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This is the development documentation of nilearn (0.10.5.dev241+g722514fcd) Switch to stable version (0.10.4)

Nilearn
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Nilearn
  • Quickstart
  • Examples
    • Basic tutorials
      • Basic numerics and plotting with Python
      • 3D and 4D niimgs: handling and visualizing
      • Basic nilearn example: manipulating and looking at data
      • Working with Surface images
      • A introduction tutorial to fMRI decoding
      • Intro to GLM Analysis: a single-run, single-subject fMRI dataset
    • Visualization of brain images
      • Glass brain plotting in nilearn
      • Visualizing Megatrawls Network Matrices from Human Connectome Project
      • Visualizing 4D probabilistic atlas maps
      • Basic Atlas plotting
      • Visualizing a probabilistic atlas: the default mode in the MSDL atlas
      • Controlling the contrast of the background when plotting
      • Visualizing multiscale functional brain parcellations
      • Matplotlib colormaps in Nilearn
      • NeuroImaging volumes visualization
      • Visualizing global patterns with a carpet plot
      • Plot Haxby masks
      • Technical point: Illustration of the volume to surface sampling schemes
      • Plotting tools in nilearn
      • Loading and plotting of a cortical surface atlas
      • More plotting tools from nilearn
      • Glass brain plotting in nilearn (all options)
      • Seed-based connectivity on the surface
      • Making a surface plot of a 3D statistical map
    • Decoding and predicting from brain images
      • Show stimuli of Haxby et al. dataset
      • FREM on Jimura et al “mixed gambles” dataset
      • Voxel-Based Morphometry on Oasis dataset with Space-Net prior
      • Decoding with FREM: face vs house vs chair object recognition
      • Decoding with ANOVA + SVM: face vs house in the Haxby dataset
      • The haxby dataset: different multi-class strategies
      • Cortical surface-based searchlight decoding
      • Searchlight analysis of face vs house recognition
      • Decoding of a dataset after GLM fit for signal extraction
      • ROI-based decoding analysis in Haxby et al. dataset
      • Setting a parameter by cross-validation
      • Voxel-Based Morphometry on Oasis dataset
      • Different classifiers in decoding the Haxby dataset
      • Understanding nilearn.decoding.Decoder
      • Example of pattern recognition on simulated data
      • Encoding models for visual stimuli from Miyawaki et al. 2008
      • Reconstruction of visual stimuli from Miyawaki et al. 2008
    • Functional connectivity
      • Computing a connectome with sparse inverse covariance
      • Extracting signals of a probabilistic atlas of functional regions
      • Connectivity structure estimation on simulated data
      • Group Sparse inverse covariance for multi-subject connectome
      • Deriving spatial maps from group fMRI data using ICA and Dictionary Learning
      • Producing single subject maps of seed-to-voxel correlation
      • Regions extraction using dictionary learning and functional connectomes
      • Classification of age groups using functional connectivity
      • Extracting signals from a brain parcellation
      • Comparing connectomes on different reference atlases
      • Extract signals on spheres and plot a connectome
      • Clustering methods to learn a brain parcellation from fMRI
    • GLM: First level analysis
      • Default Mode Network extraction of ADHD dataset
      • Analysis of an fMRI dataset with a Finite Impule Response (FIR) model
      • Generate an events.tsv file for the NeuroSpin localizer task
      • Single-subject data (two runs) in native space
      • Example of surface-based first-level analysis
