Neural Engineering Techniques for Autism Spectrum Disorder: Diagnosis and Clinical Analysis, Volume 2

  • 8h 32m
  • Ayman S. El-Baz, Jasjit Suri
  • Elsevier Science and Technology Books, Inc.
  • 2022

Neural Engineering for Autism Spectrum Disorder, Volume Two: Diagnosis and Clinical Analysis 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, magnetic resonance spectroscopy, MRI, fMRI, DTI, video analysis of sensory-motor and 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, fuzzy model and temporal fractal analysis of rest state BOLD signals and brain signals are also presented.

A clinical guide for general practitioners is provided along with a variety of assessment techniques such as magnetic resonance spectroscopy. The book is presented in two volumes, including Volume One: Imaging and Signal Analysis Techniques comprised of two Parts: Autism and Medical Imaging, and Autism and Signal Analysis. Volume Two: Diagnosis and Treatment includes Autism and Clinical Analysis: Diagnosis, and Autism and Clinical Analysis: Treatment.

  • Presents applications of Neural Engineering techniques for diagnosis of Autism Spectrum Disorder (ASD)
  • Includes in-depth technical coverage of assessment techniques, such as the functional and structural networks underlying visuospatial vs. linguistic reasoning in autism
  • Covers treatment techniques for Autism Spectrum Disorder (ASD), including social skills intervention, behavioral treatment, evidence-based treatments, and technical tools such as Magnetic Resonance Spectroscopy for ASD
  • Written by engineers for engineers, computer scientists, researchers and clinicians who need to understand the technology and applications of Neural Engineering for the detection and diagnosis of Autism Spectrum Disorder (ASD)

About the Author

Jasjit S. Suri, PhD, MBA, FIEEE, FAMIBE, FAIUM, FSVM, FAPVS is an innovator, visionary, scientist, and an internationally known world leader in Biomedical Engineering and its management. Dr. Suri received the Director General's Gold medal in 1980, is a Fellow of the Institute of Electrical and Electronics Engineers (2021), American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC (2004), American Institute of Ultrasound in Medicine (2019), Asia Pacific Vascular Society (2020) and recipient of Lifetime Achievement Award from Marquis (2018). He is currently Chairman of AtheroPoint, Roseville, CA, USA, dedicated to imaging technologies for cardiovascular and stroke. He has nearly 30,000 citations, has co-authored 60 books, and 50 US/European Patents.

Dr. El-Baz is a Professor, University Scholar, and Chair of the Bioengineering Department at the University of Louisville, KY. Dr. El-Baz earned his bachelor's and master’s degrees in Electrical Engineering in 1997 and 2001, respectively. He earned his doctoral degree in electrical engineering from the University of Louisville in 2006. In 2009, Dr. El-Baz was named a Coulter Fellow for his contributions to the field of biomedical translational research. Dr. El-Baz has 15 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 450 technical articles (105 journals, 15 books, 50 book chapters, 175 refereed-conference papers, 100 abstracts, and 15 US patents).

In this Book

  • Remote Telehealth Assessments for Autism Spectrum Disorder
  • Maternal Immune Dysregulation and Autism Spectrum Disorder
  • Reading Differences in Eye-Tracking Data as a Marker of High-Functioning Autism in Adults and Comparison to Results from Web-Related Tasks
  • Parents of Children with Autism Spectrum Disorders—Interventions with and for Them
  • Applications of Machine Learning Methods to Assist the Diagnosis of Autism Spectrum Disorder
  • Potential Approaches and Recent Advances in Biomarker Discovery in Autism Spectrum Disorders
  • Detection and Identification of Warning Signs of Autism Spectrum Disorder—Instruments and Strategies for Its Application
  • Machine Learning in Autism Spectrum Disorder Diagnosis and Treatment—Techniques and Applications
  • Inhibition of Lysine-Specific Demethylase 1 Enzyme Activity by TAK-418 as a Novel Therapy for Autism
  • Behavioral Phenotype Features of Autism
  • Development of an Animated Infographic About Autistic Spectrum Disorder
  • Fundamentals of Machine-Learning Modeling for Behavioral Screening and Diagnosis of Autism Spectrum Disorder
  • A Comprehensive Study on Atlas-Based Classification of Autism Spectrum Disorder Using Functional Connectivity Features from Resting-State Functional Magnetic Resonance Imaging
  • Event-Related Potentials and Gamma Oscillations in EEG as Functional Diagnostic Biomarkers and Outcomes in Autism Spectrum Disorder Treatment Research