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, psychology and neurology
Chapter 1: Introduction to machine learning
Chapter 2: Main concepts in machine learning
Chapter 3: Applications of machine learning to brain disorders
Chapter 4: Linear regression
Chapter 5: Linear methods for classification
Chapter 6: Support vector machine
Chapter 7: Support vector regression
Chapter 8: Multiple kernel learning
Chapter 9: Deep neural networks
Chapter 10: Convolutional neural networks
Chapter 11: Autoencoders
Chapter 12: Principal component analysis
Chapter 13: K-means clustering
Chapter 14: Dealing with missing data, small sample sizes, and heterogeneity
Chapter 15: Working with high dimensional feature spaces: the example of voxel-wise encoding models
Chapter 16: Multimodal integration
Chapter 17: Bias, noise and interpretability in machine learning: from measurements to features
Chapter 18: Ethical issues in the application of machine learning to brain disorders
Chapter 19: A step-by-step tutorial on how to build a machine learning model
- No. of pages:
- © Academic Press 2020
- 1st November 2019
- Academic Press
- Paperback 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, United Kingdom
Sandra Vieira is a researcher at the Institute of Psychiatry at King’s College London. She received her first Masters from Clinical Psychology in 2011 from the University of Coimbra, Portugal, and her second Masters in Psychiatric Research in 2014 from King’s College London. United Kingdom. She is currently completing her PhD in Psychosis Studies at the Institute of Psychiatry, Psychology & Neuroscience (King’s College London). Her research focuses on the application of advanced machine learning methods to investigate brain abnormalities in psychosis.
Researcher at the Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom.