User guide: table of contents

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• 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!
  • 1.2. 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. An introduction to decoding
    • 2.1.1. Loading and preparing the data
      • 2.1.1.1. The Haxby 2001 experiment
      • 2.1.1.2. Loading the data into nilearn
    • 2.1.2. Performing a simple decoding analysis
      • 2.1.2.1. A few definitions
      • 2.1.2.2. A first estimator
      • 2.1.2.3. Decoding made easy
      • 2.1.2.4. Measuring prediction performance
        • 2.1.2.4.1. Cross-validation
        • 2.1.2.4.2. Choosing a good cross-validation strategy
        • 2.1.2.4.3. Choice of the prediction accuracy measure
        • 2.1.2.4.4. Prediction accuracy at chance using simple strategies
      • 2.1.2.5. 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
  • 2.2. Choosing the right predictive model for neuroimaging
    • 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. Different linear models
    • 2.2.3. Setting estimator parameters
    • 2.2.4. Bagging several models
    • 2.2.5. References
  • 2.3. FREM: fast ensembling of regularized models for robust decoding
    • 2.3.1. FREM pipeline
    • 2.3.2. Empirical comparisons
      • 2.3.2.1. Decoding performance increase on Haxby dataset
      • 2.3.2.2. Spatial regularization of decoding maps on mixed gambles study
    • 2.3.3. References
  • 2.4. SpaceNet: decoding with spatial structure for better maps
    • 2.4.1. The SpaceNet decoder
    • 2.4.2. Related example
    • 2.4.3. References
  • 2.5. Searchlight : finding voxels containing information
    • 2.5.1. Principle of the Searchlight
    • 2.5.2. Preparing the data
      • 2.5.2.1. Masking
    • 2.5.3. Setting up the searchlight
      • 2.5.3.1. Classifier
      • 2.5.3.2. Score function
      • 2.5.3.3. Cross validation
      • 2.5.3.4. Sphere radius
    • 2.5.4. Visualization
      • 2.5.4.1. Searchlight
      • 2.5.4.2. Comparing to massively univariate analysis: F_score or SPM
    • 2.5.5. References
  • 2.6. Running scikit-learn functions for more control on the analysis
    • 2.6.1. Performing decoding with scikit-learn
      • 2.6.1.1. Using scikit-learn estimators
      • 2.6.1.2. Cross-validation with scikit-learn
      • 2.6.1.3. Measuring the chance level
    • 2.6.2. Going further with scikit-learn
      • 2.6.2.1. Decoding without a mask: Anova-SVM using scikit-learn
      • 2.6.2.2. Using any other model in the pipeline
    • 2.6.3. Setting estimator parameters
• 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
    • 3.1.4. A functional connectome: extracting coordinates of regions
  • 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
    • 3.2.4. Linking total and direct interactions at the group level
  • 3.3. Extracting functional brain networks: ICA and related
    • 3.3.1. Multi-subject ICA: CanICA
      • 3.3.1.1. Objective
      • 3.3.1.2. Fitting CanICA model with nilearn
      • 3.3.1.3. Visualizing results
      • 3.3.1.4. Interpreting such components
    • 3.3.2. An alternative to ICA: Dictionary learning
  • 3.4. Region Extraction for better brain parcellations
    • 3.4.1. Fetching movie-watching based functional datasets
    • 3.4.2. Brain maps using Dictionary Learning
    • 3.4.3. Visualization of Dictionary Learning maps
    • 3.4.4. Region Extraction with Dictionary Learning maps
    • 3.4.5. Visualization of Region Extraction results
    • 3.4.6. Computing functional connectivity matrices
    • 3.4.7. Visualization of functional connectivity matrices
    • 3.4.8. Validating results
  • 3.5. Clustering to parcellate the brain in regions
    • 3.5.1. Data loading: movie-watching data
    • 3.5.2. Applying clustering
    • 3.5.3. Using and visualizing the resulting parcellation
      • 3.5.3.1. Visualizing the parcellation
      • 3.5.3.2. Compressed representation
• 4. Plotting brain images
  • 4.1. Different plotting functions
  • 4.2. Different display modes
  • 4.3. Available Colormaps
  • 4.4. Adding overlays, edges, contours, contour fillings, markers, scale bar
  • 4.5. Displaying or saving to an image file
  • 4.6. Surface plotting
  • 4.7. Interactive plots
    • 4.7.1. 3D Plots of statistical maps or atlases on the cortical surface
    • 4.7.2. 3D Plots of connectomes
    • 4.7.3. 3D Plots of markers
    • 4.7.4. Interactive visualization of statistical map slices
• 5. Analyzing fMRI using GLMs
  • 5.1. An introduction to GLMs in fMRI statistical analysis
    • 5.1.1. A primer on BOLD-fMRI data analysis
      • 5.1.1.1. What is fMRI ?
