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Machine Learning

Methods and Applications to Brain Disorders

  • 1st Edition - November 14, 2019
  • Latest edition
  • Editors: Andrea Mechelli, Sandra Vieira
  • Language: English

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 us… Read more

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Description

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.

Key features

  • 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

Readership

Advanced students and researchers in behavioral neuroscience, psychology, psychiatry, psychology and neurology

Table of contents

Part I

1. Introduction to machine learning

2. Main concepts in machine learning

3. Applications of machine learning to brain disorders

Part II

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

11. Autoencoders

12. Principal component analysis

13. K-means clustering

Part III

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

Part IV

19. A step-by-step tutorial on how to build a machine learning model

Review quotes

"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

Product details

  • Edition: 1
  • Latest edition
  • Published: November 14, 2019
  • Language: English

About the editors

AM

Andrea Mechelli

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.
Affiliations and expertise
Professor of Early Intervention in Mental Health at the Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK

SV

Sandra Vieira

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.
Affiliations and expertise
Researcher at the Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK

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