User guide

Note

You can download the user guide and examples for offline viewing.

This user guide is especially directed towards grad students with a background in computer science, machine learning, computational biology or neuroscience. However, less or more advanced readers can also find interesting pieces of information throughout the guide.

Table of contents

• 1. Introduction
• 2. What is nilearn?
• 3. Using nilearn for the first time
  • 3.1. First steps with nilearn
  • 3.2. Learning with the API references
  • 3.3. Learning with examples
  • 3.4. Finding help
• 4. Machine learning applications to Neuroimaging
  • 4.1. References
• 5. Decoding and MVPA: predicting from brain images
  • 5.1. An introduction to decoding
  • 5.2. Choosing the right predictive model for neuroimaging
  • 5.3. FREM: fast ensembling of regularized models for robust decoding
  • 5.4. SpaceNet: decoding with spatial structure for better maps
  • 5.5. Searchlight : finding voxels containing information
  • 5.6. Running scikit-learn functions for more control on the analysis
• 6. Functional connectivity and resting state
  • 6.1. Extracting times series to build a functional connectome
  • 6.2. Connectome extraction: inverse covariance for direct connections
  • 6.3. Extracting functional brain networks: ICA and related
  • 6.4. Region Extraction for better brain parcellations
  • 6.5. Clustering to parcellate the brain in regions
• 7. Plotting brain images
  • 7.1. Different plotting functions
  • 7.2. Different display modes
  • 7.3. Available Colormaps
  • 7.4. Adding overlays, edges, contours, contour fillings, markers, scale bar
  • 7.5. Displaying or saving to an image file
  • 7.6. Surface plotting
  • 7.7. Interactive plots
• 8. Analyzing fMRI using GLMs
  • 8.1. An introduction to GLMs in fMRI statistical analysis
  • 8.2. First level models
  • 8.3. Second level models
  • 8.4. Difference in meanings between different toolboxes
• 9. Manipulation brain volumes with nilearn
  • 9.1. Input and output: neuroimaging data representation
  • 9.2. Manipulating images: resampling, smoothing, masking, ROIs…
  • 9.3. From neuroimaging volumes to data matrices: the masker objects
• 10. Advanced usage: manual pipelines and scaling up
  • 10.1. Building your own neuroimaging machine-learning pipeline
  • 10.2. Downloading statistical maps from the Neurovault repository