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This is the development documentation of nilearn (0.14.2.dev104+g5b27f6ea3) Switch to stable version (0.14.1)

Nilearn
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Nilearn
  • Quickstart
  • Installing Nilearn
  • Examples
    • Basic tutorials
      • 3D and 4D niimgs: handling and visualizing
      • An introduction tutorial to fMRI decoding
      • Basic nilearn example: manipulating and looking at data
      • Basic numerics and plotting with Python
      • Intro to GLM Analysis: a single-run, single-subject fMRI dataset
      • Working with Surface images
    • Visualization of brain images
      • Basic Atlas plotting
      • Colormaps in Nilearn
      • Controlling the contrast of the background when plotting
      • Glass brain plotting in nilearn
      • Glass brain plotting in nilearn (all options)
      • Loading and plotting of a cortical surface atlas
      • Making a surface plot of a 3D statistical map
      • More plotting tools from nilearn
      • NeuroImaging volumes visualization
      • Plot Haxby masks
      • Plotting images with transparent thresholding
      • Plotting tools in nilearn
      • Seed-based connectivity on the surface
      • Technical point: Illustration of the volume to surface sampling schemes
      • Using brain templates from TemplateFlow
      • Visualizing 4D probabilistic atlas maps
      • Visualizing Megatrawls Network Matrices from Human Connectome Project
      • Visualizing a probabilistic atlas
      • Visualizing global patterns with a carpet plot
      • Visualizing multiscale functional brain parcellations
    • Decoding and predicting from brain images
      • Comparing classifiers in decoding
      • Cortical surface-based searchlight decoding
      • Decoding of a dataset after GLM fit for signal extraction
      • Decoding with ANOVA + SVM
      • Decoding with fast ensembling of regularized models
      • Effect of standardization on decoding performance
      • Encoding models for visual stimuli
      • Example of pattern recognition on simulated data
      • Multi-class strategies
      • ROI-based decoding analysis
      • Reconstruction of visual stimuli
      • Regression with fast ensembling of regularized models
      • Searchlight analysis
      • Show stimuli of Haxby dataset
      • Tuning a parameter with cross-validation
      • Understanding Decoder
      • Voxel-Based Morphometry with Space-Net prior
      • Voxel-Based Morphometry: Regression
    • Functional connectivity
      • Classification of age groups using functional connectivity
      • Clustering methods to learn a brain parcellation from fMRI
      • Comparing connectomes on different reference atlases
      • Computing a connectome with sparse inverse covariance
      • Connectivity structure estimation on simulated data
      • Deriving spatial maps from group fMRI data using ICA and Dictionary Learning
      • Extract signals on spheres and plot a connectome
      • Extracting signals from a brain parcellation
      • Extracting signals of a probabilistic atlas of functional regions
      • Group Sparse inverse covariance for multi-subject connectome
      • Producing single subject maps of seed-to-voxel correlation
      • Regions extraction using dictionary learning and functional connectomes
    • GLM: First level analysis
      • Analysis of an fMRI dataset with a Finite Impulse Response (FIR) model
      • Default Mode Network extraction
      • Example of MRI response functions
      • Example of surface-based first-level analysis
      • Examples of design matrices
      • First level analysis of a complete BIDS dataset from openneuro
      • Generate an events.tsv file
      • Predicted time series and residuals
      • Simple example of two-runs fMRI model fitting
      • Single-subject data (two runs) in native space
      • Understanding parameters of the first-level model
    • GLM: Second level analysis
      • Example of generic design in second-level models
      • Example of second level design matrix
      • Second-level fMRI model: one sample test
      • Second-level fMRI model: true positive proportion in clusters
      • Second-level fMRI model: two-sample test, unpaired and paired
      • Statistical testing of a second-level analysis
      • Voxel-Based Morphometry: group level analysis
    • Manipulating brain image volumes
      • Breaking an atlas of labels in separated regions
      • Comparing the means of 2 images
      • Computing a Region of Interest (ROI) mask manually
      • Extracting signals from brain regions using the NiftiLabelsMasker
      • Image thresholding
      • Negating an image with math_img
      • Region Extraction using a t-statistical map (3D)
      • Regions Extraction of Default Mode Networks
      • Resample an image to a template
      • Simple example of NiftiMasker use
      • Smoothing an image
      • Understanding NiftiMasker and mask computation
      • Visualization of affine resamplings
    • Advanced statistical analysis of brain images
      • A short demo of the surface images & maskers
      • Advanced decoding using scikit-learn
