User guide: table of contents

• 1. Introduction: nilearn in a nutshell
  • 1.1. What is nilearn: MVPA, decoding, predictive models, functional connectivity
    • 1.1.1. Why is machine learning relevant to NeuroImaging? A few examples!
    • 1.1.2. Glossary: machine learning vocabulary
  • 1.2. Installing nilearn
  • 1.3. Python for NeuroImaging, a quick start
    • 1.3.1. Your first steps with nilearn
    • 1.3.2. Scientific computing with Python
      • 1.3.2.1. Basic numerics
      • 1.3.2.2. Scikit-learn: machine learning in Python
    • 1.3.3. Finding help
• 2. Decoding and MVPA: predicting from brain images
  • 2.1. A decoding tutorial
    • 2.1.1. Data loading and preparation
      • 2.1.1.1. The Haxby 2001 experiment
      • 2.1.1.2. Loading the data into Python
    • 2.1.2. Performing the decoding analysis
      • 2.1.2.1. The prediction engine
        • 2.1.2.1.1. An estimator object
        • 2.1.2.1.2. Applying it to data: fit (train) and predict (test)
      • 2.1.2.2. Measuring prediction performance
        • 2.1.2.2.1. Cross-validation
        • 2.1.2.2.2. Choosing a good cross-validation strategy
        • 2.1.2.2.3. Choice of the prediction accuracy measure
        • 2.1.2.2.4. Measuring the chance level
      • 2.1.2.3. Visualizing the decoder’s weights
    • 2.1.3. Decoding without a mask: Anova-SVM
      • 2.1.3.1. Dimension reduction with feature selection
      • 2.1.3.2. Visualizing the results
    • 2.1.4. Going further with scikit-learn
      • 2.1.4.1. Changing the prediction engine
      • 2.1.4.2. Changing the feature selection
  • 2.2. Choosing the right predictive model
    • 2.2.1. Predictions: regression, classification and multi-class
      • 2.2.1.1. Regression
      • 2.2.1.2. Classification: two classes or multi-class
    • 2.2.2. Setting estimator parameters
    • 2.2.3. Different linear models
  • 2.3. Decoding on simulated data
    • 2.3.1. Simple NeuroImaging-like simulations
    • 2.3.2. Running various estimators
  • 2.4. Searchlight : finding voxels containing information
    • 2.4.1. Principle of the Searchlight
    • 2.4.2. Preparing the data
      • 2.4.2.1. Loading
      • 2.4.2.2. Reshaping the data
      • 2.4.2.3. Masking
    • 2.4.3. Third Step: Setting up the searchlight
      • 2.4.3.1. Classifier
      • 2.4.3.2. Score function
      • 2.4.3.3. Cross validation
    • 2.4.4. Running Searchlight
    • 2.4.5. Visualization
      • 2.4.5.1. Searchlight
      • 2.4.5.2. Comparing to massively univariate analysis: F_score or SPM
• 3. Functional connectivity and resting state
  • 3.1. Extracting times series to build a functional connectome
    • 3.1.1. Time-series from a brain parcellation or “MaxProb” atlas
      • 3.1.1.1. Brain parcellations
      • 3.1.1.2. Extracting signals on a parcellation
    • 3.1.2. Time-series from a probabilistic atlas
      • 3.1.2.1. Probabilistic atlases
      • 3.1.2.2. Extracting signals from a probabilistic atlas
    • 3.1.3. A functional connectome: a graph of interactions
  • 3.2. Connectome extraction: inverse covariance for direct connections
    • 3.2.1. Sparse inverse covariance for functional connectomes
    • 3.2.2. Sparse inverse covariance on multiple subjects
    • 3.2.3. Comparing the different approaches on simulated data
  • 3.3. Extracting resting-state networks with ICA
    • 3.3.1. Data preparation: retrieving example data
    • 3.3.2. Applying CanICA
    • 3.3.3. Visualizing the results
  • 3.4. Parcellating the brain in regions
    • 3.4.1. A resting-state dataset
    • 3.4.2. Preprocessing: loading and masking
    • 3.4.3. Applying Ward clustering
      • 3.4.3.1. Running the Ward algorithm
    • 3.4.4. Post-Processing and visualizing the parcels
      • 3.4.4.1. Unmasking
      • 3.4.4.2. Label visualization
      • 3.4.4.3. Compressed picture
• 4. Image manipulation and visualization
  • 4.1. Plotting brain images
    • 4.1.1. Different plotting functions
    • 4.1.2. Different display modes
    • 4.1.3. Adding overlays, edges and contours
    • 4.1.4. Saving to an image file
  • 4.2. Data preparation: loading and basic transformation
    • 4.2.1. The concept of “masker” objects
    • 4.2.2. NiftiMasker: loading, masking and filtering
      • 4.2.2.1. Custom data loading
      • 4.2.2.2. Controlling how the mask is computed from the data
        • 4.2.2.2.1. Computing the mask
      • 4.2.2.3. Common data preparation steps: resampling, smoothing, filtering
        • 4.2.2.3.1. Resampling
        • 4.2.2.3.2. Smoothing
        • 4.2.2.3.3. Temporal Filtering
      • 4.2.2.4. Inverse transform: unmasking data
    • 4.2.3. Extraction of signals from regions: NiftiLabelsMasker, NiftiMapsMasker.