      • Example of MRI response functions
      • Predicted time series and residuals
      • Examples of design matrices
      • First level analysis of a complete BIDS dataset from openneuro
      • Simple example of two-runs fMRI model fitting
      • Understanding parameters of the first-level model
    • GLM: Second level analysis
      • Example of second level design matrix
      • Second-level fMRI model: true positive proportion in clusters
      • Statistical testing of a second-level analysis
      • Second-level fMRI model: two-sample test, unpaired and paired
      • Voxel-Based Morphometry on OASIS dataset
      • Second-level fMRI model: one sample test
      • Example of generic design in second-level models
    • Manipulating brain image volumes
      • Negating an image with math_img
      • Comparing the means of 2 images
      • Smoothing an image
      • Breaking an atlas of labels in separated regions
      • Regions Extraction of Default Mode Networks using Smith Atlas
      • Resample an image to a template
      • Simple example of NiftiMasker use
      • Region Extraction using a t-statistical map (3D)
      • Extracting signals from brain regions using the NiftiLabelsMasker
      • Understanding NiftiMasker and mask computation
      • Visualization of affine resamplings
      • Computing a Region of Interest (ROI) mask manually
    • Advanced statistical analysis of brain images
      • Multivariate decompositions: Independent component analysis of fMRI
      • Massively univariate analysis of a calculation task from the Localizer dataset
      • BIDS dataset first and second level analysis
      • Copying headers from input images with math_img
      • Surface-based dataset first and second level analysis of a dataset
      • NeuroVault meta-analysis of stop-go paradigm studies
      • Functional connectivity predicts age group
      • NeuroVault cross-study ICA maps
      • Massively univariate analysis of a motor task from the Localizer dataset
      • Massively univariate analysis of face vs house recognition
      • A short demo of the surface images & maskers
      • Advanced decoding using scikit learn
      • Beta-Series Modeling for Task-Based Functional Connectivity and Decoding
    • Examples for experimental modules
  • 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
    • 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
  • API References
    • nilearn.connectome: Functional Connectivity
      • nilearn.connectome.ConnectivityMeasure
      • nilearn.connectome.GroupSparseCovariance
      • nilearn.connectome.GroupSparseCovarianceCV
      • nilearn.connectome.sym_matrix_to_vec
      • nilearn.connectome.vec_to_sym_matrix
      • nilearn.connectome.group_sparse_covariance
      • nilearn.connectome.cov_to_corr
      • nilearn.connectome.prec_to_partial
    • nilearn.datasets: Automatic Dataset Fetching
      • nilearn.datasets.fetch_icbm152_2009
      • nilearn.datasets.fetch_icbm152_brain_gm_mask
      • nilearn.datasets.fetch_surf_fsaverage
      • nilearn.datasets.load_fsaverage
      • nilearn.datasets.load_fsaverage_data
      • nilearn.datasets.load_mni152_brain_mask
      • nilearn.datasets.load_mni152_gm_mask
      • nilearn.datasets.load_mni152_gm_template
      • nilearn.datasets.load_mni152_template
      • nilearn.datasets.load_mni152_wm_mask
      • nilearn.datasets.load_mni152_wm_template
      • ICBM 152 template
      • fsaverage template
      • fsaverage3 template
      • fsaverage4 template
      • fsaverage5 template
      • fsaverage6 template
      • nilearn.datasets.fetch_atlas_aal
      • nilearn.datasets.fetch_atlas_allen_2011
      • nilearn.datasets.fetch_atlas_basc_multiscale_2015
      • nilearn.datasets.fetch_atlas_craddock_2012
