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Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2
Diagnosis and Clinical Analysis
- 1st Edition - October 17, 2022
- Editors: Jasjit S. Suri, Ayman S. El-Baz
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 4 4 2 1 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 4 4 2 2 - 7
Neural Engineering for Autism Spectrum Disorder, Volume Two: Diagnosis and Clinical Analysis presents the latest advances in neural engineering and biomedical engineering as applie… Read more
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Request a sales quoteNeural 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)
Biomedical Engineers and researchers in neural engineering, medical imaging, and neural networks. Students, researchers and clinicians in autism, and a variety of other specialties
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- About the editors
- Acknowledgments
- Part 1: Autism and clinical analysis: Diagnosis
- Chapter 1. Remote telehealth assessments for autism spectrum disorder
- Abstract
- 1.1 Introduction
- 1.2 Telehealth assessments
- 1.3 Implications
- References
- Chapter 2. Maternal immune dysregulation and autism spectrum disorder
- Abstract
- 2.1 Introduction
- 2.2 Cytokines and chemokines (overview)
- 2.3 Autoantibodies reactive to brain antigens
- 2.4 Concluding remarks
- References
- Chapter 3. Reading differences in eye-tracking data as a marker of high-functioning autism in adults and comparison to results from web-related tasks
- Abstract
- 3.1 Introduction
- 3.2 Related work
- 3.3 Automated detection of high-functioning autism in adults with eye-tracking data from web tasks
- 3.4 The proposed approach
- 3.5 Experiments
- 3.6 Results
- 3.7 Discussion
- 3.8 Conclusion
- 3.9 Open data
- References
- Chapter 4. Parents of children with autism spectrum disorders: interventions with and for them
- Abstract
- 4.1 Introduction
- 4.2 Parent participation in early comprehensive intervention programs
- 4.3 Programs for the development of parent–child interaction
- 4.4 Parent–child intervention based on anxiety reduction
- 4.5 Conclusion
- References
- Chapter 5. Applications of machine learning methods to assist the diagnosis of autism spectrum disorder
- Abstract
- 5.1 Introduction
- 5.2 Background and related work
- 5.3 Data description
- 5.4 Unsupervised learning: clustering of eye-tracking scanpaths
- 5.5 Supervised learning: classification model
- 5.6 Demo application
- 5.7 Limitations
- 5.8 Conclusions
- References
- Chapter 6. Potential approaches and recent advances in biomarker discovery in autism spectrum disorders
- Abstract
- 6.1 Introduction
- 6.2 Diagnosis and categories of biomarkers
- 6.3 Conclusion
- References
- Chapter 7. Detection and identification of warning signs of autism spectrum disorder: instruments and strategies for its application
- Abstract
- 7.1 Introduction
- 7.2 Importance of early detection
- 7.3 Differential diagnosis
- 7.4 Detection and screening process
- 7.5 Symptom detection vs Diagnosis
- 7.6 Impact on the family of detecting and diagnosing Autism Spectrum Disorder
- 7.7 Choice of screening instruments according to age of application and cultural environment of implementation
- 7.8 Discussion
- 7.9 Conclusions
- References
- Chapter 8. Machine learning in autism spectrum disorder diagnosis and treatment: techniques and applications
- Abstract
- 8.1 Introduction
- 8.2 Utilizing machine learning algorithms to diagnose autism spectrum disorder
- 8.3 Feature analysis
- 8.4 Technological applications
- 8.5 Conclusion
- References
- Chapter 9. Inhibition of lysine-specific demethylase 1 enzyme activity by TAK-418 as a novel therapy for autism
- Abstract
- 9.1 Introduction
- 9.2 Lysine-specific demethylase 1 as the potential therapeutic target for autism spectrum disorder
- 9.3 Discovery of the “enzyme activity-specific” inhibitors of lysine-specific demethylase 1
- 9.4 Discussion
- 9.5 Conclusion
- References
- Chapter 10. Behavioral phenotype features of autism
- Abstract
- 10.1 Introduction
- 10.2 Eye movement behavior phenotype of autism
- 10.3 Action behavior phenotype
- 10.4 Drawing behavior phenotype
- 10.5 Discussion and conclusion
- References
- Chapter 11. Development of an animated infographic about autistic spectrum disorder
- Abstract
- 11.1 Introduction
- 11.2 Infographics
- 11.3 Results
- 11.4 Discussion
- 11.5 Conclusion
- References
- Chapter 12. Fundamentals of machine-learning modeling for behavioral screening and diagnosis of autism spectrum disorder
- Abstract
- 12.1 Introduction
- 12.2 Current autism spectrum disorder screening and diagnostic practices
- 12.3 Machine learning-based assessment of autism spectrum disorder
- 12.4 Conclusion
- References
- Chapter 13. A comprehensive study on atlas-based classification of autism spectrum disorder using functional connectivity features from resting-state functional magnetic resonance imaging
- Abstract
- 13.1 Introduction
- 13.2 Overview of functional magnetic resonance imaging
- 13.3 Literature review
- 13.4 Materials and methods
- 13.5 Experimental results and analysis
- 13.6 Conclusion
- 13.7 Future work
- References
- Chapter 14. Event-related potentials and gamma oscillations in EEG as functional diagnostic biomarkers and outcomes in autism spectrum disorder treatment research
- Abstract
- 14.1 Introduction
- 14.2 Neurophysiological biomarkers
- 14.3 Gamma oscillations as potential neuromarkers in neurodevelopmental disorders
- 14.4 ERP and induced gamma oscillations in facial categorization task in ASD, ADHD, and TD groups
- 14.5 Evoked and induced EEG data acquisition and processing in Kanizsa oddball task
- 14.6 Conclusions
- References
- Index
- No. of pages: 346
- Language: English
- Edition: 1
- Published: October 17, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128244210
- eBook ISBN: 9780128244227
JS
Jasjit S. Suri
Dr. Jasjit Suri, PhD, MBA, is a renowned innovator and scientist. He received the Director General’s Gold Medal in 1980 and is a Fellow of several prestigious organizations, including the American Institute of Medical and Biological Engineering and the Institute of Electrical and Electronics Engineers. Dr. Suri has been honored with lifetime achievement awards from Marcus, NJ, USA, and Graphics Era University, India. He has published nearly 300 peer-reviewed AI articles, 100 books, and holds 100 innovations/trademarks, achieving an H-index of nearly 100 with about 43,000 citations. Dr. Suri has served as chairman of AtheroPoint, IEEE Denver section, and as an advisory board member to various healthcare industries and universities globally.
Affiliations and expertise
Chairman, AtheroPoint LLC, USAAS
Ayman S. El-Baz
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).
Affiliations and expertise
University of Louisville, USARead Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2 on ScienceDirect