      • 4.2.3.1. Regions definition
      • 4.2.3.2. NiftiMapsMasker Usage
      • 4.2.3.3. NiftiLabelsMasker Usage
  • 4.3. Manipulating brain volume: input/output, masking, ROIs, smoothing...
    • 4.3.1. Loading data
      • 4.3.1.1. Fetching datasets
      • 4.3.1.2. Loading your own data
    • 4.3.2. Understanding Neuroimaging data
      • 4.3.2.1. Nifti and Analyze files
      • 4.3.2.2. Niimg-like objects
      • 4.3.2.3. Text files: phenotype or behavior
    • 4.3.3. Masking data manually
      • 4.3.3.1. Extracting a brain mask
        • 4.3.3.1.1. nilearn.masking.compute_epi_mask
      • 4.3.3.2. From 4D to 2D arrays
    • 4.3.4. Functions for data preparation steps
    • 4.3.5. Image operations: creating a ROI mask manually
      • 4.3.5.1. Smoothing
      • 4.3.5.2. Selecting features
      • 4.3.5.3. Thresholding
      • 4.3.5.4. Mask intersection
      • 4.3.5.5. Mask dilation
      • 4.3.5.6. Extracting connected components
      • 4.3.5.7. Saving the result
• 5. Advanced usage: manual pipelines and scaling up
  • 5.1. Building your own neuroimaging machine-learning pipeline
    • 5.1.1. Data loading and preprocessing
      • 5.1.1.1. Downloading the data
      • 5.1.1.2. Loading non image data: experiment description
      • 5.1.1.3. Masking the data: from 4D image to 2D array
        • 5.1.1.3.1. Applying a mask
        • 5.1.1.3.2. Automatically computing a mask
    • 5.1.2. Applying a scikit-learn machine learning method
    • 5.1.3. Unmasking (inverse_transform)
    • 5.1.4. Visualizing results
    • 5.1.5. Going further
• 6. Reference documentation: all nilearn functions
  • 6.1. nilearn.datasets: Automatic Dataset Fetching
    • 6.1.1. nilearn.datasets.fetch_atlas_craddock_2012
    • 6.1.2. nilearn.datasets.fetch_atlas_destrieux_2009
    • 6.1.3. nilearn.datasets.fetch_atlas_harvard_oxford
    • 6.1.4. nilearn.datasets.fetch_atlas_msdl
    • 6.1.5. nilearn.datasets.fetch_atlas_power_2011
    • 6.1.6. nilearn.datasets.fetch_atlas_smith_2009
    • 6.1.7. nilearn.datasets.fetch_atlas_yeo_2011
    • 6.1.8. nilearn.datasets.fetch_abide_pcp
    • 6.1.9. nilearn.datasets.fetch_adhd
    • 6.1.10. nilearn.datasets.fetch_haxby
    • 6.1.11. nilearn.datasets.fetch_haxby_simple
    • 6.1.12. nilearn.datasets.fetch_icbm152_2009
    • 6.1.13. nilearn.datasets.fetch_localizer_contrasts
    • 6.1.14. nilearn.datasets.fetch_localizer_calculation_task
    • 6.1.15. nilearn.datasets.fetch_miyawaki2008
    • 6.1.16. nilearn.datasets.fetch_nyu_rest
    • 6.1.17. nilearn.datasets.fetch_oasis_vbm
  • 6.2. nilearn.decoding: Decoding
  • 6.3. nilearn.decompositon: Multivariate decompositions
  • 6.4. nilearn.image: Image processing and resampling utilities
  • 6.5. nilearn.input_data: Loading and Processing files easily
  • 6.6. nilearn.masking: Data Masking Utilities
    • 6.6.1. nilearn.masking.compute_epi_mask
    • 6.6.2. nilearn.masking.compute_multi_epi_mask
    • 6.6.3. nilearn.masking.compute_background_mask
    • 6.6.4. nilearn.masking.compute_multi_background_mask
    • 6.6.5. nilearn.masking.apply_mask
    • 6.6.6. nilearn.masking.unmask
  • 6.7. nilearn.region: Operating on regions
    • 6.7.1. nilearn.region.img_to_signals_labels
    • 6.7.2. nilearn.region.signals_to_img_labels
    • 6.7.3. nilearn.region.img_to_signals_maps
    • 6.7.4. nilearn.region.signals_to_img_maps
  • 6.8. nilearn.mass_univariate: Mass-univariate analysis
  • 6.9. nilearn.plotting: Plotting brain data
    • 6.9.1. nilearn.plotting.displays.OrthoSlicer
  • 6.10. nilearn.signal: Preprocessing Time Series
    • 6.10.1. nilearn.signal.clean