      • 5.1.1.2. fMRI data modelling
      • 5.1.1.3. fMRI statistical analysis
      • 5.1.1.4. Multiple Comparisons
  • 5.2. First level models
    • 5.2.1. HRF models
    • 5.2.2. Design matrix: event-based and time series-based
      • 5.2.2.1. Event-based
      • 5.2.2.2. Time series-based
    • 5.2.3. Fitting a first level model
      • 5.2.3.1. Computing contrasts
    • 5.2.4. Extracting predicted time series and residuals
    • 5.2.5. Surface-based analysis
  • 5.3. Second level models
    • 5.3.1. Fitting a second level model
    • 5.3.2. Thresholding statistical maps
    • 5.3.3. Multiple comparisons correction
    • 5.3.4. Voxel based morphometry
• 6. Manipulation brain volumes with nilearn
  • 6.1. Input and output: neuroimaging data representation
    • 6.1.1. Inputing data: file names or image objects
      • 6.1.1.1. File names and objects, 3D and 4D images
      • 6.1.1.2. File name matching: “globbing” and user path expansion
    • 6.1.2. Fetching open datasets from Internet
    • 6.1.3. Understanding neuroimaging data
      • 6.1.3.1. Nifti and Analyze data
      • 6.1.3.2. Niimg-like objects
      • 6.1.3.3. Text files: phenotype or behavior
  • 6.2. Manipulating images: resampling, smoothing, masking, ROIs…
    • 6.2.1. Functions for data preparation and image transformation
    • 6.2.2. Resampling images
      • 6.2.2.1. Resampling one image to match another one
      • 6.2.2.2. Resampling to a specific target affine, shape, or resolution
    • 6.2.3. Accessing individual volumes in 4D images
    • 6.2.4. Computing and applying spatial masks
      • 6.2.4.1. Extracting a brain mask
      • 6.2.4.2. Masking data: from 4D Nifti images to 2D data arrays
    • 6.2.5. Image operations: creating a ROI mask manually
  • 6.3. From neuroimaging volumes to data matrices: the masker objects
    • 6.3.1. The concept of “masker” objects
    • 6.3.2. NiftiMasker: applying a mask to load time-series
      • 6.3.2.1. Custom data loading: loading only the first 100 time points
      • 6.3.2.2. Controlling how the mask is computed from the data
        • 6.3.2.2.1. Visualizing the computed mask
        • 6.3.2.2.2. Different masking strategies
        • 6.3.2.2.3. Extra mask parameters: opening, cutoff…
      • 6.3.2.3. Common data preparation steps: smoothing, filtering, resampling
        • 6.3.2.3.1. Smoothing
        • 6.3.2.3.2. Temporal Filtering and confound removal
        • 6.3.2.3.3. Resampling: resizing and changing resolutions of images
      • 6.3.2.4. Inverse transform: unmasking data
    • 6.3.3. Extraction of signals from regions: NiftiLabelsMasker, NiftiMapsMasker
      • 6.3.3.1. Regions definition
      • 6.3.3.2. NiftiLabelsMasker Usage
      • 6.3.3.3. NiftiMapsMasker Usage
    • 6.3.4. Extraction of signals from seeds: NiftiSpheresMasker
• 7. Advanced usage: manual pipelines and scaling up
  • 7.1. Building your own neuroimaging machine-learning pipeline
    • 7.1.1. Data loading and preprocessing
      • 7.1.1.1. Downloading the data
      • 7.1.1.2. Loading non image data: experiment description
      • 7.1.1.3. Masking the data: from 4D image to 2D array
        • 7.1.1.3.1. Applying a mask
        • 7.1.1.3.2. Automatically computing a mask
