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.

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.

nilearn.datasets: Automatic Dataset Fetching

Helper functions to download NeuroImaging datasets

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

Templates

Functions:

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 the 'gm' mask.
fetch_surf_fsaverage([mesh, data_dir])	Download a Freesurfer fsaverage surface.
load_mni152_brain_mask([resolution, threshold])	Load the MNI152 whole-brain mask.
load_mni152_gm_mask([resolution, threshold, ...])	Load the MNI152 grey-matter mask.
load_mni152_gm_template([resolution])	Load the MNI152 grey-matter template.
load_mni152_template([resolution])	Load the MNI152 skullstripped T1 template.
load_mni152_wm_mask([resolution, threshold, ...])	Load the MNI152 white-matter mask.
load_mni152_wm_template([resolution])	Load the MNI152 white-matter template.

Atlases

Functions:

fetch_atlas_aal([version, data_dir, url, ...])	Downloads and returns the AAL template for SPM 12.
fetch_atlas_allen_2011([data_dir, url, ...])	Download and return file names for the Allen and MIALAB ICA Probabilistic atlas (dated 2011).
fetch_atlas_basc_multiscale_2015([version, ...])	Downloads and loads multiscale functional brain parcellations.
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 deterministic atlas (dated 2009).
fetch_atlas_difumo([dimension, ...])	Fetch DiFuMo brain atlas.
fetch_atlas_harvard_oxford(atlas_name[, ...])	Load Harvard-Oxford parcellations from FSL.
fetch_atlas_juelich(atlas_name[, data_dir, ...])	Load Juelich parcellations from FSL.
fetch_atlas_msdl([data_dir, url, resume, ...])	Download and load the MSDL brain Probabilistic atlas.
fetch_atlas_pauli_2017([version, data_dir, ...])	Download the Pauli et al. (2017) atlas.
fetch_atlas_schaefer_2018([n_rois, ...])	Download and return file names for the Schaefer 2018 parcellation.
fetch_atlas_smith_2009([data_dir, mirror, ...])	Download and load the Smith ICA and BrainMap Probabilistic atlas (2009).
fetch_atlas_surf_destrieux([data_dir, url, ...])	Download and load Destrieux et al, 2010 cortical Deterministic atlas.
fetch_atlas_talairach(level_name[, ...])	Download the Talairach Deterministic atlas.
fetch_atlas_yeo_2011([data_dir, url, ...])	Download and return file names for the Yeo 2011 parcellation.
fetch_coords_dosenbach_2010([...])	Load the Dosenbach et al. 160 ROIs.
fetch_coords_power_2011([legacy_format])	Download and load the Power et al. brain atlas composed of 264 ROIs.
fetch_coords_seitzman_2018([...])	Load the Seitzman et al. 300 ROIs.

Preprocessed datasets

Functions:

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_bids_langloc_dataset([data_dir, verbose])	Download language localizer example bids dataset.
fetch_development_fmri([n_subjects, ...])	Fetch movie watching based brain development dataset (fMRI)
fetch_ds000030_urls([data_dir, verbose])	Fetch URLs for files from the ds000030 BIDS dataset.
fetch_fiac_first_level([data_dir, verbose])	Download a first-level fiac fMRI dataset (2 sessions)
fetch_haxby([data_dir, subjects, ...])	Download and loads complete haxby dataset.
fetch_language_localizer_demo_dataset([...])	Download language localizer demo dataset.
fetch_localizer_first_level([data_dir, verbose])	Download a first-level localizer fMRI dataset
fetch_miyawaki2008([data_dir, url, resume, ...])	Download and loads Miyawaki et al. 2008 dataset (153MB).
fetch_openneuro_dataset_index([data_dir, ...])	Download a file with OpenNeuro BIDS dataset index.
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_surf_nki_enhanced([n_subjects, ...])	Download and load the NKI enhanced resting-state dataset, preprocessed and projected to the fsaverage5 space surface.

