Data Science for Neuroimaging: An Introduction

Cover
Contents
Preface
1. Introduction
	1.1 Why Data Science?
	1.2 Who This Book Is For
	1.3 How We Wrote This Book
	1.4 How You Might Read This Book
	1.5 Additional Resources
PART I. The Data Science Toolbox
	2. The Unix Operating System
		2.1 Using Unix
		2.2 More About Unix
		2.3 Additional Resources
	3. Version Control
		3.1 Getting Started with Git
		3.2 Working with Git at the First Level: Tracking Changes That You Make
		3.3 Working with Git at the Second Level: Branching and Merging
		3.4 Working with Git at the Third Level: Collaborating with Others
		3.5 Additional Resources
	4. Computational Environments and Computational Containers
		4.1 Creating Virtual Environments with Conda
		4.2 Containerization with Docker
		4.3 Setting Up
		4.4 Additional Resources
PART II. Programming
	5. A brief Introduction to Python
		5.1 What Is Python?
		5.2 Variables and Basic Types
		5.3 Collections
		5.4 Everything in Python Is an Object
		5.5 Control Flow
		5.6 Namespaces and Imports
		5.7 Functions
		5.8 Classes
		5.9 Additional Resources
	6. The Python Environment
		6.1 Choosing a Good Editor
		6.2 Debugging
		6.3 Testing
		6.4 Profiling Code
		6.5 Summary
		6.6 Additional Resources
	7. Sharing Code with Others
		7.1 What Should Be Shareable?
		7.2 From Notebook to Module
		7.3 From Module to Package
		7.4 The Setup File
		7.5 A Complete Project
		7.6 Summary
		7.7 Additional Resources
PART III. Scientific Computing
	8. The Scientific Python Ecosystem
		8.1 Numerical Computing in Python
		8.2 Introducing NumPy
		8.3 Additional Resources
	9. Manipulating Tabular Data with Pandas
		9.1 Summarizing DataFrames
		9.2 Indexing into DataFrames
		9.3 Computing with DataFrames
		9.4 Joining Different Tables
		9.5 Additional Resources
	10. Visualizing Data with Python
		10.1 Creating Pictures from Data
		10.2 Scatter Plots
		10.3 Statistical Visualizations
		10.4 Additional Resources
PART IV. Neuroimaging in Python
	11. Data Science Tools for Neuroimaging
		11.1 Neuroimaging in Python
		11.2 The Brain Imaging Data Structure Standard
		11.3 Additional Resources
	12. Reading Neuroimaging Data with NiBabel
		12.1 Assessing MRI Data Quality
		12.2 Additional Resources
	13. Using Nibabel to Align Different Measurements
		13.1 Coordinate Frames
		13.2 Multiplying Matrices in Python
		13.3 Using the Affine
		13.4 Additional Resources
PART V. Image Processing
	14. Image Processing
		14.1 Images Are Arrays
		14.2 Images Can Have Two Dimensions or More
		14.3 Images Can Have Other Special Dimensions
		14.4 Operations with Images
		14.5 Additional Resources
	15. Image Segmentation
		15.1 Intensity-Based Segmentation
		15.2 Edge-Based Segmentation
		15.3 Additional Resources
	16. Image Registration
		16.1 Affine Registration
		16.2 Summary
		16.3 Additional Resources
PART VI. Machine Learning
	17. The Core Concepts of Machine Learning
		17.1 What Is Machine Learning?
		17.2 Supervised versus Unsupervised Learning
		17.3 Supervised Learning: Classification versus Regression
		17.4 Unsupervised Learning: Clustering and Dimensionality Reduction
		17.5 Additional Resources
	18. The Scikit-Learn Package
		18.1 The ABIDE II Data set
		18.2 Regression Example: Brain-Age Prediction
		18.3 Classification Example: Autism Classification
		18.4 Clustering Example: Are There Neural Subtypes of Autism?
		18.5 Additional Resources
	19. Overfitting
		19.1 Understanding Overfitting
		19.2 Additional Resources
	20. Validation
		20.1 Cross-Validation
		20.2 Learning and Validation Curves
		20.3 Additional Resources
	21. Model Selection
		21.1 Bias and Variance
		21.2 Regularization
		21.3 Beyond Linear Regression
		21.4 Additional Resources
	22. Deep Learning
		22.1 Artificial Neural Networks
		22.2 Learning through Gradient Descent and Back Propagation
		22.3 Introducing Keras
		22.4 Convolutional Neural Networks
		22.5 Additional Resources
PART VII. Appendices
	Appendix 1: Solutions to Exercises
		A1.1 The Data Science Toolbox
		A1.2 Programming
		A1.3 Scientific Computing
		A1.4 Neuroimaging in Python
		A1.5 Image Processing
		A1.6 Machine Learning
	Appendix 2: ndslib Function Reference
Bibliography
Index