    • 7.1.2. Applying a scikit-learn machine learning method
    • 7.1.3. Unmasking (inverse_transform)
    • 7.1.4. Visualizing results
    • 7.1.5. Going further
  • 7.2. Downloading statistical maps from the Neurovault repository
    • 7.2.1. Specific images or collections
    • 7.2.2. Selection filters
    • 7.2.3. Output
    • 7.2.4. Neurosynth annotations
    • 7.2.5. Examples using Neurovault
    • 7.2.6. References
• 8. Reference documentation: all nilearn functions
  • 8.1. nilearn.connectome: Functional Connectivity
    • 8.1.1. nilearn.connectome.ConnectivityMeasure
      • 8.1.1.1. Examples using nilearn.connectome.ConnectivityMeasure
    • 8.1.2. nilearn.connectome.GroupSparseCovariance
    • 8.1.3. nilearn.connectome.GroupSparseCovarianceCV
      • 8.1.3.1. Examples using nilearn.connectome.GroupSparseCovarianceCV
    • 8.1.4. nilearn.connectome.sym_matrix_to_vec
    • 8.1.5. nilearn.connectome.vec_to_sym_matrix
    • 8.1.6. nilearn.connectome.group_sparse_covariance
    • 8.1.7. nilearn.connectome.cov_to_corr
    • 8.1.8. nilearn.connectome.prec_to_partial
  • 8.2. nilearn.datasets: Automatic Dataset Fetching
    • 8.2.1. nilearn.datasets.fetch_atlas_craddock_2012
    • 8.2.2. nilearn.datasets.fetch_atlas_destrieux_2009
      • 8.2.2.1. Examples using nilearn.datasets.fetch_atlas_destrieux_2009
    • 8.2.3. nilearn.datasets.fetch_atlas_harvard_oxford
      • 8.2.3.1. Examples using nilearn.datasets.fetch_atlas_harvard_oxford
    • 8.2.4. nilearn.datasets.fetch_atlas_juelich
      • 8.2.4.1. Examples using nilearn.datasets.fetch_atlas_juelich
    • 8.2.5. nilearn.datasets.fetch_atlas_msdl
      • 8.2.5.1. Examples using nilearn.datasets.fetch_atlas_msdl
    • 8.2.6. nilearn.datasets.fetch_atlas_difumo
      • 8.2.6.1. Examples using nilearn.datasets.fetch_atlas_difumo
    • 8.2.7. nilearn.datasets.fetch_coords_power_2011
      • 8.2.7.1. Examples using nilearn.datasets.fetch_coords_power_2011
    • 8.2.8. nilearn.datasets.fetch_coords_seitzman_2018
    • 8.2.9. nilearn.datasets.fetch_atlas_smith_2009
      • 8.2.9.1. Examples using nilearn.datasets.fetch_atlas_smith_2009
    • 8.2.10. nilearn.datasets.fetch_atlas_yeo_2011
      • 8.2.10.1. Examples using nilearn.datasets.fetch_atlas_yeo_2011
    • 8.2.11. nilearn.datasets.fetch_atlas_aal
    • 8.2.12. nilearn.datasets.fetch_atlas_basc_multiscale_2015
      • 8.2.12.1. Examples using nilearn.datasets.fetch_atlas_basc_multiscale_2015
    • 8.2.13. nilearn.datasets.fetch_atlas_allen_2011
      • 8.2.13.1. Examples using nilearn.datasets.fetch_atlas_allen_2011
    • 8.2.14. nilearn.datasets.fetch_atlas_pauli_2017
      • 8.2.14.1. Examples using nilearn.datasets.fetch_atlas_pauli_2017
    • 8.2.15. nilearn.datasets.fetch_coords_dosenbach_2010
      • 8.2.15.1. Examples using nilearn.datasets.fetch_coords_dosenbach_2010
    • 8.2.16. nilearn.datasets.fetch_abide_pcp
    • 8.2.17. nilearn.datasets.fetch_adhd
      • 8.2.17.1. Examples using nilearn.datasets.fetch_adhd
    • 8.2.18. nilearn.datasets.fetch_development_fmri
      • 8.2.18.1. Examples using nilearn.datasets.fetch_development_fmri
    • 8.2.19. nilearn.datasets.fetch_haxby
      • 8.2.19.1. Examples using nilearn.datasets.fetch_haxby
    • 8.2.20. nilearn.datasets.fetch_icbm152_2009
