Contents

• Preface to the Series
• Preface
• Acknowledgements
  Olivier Colliot.
• Abbreviations
• Contributors
• I. Machine Learning Fundamentals
  • 1. A Non-technical Introduction to Machine Learning
    Olivier Colliot.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. A Bit of History
    • 3. Main Machine Learning Concepts
    • 4. Conclusion
    • Acknowledgements
    • References
  • 2. Classic Machine Learning Methods
    Johann Faouzi and Olivier Colliot.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Notations
    • 3. Nearest Neighbor Methods
    • 4. Linear Regression
    • 5. Logistic Regression
    • 6. Overfitting and Regularization
    • 7. Penalized Models
    • 8. Support Vector Machine
    • 9. Multiclass Classification
    • 10. Decision Functions with Normal Distributions
    • 11. Tree-Based Methods
    • 12. Clustering
    • 13. Dimensionality Reduction
    • 14. Kernel Methods
    • 15. Conclusion
    • Acknowledgements
    • References
  • 3. Deep Learning: Basics and Convolutional Neural Networks (CNNs)
    Maria Vakalopoulou, Stergios Christodoulidis, Ninon Burgos, Olivier Colliot, and Vincent Lepetit.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Deep Feedforward Networks
    • 3. Optimization of Deep Neural Networks
    • 4. Convolutional Neural Networks
    • 5. Autoencoders
    • 6. Conclusion
    • Acknowledgements
    • References
  • 4. Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research
    Susmita Das, Amara Tariq, Thiago Santos, Sai Sandeep Kantareddy, and Imon Banerjee.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Popular RNN Architectures
    • 3. Tips and Tricks for RNN Training
    • 4. RNN Applications in Language Modeling
    • 5. Conclusion
    • References
  • 5. Generative Adversarial Networks and Other Generative Models
    Markus Wenzel.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Generative Models
    • 3. Generative Adversarial Networks
    • 4. Selected GAN Architectures You Should Know
    • 5. Other Generative Models
    • Acknowledgements
    • References
  • 6. Transformers and Visual Transformers
    Robin Courant, Maika Edberg, Nicolas Dufour, and Vicky Kalogeiton.
    Published online: July 23, 2023.
    • 1. Attention
    • 2. Visual Transformers
    • 3. Improvements over the Vision Transformer
    • 4. Vision Transformers for Tasks Other than Classification
    • 5. Vision Transformers for Other Domains
    • 6. Conclusion
    • References
• II. Data
  • 7. Clinical Assessment of Brain Disorders
    Stéphane Epelbaum and Federica Cacciamani.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. The Neurological Examination
    • 3. Neuropsychological Assessment
    • 4. Clinical Examination by Pathology
    • 5. Conclusion
    • Acknowledgments
    • References
  • 8. Neuroimaging in Machine Learning for Brain Disorders
    Ninon Burgos.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Manipulating Neuroimages
    • 3. Magnetic Resonance Imaging
    • 4. X-Ray Imaging
    • 5. Nuclear Imaging
    • 6. Conclusion
    • Acknowledgements
    • References
  • 9. Electroencephalography and Magnetoencephalography
    Marie-Constance Corsi.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Basic Principles
    • 3. M/EEG Experiments
    • 4. Data Analysis
    • 5. Feature Extraction and Selection
    • 6. M/EEG and Brain Disorders
    • 7. Conclusion
    • Acknowledgements
    • References
  • 10. Working with Omics Data: An Interdisciplinary Challenge at the Crossroads of Biology and Computer Science
    Thibault Poinsignon, Pierre Poulain, Mélina Gallopin, and Gaëlle Lelandais.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. What Are Omics Data?
    • 3. How to Generate Omics Data?
    • 4. Which Applications for Omics Data?
