBD. Simple strategies such as parental education, maintaining a bladder bowel diary and treatment of constipation may result in resolution of symptoms.

scholarly journals Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 51 ◽  
Author(s):  
Aitana Pascual-Belda ◽  
Antonio Díaz-Parra ◽  
David Moratal
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Typical Development ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Management Functions ◽  
The Brain

The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson’s correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.

scholarly journals A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

2020 ◽  
Vol 10 (12) ◽  
pp. 949
Author(s):  
Md. Mokhlesur Rahman ◽  
Opeyemi Lateef Usman ◽  
Ravie Chandren Muniyandi ◽  
Shahnorbanun Sahran ◽  
Suziyani Mohamed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Early Identification ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Assessment Tools ◽  
Learning Methods ◽  
Machine Learning Methods

Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning’s speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.

scholarly journals Altered brain connectivity in 3-to 7-year-old children with autism spectrum disorder

2013 ◽  
Vol 2 ◽  
pp. 394-401 ◽  
Author(s):  
Mitsuru Kikuchi ◽  
Kiyomi Shitamichi ◽  
Yuko Yoshimura ◽  
Sanae Ueno ◽  
Hirotoshi Hiraishi ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Connectivity ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder
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