      • 8.2.20.1. Examples using nilearn.datasets.fetch_icbm152_2009
    • 8.2.21. nilearn.datasets.fetch_icbm152_brain_gm_mask
      • 8.2.21.1. Examples using nilearn.datasets.fetch_icbm152_brain_gm_mask
    • 8.2.22. nilearn.datasets.fetch_localizer_button_task
      • 8.2.22.1. Examples using nilearn.datasets.fetch_localizer_button_task
    • 8.2.23. nilearn.datasets.fetch_localizer_contrasts
      • 8.2.23.1. Examples using nilearn.datasets.fetch_localizer_contrasts
    • 8.2.24. nilearn.datasets.fetch_localizer_calculation_task
      • 8.2.24.1. Examples using nilearn.datasets.fetch_localizer_calculation_task
    • 8.2.25. nilearn.datasets.fetch_miyawaki2008
      • 8.2.25.1. Examples using nilearn.datasets.fetch_miyawaki2008
    • 8.2.26. nilearn.datasets.fetch_surf_nki_enhanced
      • 8.2.26.1. Examples using nilearn.datasets.fetch_surf_nki_enhanced
    • 8.2.27. nilearn.datasets.fetch_surf_fsaverage
      • 8.2.27.1. Examples using nilearn.datasets.fetch_surf_fsaverage
    • 8.2.28. nilearn.datasets.fetch_atlas_surf_destrieux
      • 8.2.28.1. Examples using nilearn.datasets.fetch_atlas_surf_destrieux
    • 8.2.29. nilearn.datasets.fetch_atlas_talairach
    • 8.2.30. nilearn.datasets.fetch_atlas_schaefer_2018
    • 8.2.31. nilearn.datasets.fetch_oasis_vbm
      • 8.2.31.1. Examples using nilearn.datasets.fetch_oasis_vbm
    • 8.2.32. nilearn.datasets.fetch_megatrawls_netmats
      • 8.2.32.1. Examples using nilearn.datasets.fetch_megatrawls_netmats
    • 8.2.33. nilearn.datasets.fetch_cobre
    • 8.2.34. nilearn.datasets.fetch_neurovault
      • 8.2.34.1. Examples using nilearn.datasets.fetch_neurovault
    • 8.2.35. nilearn.datasets.fetch_neurovault_ids
      • 8.2.35.1. Examples using nilearn.datasets.fetch_neurovault_ids
    • 8.2.36. nilearn.datasets.fetch_neurovault_auditory_computation_task
      • 8.2.36.1. Examples using nilearn.datasets.fetch_neurovault_auditory_computation_task
    • 8.2.37. nilearn.datasets.fetch_neurovault_motor_task
      • 8.2.37.1. Examples using nilearn.datasets.fetch_neurovault_motor_task
    • 8.2.38. nilearn.datasets.get_data_dirs
      • 8.2.38.1. Examples using nilearn.datasets.get_data_dirs
    • 8.2.39. nilearn.datasets.load_mni152_template
      • 8.2.39.1. Examples using nilearn.datasets.load_mni152_template
    • 8.2.40. nilearn.datasets.load_mni152_gm_template
    • 8.2.41. nilearn.datasets.load_mni152_wm_template
    • 8.2.42. nilearn.datasets.load_mni152_brain_mask
      • 8.2.42.1. Examples using nilearn.datasets.load_mni152_brain_mask
    • 8.2.43. nilearn.datasets.load_mni152_gm_mask
    • 8.2.44. nilearn.datasets.load_mni152_wm_mask
    • 8.2.45. nilearn.datasets.fetch_language_localizer_demo_dataset
      • 8.2.45.1. Examples using nilearn.datasets.fetch_language_localizer_demo_dataset
    • 8.2.46. nilearn.datasets.fetch_bids_langloc_dataset
    • 8.2.47. nilearn.datasets.fetch_openneuro_dataset_index
      • 8.2.47.1. Examples using nilearn.datasets.fetch_openneuro_dataset_index
    • 8.2.48. nilearn.datasets.select_from_index
      • 8.2.48.1. Examples using nilearn.datasets.select_from_index
    • 8.2.49. nilearn.datasets.patch_openneuro_dataset
    • 8.2.50. nilearn.datasets.fetch_openneuro_dataset