Statistical maps/derivatives

Functions:

fetch_localizer_button_task([data_dir, url, ...])	Fetch left vs right button press contrast maps from the localizer.
fetch_localizer_calculation_task([...])	Fetch calculation task contrast maps from the localizer.
fetch_localizer_contrasts(contrasts[, ...])	Download and load Brainomics/Localizer dataset (94 subjects).
fetch_megatrawls_netmats([dimensionality, ...])	Downloads and returns Network Matrices data from MegaTrawls release in HCP.
fetch_mixed_gambles([n_subjects, data_dir, ...])	Fetch Jimura "mixed gambles" dataset.
fetch_oasis_vbm([n_subjects, ...])	Download and load Oasis "cross-sectional MRI" dataset (416 subjects).
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.

General functions

Functions:

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_openneuro_dataset([urls, data_dir, ...])	Download OpenNeuro BIDS dataset.
get_data_dirs([data_dir])	Returns the directories in which nilearn looks for data.
patch_openneuro_dataset(file_list)	Add symlinks for files not named according to BIDS conventions.
select_from_index(urls[, inclusion_filters, ...])	Select subset of urls with given filters.

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.

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 [R637c2563345c-1] [R637c2563345c-2].
DictLearning([n_components, n_epochs, ...])	Perform a map learning algorithm based on spatial component sparsity, over a CanICA initialization [Rd0eec3116114-1].

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:

binarize_img(img[, threshold, mask_img])	Binarize an image such that its values are either 0 or 1.
clean_img(imgs[, runs, 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[, ...])	Threshold the given input image, mostly statistical or atlas images.

nilearn.interfaces: Loading components from interfaces

Interfaces for Nilearn.

nilearn.interfaces.bids

The nilearn.interfaces.bids module includes tools to work with BIDS format data.

Functions:

get_bids_files(main_path[, file_tag, ...])	Search for files in a BIDS dataset following given constraints.
parse_bids_filename(img_path)	Return dictionary with parsed information from file path.
save_glm_to_bids(model, contrasts[, ...])	Save GLM results to BIDS-like files.

nilearn.interfaces.fmriprep

The nilearn.interfaces.fmriprep module includes tools to preprocess neuroimaging data and access fMRIPrep generated confounds.

Functions:

load_confounds(img_files[, strategy, ...])	Use confounds from fMRIPrep.
load_confounds_strategy(img_files[, ...])	Use preset strategy to load confounds from fMRIPrep.

nilearn.interfaces.fsl

Functions for working with the FSL library.

Functions:

get_design_from_fslmat(fsl_design_matrix_path)

Extract design matrix dataframe from FSL mat file.

nilearn.maskers: Extracting Signals from Brain Images

The nilearn.maskers contains masker objects.

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

Classes:

BaseMasker()	Base class for NiftiMaskers.
NiftiMasker([mask_img, runs, ...])	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[, labels, ...])	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.

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 numpy.ndarray.
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, grey-matter or white-matter mask.
compute_multi_brain_mask(target_imgs[, ...])	Compute the whole-brain, grey-matter or white-matter mask 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.

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.
HierarchicalKMeans(n_clusters[, init, ...])	Hierarchical KMeans: First clusterize the samples into big clusters.

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.

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_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, mask_labels, ...])	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 pandas.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.

nilearn.plotting.displays: Interacting with figures

Display objects and utilities.

These objects are returned by plotting functions from the plotting module.

Functions:

get_projector(display_mode)	Retrieve a projector from a given display mode.
get_slicer(display_mode)	Retrieve a slicer from a given display mode.

Classes:

