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.
1. 1: Introduction - p. 237 2. 2: Background knowledge and motivation - p. 240 3. 3: The two-step method for cyclic and large-scale causal discovery - p. 243 4. 4: Identifiability of causal model with two-step method - p. 246 5. 5: Autism spectrum disorder diagnosis on ABIDE dataset - p. 248 6. 6: Discussions - p. 262 7. 7: Conclusions and future work - p. 264 8. Acknowledgements - p. 264 9. 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 - p. 269 10. Chapter 14 Machine learning–based patient-specific processor for the early intervention in autistic children through emotion detection - p. 287 11. Chapter 15 Autism spectrum disorders and anxiety: measurement and treatment - p. 315 12. Chapter 16 Extract image markers of autism using hierarchical feature selection technique - p. 333 13. Chapter 17 Early autism analysis and diagnosis system using task-based fMRI in a response to speech task - p. 345 14. Chapter 18 Identifying brain pathological abnormalities of autism for classification using diffusion tensor imaging - p. 361 15. Index - p. 377
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.
1. 1: Introduction - p. 237 2. 2: Background knowledge and motivation - p. 240 3. 3: The two-step method for cyclic and large-scale causal discovery - p. 243 4. 4: Identifiability of causal model with two-step method - p. 246 5. 5: Autism spectrum disorder diagnosis on ABIDE dataset - p. 248 6. 6: Discussions - p. 262 7. 7: Conclusions and future work - p. 264 8. Acknowledgements - p. 264 9. 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 - p. 269 10. Chapter 14 Machine learning–based patient-specific processor for the early intervention in autistic children through emotion detection - p. 287 11. Chapter 15 Autism spectrum disorders and anxiety: measurement and treatment - p. 315 12. Chapter 16 Extract image markers of autism using hierarchical feature selection technique - p. 333 13. Chapter 17 Early autism analysis and diagnosis system using task-based fMRI in a response to speech task - p. 345 14. Chapter 18 Identifying brain pathological abnormalities of autism for classification using diffusion tensor imaging - p. 361 15. Index - p. 377