      • 8.2.50.1. Examples using nilearn.datasets.fetch_openneuro_dataset
    • 8.2.51. nilearn.datasets.fetch_localizer_first_level
      • 8.2.51.1. Examples using nilearn.datasets.fetch_localizer_first_level
    • 8.2.52. nilearn.datasets.fetch_spm_auditory
      • 8.2.52.1. Examples using nilearn.datasets.fetch_spm_auditory
    • 8.2.53. nilearn.datasets.fetch_spm_multimodal_fmri
      • 8.2.53.1. Examples using nilearn.datasets.fetch_spm_multimodal_fmri
    • 8.2.54. nilearn.datasets.fetch_fiac_first_level
      • 8.2.54.1. Examples using nilearn.datasets.fetch_fiac_first_level
  • 8.3. nilearn.decoding: Decoding
    • 8.3.1. nilearn.decoding.Decoder
      • 8.3.1.1. Examples using nilearn.decoding.Decoder
    • 8.3.2. nilearn.decoding.DecoderRegressor
      • 8.3.2.1. Examples using nilearn.decoding.DecoderRegressor
    • 8.3.3. nilearn.decoding.FREMClassifier
      • 8.3.3.1. Examples using nilearn.decoding.FREMClassifier
    • 8.3.4. nilearn.decoding.FREMRegressor
      • 8.3.4.1. Examples using nilearn.decoding.FREMRegressor
    • 8.3.5. nilearn.decoding.SpaceNetClassifier
    • 8.3.6. nilearn.decoding.SpaceNetRegressor
      • 8.3.6.1. Examples using nilearn.decoding.SpaceNetRegressor
    • 8.3.7. nilearn.decoding.SearchLight
      • 8.3.7.1. Examples using nilearn.decoding.SearchLight
  • 8.4. nilearn.decomposition: Multivariate Decompositions
    • 8.4.1. nilearn.decomposition.CanICA
      • 8.4.1.1. Examples using nilearn.decomposition.CanICA
    • 8.4.2. nilearn.decomposition.DictLearning
      • 8.4.2.1. Examples using nilearn.decomposition.DictLearning
  • 8.5. nilearn.image: Image Processing and Resampling Utilities
    • 8.5.1. nilearn.image.binarize_img
    • 8.5.2. nilearn.image.clean_img
      • 8.5.2.1. Examples using nilearn.image.clean_img
    • 8.5.3. nilearn.image.concat_imgs
      • 8.5.3.1. Examples using nilearn.image.concat_imgs
    • 8.5.4. nilearn.image.coord_transform
      • 8.5.4.1. Examples using nilearn.image.coord_transform
    • 8.5.5. nilearn.image.copy_img
    • 8.5.6. nilearn.image.crop_img
    • 8.5.7. nilearn.image.get_data
      • 8.5.7.1. Examples using nilearn.image.get_data
    • 8.5.8. nilearn.image.high_variance_confounds
      • 8.5.8.1. Examples using nilearn.image.high_variance_confounds
    • 8.5.9. nilearn.image.index_img
      • 8.5.9.1. Examples using nilearn.image.index_img
    • 8.5.10. nilearn.image.iter_img
      • 8.5.10.1. Examples using nilearn.image.iter_img
    • 8.5.11. nilearn.image.largest_connected_component_img
    • 8.5.12. nilearn.image.load_img
      • 8.5.12.1. Examples using nilearn.image.load_img
    • 8.5.13. nilearn.image.math_img
      • 8.5.13.1. Examples using nilearn.image.math_img
    • 8.5.14. nilearn.image.mean_img
      • 8.5.14.1. Examples using nilearn.image.mean_img
    • 8.5.15. nilearn.image.new_img_like
      • 8.5.15.1. Examples using nilearn.image.new_img_like
    • 8.5.16. nilearn.image.resample_img
      • 8.5.16.1. Examples using nilearn.image.resample_img
    • 8.5.17. nilearn.image.resample_to_img
      • 8.5.17.1. Examples using nilearn.image.resample_to_img
    • 8.5.18. nilearn.image.reorder_img
    • 8.5.19. nilearn.image.smooth_img
      • 8.5.19.1. Examples using nilearn.image.smooth_img
