8. Reference documentation: all nilearn functions

This is the class and function reference of nilearn. Please refer to the user guide for more information and usage examples.

8.1. nilearn.connectome: Functional Connectivity

Tools for computing functional connectivity matrices and also implementation of algorithm for sparse multi subjects learning of Gaussian graphical models.

Classes:

ConnectivityMeasure([cov_estimator, kind, …])	A class that computes different kinds of functional connectivity matrices.
GroupSparseCovariance([alpha, tol, …])	Covariance and precision matrix estimator.
GroupSparseCovarianceCV([alphas, …])	Sparse inverse covariance w/ cross-validated choice of the parameter.

Functions:

sym_matrix_to_vec(symmetric[, discard_diagonal])	Return the flattened lower triangular part of an array.
vec_to_sym_matrix(vec[, diagonal])	Return the symmetric matrix given its flattened lower triangular part.
group_sparse_covariance(subjects, alpha[, …])	Compute sparse precision matrices and covariance matrices.
cov_to_corr(covariance)	Return correlation matrix for a given covariance matrix.
prec_to_partial(precision)	Return partial correlation matrix for a given precision matrix.

8.2. nilearn.datasets: Automatic Dataset Fetching

Helper functions to download NeuroImaging datasets

User guide: See the Fetching open datasets from Internet section for further details.

Functions:

fetch_atlas_craddock_2012([data_dir, url, …])	Download and return file names for the Craddock 2012 parcellation
fetch_atlas_destrieux_2009([lateralized, …])	Download and load the Destrieux cortical atlas (dated 2009)
fetch_atlas_harvard_oxford(atlas_name[, …])	Load Harvard-Oxford parcellations from FSL.
fetch_atlas_msdl([data_dir, url, resume, …])	Download and load the MSDL brain atlas.
fetch_coords_power_2011()	Download and load the Power et al.
fetch_coords_seitzman_2018([ordered_regions])	Load the Seitzman et al.
fetch_atlas_smith_2009([data_dir, mirror, …])	Download and load the Smith ICA and BrainMap atlas (dated 2009)
fetch_atlas_yeo_2011([data_dir, url, …])	Download and return file names for the Yeo 2011 parcellation.
fetch_atlas_aal([version, data_dir, url, …])	Downloads and returns the AAL template for SPM 12.
fetch_atlas_basc_multiscale_2015([version, …])	Downloads and loads multiscale functional brain parcellations
fetch_atlas_allen_2011([data_dir, url, …])	Download and return file names for the Allen and MIALAB ICA atlas (dated 2011).
fetch_atlas_pauli_2017([version, data_dir, …])	Download the Pauli et al.
fetch_coords_dosenbach_2010([ordered_regions])	Load the Dosenbach et al.
fetch_abide_pcp([data_dir, n_subjects, …])	Fetch ABIDE dataset
fetch_adhd([n_subjects, data_dir, url, …])	Download and load the ADHD resting-state dataset.
fetch_development_fmri([n_subjects, …])	Fetch movie watching based brain development dataset (fMRI)
fetch_haxby([data_dir, subjects, …])	Download and loads complete haxby dataset
fetch_icbm152_2009([data_dir, url, resume, …])	Download and load the ICBM152 template (dated 2009)
fetch_icbm152_brain_gm_mask([data_dir, …])	Downloads ICBM152 template first, then loads ‘gm’ mask image.
fetch_localizer_button_task([data_dir, url, …])	Fetch left vs right button press contrast maps from the localizer.
fetch_localizer_contrasts(contrasts[, …])	Download and load Brainomics/Localizer dataset (94 subjects).
fetch_localizer_calculation_task([…])	Fetch calculation task contrast maps from the localizer.
fetch_miyawaki2008([data_dir, url, resume, …])	Download and loads Miyawaki et al.
fetch_nyu_rest([n_subjects, sessions, …])	Download and loads the NYU resting-state test-retest dataset.
fetch_surf_nki_enhanced([n_subjects, …])	Download and load the NKI enhanced resting-state dataset,
fetch_surf_fsaverage([mesh, data_dir])	Download a Freesurfer fsaverage surface
fetch_atlas_surf_destrieux([data_dir, url, …])	Download and load Destrieux et al, 2010 cortical atlas.
fetch_atlas_talairach(level_name[, …])	Download the Talairach atlas.
fetch_atlas_schaefer_2018([n_rois, …])	Download and return file names for the Schaefer 2018 parcellation
fetch_oasis_vbm([n_subjects, …])	Download and load Oasis “cross-sectional MRI” dataset (416 subjects).
fetch_megatrawls_netmats([dimensionality, …])	Downloads and returns Network Matrices data from MegaTrawls release in HCP.
fetch_cobre([n_subjects, data_dir, url, verbose])	DEPRECATED: ‘fetch_cobre’ has been deprecated and will be removed in release 0.9 .
fetch_neurovault([max_images, …])	Download data from neurovault.org that match certain criteria.
fetch_neurovault_ids([collection_ids, …])	Download specific images and collections from neurovault.org.
fetch_neurovault_auditory_computation_task([…])	Fetch a contrast map from NeuroVault showing the effect of mental subtraction upon auditory instructions
fetch_neurovault_motor_task([data_dir, verbose])	Fetch left vs right button press group contrast map from NeuroVault.
get_data_dirs([data_dir])	Returns the directories in which nilearn looks for data.
load_mni152_template()	Load skullstripped 2mm version of the MNI152 originally distributed with FSL
load_mni152_brain_mask()	Load brain mask from MNI152 T1 template
fetch_language_localizer_demo_dataset([…])	Download language localizer demo dataset.
fetch_bids_langloc_dataset([data_dir, verbose])	Download language localizer example bids dataset.
fetch_openneuro_dataset_index([data_dir, …])	Download a file with OpenNeuro BIDS dataset index.
select_from_index(urls[, inclusion_filters, …])	Select subset of urls with given filters.
patch_openneuro_dataset(file_list)	Add symlinks for files not named according to latest BIDS conventions.
fetch_openneuro_dataset([urls, data_dir, …])	Download OpenNeuro BIDS dataset.
fetch_localizer_first_level([data_dir, verbose])	Download a first-level localizer fMRI dataset
fetch_spm_auditory([data_dir, data_name, …])	Function to fetch SPM auditory single-subject data.
fetch_spm_multimodal_fmri([data_dir, …])	Fetcher for Multi-modal Face Dataset.
fetch_fiac_first_level([data_dir, verbose])	Download a first-level fiac fMRI dataset (2 sessions)

