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.
List of modules
nilearn.connectome
: Functional Connectivitynilearn.datasets
: Automatic Dataset Fetchingnilearn.decoding
: Decodingnilearn.decomposition
: Multivariate Decompositionsnilearn.image
: Image Processing and Resampling Utilitiesnilearn.input_data
: Loading and Processing Files Easilynilearn.masking
: Data Masking Utilitiesnilearn.regions
: Operating on Regionsnilearn.mass_univariate
: Mass-Univariate Analysisnilearn.plotting
: Plotting Brain Datanilearn.signal
: Preprocessing Time Seriesnilearn.glm
: Generalized Linear Modelsnilearn.reporting
: Reporting Functionsnilearn.surface
: Manipulating Surface Data
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. |