    • 8.5.20. nilearn.image.swap_img_hemispheres
    • 8.5.21. nilearn.image.threshold_img
      • 8.5.21.1. Examples using nilearn.image.threshold_img
  • 8.6. nilearn.input_data: Loading and Processing Files Easily
    • 8.6.1. nilearn.input_data.NiftiMasker
      • 8.6.1.1. Examples using nilearn.input_data.NiftiMasker
    • 8.6.2. nilearn.input_data.MultiNiftiMasker
      • 8.6.2.1. Examples using nilearn.input_data.MultiNiftiMasker
    • 8.6.3. nilearn.input_data.NiftiLabelsMasker
      • 8.6.3.1. Examples using nilearn.input_data.NiftiLabelsMasker
    • 8.6.4. nilearn.input_data.NiftiMapsMasker
      • 8.6.4.1. Examples using nilearn.input_data.NiftiMapsMasker
    • 8.6.5. nilearn.input_data.NiftiSpheresMasker
      • 8.6.5.1. Examples using nilearn.input_data.NiftiSpheresMasker
  • 8.7. nilearn.masking: Data Masking Utilities
    • 8.7.1. nilearn.masking.compute_epi_mask
      • 8.7.1.1. Examples using nilearn.masking.compute_epi_mask
    • 8.7.2. nilearn.masking.compute_multi_epi_mask
    • 8.7.3. nilearn.masking.compute_brain_mask
    • 8.7.4. nilearn.masking.compute_multi_brain_mask
    • 8.7.5. nilearn.masking.compute_background_mask
    • 8.7.6. nilearn.masking.compute_multi_background_mask
    • 8.7.7. nilearn.masking.intersect_masks
    • 8.7.8. nilearn.masking.apply_mask
      • 8.7.8.1. Examples using nilearn.masking.apply_mask
    • 8.7.9. nilearn.masking.unmask
      • 8.7.9.1. Examples using nilearn.masking.unmask
  • 8.8. nilearn.regions: Operating on Regions
    • 8.8.1. nilearn.regions.connected_regions
      • 8.8.1.1. Examples using nilearn.regions.connected_regions
    • 8.8.2. nilearn.regions.connected_label_regions
      • 8.8.2.1. Examples using nilearn.regions.connected_label_regions
    • 8.8.3. nilearn.regions.img_to_signals_labels
    • 8.8.4. nilearn.regions.signals_to_img_labels
    • 8.8.5. nilearn.regions.img_to_signals_maps
    • 8.8.6. nilearn.regions.signals_to_img_maps
    • 8.8.7. nilearn.regions.RegionExtractor
      • 8.8.7.1. Examples using nilearn.regions.RegionExtractor
    • 8.8.8. nilearn.regions.Parcellations
      • 8.8.8.1. Examples using nilearn.regions.Parcellations
    • 8.8.9. nilearn.regions.ReNA
  • 8.9. nilearn.mass_univariate: Mass-Univariate Analysis
    • 8.9.1. nilearn.mass_univariate.permuted_ols
      • 8.9.1.1. Examples using nilearn.mass_univariate.permuted_ols
  • 8.10. nilearn.plotting: Plotting Brain Data
    • 8.10.1. nilearn.plotting.find_cut_slices
    • 8.10.2. nilearn.plotting.find_xyz_cut_coords
      • 8.10.2.1. Examples using nilearn.plotting.find_xyz_cut_coords
    • 8.10.3. nilearn.plotting.find_parcellation_cut_coords
      • 8.10.3.1. Examples using nilearn.plotting.find_parcellation_cut_coords
    • 8.10.4. nilearn.plotting.find_probabilistic_atlas_cut_coords
      • 8.10.4.1. Examples using nilearn.plotting.find_probabilistic_atlas_cut_coords
    • 8.10.5. nilearn.plotting.plot_anat
      • 8.10.5.1. Examples using nilearn.plotting.plot_anat
    • 8.10.6. nilearn.plotting.plot_img
      • 8.10.6.1. Examples using nilearn.plotting.plot_img
    • 8.10.7. nilearn.plotting.plot_epi
      • 8.10.7.1. Examples using nilearn.plotting.plot_epi
    • 8.10.8. nilearn.plotting.plot_matrix