      • nilearn.datasets.fetch_atlas_destrieux_2009
      • nilearn.datasets.fetch_atlas_difumo
      • nilearn.datasets.fetch_atlas_harvard_oxford
      • nilearn.datasets.fetch_atlas_juelich
      • nilearn.datasets.fetch_atlas_msdl
      • nilearn.datasets.fetch_atlas_pauli_2017
      • nilearn.datasets.fetch_atlas_schaefer_2018
      • nilearn.datasets.fetch_atlas_smith_2009
      • nilearn.datasets.fetch_atlas_surf_destrieux
      • nilearn.datasets.fetch_atlas_talairach
      • nilearn.datasets.fetch_atlas_yeo_2011
      • nilearn.datasets.fetch_coords_dosenbach_2010
      • nilearn.datasets.fetch_coords_power_2011
      • nilearn.datasets.fetch_coords_seitzman_2018
      • AAL atlas
      • Allen 2011 atlas
      • BASC multiscale atlas
      • Craddock 2012 atlas
      • Destrieux atlas
      • DiFuMo atlas
      • Harvard Oxford atlas
      • Juelich atlas
      • MSDL atlas
      • Pauli 2007 atlas
      • Schaefer 2018 atlas
      • Smith 2009 atlas
      • Talairach atlas
      • Yeo 2011 atlas
      • Dosenbach 2010 atlas
      • Power 2011 atlas
      • Seitzman 2018 atlas
      • nilearn.datasets.fetch_abide_pcp
      • nilearn.datasets.fetch_adhd
      • nilearn.datasets.fetch_bids_langloc_dataset
      • nilearn.datasets.fetch_development_fmri
      • nilearn.datasets.fetch_ds000030_urls
      • nilearn.datasets.fetch_fiac_first_level
      • nilearn.datasets.fetch_haxby
      • nilearn.datasets.fetch_language_localizer_demo_dataset
      • nilearn.datasets.fetch_localizer_first_level
      • nilearn.datasets.fetch_miyawaki2008
      • nilearn.datasets.fetch_spm_auditory
      • nilearn.datasets.fetch_spm_multimodal_fmri
      • nilearn.datasets.fetch_surf_nki_enhanced
      • nilearn.datasets.load_nki
      • ABIDE PCP dataset
      • ADHD dataset
      • BIDS language localizer
      • development fMRI dataset
      • 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
      • nilearn.datasets.fetch_localizer_button_task
      • nilearn.datasets.fetch_localizer_calculation_task
      • nilearn.datasets.fetch_localizer_contrasts
      • nilearn.datasets.fetch_megatrawls_netmats
      • nilearn.datasets.fetch_mixed_gambles
      • nilearn.datasets.fetch_oasis_vbm
      • nilearn.datasets.fetch_neurovault_auditory_computation_task
      • nilearn.datasets.fetch_neurovault_motor_task
      • MegaTrawls Network Matrices HCP
      • Mixed gambles statistical maps
      • OASIS volume based morphometry maps
      • nilearn.datasets.fetch_neurovault
      • nilearn.datasets.fetch_neurovault_ids
      • nilearn.datasets.fetch_openneuro_dataset
      • nilearn.datasets.get_data_dirs
      • nilearn.datasets.patch_openneuro_dataset
      • nilearn.datasets.select_from_index
      • nilearn.datasets.load_sample_motor_activation_image
      • Neurovault statistical maps
    • nilearn.decoding: Decoding
      • nilearn.decoding.Decoder
      • nilearn.decoding.DecoderRegressor
      • nilearn.decoding.FREMClassifier
      • nilearn.decoding.FREMRegressor
      • nilearn.decoding.SpaceNetClassifier
      • nilearn.decoding.SpaceNetRegressor
      • nilearn.decoding.SearchLight
    • nilearn.decomposition: Multivariate Decompositions
      • nilearn.decomposition.CanICA
      • nilearn.decomposition.DictLearning
    • nilearn.glm: Generalized Linear Models
      • nilearn.glm.Contrast
      • nilearn.glm.FContrastResults
      • nilearn.glm.TContrastResults
      • nilearn.glm.ARModel
      • nilearn.glm.OLSModel
      • nilearn.glm.LikelihoodModelResults
      • nilearn.glm.RegressionResults
      • nilearn.glm.SimpleRegressionResults
      • nilearn.glm.compute_contrast
      • nilearn.glm.compute_fixed_effects
      • nilearn.glm.expression_to_contrast_vector
