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.
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Table of Contents
1. Remote Telehealth Assessments for Autism Spectrum Disorder 2. Maternal Immune Dysregulation and Autism Spectrum Disorder 3. Reading differences in eye-tracking data as a marker of high-functioning autism in adults and comparison to results from web-related tasks 4. Parents of Children with Autism Spectrum Disorders: Intervention with and for Them 5. Applications of Machine Learning to Assist the Diagnosis of Autism Spectrum Disorder 6. Potential Approaches and recent advances in biomarker discovery in autism spectrum disorder 7. Detection and Identification of warning signs of autism spectrum disorder: instruments and strategies for its application 8. Machine Learning in Autism Spectrum Disorder Diagnosis and treatment: techniques and applications 9. Inhibition of LSD1 Enzyme activity by TAK-418 as a novel therapy for autism 10. Behavioral Phenotype features of Autism Spectrum Disorder 11. Development of an animated infographic about autism spectrum disorder 12. Fundamentals of Machine Learning modelling in Behavioral screening and diagnosis of autism spectrum disorder 13. A comprehensive study on Atlas-based classification of autism spectrum disorder using functional connectivity features from resting state fMRI 14. Event-related potentials and gamma oscillations in EEG as functional diagnostic biomarkers and outcomes in autism spectrum disorder treatment research
Authors
Jasjit Suri Chairman, AtheroPoint LLC.Dr. Jasjit Suri, PhD, MBA, is an innovator, visionary, scientist, and internationally known world leader. Dr Suri received the Director General's Gold medal in 1980 and Fellow of (i) American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC, (ii) Institute of Electrical and Electronics Engineers, (iii) American Institute of Ultrasound in Medicine, (iv) Society of Vascular Medicine, (v) Asia Pacific Vascular Society, and (vi) Asia Association of Artificial Intelligence. Dr. Suri was honored with life time achievement awards by Marcus, NJ, USA and Graphics Era University, Dehradun, India. He has published nearly 300 peer-reviewed Artificial Intelligence articles, nearly 2000 Google Scholar Publications, 100 books, and 100 innovations/trademarks leading to an H-index of nearly 100 with about 43,000 citations. He has held positions as chairman of AtheroPoint, CA, USA, IEEE Denver section, Colorado, USA, and advisory board member to healthcare industries and several universities in the United States of America and abroad.
Ayman S. El-Baz University of Louisville. 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).