      • 8.10.8.1. Examples using nilearn.plotting.plot_matrix
    • 8.10.9. nilearn.plotting.plot_roi
      • 8.10.9.1. Examples using nilearn.plotting.plot_roi
    • 8.10.10. nilearn.plotting.plot_stat_map
      • 8.10.10.1. Examples using nilearn.plotting.plot_stat_map
    • 8.10.11. nilearn.plotting.plot_glass_brain
      • 8.10.11.1. Examples using nilearn.plotting.plot_glass_brain
    • 8.10.12. nilearn.plotting.plot_connectome
      • 8.10.12.1. Examples using nilearn.plotting.plot_connectome
    • 8.10.13. nilearn.plotting.plot_connectome_strength
    • 8.10.14. nilearn.plotting.plot_markers
      • 8.10.14.1. Examples using nilearn.plotting.plot_markers
    • 8.10.15. nilearn.plotting.plot_prob_atlas
      • 8.10.15.1. Examples using nilearn.plotting.plot_prob_atlas
    • 8.10.16. nilearn.plotting.plot_carpet
      • 8.10.16.1. Examples using nilearn.plotting.plot_carpet
    • 8.10.17. nilearn.plotting.plot_surf
    • 8.10.18. nilearn.plotting.plot_surf_roi
      • 8.10.18.1. Examples using nilearn.plotting.plot_surf_roi
    • 8.10.19. nilearn.plotting.plot_surf_contours
      • 8.10.19.1. Examples using nilearn.plotting.plot_surf_contours
    • 8.10.20. nilearn.plotting.plot_surf_stat_map
      • 8.10.20.1. Examples using nilearn.plotting.plot_surf_stat_map
    • 8.10.21. nilearn.plotting.plot_img_on_surf
      • 8.10.21.1. Examples using nilearn.plotting.plot_img_on_surf
    • 8.10.22. nilearn.plotting.plot_img_comparison
      • 8.10.22.1. Examples using nilearn.plotting.plot_img_comparison
    • 8.10.23. nilearn.plotting.plot_design_matrix
      • 8.10.23.1. Examples using nilearn.plotting.plot_design_matrix
    • 8.10.24. nilearn.plotting.plot_event
      • 8.10.24.1. Examples using nilearn.plotting.plot_event
    • 8.10.25. nilearn.plotting.plot_contrast_matrix
      • 8.10.25.1. Examples using nilearn.plotting.plot_contrast_matrix
    • 8.10.26. nilearn.plotting.view_surf
      • 8.10.26.1. Examples using nilearn.plotting.view_surf
    • 8.10.27. nilearn.plotting.view_img_on_surf
      • 8.10.27.1. Examples using nilearn.plotting.view_img_on_surf
    • 8.10.28. nilearn.plotting.view_connectome
      • 8.10.28.1. Examples using nilearn.plotting.view_connectome
    • 8.10.29. nilearn.plotting.view_markers
    • 8.10.30. nilearn.plotting.view_img
      • 8.10.30.1. Examples using nilearn.plotting.view_img
    • 8.10.31. nilearn.plotting.show
      • 8.10.31.1. Examples using nilearn.plotting.show
    • 8.10.32. nilearn.plotting.displays.OrthoSlicer
      • 8.10.32.1. Examples using nilearn.plotting.displays.OrthoSlicer
  • 8.11. nilearn.signal: Preprocessing Time Series
    • 8.11.1. nilearn.signal.clean
    • 8.11.2. nilearn.signal.high_variance_confounds
  • 8.12. nilearn.glm: Generalized Linear Models
    • 8.12.1. nilearn.glm.Contrast
      • 8.12.1.1. Examples using nilearn.glm.Contrast
    • 8.12.2. nilearn.glm.FContrastResults
    • 8.12.3. nilearn.glm.TContrastResults
    • 8.12.4. nilearn.glm.ARModel
    • 8.12.5. nilearn.glm.OLSModel
    • 8.12.6. nilearn.glm.LikelihoodModelResults
    • 8.12.7. nilearn.glm.RegressionResults
    • 8.12.8. nilearn.glm.SimpleRegressionResults
    • 8.12.9. nilearn.glm.compute_contrast
      • 8.12.9.1. Examples using nilearn.glm.compute_contrast