      • nilearn.glm.fdr_threshold
      • nilearn.glm.cluster_level_inference
      • nilearn.glm.threshold_stats_img
      • nilearn.glm.first_level.FirstLevelModel
      • nilearn.glm.first_level.check_design_matrix
      • nilearn.glm.first_level.compute_regressor
      • nilearn.glm.first_level.first_level_from_bids
      • nilearn.glm.first_level.glover_dispersion_derivative
      • nilearn.glm.first_level.glover_hrf
      • nilearn.glm.first_level.glover_time_derivative
      • nilearn.glm.first_level.make_first_level_design_matrix
      • nilearn.glm.first_level.mean_scaling
      • nilearn.glm.first_level.run_glm
      • nilearn.glm.first_level.spm_dispersion_derivative
      • nilearn.glm.first_level.spm_hrf
      • nilearn.glm.first_level.spm_time_derivative
      • nilearn.glm.second_level.SecondLevelModel
      • nilearn.glm.second_level.make_second_level_design_matrix
      • nilearn.glm.second_level.non_parametric_inference
    • nilearn.image: Image Processing and Resampling Utilities
      • nilearn.image.binarize_img
      • nilearn.image.clean_img
      • nilearn.image.concat_imgs
      • nilearn.image.coord_transform
      • nilearn.image.copy_img
      • nilearn.image.crop_img
      • nilearn.image.get_data
      • nilearn.image.high_variance_confounds
      • nilearn.image.index_img
      • nilearn.image.iter_img
      • nilearn.image.largest_connected_component_img
      • nilearn.image.load_img
      • nilearn.image.math_img
      • nilearn.image.mean_img
      • nilearn.image.new_img_like
      • nilearn.image.resample_img
      • nilearn.image.resample_to_img
      • nilearn.image.reorder_img
      • nilearn.image.smooth_img
      • nilearn.image.swap_img_hemispheres
      • nilearn.image.threshold_img
    • nilearn.interfaces: Loading components from interfaces
      • nilearn.interfaces.bids.get_bids_files
      • nilearn.interfaces.bids.parse_bids_filename
      • nilearn.interfaces.bids.save_glm_to_bids
      • nilearn.interfaces.fmriprep.load_confounds
      • nilearn.interfaces.fmriprep.load_confounds_strategy
      • nilearn.interfaces.fsl.get_design_from_fslmat
    • nilearn.maskers: Extracting Signals from Brain Images
      • nilearn.maskers.BaseMasker
      • nilearn.maskers.NiftiMasker
      • nilearn.maskers.MultiNiftiMasker
      • nilearn.maskers.NiftiLabelsMasker
      • nilearn.maskers.MultiNiftiLabelsMasker
      • nilearn.maskers.NiftiMapsMasker
      • nilearn.maskers.MultiNiftiMapsMasker
      • nilearn.maskers.NiftiSpheresMasker
      • nilearn.maskers.SurfaceLabelsMasker
      • nilearn.maskers.SurfaceMasker
      • Examples masker reports
    • nilearn.masking: Data Masking Utilities
      • nilearn.masking.compute_epi_mask
      • nilearn.masking.compute_multi_epi_mask
      • nilearn.masking.compute_brain_mask
      • nilearn.masking.compute_multi_brain_mask
      • nilearn.masking.compute_background_mask
      • nilearn.masking.compute_multi_background_mask
      • nilearn.masking.intersect_masks
      • nilearn.masking.apply_mask
      • nilearn.masking.unmask
    • nilearn.mass_univariate: Mass-Univariate Analysis
      • nilearn.mass_univariate.permuted_ols
    • nilearn.plotting: Plotting Brain Data
      • nilearn.plotting.find_cut_slices
      • nilearn.plotting.find_xyz_cut_coords
      • nilearn.plotting.find_parcellation_cut_coords
      • nilearn.plotting.find_probabilistic_atlas_cut_coords
      • nilearn.plotting.plot_anat
      • nilearn.plotting.plot_img
      • nilearn.plotting.plot_epi
      • nilearn.plotting.plot_matrix
      • nilearn.plotting.plot_roi
      • nilearn.plotting.plot_stat_map
      • nilearn.plotting.plot_glass_brain
      • nilearn.plotting.plot_connectome
      • nilearn.plotting.plot_markers
      • nilearn.plotting.plot_prob_atlas
