Table of Contents

Preface

Acknowledgement

Dedication

Chapter 1. Machine Learning
Applications to Recognize Autism and Alzheimer's disease

Chapter 2. Neuropathology
and Neuroimaging of Alzheimer¿s disease

Chapter 3. Retinal Imaging
in Alzheimer's Disease

Chapter 4. Clinically
Relevant Depression And Risk Of Alzheimer Disease In Olders: Meta-Analysis Of
Cohort Studies

Chapter 5. The Implication
of Genetic Factors in Autism Spectrum Disorder and Alzheimer's disease

Chapter 6. Nuclear Neurology
of Autism Spectrum Disorders

Chapter 7. Ethylene and ammonia
in neurobehavioral disorders

Chapter 8. Focusing on
Parental Behavior Following a Diagnosis of Autism: The Important Role Parents
Play and How Stress Can Impact this Role

Chapter 9. Visual saliency
for medical imaging and computer-aided diagnosis

Chapter 10. The Early
Diagnosis of Alzheimer's Disease Using Advanced Biomedical Engineering
Technology

Chapter 11. A Local/Regional
Computer Aided System for the Diagnosis of the Mild Cognitive Impairment

Chapter 12. Identifying
Alzheimer's Disease using Feature Reduction of GLCM and Supervised
Classification Techniques

Chapter 13. Current Trends
and Considerations of Alzheimer's Disease

Chapter 14. A Non-Invasive
Image-Based Approach Toward an Early Diagnosis of Autism

Chapter 15. Towards a Robust
CAD System for Early Diagnosis of Autism Using Structural MRI

Chapter 16. Computational
Analysis Techniques: A Case Study on fMRI for Autism Spectrum Disorder

Chapter 17. Autism Diagnosis
Using Task-Based Functional MRI

Autism spectrum disorder (ASD) and Alzheimer's disease (AD) are two significant neurological disorders, which represent the scope of this book. ASD can be characterized by different conditions such as social skills, repetitive behaviors, speech and nonverbal communication, as well as having unique strengths and differences. According to the Centres for Disease Control and Prevention (CDC), the estimate of autism's prevalence is 1 in 68 children in the United States (i.e., 1 in 42 boys and 1 in 189 girls). Additionally, it is estimated that 50,000 teens with autism become adults each year.

AD is a chronic neurodegenerative disorder marked by cognitive and behavioral impairments. Statistically, 42% of AD sufferers are people older than 85 with the percentage decreasing to only 6% for people of 70-74 years old. Although the probability is small, younger individuals may also be affected. Several state-of-the-art machine learning techniques for the early diagnosis of ASD are presented in this book. Moreover, various studies are discussed to demonstrate the formation, cause, and medical treatments for the AD foetal disorder.

Ayman El-Baz is a professor, university scholar and the chair of the Bioengineering Department at the University of Louisville, Kentucky. He earned his BSc and MSc in electrical engineering in 1997 and 2001, respectively. He earned his PhD in electrical engineering from the University of Louisville in 2006, and has 17 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems.

Jasjit S Suri is an innovator, scientist, visionary, industrialist and internationally known world leader in biomedical engineering. He has spent more than 25 years in the field of biomedical engineering and its management, and in 2018 he was awarded the Marquis Life Time Achievement Award for his outstanding contributions and dedication to medical imaging and its management.