    • 8.12.10. nilearn.glm.compute_fixed_effects
      • 8.12.10.1. Examples using nilearn.glm.compute_fixed_effects
    • 8.12.11. nilearn.glm.expression_to_contrast_vector
    • 8.12.12. nilearn.glm.fdr_threshold
    • 8.12.13. nilearn.glm.cluster_level_inference
      • 8.12.13.1. Examples using nilearn.glm.cluster_level_inference
    • 8.12.14. nilearn.glm.threshold_stats_img
      • 8.12.14.1. Examples using nilearn.glm.threshold_stats_img
    • 8.12.15. nilearn.glm.first_level
      • 8.12.15.1. nilearn.glm.first_level.FirstLevelModel
        • 8.12.15.1.1. Examples using nilearn.glm.first_level.FirstLevelModel
      • 8.12.15.2. nilearn.glm.first_level.check_design_matrix
      • 8.12.15.3. nilearn.glm.first_level.compute_regressor
        • 8.12.15.3.1. Examples using nilearn.glm.first_level.compute_regressor
      • 8.12.15.4. nilearn.glm.first_level.first_level_from_bids
        • 8.12.15.4.1. Examples using nilearn.glm.first_level.first_level_from_bids
      • 8.12.15.5. nilearn.glm.first_level.glover_dispersion_derivative
      • 8.12.15.6. nilearn.glm.first_level.glover_hrf
      • 8.12.15.7. nilearn.glm.first_level.glover_time_derivative
      • 8.12.15.8. nilearn.glm.first_level.make_first_level_design_matrix
        • 8.12.15.8.1. Examples using nilearn.glm.first_level.make_first_level_design_matrix
      • 8.12.15.9. nilearn.glm.first_level.mean_scaling
      • 8.12.15.10. nilearn.glm.first_level.run_glm
        • 8.12.15.10.1. Examples using nilearn.glm.first_level.run_glm
      • 8.12.15.11. nilearn.glm.first_level.spm_dispersion_derivative
      • 8.12.15.12. nilearn.glm.first_level.spm_hrf
      • 8.12.15.13. nilearn.glm.first_level.spm_time_derivative
    • 8.12.16. nilearn.glm.second_level
      • 8.12.16.1. nilearn.glm.second_level.SecondLevelModel
        • 8.12.16.1.1. Examples using nilearn.glm.second_level.SecondLevelModel
      • 8.12.16.2. nilearn.glm.second_level.make_second_level_design_matrix
        • 8.12.16.2.1. Examples using nilearn.glm.second_level.make_second_level_design_matrix
      • 8.12.16.3. nilearn.glm.second_level.non_parametric_inference
        • 8.12.16.3.1. Examples using nilearn.glm.second_level.non_parametric_inference
  • 8.13. nilearn.reporting: Reporting Functions
    • 8.13.1. nilearn.reporting.get_clusters_table
      • 8.13.1.1. Examples using nilearn.reporting.get_clusters_table
    • 8.13.2. nilearn.reporting.make_glm_report
      • 8.13.2.1. Examples using nilearn.reporting.make_glm_report
  • 8.14. nilearn.surface: Manipulating Surface Data
    • 8.14.1. nilearn.surface.load_surf_data
      • 8.14.1.1. Examples using nilearn.surface.load_surf_data
    • 8.14.2. nilearn.surface.load_surf_mesh
      • 8.14.2.1. Examples using nilearn.surface.load_surf_mesh
    • 8.14.3. nilearn.surface.vol_to_surf
      • 8.14.3.1. Examples using nilearn.surface.vol_to_surf
• 9. Nilearn usage examples
  • 9.1. Tutorial examples
  • 9.2. Visualization of brain images
  • 9.3. Decoding and predicting from brain images
  • 9.4. Functional connectivity
  • 9.5. GLM: First level analysis examples
  • 9.6. GLM : Second level analysis examples
  • 9.7. Manipulating brain image volumes
  • 9.8. Advanced statistical analysis of brain images