8.3. nilearn.decoding: Decoding

Decoding tools and algorithms.

Classes:

Decoder([estimator, mask, cv, param_grid, …])	A wrapper for popular classification strategies in neuroimaging.
DecoderRegressor([estimator, mask, cv, …])	A wrapper for popular regression strategies in neuroimaging.
FREMClassifier([estimator, mask, cv, …])	State of the art decoding scheme applied to usual classifiers.
FREMRegressor([estimator, mask, cv, …])	State of the art decoding scheme applied to usual regression estimators.
SpaceNetClassifier([penalty, loss, …])	Classification learners with sparsity and spatial priors.
SpaceNetRegressor([penalty, l1_ratios, …])	Regression learners with sparsity and spatial priors.
SearchLight(mask_img[, process_mask_img, …])	Implement search_light analysis using an arbitrary type of classifier.

8.4. nilearn.decomposition: Multivariate Decompositions

The nilearn.decomposition module includes a subject level variant of the ICA called Canonical ICA.

Classes:

CanICA([mask, n_components, smoothing_fwhm, …])	Perform Canonical Independent Component Analysis.
DictLearning([n_components, n_epochs, …])	Perform a map learning algorithm based on spatial component sparsity, over a CanICA initialization.

8.5. nilearn.image: Image Processing and Resampling Utilities

Mathematical operations working on Niimg-like objects like a (3+)D block of data, and an affine.

