Generative Artificial Intelligence in Neuroimaging: Methods and Applications offers a clear and practical guide for biomedical engineers and data scientists interested in using generative AI to improve neuroimaging techniques. The book explains key generative models, such as GANs, VAEs, and diffusion models, and shows how these methods can enhance data analysis, improve image quality, and support personalized medicine. It includes real-world examples that demonstrate the successful use of AI in diagnosing diseases and developing brain-computer interfaces. The book also discusses important ethical considerations and best practices for using AI responsibly in healthcare.
Finally, the book addresses technical challenges and highlights future research opportunities in the field of AI and biomedical engineering. Whether you are an experienced professional or a new researcher, this book provides the knowledge and tools needed to advance neuroimaging and contribute to better patient care.
1. Chapter 1. Introduction to neuroscience imaging modalities: fMRI, EEG, MEG, and their challenges - p. 3 2. Chapter 2. A primer on generative AI: Key concepts and architectures - p. 27 3. Chapter 3. Data handling and preprocessing in neuroimaging for generative AI. Addressing noise, artifacts, and variability - p. 49 4. Part II. Methodological advancements - p. P2 5. Chapter 4. Generative adversarial networks (GANs) for neuroimaging networks - p. 69 6. Chapter 5. Variational Autoencoders (VAEs) for neuroimaging dimensionality reduction, data augmentation and latent space analysis - p. 95 7. Chapter 6. Diffusion models for high-fidelity neuroimage generation and enhancement - p. 117 8. Chapter 7. Flow-based generative models for neuroimaging: Density estimation and data augmentation - p. 145 9. Chapter 8. Hybrid and novel generative models for neuroimaging: Exploring emerging architectures and combinations - p. 167 10. Part III. Applications in neuroscience - p. P3 11. Chapter 9. Generative AI for disease diagnosis and prognosis (Alzheimer's, Parkinson's, stroke, etc.) - p. 209 12. Chapter 10. Generative AI for brain connectivity and network analysis: Understanding brain organization and its alterations in disease - p. 225 13. Chapter 11. Generative AI for simulating brain development and aging: Modeling normal and pathological processes - p. 245 14. Chapter 12. Generative AI for cognitive neuroscience: Investigating the neural basis of cognition - p. 281 15. Part IV. Challenges and future directions - p. P4 16. Chapter 13. Challenges and limitations: Addressing data scarcity, interpretability, computational costs, and ethical concerns - p. 313 17. Chapter 14. Generative Al for neurological diseases: A review toward future directions - p. 335 18. Index - p. 355
Generative Artificial Intelligence in Neuroimaging: Methods and Applications offers a clear and practical guide for biomedical engineers and data scientists interested in using generative AI to improve neuroimaging techniques. The book explains key generative models, such as GANs, VAEs, and diffusion models, and shows how these methods can enhance data analysis, improve image quality, and support personalized medicine. It includes real-world examples that demonstrate the successful use of AI in diagnosing diseases and developing brain-computer interfaces. The book also discusses important ethical considerations and best practices for using AI responsibly in healthcare.
Finally, the book addresses technical challenges and highlights future research opportunities in the field of AI and biomedical engineering. Whether you are an experienced professional or a new researcher, this book provides the knowledge and tools needed to advance neuroimaging and contribute to better patient care.
1. Chapter 1. Introduction to neuroscience imaging modalities: fMRI, EEG, MEG, and their challenges - p. 3 2. Chapter 2. A primer on generative AI: Key concepts and architectures - p. 27 3. Chapter 3. Data handling and preprocessing in neuroimaging for generative AI. Addressing noise, artifacts, and variability - p. 49 4. Part II. Methodological advancements - p. P2 5. Chapter 4. Generative adversarial networks (GANs) for neuroimaging networks - p. 69 6. Chapter 5. Variational Autoencoders (VAEs) for neuroimaging dimensionality reduction, data augmentation and latent space analysis - p. 95 7. Chapter 6. Diffusion models for high-fidelity neuroimage generation and enhancement - p. 117 8. Chapter 7. Flow-based generative models for neuroimaging: Density estimation and data augmentation - p. 145 9. Chapter 8. Hybrid and novel generative models for neuroimaging: Exploring emerging architectures and combinations - p. 167 10. Part III. Applications in neuroscience - p. P3 11. Chapter 9. Generative AI for disease diagnosis and prognosis (Alzheimer's, Parkinson's, stroke, etc.) - p. 209 12. Chapter 10. Generative AI for brain connectivity and network analysis: Understanding brain organization and its alterations in disease - p. 225 13. Chapter 11. Generative AI for simulating brain development and aging: Modeling normal and pathological processes - p. 245 14. Chapter 12. Generative AI for cognitive neuroscience: Investigating the neural basis of cognition - p. 281 15. Part IV. Challenges and future directions - p. P4 16. Chapter 13. Challenges and limitations: Addressing data scarcity, interpretability, computational costs, and ethical concerns - p. 313 17. Chapter 14. Generative Al for neurological diseases: A review toward future directions - p. 335 18. Index - p. 355
