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Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners.
- Provides a non-technical introduction to machine learning and applications to brain disorders
- Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches
- Covers the main methodological challenges in the application of machine learning to brain disorders
- Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python
Advanced students and researchers in behavioral neuroscience, psychology, psychiatry, and neurology
1. Introduction to machine learning
2. Main concepts in machine learning
3. Applications of machine learning to brain disorders
4. Linear regression
5. Linear methods for classification
6. Support vector machine
7. Support vector regression
8. Multiple kernel learning
9. Deep neural networks
10. Convolutional neural networks
12. Principal component analysis
13. K-means clustering
14. Dealing with missing data, small sample sizes, and heterogeneity
15. Working with high dimensional feature spaces: the example of voxel-wise encoding models
16. Multimodal integration
17. Bias, noise and interpretability in machine learning: from measurements to features
18. Ethical issues in the application of machine learning to brain disorders
19. A step-by-step tutorial on how to build a machine learning model
- No. of pages:
- © Academic Press 2020
- 15th November 2019
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Andrea Mechelli is a clinical psychologist and a neuroscientist with an interest in the early detection and treatment of mental illness. After studying Psychology at the University of Padua (1999), he completed a PhD in Neurological Sciences at University College London in 2002 and became an academic member of staff at King's College London in 2004. He currently holds the position of Professor of Early Intervention in Mental Health at the Institute of Psychiatry, Psychology & Neuroscience at King's College London. Prof. Mechelli's research involves the application of advanced machine learning methods to clinical, neuroimaging and smartphone data, with the aim of developing and validating novel tools for early detection and treatment.
Professor of Early Intervention in Mental Health at the Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK
Sandra Vieira is a postdoctoral researcher at the Institute Psychiatry, Psychology & Neuroscience (King's College London). After completing a degree in Psychology (2009) and a Masters in Clinical Psychology (2011) at the University of Coimbra, she joined the Institute Psychiatry, Psychology & Neuroscience. Here she obtained a Masters in Psychiatric Research in 2014 and a PhD in Psychosis Studies in 2019. Her research focuses on the integration of advanced machine learning methods and multi-modal neuroimaging to investigate the neural basis of mental illness and develop imaging-based clinical tools.
Researcher at the Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK
"This is a fantastic resource for researchers and clinicians interested in the application of artificial intelligence to brain disorders. The most up-to-date approaches are covered, using a rigorous yet accessible language. The step-by-step practical guide will be particularly useful to those taking their first steps in this field."-- Qiyong Gong, MD, PhD