Functions:

clean_img(imgs[, sessions, detrend, …])	Improve SNR on masked fMRI signals.
concat_imgs(niimgs[, dtype, ensure_ndim, …])	Concatenate a list of 3D/4D niimgs of varying lengths.
coord_transform(x, y, z, affine)	Convert the x, y, z coordinates from one image space to another
copy_img(img)	Copy an image to a nibabel.Nifti1Image.
crop_img(img[, rtol, copy, pad, return_offset])	Crops an image as much as possible.
get_data(img)	Get the image data as a numpy.ndarray.
high_variance_confounds(imgs[, n_confounds, …])	Return confounds signals extracted from input signals with highest variance.
index_img(imgs, index)	Indexes into a 4D Niimg-like object in the fourth dimension.
iter_img(imgs)	Iterates over a 4D Niimg-like object in the fourth dimension.
largest_connected_component_img(imgs)	Return the largest connected component of an image or list of images.
load_img(img[, wildcards, dtype])	Load a Niimg-like object from filenames or list of filenames.
math_img(formula, **imgs)	Interpret a numpy based string formula using niimg in named parameters.
mean_img(imgs[, target_affine, …])	Compute the mean of the images over time or the 4th dimension.
new_img_like(ref_niimg, data[, affine, …])	Create a new image of the same class as the reference image
resample_img(img[, target_affine, …])	Resample a Niimg-like object
resample_to_img(source_img, target_img[, …])	Resample a Niimg-like source image on a target Niimg-like image (no registration is performed: the image should already be aligned).
reorder_img(img[, resample])	Returns an image with the affine diagonal (by permuting axes).
smooth_img(imgs, fwhm)	Smooth images by applying a Gaussian filter.
swap_img_hemispheres(img)	Performs swapping of hemispheres in the indicated NIfTI image.
threshold_img(img, threshold[, mask_img, copy])	Threshold the given input image, mostly statistical or atlas images.

8.6. nilearn.input_data: Loading and Processing Files Easily

The nilearn.input_data module includes scikit-learn tranformers and tools to preprocess neuro-imaging data.

User guide: See the NiftiMasker: applying a mask to load time-series section for further details.

Classes:

NiftiMasker([mask_img, sessions, …])	Applying a mask to extract time-series from Niimg-like objects.
MultiNiftiMasker([mask_img, smoothing_fwhm, …])	Class for masking of Niimg-like objects.
NiftiLabelsMasker(labels_img[, …])	Class for masking of Niimg-like objects.
NiftiMapsMasker(maps_img[, mask_img, …])	Class for masking of Niimg-like objects.
NiftiSpheresMasker(seeds[, radius, …])	Class for masking of Niimg-like objects using seeds.

8.7. nilearn.masking: Data Masking Utilities

Utilities to compute and operate on brain masks

User guide: See the Masking the data: from 4D image to 2D array section for further details.

Functions:

compute_epi_mask(epi_img[, lower_cutoff, …])	Compute a brain mask from fMRI data in 3D or 4D ndarrays.
compute_multi_epi_mask(epi_imgs[, …])	Compute a common mask for several sessions or subjects of fMRI data.
compute_brain_mask(target_img[, threshold, …])	Compute the whole-brain mask.
compute_multi_gray_matter_mask(target_imgs)	Compute a mask corresponding to the gray matter part of the brain for a list of images.
compute_background_mask(data_imgs[, …])	Compute a brain mask for the images by guessing the value of the background from the border of the image.
compute_multi_background_mask(data_imgs[, …])	Compute a common mask for several sessions or subjects of data.
intersect_masks(mask_imgs[, threshold, …])	Compute intersection of several masks
apply_mask(imgs, mask_img[, dtype, …])	Extract signals from images using specified mask.
unmask(X, mask_img[, order])	Take masked data and bring them back into 3D/4D

8.8. nilearn.regions: Operating on Regions

The nilearn.regions class module includes region extraction procedure on a 4D statistical/atlas maps and its function.

Functions:

connected_regions(maps_img[, …])	Extraction of brain connected regions into separate regions.
connected_label_regions(labels_img[, …])	Extract connected regions from a brain atlas image defined by labels (integers).
img_to_signals_labels(imgs, labels_img[, …])	Extract region signals from image.
signals_to_img_labels(signals, labels_img[, …])	Create image from region signals defined as labels.
img_to_signals_maps(imgs, maps_img[, mask_img])	Extract region signals from image.
signals_to_img_maps(region_signals, maps_img)	Create image from region signals defined as maps.

Classes:

RegionExtractor(maps_img[, mask_img, …])	Class for brain region extraction.
Parcellations(method[, n_parcels, …])	Learn parcellations on fMRI images.
ReNA(mask_img[, n_clusters, scaling, …])	Recursive Neighbor Agglomeration (ReNA): Recursively merges the pair of clusters according to 1-nearest neighbors criterion.