    • 5. Conclusion
    • Acknowledgments
    • References
  • 11. Electronic Health Records as Source of Research Data
    Wenjuan Wang, Davide Ferrari, Gabriel Haddon-Hill, and Vasa Curcin.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Data Quality in EHR
    • 3. Clinical Coding Systems
    • 4. Protection and Governance of EHR Data
    • 5. Conclusion
    • Acknowledgements
    • References
  • 12. Mobile Devices, Connected Objects, and Sensors
    Sirenia Lizbeth Mondragón-González, Eric Burguière, and Karim N’diaye.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Data Available from Mobile Device, Sensors, and Connected Objects for Brain Disorders
    • 3. Applications to Brain Disorders
    • 4. Considerations and Challenges
    • 5. Discussion
    • Acknowledgments
    • References
• III. Methodologies
  • 13. Medical Image Segmentation Using Deep Learning
    Han Liu, Dewei Hu, Hao Li, and Ipek Oguz.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Methods
    • 3. Medical Image Segmentation Challenges
    • 4. Conclusion
    • References
  • 14. Image Registration: Fundamentals and Recent Advances Based on Deep Learning
    Min Chen, Nicholas J. Tustison, Rohit Jena, and James C. Gee.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Fundamentals of Image Registration
    • 3. Learning-Based Models for Registration
    • 4. Registration in the Study of Brain Disorders
    • 5. Conclusion
    • Acknowledgements
    • References
  • 15. Computer-Aided Diagnosis and Prediction in Brain Disorders
    Vikram Venkatraghavan, Sebastian R. van der Voort, Daniel Bos, Marion Smits, Frederik Barkhof, Wiro J. Niessen, Stefan Klein, and Esther E. Bron.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Method Evaluation
    • 3. Clinical Impact
    • 4. Roadmap for Clinical Translation
    • 5. Final Summary and Conclusion
    • 6. Conflict of Interest
    • Acknowledgements
    • References
  • 16. Subtyping Brain Diseases from Imaging Data
    Junhao Wen, Erdem Varol, Zhijian Yang, Gyujoon Hwang, Dominique Dwyer, Anahita Fathi Kazerooni, Paris Alexandros Lalousis, and Christos Davatzikos.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Methodological Development Using Machine Learning and Neuroimaging
    • 3. Application to Brain Disorders
    • 4. Conclusion
    • Acknowledgements
    • References
  • 17. Data-Driven Disease Progression Modeling
    Neil P. Oxtoby.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Data Preprocessing
    • 3. Models for Cross-Sectional Data
    • 4. Models for Longitudinal Data
    • 5. Conclusion
    • Acknowledgements
    • Appendix
    • References
  • 18. Computational Pathology for Brain Disorders
    Gabriel Jiménez and Daniel Racoceanu.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Understanding Histological Images
    • 3. Histopathological Image Analysis
    • 4. Methods in Brain Computational Pathology
    • 5. Perspectives
    • Acknowledgements
    • References
  • 19. Integration of Multimodal Data
    Marco Lorenzi, Marie Deprez, Irene Balelli, Ana L. Aguila, and Andre Altmann.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Methodological Background
    • 3. Bayesian Frameworks for Latent Variable Models
    • 4. Biologically Inspired Data Integration Strategies
    • 5. Conclusions
    • Acknowledgements
    • References
• IV. Validation and Datasets
  • 20. Evaluating Machine Learning Models and Their Diagnostic Value
    Gael Varoquaux and Olivier Colliot.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Performance Metrics
    • 3. Evaluation Strategies
    • 4. Conclusion
    • Acknowledgements
    • Appendix
    • References
  • 21. Reproducibility in Machine Learning for Medical Imaging
    Olivier Colliot, Elina Thibeau-Sutre, and Ninon Burgos.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. The Polysemy of Reproducibility
    • 3. Exact Reproducibility
    • 4. Statistical Reproducibility
    • 5. Conceptual Reproducibility
    • 6. Measurement Reproducibility
    • 7. Discussion
    • Acknowledgements
    • References
  • 22. Interpretability of Machine Learning Methods Applied to Neuroimaging
    Elina Thibeau-Sutre, Sasha Collin, Ninon Burgos, and Olivier Colliot.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Interpretability Methods
    • 3. Application of Interpretability Methods to Neuroimaging Data
    • 4. Limitations and Recommendations
    • 5. Conclusion
    • Acknowledgements
    • Appendices
    • References
  • 23. A Regulatory Science Perspective on Performance Assessment of Machine Learning Algorithms in Imaging
    Weijie Chen, Daniel Krainak, Berkman Sahiner, and Nicholas Petrick.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Regulatory Framework
    • 3. Study Design and Data Collection
    • 4. Algorithm Documentation
    • 5. Reference Standard
    • 6. Standalone Performance Assessment
    • 7. Clinical Performance Assessment with a Reader Study
    • 8. Statistical Analysis
    • 9. Summary Remarks
    • Acknowledgments
    • References
  • 24. Main Existing Datasets for Open Brain Research on Humans
    Baptiste Couvy-Duchesne, Simona Bottani, Etienne Camenen, Fang Fang, Mulusew Fikere, Juliana Gonzalez-Astudillo, Joshua Harvey, Ravi Hassanaly, Irfahan Kassam, Penelope A. Lind, Qianwei Liu, Yi Lu, Marta Nabais, Thibault Rolland, Julia Sidorenko, Lachlan Strike, and Margie Wright.