      • BIDS dataset first- and second-level analysis
      • Beta-Series Modeling for Task-Based Functional Connectivity
      • Copying headers from input images with math_img
      • Functional connectivity predicts age group
      • Independent Component Analysis (ICA) of NeuroVault maps
      • Independent Component Analysis (ICA) of fMRI timeseries
      • Massively univariate analysis of a calculation task
      • Massively univariate analysis of a motor task
      • Massively univariate analysis of a visual task
      • NeuroVault meta-analysis of stop-go paradigm studies
      • SearchLight with a custom correlation estimator
      • Surface-based dataset first and second level analysis of a dataset
      • Working with long time series fMRI images
  • User guide
    • 1. Introduction
    • 2. What is nilearn?
    • 3. Using nilearn for the first time
    • 4. Machine learning applications to Neuroimaging
    • 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
        • 6.2.3.1. Group-sparse covariance estimation
      • 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
    • 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. Leverage GPUs for your neuroimaging machine-learning pipeline
      • 10.3. Downloading statistical maps from the Neurovault repository
  • API References
    • nilearn.connectome: Functional Connectivity
      • ConnectivityMeasure
      • GroupSparseCovariance
      • GroupSparseCovarianceCV
      • sym_matrix_to_vec
      • vec_to_sym_matrix
      • group_sparse_covariance
      • cov_to_corr
      • prec_to_partial
    • nilearn.datasets: Automatic Dataset Fetching
      • fetch_icbm152_2009
      • fetch_icbm152_brain_gm_mask
      • fetch_surf_fsaverage
      • load_fsaverage
      • load_fsaverage_data
      • load_mni152_brain_mask
      • load_mni152_gm_mask
      • load_mni152_gm_template
      • load_mni152_template
      • load_mni152_wm_mask
      • load_mni152_wm_template
      • ICBM 152 template
      • fsaverage template
      • fsaverage meshes
      • fetch_atlas_aal
      • fetch_atlas_basc_multiscale_2015
      • fetch_atlas_destrieux_2009
      • fetch_atlas_harvard_oxford
      • fetch_atlas_juelich
      • fetch_atlas_pauli_2017
      • fetch_atlas_schaefer_2018
      • fetch_atlas_surf_destrieux
      • fetch_atlas_talairach
      • fetch_atlas_yeo_2011
      • fetch_coords_dosenbach_2010
      • fetch_coords_power_2011
      • fetch_coords_seitzman_2018
      • fetch_atlas_allen_2011
      • fetch_atlas_craddock_2012
      • fetch_atlas_difumo
      • fetch_atlas_harvard_oxford
      • fetch_atlas_juelich
      • fetch_atlas_msdl
      • fetch_atlas_pauli_2017
      • fetch_atlas_smith_2009
      • AAL atlas
      • Allen 2011 atlas
      • BASC multiscale atlas
      • Craddock 2012 atlas
      • DiFuMo atlas
      • Destrieux atlas
      • Dosenbach 2010 atlas
      • Harvard Oxford atlas
      • Juelich atlas
      • MSDL atlas
      • Pauli 2007 atlas
      • Power 2011 atlas
      • Schaefer 2018 atlas
      • Seitzman 2018 atlas
      • Smith 2009 atlas
      • Destrieux atlas
      • Talairach atlas
      • Yeo 2011 atlas
      • fetch_abide_pcp
      • fetch_adhd
      • fetch_development_fmri
      • fetch_ds000030_urls
      • fetch_fiac_first_level
      • fetch_haxby
      • fetch_language_localizer_demo_dataset
      • fetch_localizer_first_level
      • fetch_miyawaki2008
      • fetch_spm_auditory
      • fetch_spm_multimodal_fmri
      • fetch_surf_nki_enhanced
      • load_nki
      • ABIDE PCP dataset
      • ADHD dataset
      • development fMRI dataset
      • ds000030
      • fiac first level dataset
      • Haxby dataset
      • language localizer demo dataset
      • localizer first level dataset
      • Miyawaki 2008 dataset
      • SPM auditory dataset
      • SPM multimodal dataset
      • NKI enhanced surface dataset
      • Brainomics Localizer
      • fetch_localizer_button_task
      • fetch_localizer_calculation_task
      • fetch_localizer_contrasts
      • fetch_megatrawls_netmats
      • fetch_mixed_gambles
      • fetch_oasis_vbm
      • fetch_neurovault_auditory_computation_task
      • MegaTrawls Network Matrices HCP
      • Mixed gambles statistical maps
      • OASIS volume based morphometry maps
      • fetch_neurovault
      • fetch_neurovault_ids
      • fetch_openneuro_dataset
      • get_data_dirs
      • patch_openneuro_dataset
      • select_from_index
      • load_sample_motor_activation_image
      • Neurovault statistical maps
    • nilearn.decoding: Decoding
      • Decoder
      • DecoderRegressor
      • FREMClassifier
      • FREMRegressor
      • SpaceNetClassifier
      • SpaceNetRegressor
      • SearchLight
    • nilearn.decomposition: Multivariate Decompositions
      • CanICA
      • DictLearning
    • nilearn.exceptions: Exceptions and warnings
      • MaskWarning
      • NotImplementedWarning
      • AllVolumesRemovedError
      • DimensionError
      • MeshDimensionError
    • nilearn.glm: Generalized Linear Models
      • Contrast
      • FContrastResults
      • TContrastResults
      • ARModel
      • OLSModel
      • LikelihoodModelResults
      • RegressionResults
