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.
1. Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2 - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Dedication - p. v 6. Table of Contents - p. vii 7. List of contributors - p. xiii 8. About the editors - p. xvii 9. Acknowledgments - p. xix 10. Chapter 1 Remote telehealth assessments for autism spectrum disorder - p. 3 11. Chapter 2 Maternal immune dysregulation and autism spectrum disorder - p. 21 12. 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 - p. 63 13. Chapter 4 Parents of children with autism spectrum disorders: interventions with and for them - p. 81 14. Chapter 5 Applications of machine learning methods to assist the diagnosis of autism spectrum disorder - p. 99 15. Chapter 6 Potential approaches and recent advances in biomarker discovery in autism spectrum disorders - p. 121 16. Chapter 7 Detection and identification of warning signs of autism spectrum disorder: instruments and strategies for its application - p. 147 17. Chapter 8 Machine learning in autism spectrum disorder diagnosis and treatment: techniques and applications - p. 173 18. Chapter 9 Inhibition of lysine-specific demethylase 1 enzyme activity by TAK-418 as a novel therapy for autism - p. 195 19. Chapter 10 Behavioral phenotype features of autism - p. 213 20. Chapter 11 Development of an animated infographic about autistic spectrum disorder - p. 239 21. Chapter 12 Fundamentals of machine-learning modeling for behavioral screening and diagnosis of autism spectrum disorder - p. 253 22. Chapter 13 A comprehensive study on atlas-based classification of autism spectrum disorder using functional connectivity features from resting-state functional magnetic resonance imaging - p. 269 23. Chapter 14 Event-related potentials and gamma oscillations in EEG as functional diagnostic biomarkers and outcomes in autism spectrum disorder treatment research - p. 297 24. Index - p. 321
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.
1. Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2 - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Dedication - p. v 6. Table of Contents - p. vii 7. List of contributors - p. xiii 8. About the editors - p. xvii 9. Acknowledgments - p. xix 10. Chapter 1 Remote telehealth assessments for autism spectrum disorder - p. 3 11. Chapter 2 Maternal immune dysregulation and autism spectrum disorder - p. 21 12. 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 - p. 63 13. Chapter 4 Parents of children with autism spectrum disorders: interventions with and for them - p. 81 14. Chapter 5 Applications of machine learning methods to assist the diagnosis of autism spectrum disorder - p. 99 15. Chapter 6 Potential approaches and recent advances in biomarker discovery in autism spectrum disorders - p. 121 16. Chapter 7 Detection and identification of warning signs of autism spectrum disorder: instruments and strategies for its application - p. 147 17. Chapter 8 Machine learning in autism spectrum disorder diagnosis and treatment: techniques and applications - p. 173 18. Chapter 9 Inhibition of lysine-specific demethylase 1 enzyme activity by TAK-418 as a novel therapy for autism - p. 195 19. Chapter 10 Behavioral phenotype features of autism - p. 213 20. Chapter 11 Development of an animated infographic about autistic spectrum disorder - p. 239 21. Chapter 12 Fundamentals of machine-learning modeling for behavioral screening and diagnosis of autism spectrum disorder - p. 253 22. Chapter 13 A comprehensive study on atlas-based classification of autism spectrum disorder using functional connectivity features from resting-state functional magnetic resonance imaging - p. 269 23. Chapter 14 Event-related potentials and gamma oscillations in EEG as functional diagnostic biomarkers and outcomes in autism spectrum disorder treatment research - p. 297 24. Index - p. 321