8.9. nilearn.mass_univariate: Mass-Univariate Analysis

Defines a Massively Univariate Linear Model estimated with OLS and permutation test

Functions:

permuted_ols(tested_vars, target_vars[, …])

Massively univariate group analysis with permuted OLS.

8.10. nilearn.plotting: Plotting Brain Data

Plotting code for nilearn

Functions:

find_cut_slices(img[, direction, n_cuts, …])	Find ‘good’ cross-section slicing positions along a given axis.
find_xyz_cut_coords(img[, mask_img, …])	Find the center of the largest activation connected component.
find_parcellation_cut_coords(labels_img[, …])	Return coordinates of center of mass of 3D parcellation atlas
find_probabilistic_atlas_cut_coords(maps_img)	Return coordinates of center probabilistic atlas 4D image
plot_anat([anat_img, cut_coords, …])	Plot cuts of an anatomical image (by default 3 cuts: Frontal, Axial, and Lateral)
plot_img(img[, cut_coords, output_file, …])	Plot cuts of a given image (by default Frontal, Axial, and Lateral)
plot_epi([epi_img, cut_coords, output_file, …])	Plot cuts of an EPI image (by default 3 cuts: Frontal, Axial, and Lateral)
plot_matrix(mat[, title, labels, figure, …])	Plot the given matrix.
plot_roi(roi_img[, bg_img, cut_coords, …])	Plot cuts of an ROI/mask image (by default 3 cuts: Frontal, Axial, and Lateral)
plot_stat_map(stat_map_img[, bg_img, …])	Plot cuts of an ROI/mask image (by default 3 cuts: Frontal, Axial, and Lateral)
plot_glass_brain(stat_map_img[, …])	Plot 2d projections of an ROI/mask image (by default 3 projections: Frontal, Axial, and Lateral).
plot_connectome(adjacency_matrix, node_coords)	Plot connectome on top of the brain glass schematics.
plot_connectome_strength(adjacency_matrix, …)	Plot connectome strength on top of the brain glass schematics.
plot_markers(node_values, node_coords[, …])	Plot network nodes (markers) on top of the brain glass schematics.
plot_prob_atlas(maps_img[, bg_img, …])	Plot the probabilistic atlases onto the anatomical image by default MNI template
plot_carpet(img[, mask_img, detrend, …])	Plot an image representation of voxel intensities across time.
plot_surf(surf_mesh[, surf_map, bg_map, …])	Plotting of surfaces with optional background and data
plot_surf_roi(surf_mesh, roi_map[, bg_map, …])	Plotting ROI on a surface mesh with optional background
plot_surf_contours(surf_mesh, roi_map[, …])	Plotting contours of ROIs on a surface, optionally over a statistical map.
plot_surf_stat_map(surf_mesh, stat_map[, …])	Plotting a stats map on a surface mesh with optional background
plot_img_on_surf(stat_map[, surf_mesh, …])	Convenience function to plot multiple views of plot_surf_stat_map in a single figure.
plot_img_comparison(ref_imgs, src_imgs, masker)	Creates plots to compare two lists of images and measure correlation.
plot_design_matrix(design_matrix[, rescale, …])	Plot a design matrix provided as a DataFrame
plot_event(model_event[, cmap, output_file])	Creates plot for event visualization.
plot_contrast_matrix(contrast_def, design_matrix)	Creates plot for contrast definition.
view_surf(surf_mesh[, surf_map, bg_map, …])	Insert a surface plot of a surface map into an HTML page.
view_img_on_surf(stat_map_img[, surf_mesh, …])	Insert a surface plot of a statistical map into an HTML page.
view_connectome(adjacency_matrix, node_coords)	Insert a 3d plot of a connectome into an HTML page.
view_markers(marker_coords[, marker_color, …])	Insert a 3d plot of markers in a brain into an HTML page.
view_img(stat_map_img[, bg_img, cut_coords, …])	Interactive html viewer of a statistical map, with optional background
show()	Show all the figures generated by nilearn and/or matplotlib.

Classes:

OrthoSlicer(cut_coords[, axes, black_bg, …])

A class to create 3 linked axes for plotting orthogonal cuts of 3D maps.

8.11. nilearn.signal: Preprocessing Time Series

Preprocessing functions for time series.

