6. Reference documentation: all nilearn functions

This is the class and function reference of nilearn. Please refer to the full user guide for further details, as the class and function raw specifications may not be enough to give full guidelines on their uses.

6.1. nilearn.datasets: Automatic Dataset Fetching

Utilities to download NeuroImaging datasets

User guide: See the Fetching datasets 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 parcellation from FSL if installed or download it.
fetch_atlas_msdl([data_dir, url, resume, ...])	Download and load the MSDL brain atlas.
fetch_atlas_power_2011()	Download and load the Power 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_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_haxby([data_dir, n_subjects, ...])	Download and loads complete haxby dataset
fetch_haxby_simple([data_dir, url, resume, ...])	Download and load an example haxby dataset
fetch_icbm152_2009([data_dir, url, resume, ...])	Download and load the ICBM152 template (dated 2009)
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_oasis_vbm([n_subjects, ...])	Download and load Oasis “cross-sectional MRI” dataset (416 subjects).

6.2. nilearn.decoding: Decoding

Decoding tools and algorithms

Classes:

6.3. nilearn.decompositon: Multivariate decompositions

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

Classes:

6.4. nilearn.image: Image processing and resampling utilities

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

Functions:

6.5. 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: loading, masking and filtering section for further details.

Classes:

6.6. 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_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_mask
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

6.7. nilearn.region: Operating on regions

Functions for extracting region-defined signals.

Two ways of defining regions are supported: as labels in a single 3D image, or as weights in one image per region (maps).

Functions:

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.

See also

nilearn.masking.apply_mask, nilearn.masking.unmask

6.8. nilearn.mass_univariate: Mass-univariate analysis

Functions:

6.9. nilearn.plotting: Plotting brain data

Plotting code for nilearn

Functions:

Classes:

OrthoSlicer(cut_coords[, axes, black_bg])

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

6.10. 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[, detrend, standardize, ...])

Improve SNR on masked fMRI signals.