    Published online: July 23, 2023.
    • 1. Aims
    • 2. Introduction
    • 3. Neuroimaging
    • 4. EEG/MEG
    • 5. Genetics
    • 6. Genomics
    • 7. Electronic Health Records
    • 8. Smartphone and Sensors
    • Acknowledgments and Fundings
    • References
• V. Disorders
  • 25. Machine Learning for Alzheimer’s Disease and Related Dementias
    Marc Modat, David M. Cash, Liane Dos Santos Canas, Martina Bocchetta, and Sébastien Ourselin.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Features and Markers of Dementia
    • 3. Challenges for Machine Learning
    • 4. Machine Learning Developments
    • 5. What Is Next?
    • 6. Conclusion
    • Acknowledgements
    • References
  • 26. Machine Learning for Parkinson’s Disease and Related Disorders
    Johann Faouzi, Olivier Colliot, and Jean-Christophe Corvol.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Diagnosis
    • 3. Symptom Detection and Quantification
    • 4. Disease Progression
    • 5. Treatment Adjustment and Adverse Event Prevention
    • 6. Conclusion
    • Acknowledgments
    • References
  • 27. Machine Learning in Neuroimaging of Epilepsy
    Hyo Min Lee, Ravnoor Singh Gill, Neda Bernasconi, and Andrea Bernasconi.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Lesion Mapping
    • 3. Prediction of Clinical Outcomes
    • 4. Conclusion and Future Perspectives
    • References
  • 28. Machine Learning in Multiple Sclerosis
    Bas Jasperse and Frederik Barkhof.
    Published online: July 23, 2023.
    • 1. Introduction to MS
    • 2. Machine Learning to Aid in the Differential Diagnosis of MS
    • 3. Machine Learning for Lesion Segmentation and Quantification
    • 4. Machine Learning to Improve Detection of Tissue Properties from Conventional MRI Sequences
    • 5. Machine Learning to Characterize Neurodegeneration in MS
    • 6. Machine Learning to Predict Disease Progression
    • 7. Concluding Remarks
    • Acknowledgments
    • References
  • 29. Machine Learning for Cerebrovascular Disorders
    Yannan Yu and David Yen-Ting Chen.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Ischemic Stroke
    • 3. Hemorrhagic Stroke or Intracranial Hemorrhage
    • 4. Cerebral Vascular Malformation
    • 5. Intracranial Aneurysms
    • 6. Cerebral Small Vessel Disease
    • 7. Conclusion
    • Acknowledgments
    • References
  • 30. The Role of Artificial Intelligence in Neuro-oncology Imaging
    Jennifer Soun, Lu-Aung Yosuke Masudathaya, Arabdha Biswas, and Daniel S. Chow.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Brain Tumor Epidemiology
    • 3. Present Challenges with Brain Tumor Imaging
    • 4. Potential Applications for Machine Learning
    • 5. Summary
    • Acknowledgments
    • References
  • 31. Machine Learning for Neurodevelopmental Disorders
    Clara Moreau, Christine Deruelle, and Guillaume Auzias.
    Published online: July 23, 2023.
    • 1. A Brief Introduction to Neurodevelopmental Disorders
    • 2. What Are the Main Challenges in These Conditions That Can Be Addressed Using Machine Learning?
    • 3. A Non-exhaustive Survey of Existing Papers on Machine Learning for NDDs and Their Limitations
    • 4. Open Challenges and Conclusion
    • Acknowledgments
    • References
  • 32. Machine Learning and Brain Imaging for Psychiatric Disorders: New Perspectives
    Ivan Brossollet, Quentin Gallet, Pauline Favre, and Josselin Houenou.
    Published online: July 23, 2023.
    • 1. Introduction
    • 2. Challenges for Machine Learning in Psychiatry
    • 3. MRI and Machine Learning in Psychiatry: State of the Art (Table 1)
    • 4. Limitations and Perspectives
    • Acknowledgments
    • References
• Disclosure Statement of the Editor