All functions in this module should take X matrices with samples x features

Functions:

clean(signals[, sessions, detrend, …])	Improve SNR on masked fMRI signals.
high_variance_confounds(series[, …])	Return confounds time series extracted from series with highest variance.

8.12. nilearn.glm: Generalized Linear Models

Analysing fMRI data using GLMs.

Note that the nilearn.glm module is experimental.

It may change in any future (>0.7.0) release of Nilearn.

Classes:

Contrast(effect, variance[, dim, dof, …])	The contrast class handles the estimation of statistical contrasts on a given model: student (t) or Fisher (F).
FContrastResults(effect, covariance, F, df_num)	Results from an F contrast of coefficients in a parametric model.
TContrastResults(t, sd, effect[, df_den])	Results from a t contrast of coefficients in a parametric model.
ARModel(design, rho)	A regression model with an AR(p) covariance structure.
OLSModel(design)	A simple ordinary least squares model.
LikelihoodModelResults(theta, Y, model[, …])	Class to contain results from likelihood models
RegressionResults(theta, Y, model, …[, …])	This class summarizes the fit of a linear regression model.
SimpleRegressionResults(results)	This class contains only information of the model fit necessary for contast computation.

Functions:

compute_contrast(labels, regression_result, …)	Compute the specified contrast given an estimated glm
compute_fixed_effects(contrast_imgs, …[, …])	Compute the fixed effects, given images of effects and variance
expression_to_contrast_vector(expression, …)	Converts a string describing a contrast to a contrast vector
fdr_threshold(z_vals, alpha)	return the Benjamini-Hochberg FDR threshold for the input z_vals
cluster_level_inference(stat_img[, …])	Report the proportion of active voxels for all clusters defined by the input threshold.
threshold_stats_img([stat_img, mask_img, …])	Compute the required threshold level and return the thresholded map

8.12.15. nilearn.glm.first_level

Classes:

FirstLevelModel([t_r, slice_time_ref, …])

Implementation of the General Linear Model for single session fMRI data.

Functions:

check_design_matrix(design_matrix)	Check that the provided DataFrame is indeed a valid design matrix descriptor, and returns a triplet of fields
compute_regressor(exp_condition, hrf_model, …)	This is the main function to convolve regressors with hrf model
first_level_from_bids(dataset_path, task_label)	Create FirstLevelModel objects and fit arguments from a BIDS dataset.
glover_dispersion_derivative(tr[, …])	Implementation of the Glover dispersion derivative hrf model
glover_hrf(tr[, oversampling, time_length, …])	Implementation of the Glover hrf model
glover_time_derivative(tr[, oversampling, …])	Implementation of the Glover time derivative hrf (dhrf) model
make_first_level_design_matrix(frame_times)	Generate a design matrix from the input parameters
mean_scaling(Y[, axis])	Scaling of the data to have percent of baseline change along the specified axis
run_glm(Y, X[, noise_model, bins, n_jobs, …])	GLM fit for an fMRI data matrix
spm_dispersion_derivative(tr[, …])	Implementation of the SPM dispersion derivative hrf model
spm_hrf(tr[, oversampling, time_length, onset])	Implementation of the SPM hrf model
spm_time_derivative(tr[, oversampling, …])	Implementation of the SPM time derivative hrf (dhrf) model

8.12.16. nilearn.glm.second_level

Classes:

SecondLevelModel([mask_img, target_affine, …])

Implementation of the General Linear Model for multiple subject fMRI data

Functions:

make_second_level_design_matrix(subjects_label)	Sets up a second level design.
non_parametric_inference(second_level_input)	Generate p-values corresponding to the contrasts provided based on permutation testing.

8.13. nilearn.reporting: Reporting Functions

Reporting code for nilearn

Functions:

get_clusters_table(stat_img, stat_threshold)	Creates pandas dataframe with img cluster statistics.
make_glm_report(model, contrasts[, title, …])	Returns HTMLDocument object for a report which shows all important aspects of a fitted GLM.

8.14. nilearn.surface: Manipulating Surface Data

Functions for surface manipulation.

Functions:

load_surf_data(surf_data)	Loading data to be represented on a surface mesh.
load_surf_mesh(surf_mesh)	Loading a surface mesh geometry
vol_to_surf(img, surf_mesh[, radius, …])	Extract surface data from a Nifti image.