      • SimpleRegressionResults
      • compute_contrast
      • compute_fixed_effects
      • expression_to_contrast_vector
      • fdr_threshold
      • cluster_level_inference
      • threshold_stats_img
      • save_glm_to_bids
      • FirstLevelModel
      • check_design_matrix
      • compute_regressor
      • first_level_from_bids
      • glover_dispersion_derivative
      • glover_hrf
      • glover_time_derivative
      • make_first_level_design_matrix
      • mean_scaling
      • run_glm
      • spm_dispersion_derivative
      • spm_hrf
      • spm_time_derivative
      • SecondLevelModel
      • make_second_level_design_matrix
      • non_parametric_inference
    • nilearn.image: Image Processing and Resampling Utilities
      • binarize_img
      • check_niimg
      • check_niimg_3d
      • check_niimg_4d
      • clean_img
      • concat_imgs
      • coord_transform
      • copy_img
      • crop_img
      • get_data
      • high_variance_confounds
      • index_img
      • iter_img
      • largest_connected_component_img
      • load_img
      • math_img
      • mean_img
      • new_img_like
      • resample_img
      • resample_to_img
      • reorder_img
      • smooth_img
      • swap_img_hemispheres
      • threshold_img
    • nilearn.interfaces: Loading components from interfaces
      • get_bids_files
      • parse_bids_filename
      • load_confounds
      • load_confounds_strategy
      • get_design_from_fslmat
    • nilearn.maskers: Extracting Signals from Brain Images
      • BaseMasker
      • NiftiMasker
      • MultiNiftiMasker
      • NiftiLabelsMasker
      • MultiNiftiLabelsMasker
      • NiftiMapsMasker
      • MultiNiftiMapsMasker
      • NiftiSpheresMasker
      • SurfaceMasker
      • MultiSurfaceMasker
      • SurfaceLabelsMasker
      • MultiSurfaceLabelsMasker
      • SurfaceMapsMasker
      • MultiSurfaceMapsMasker
      • Examples of masker reports
    • nilearn.masking: Data Masking Utilities
      • compute_epi_mask
      • compute_multi_epi_mask
      • compute_brain_mask
      • compute_multi_brain_mask
      • compute_background_mask
      • compute_multi_background_mask
      • intersect_masks
      • apply_mask
      • unmask
    • nilearn.mass_univariate: Mass-Univariate Analysis
      • permuted_ols
    • nilearn.plotting: Plotting Brain Data
      • find_cut_slices
      • find_xyz_cut_coords
      • find_parcellation_cut_coords
      • find_probabilistic_atlas_cut_coords
      • plot_anat
      • plot_carpet
      • plot_connectome
      • plot_contrast_matrix
      • plot_design_matrix
      • plot_design_matrix_correlation
      • plot_epi
      • plot_event
      • plot_glass_brain
      • plot_img
      • plot_img_on_surf
      • plot_markers
      • plot_matrix
      • plot_prob_atlas
      • plot_roi
      • plot_stat_map
      • plot_surf
      • plot_surf_contours
      • plot_surf_roi
      • plot_surf_stat_map
      • show
      • view_surf
      • view_img_on_surf
      • view_connectome
      • view_markers
      • view_img
      • plot_bland_altman
      • plot_img_comparison
      • PlotlySurfaceFigure
      • BaseAxes
      • CutAxes
      • GlassBrainAxes
      • OrthoProjector
      • LZRYProjector
      • LYRZProjector
      • LYRProjector
      • LZRProjector
      • LRProjector
      • LProjector
      • RProjector
      • XZProjector
      • YZProjector
      • YXProjector
      • XProjector
      • YProjector
      • ZProjector
      • BaseSlicer
      • OrthoSlicer
      • MosaicSlicer
      • TiledSlicer
      • XSlicer
      • XZSlicer
      • YXSlicer
      • YZSlicer
      • YSlicer
      • ZSlicer
      • get_projector
      • get_slicer
    • nilearn.regions: Operating on Regions
      • RegionExtractor
      • Parcellations
      • ReNA
      • HierarchicalKMeans
      • connected_regions
      • connected_label_regions
      • img_to_signals_labels
      • signals_to_img_labels
      • img_to_signals_maps
      • signals_to_img_maps
      • recursive_neighbor_agglomeration
    • nilearn.reporting: Reporting Functions
      • HTMLReport
      • get_clusters_table
      • Examples of GLM reports
    • nilearn.signal: Preprocessing Time Series
      • butterworth
      • clean
      • high_variance_confounds
    • nilearn.surface: Manipulating Surface Data
      • FileMesh
      • InMemoryMesh
      • PolyData
      • PolyMesh
      • SurfaceImage
      • SurfaceMesh
      • load_surf_data
      • load_surf_mesh
      • vol_to_surf
    • nilearn.utils: Utilities
      • InputTags
      • all_displays
      • all_estimators
      • all_functions
  • Glossary

Development

  • Contributing
  • Continuous integration
  • Maintenance
  • What’s new
  • Team
  • Versions
  • GitHub Repository
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nilearn.utils: Utilities¶

Utilities for nilearn users.

nilearn.utils¶

InputTags([one_d_array, two_d_array, ...])

Tags for the input data.

all_displays([type_filter])

Get a list of all 'displays' objects from nilearn.

all_estimators([type_filter])

Get a list of all estimators from nilearn.

all_functions()

Get a list of all functions from nilearn.

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