- Preface to the Series
- Preface
- Acknowledgements
- Abbreviations
- Contributors
- I. Machine Learning Fundamentals
- 1. A Non-technical Introduction to Machine Learning
- 2. Classic Machine Learning Methods
- 3. Deep Learning: Basics and Convolutional Neural Networks (CNNs)
- 4. Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research
- 5. Generative Adversarial Networks and Other Generative Models
- 6. Transformers and Visual Transformers
- II. Data
- 7. Clinical Assessment of Brain Disorders
- 8. Neuroimaging in Machine Learning for Brain Disorders
- 9. Electroencephalography and Magnetoencephalography
- 10. Working with Omics Data: An Interdisciplinary Challenge at the Crossroads of Biology and Computer Science
- 11. Electronic Health Records as Source of Research Data
- 12. Mobile Devices, Connected Objects, and Sensors
- III. Methodologies
- 13. Medical Image Segmentation Using Deep Learning
- 14. Image Registration: Fundamentals and Recent Advances Based on Deep Learning
- 15. Computer-Aided Diagnosis and Prediction in Brain Disorders
- 16. Subtyping Brain Diseases from Imaging Data
- 17. Data-Driven Disease Progression Modeling
- 18. Computational Pathology for Brain Disorders
- 19. Integration of Multimodal Data
- IV. Validation and Datasets
- 20. Evaluating Machine Learning Models and Their Diagnostic Value
- 21. Reproducibility in Machine Learning for Medical Imaging
- 22. Interpretability of Machine Learning Methods Applied to Neuroimaging
- 23. A Regulatory Science Perspective on Performance Assessment of Machine Learning Algorithms in Imaging
- 24. Main Existing Datasets for Open Brain Research on Humans
- V. Disorders
- 25. Machine Learning for Alzheimer’s Disease and Related Dementias
- 26. Machine Learning for Parkinson’s Disease and Related Disorders
- 27. Machine Learning in Neuroimaging of Epilepsy
- 28. Machine Learning in Multiple Sclerosis
- 29. Machine Learning for Cerebrovascular Disorders
- 30. The Role of Artificial Intelligence in Neuro-oncology Imaging
- 31. Machine Learning for Neurodevelopmental Disorders
- 32. Machine Learning and Brain Imaging for Psychiatric Disorders: New Perspectives
- Disclosure Statement of the Editor