      • nilearn.plotting.plot_carpet
      • nilearn.plotting.plot_surf
      • nilearn.plotting.plot_surf_roi
      • nilearn.plotting.plot_surf_contours
      • nilearn.plotting.plot_surf_stat_map
      • nilearn.plotting.plot_img_on_surf
      • nilearn.plotting.plot_img_comparison
      • nilearn.plotting.plot_design_matrix
      • nilearn.plotting.plot_design_matrix_correlation
      • nilearn.plotting.plot_event
      • nilearn.plotting.plot_contrast_matrix
      • nilearn.plotting.view_surf
      • nilearn.plotting.view_img_on_surf
      • nilearn.plotting.view_connectome
      • nilearn.plotting.view_markers
      • nilearn.plotting.view_img
      • nilearn.plotting.show
      • nilearn.plotting.displays.OrthoProjector
      • nilearn.plotting.displays.XZProjector
      • nilearn.plotting.displays.YZProjector
      • nilearn.plotting.displays.YXProjector
      • nilearn.plotting.displays.XProjector
      • nilearn.plotting.displays.YProjector
      • nilearn.plotting.displays.ZProjector
      • nilearn.plotting.displays.LZRYProjector
      • nilearn.plotting.displays.LYRZProjector
      • nilearn.plotting.displays.LYRProjector
      • nilearn.plotting.displays.LZRProjector
      • nilearn.plotting.displays.LRProjector
      • nilearn.plotting.displays.LProjector
      • nilearn.plotting.displays.RProjector
      • nilearn.plotting.displays.BaseAxes
      • nilearn.plotting.displays.CutAxes
      • nilearn.plotting.displays.GlassBrainAxes
      • nilearn.plotting.displays.BaseSlicer
      • nilearn.plotting.displays.OrthoSlicer
      • nilearn.plotting.displays.PlotlySurfaceFigure
      • nilearn.plotting.displays.TiledSlicer
      • nilearn.plotting.displays.MosaicSlicer
      • nilearn.plotting.displays.XZSlicer
      • nilearn.plotting.displays.YZSlicer
      • nilearn.plotting.displays.YXSlicer
      • nilearn.plotting.displays.XSlicer
      • nilearn.plotting.displays.YSlicer
      • nilearn.plotting.displays.ZSlicer
      • nilearn.plotting.displays.get_projector
      • nilearn.plotting.displays.get_slicer
    • nilearn.regions: Operating on Regions
      • nilearn.regions.RegionExtractor
      • nilearn.regions.Parcellations
      • nilearn.regions.ReNA
      • nilearn.regions.HierarchicalKMeans
      • nilearn.regions.connected_regions
      • nilearn.regions.connected_label_regions
      • nilearn.regions.img_to_signals_labels
      • nilearn.regions.signals_to_img_labels
      • nilearn.regions.img_to_signals_maps
      • nilearn.regions.signals_to_img_maps
      • nilearn.regions.recursive_neighbor_agglomeration
    • nilearn.reporting: Reporting Functions
      • nilearn.reporting.HTMLReport
      • nilearn.reporting.get_clusters_table
      • nilearn.reporting.make_glm_report
      • Examples of GLM reports
    • nilearn.signal: Preprocessing Time Series
      • nilearn.signal.butterworth
      • nilearn.signal.clean
      • nilearn.signal.high_variance_confounds
    • nilearn.surface: Manipulating Surface Data
      • nilearn.surface.FileMesh
      • nilearn.surface.InMemoryMesh
      • nilearn.surface.PolyData
      • nilearn.surface.PolyMesh
      • nilearn.surface.SurfaceImage
      • nilearn.surface.SurfaceMesh
      • nilearn.surface.load_surf_data
      • nilearn.surface.load_surf_mesh
      • nilearn.surface.vol_to_surf
  • Glossary

Development

  • Contributing
  • Maintenance
  • What’s new
  • Team
  • GitHub Repository
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BIDS language localizer¶

Warning

This dataset is a duplicate of language localizer demo dataset. It will be deprecated in Nilearn 0.13.0.

Use nilearn.datasets.fetch_language_localizer_demo_dataset instead.

Notes¶

More info at: https://osf.io/u5mzy

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