OrthoProjector(cut_coords[, axes, black_bg, ...])	A class to create linked axes for plotting orthogonal projections of 3D maps.
XZProjector(cut_coords[, axes, black_bg, ...])	The XZProjector class enables to combine sagittal and axial views on the same figure through 2D projections with plot_glass_brain.
YZProjector(cut_coords[, axes, black_bg, ...])	The YZProjector class enables to combine coronal and axial views on the same figure through 2D projections with plot_glass_brain.
YXProjector(cut_coords[, axes, black_bg, ...])	The YXProjector class enables to combine coronal and sagittal views on the same figure through 2D projections with plot_glass_brain.
XProjector(cut_coords[, axes, black_bg, ...])	The XProjector class enables sagittal visualization through 2D projections with plot_glass_brain.
YProjector(cut_coords[, axes, black_bg, ...])	The YProjector class enables coronal visualization through 2D projections with plot_glass_brain.
ZProjector(cut_coords[, axes, black_bg, ...])	The ZProjector class enables axial visualization through 2D projections with plot_glass_brain.
LZRYProjector(cut_coords[, axes, black_bg, ...])	The LZRYProjector class enables ? visualization on the same figure through 2D projections with plot_glass_brain.
LYRZProjector(cut_coords[, axes, black_bg, ...])	The LYRZProjector class enables ? visualization on the same figure through 2D projections with plot_glass_brain.
LYRProjector(cut_coords[, axes, black_bg, ...])	The LYRProjector class enables ? visualization on the same figure through 2D projections with plot_glass_brain.
LZRProjector(cut_coords[, axes, black_bg, ...])	The LZRProjector class enables hemispheric sagittal visualization on the same figure through 2D projections with plot_glass_brain.
LRProjector(cut_coords[, axes, black_bg, ...])	The LRProjector class enables left-right visualization on the same figure through 2D projections with plot_glass_brain.
LProjector(cut_coords[, axes, black_bg, ...])	The LProjector class enables the visualization of left 2D projection with plot_glass_brain.
RProjector(cut_coords[, axes, black_bg, ...])	The RProjector class enables the visualization of right 2D projection with plot_glass_brain.
BaseAxes(ax, direction, coord)	An MPL axis-like object that displays a 2D view of 3D volumes.
CutAxes(ax, direction, coord)	An MPL axis-like object that displays a cut of 3D volumes.
GlassBrainAxes(ax, direction, coord[, plot_abs])	An MPL axis-like object that displays a 2D projection of 3D volumes with a schematic view of the brain.
BaseSlicer(cut_coords[, axes, black_bg, ...])	BaseSlicer implementation which main purpose is to auto adjust the axes size to the data with different layout of cuts.
OrthoSlicer(cut_coords[, axes, black_bg, ...])	A class to create 3 linked axes for plotting orthogonal cuts of 3D maps.
PlotlySurfaceFigure([figure, output_file])	Implementation of a surface figure obtained with plotly engine.
TiledSlicer(cut_coords[, axes, black_bg, ...])	A class to create 3 axes for plotting orthogonal cuts of 3D maps, organized in a 2x2 grid.
MosaicSlicer(cut_coords[, axes, black_bg, ...])	A class to create 3 Axes for plotting cuts of 3D maps, in multiple rows and columns.
XZSlicer(cut_coords[, axes, black_bg, ...])	The XZSlicer class enables to combine sagittal and axial views on the same figure with plotting functions of Nilearn like nilearn.plotting.plot_img.
YZSlicer(cut_coords[, axes, black_bg, ...])	The YZSlicer class enables to combine coronal and axial views on the same figure with plotting functions of Nilearn like nilearn.plotting.plot_img.
YXSlicer(cut_coords[, axes, black_bg, ...])	The YXSlicer class enables to combine coronal and sagittal views on the same figure with plotting functions of Nilearn like nilearn.plotting.plot_img.
XSlicer(cut_coords[, axes, black_bg, ...])	The XSlicer class enables sagittal visualization with plotting functions of Nilearn like nilearn.plotting.plot_img.
YSlicer(cut_coords[, axes, black_bg, ...])	The YSlicer class enables coronal visualization with plotting functions of Nilearn like nilearn.plotting.plot_img.
ZSlicer(cut_coords[, axes, black_bg, ...])	The ZSlicer class enables axial visualization with plotting functions of Nilearn like nilearn.plotting.plot_img.

nilearn.signal: Preprocessing Time Series

Preprocessing functions for time series.

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

Functions:

butterworth(signals, sampling_rate[, ...])	Apply a low-pass, high-pass or band-pass Butterworth filter.
clean(signals[, runs, detrend, standardize, ...])	Improve SNR on masked fMRI signals.
high_variance_confounds(series[, ...])	Return confounds time series extracted from series with highest variance.

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 contrast 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

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

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.

nilearn.reporting: Reporting Functions

Reporting code for nilearn

Classes:

HTMLReport(head_tpl, body[, head_values])

A report written as HTML.

Functions:

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

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.