Neural Engineering Techniques for Autism Spectrum Disorder

Volume 1: Imaging and Signal Analysis

Ayman S. El-Baz, Jasjit Suri, Jasjit S. Suri

• Publisher: Academic Press
• Year: 2021
• Print ISBN: 9780128228227
• eBook ISBN: 9780128230657
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

Neural Engineering for Autism Spectrum Disorder, Volume One: Imaging and Signal Analysis Techniques presents the latest advances in neural engineering and biomedical engineering as applied to the clinical diagnosis and treatment of Autism Spectrum Disorder (ASD). Advances in the role of neuroimaging, infrared spectroscopy, sMRI, fMRI, DTI, social behaviors and suitable data analytics useful for clinical diagnosis and research applications for Autism Spectrum Disorder are covered, including relevant case studies. The application of brain signal evaluation, EEG analytics, feature selection, and analysis of blood oxygen level-dependent (BOLD) signals are presented for detection and estimation of the degree of ASD. - Presents applications of Neural Engineering and other Machine Learning techniques for the diagnosis of Autism Spectrum Disorder (ASD) - Includes in-depth technical coverage of imaging and signal analysis techniques, including coverage of functional MRI, neuroimaging, infrared spectroscopy, sMRI, fMRI, DTI, and neuroanatomy of autism - Covers Signal Analysis for the detection and estimation of Autism Spectrum Disorder (ASD), including brain signal analysis, EEG analytics, feature selection, and analysis of blood oxygen level-dependent (BOLD) signals for ASD - Written to help engineers, computer scientists, researchers and clinicians understand the technology and applications of Neural Engineering for the detection and diagnosis of Autism Spectrum Disorder (ASD)

Table of contents

• Neural Engineering Techniques for Autism Spectrum Disorder
• Cover
• Title Page
• Copyright
• Contents
• Dedication
• Contributors
• Biography of the editors
• Acknowledgments
• Chapter 1 Prediction of outcome in children with autism spectrum disorders
• Chapter 2 Autism spectrum disorder and sleep: pharmacology management
• Chapter 3 Diagnosis of autism spectrum disorder with convolutional autoencoder and structural MRI images
• Chapter 4 Explainable and scalable machine learning algorithms for detection of autism spectrum disorder using fMRI data
• Chapter 5 Smart architectures for evaluating the autonomy and behaviors of people with autism spectrum disorder in smart homes
• Chapter 6 Data mining and machine learning techniques for early detection in autism spectrum disorder
• Chapter 7 Altered gut–brain signaling in autism spectrum disorders—from biomarkers to possible intervention strategies
• Chapter 8 Machine learning methods for autism spectrum disorder classification
• Chapter 9 Exploring tree-based machine learning methods to predict autism spectrum disorder
• Chapter 10 Blood serum–infrared spectra-based chemometric models for auxiliary diagnosis of autism spectrum disorder
• Chapter 11 A deep learning predictive classifier for autism screening and diagnosis
• Chapter 12 Diagnosis of autism spectrum disorder by causal influence strength learned from resting-state fMRI data
• Chapter 13 Adapting multisystemic therapy to the treatment of disruptive behavior problems in youths with autism spectrum disorder: toward improving the practice of health care
• Chapter 14 Machine learning–based patient-specific processor for the early intervention in autistic children through emotion detection
• Chapter 15 Autism spectrum disorders and anxiety: measurement and treatment
• Chapter 16 Extract image markers of autism using hierarchical feature selection technique
• Chapter 17 Early autism analysis and diagnosis system using task-based fMRI in a response to speech task
• Chapter 18 Identifying brain pathological abnormalities of autism for classification using diffusion tensor imaging
• Index