Main description:

This Open Access volume provides readers with an up-to-date and comprehensive guide to both methodological and applicative aspects of machine learning (ML) for brain disorders. The chapters in this book are organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the main types of data used to characterize brain disorders, including clinical assessments, neuroimaging, electro- and magnetoencephalography, genetics and omics data, electronic health records, mobile devices, connected objects and sensors. Part Three covers the core methodologies of ML in brain disorders and the latest techniques used to study them. Part Four is dedicated to validation and datasets, and Part Five discusses applications of ML to various neurological and psychiatric disorders. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.

Comprehensive and cutting, Machine Learning for Brain Disorders is a valuable resource for researchers and graduate students who are new to this field, as well as experienced researchers who would like to further expand their knowledge in this area. This book will be useful to students and researchers from various backgrounds such as engineers, computer scientists, neurologists, psychiatrists, radiologists, and neuroscientists.

Contents:

Preface to the Series...
Acknowledgements...
Preface...
Table of Contents...
Contributing Authors...

Part I Machine Learning Fundamentals

1. A Non-Technical Introduction to Machine Learning
Olivier Colliot

2. Classic Machine Learning Methods
Johann Faouzi and Olivier Colliot

3. Deep Learning: Basics and Convolutional Neural Networks (CNN)
Maria Vakalopoulou, Stergios Christodoulidis, Ninon Burgos, Olivier Colliot, and Vincent Lepetit

4. Recurrent Neural Networks (RNN) - Architectures, Training Tricks, and Introduction to Influential Research
Susmita Das, Amara Tariq, Thiago Santos, Sai Sandeep Kantareddy, and Imon Banerjee

5. Generative Adversarial Networks and Other Generative Models
Markus Wenzel

6. Transformers and Visual Transformers
Robin Courant, Maika Edberg, Nicolas Dufour, and Vicky Kalogeiton

Part II Data

7. Clinical Assessment of Brain Disorders
Stephane Epelbaum and Federica Cacciamani

8. Neuroimaging in Machine Learning for Brain Disorders
Ninon Burgos

9. Electroencephalography and Magnetoencephalography
Marie-Constance Corsi

10. Working with Omics Data, An Interdisciplinary Challenge at the Crossroads of Biology and Computer Science
Thibault Poinsignon, Pierre Poulain, Melina Gallopin, and Gaelle Lelandais

11. Electronic Health Records as Source of Research Data
Wenjuan Wang, Davide Ferrari, Gabriel Haddon-Hill, and Vasa Curcin

12. Mobile Devices, Connected Objects, and Sensors
Sirenia Lizbeth Mondragon-Gonzalez, Eric Burguiere, and Karim N'Diaye

Part III Methodologies

13. Medical Image Segmentation using Deep Learning
Han Liu, Dewei Hu, Hao Li, and Ipek Oguz

14. Image Registration: Fundamentals and Recent Advances Based on Deep Learning
Min Chen, Nicholas J. Tustison, Rohit Jena, and James C. Gee

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

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

17. Data-Driven Disease Progression Modelling
Neil P. Oxtoby

18. Computational Pathology for Brain Disorders
Gabriel Jimenez and Daniel Racoceanu

19. Integration of Multimodal Data
Marco Lorenzi, Marie Deprez, Irene Balelli, Ana L. Aguila, and Andre Altmann

Part IV Validation and Datasets

20. Evaluating Machine Learning Models and their Diagnostic Value
Gael Varoquaux and Olivier Colliot

21. Reproducibility in Machine Learning for Medical Imaging
Olivier Colliot, Elina Thibeau-Sutre, and Ninon Burgos

22. Interpretability of Machine Learning Methods Applied to Neuroimaging
Elina Thibeau-Sutre, Sasha Collin, Ninon Burgos, and Olivier Colliot

23. A Regulatory Science Perspective on Performance Assessment of Machine Learning Algorithms in Imaging
Weijie Chen, Daniel Krainak, Berkman Sahiner, and Nicholas Petrick

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

Part V Disorders

25. Machine Learning for Alzheimer's Disease and Related Dementias
Marc Modat, David M. Cash, Liane Dos Santos Canas, Martina Bocchetta, and Sebastien Ourselin

26. Machine Learning for Parkinson's Disease and Related Disorders
Johann Faouzi, Olivier Colliot, and Jean-Christophe Corvol

27. Machine Learning in Neuroimaging of Epilepsy
Hyo Min Lee, Ravnoor Singh Gill, Neda Bernasconi, and Andrea Bernasconi

28. Machine Learning in Multiple Sclerosis
Bas Jasperse and Frederik Barkhof

29. Machine Learning for Cerebrovascular Disorders
Yannan Yu and David Yen-Ting Chen

30. The Role of Artificial Intelligence in Neuro-Oncology Imaging
Jennifer Soun, Lu-Aung Yosuke Masudathaya, Arabdha Biswas, and Daniel S. Chow

31. Machine Learning for Neurodevelopmental Disorders
Clara Moreau, Christine Deruelle, and Guillaume Auzias

32. Machine Learning and Brain Imaging for Psychiatric Disorders: New Perspectives
Ivan Brossollet, Quentin Gallet, Pauline Favre, and Josselin Houenou

Disclosure of Interests of the Editor...
Abbreviations...
Index...