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.interfaces: Loading components from interfacesnilearn.maskers: Extracting Signals from Brain Imagesnilearn.masking: Data Masking Utilitiesnilearn.regions: Operating on Regionsnilearn.mass_univariate: Mass-Univariate Analysisnilearn.plotting: Plotting Brain Datanilearn.plotting.displays: Interacting with figures
nilearn.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:
| A class that computes different kinds of functional connectivity matrices. |
| Covariance and precision matrix estimator. |
| Sparse inverse covariance w/ cross-validated choice of the parameter. |
Functions:
| Return the flattened lower triangular part of an array. |
| Return the symmetric matrix given its flattened lower triangular part. |
| Compute sparse precision matrices and covariance matrices. |
| Return correlation matrix for a given covariance matrix. |
| 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.
8.2.1. Templates¶
Functions:
| Download and load the ICBM152 template (dated 2009). |
| Downloads ICBM152 template first, then loads the ‘gm’ mask. |
| Download a Freesurfer fsaverage surface. |
| Load the MNI152 whole-brain mask. |
| Load the MNI152 grey-matter mask. |
| Load the MNI152 grey-matter template. |
| Load the MNI152 skullstripped T1 template. |
| Load the MNI152 white-matter mask. |
| Load the MNI152 white-matter template. |
8.2.2. Atlases¶
Functions:
| Downloads and returns the AAL template for SPM 12. |
| Download and return file names for the Allen and MIALAB ICA Probabilistic atlas (dated 2011). |
| Downloads and loads multiscale functional brain parcellations. |
| Download and return file names for the Craddock 2012 parcellation. |
| Download and load the Destrieux cortical deterministic atlas (dated 2009). |
| Fetch DiFuMo brain atlas. |
| Load Harvard-Oxford parcellations from FSL. |
| Load Juelich parcellations from FSL. |
| Download and load the MSDL brain Probabilistic atlas. |
| Download the Pauli et al. (2017) atlas. |
| Download and return file names for the Schaefer 2018 parcellation. |
| Download and load the Smith ICA and BrainMap Probabilistic atlas (2009). |
| Download and load Destrieux et al, 2010 cortical Deterministic atlas. |
| Download the Talairach Deterministic atlas. |
| Download and return file names for the Yeo 2011 parcellation. |
Load the Dosenbach et al. 160 ROIs. | |
| Download and load the Power et al. brain atlas composed of 264 ROIs. |
Load the Seitzman et al. 300 ROIs. |
8.2.3. Preprocessed datasets¶
Functions:
| Fetch ABIDE dataset. |
| Download and load the ADHD resting-state dataset. |
| Download language localizer example bids dataset. |
| Fetch movie watching based brain development dataset (fMRI) |
| Download a first-level fiac fMRI dataset (2 sessions) |
| Download and loads complete haxby dataset. |
Download language localizer demo dataset. | |
| Download a first-level localizer fMRI dataset |
| Download and loads Miyawaki et al. 2008 dataset (153MB). |
| Download a file with OpenNeuro BIDS dataset index. |
| Function to fetch SPM auditory single-subject data. |
| Fetcher for Multi-modal Face Dataset. |
| Download and load the NKI enhanced resting-state dataset, preprocessed and projected to the fsaverage5 space surface. |
8.2.4. Statistical maps/derivatives¶
Functions:
| Fetch left vs right button press contrast maps from the localizer. |
Fetch calculation task contrast maps from the localizer. | |
| Download and load Brainomics/Localizer dataset (94 subjects). |
| Downloads and returns Network Matrices data from MegaTrawls release in HCP. |
| Fetch Jimura “mixed gambles” dataset. |
| Download and load Oasis “cross-sectional MRI” dataset (416 subjects). |
Fetch a contrast map from NeuroVault showing the effect of mental subtraction upon auditory instructions | |
| Fetch left vs right button press group contrast map from NeuroVault. |
8.2.5. General functions¶
Functions:
| Download data from neurovault.org that match certain criteria. |
| Download specific images and collections from neurovault.org. |
| Download OpenNeuro BIDS dataset. |
| Returns the directories in which nilearn looks for data. |
| Add symlinks for files not named according to latest BIDS conventions. |
| Select subset of urls with given filters. |
8.3. nilearn.decoding: Decoding¶
Decoding tools and algorithms.
Classes:
| A wrapper for popular classification strategies in neuroimaging. |
| A wrapper for popular regression strategies in neuroimaging. |
| State of the art decoding scheme applied to usual classifiers. |
| State of the art decoding scheme applied to usual regression estimators. |
| Classification learners with sparsity and spatial priors. |
| Regression learners with sparsity and spatial priors. |
| 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:
| Perform Canonical Independent Component Analysis [R637c2563345c-1] [R637c2563345c-2]. |
| Perform a map learning algorithm based on spatial component sparsity, over a CanICA initialization [Rd0eec3116114-1]. |
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:
| Binarize an image such that its values are either 0 or 1. |
| Improve SNR on masked fMRI signals. |
| Concatenate a list of 3D/4D niimgs of varying lengths. |
| Convert the x, y, z coordinates from one image space to another |
| Copy an image to a nibabel.Nifti1Image. |
| Crops an image as much as possible. |
| Get the image data as a |
| Return confounds signals extracted from input signals with highest variance. |
| Indexes into a 4D Niimg-like object in the fourth dimension. |
| Iterates over a 4D Niimg-like object in the fourth dimension. |
Return the largest connected component of an image or list of images. | |
| Load a Niimg-like object from filenames or list of filenames. |
| Interpret a numpy based string formula using niimg in named parameters. |
| Compute the mean of the images over time or the 4th dimension. |
| Create a new image of the same class as the reference image |
| Resample a Niimg-like object |
| Resample a Niimg-like source image on a target Niimg-like image (no registration is performed: the image should already be aligned). |
| Returns an image with the affine diagonal (by permuting axes). |
| Smooth images by applying a Gaussian filter. |
| Performs swapping of hemispheres in the indicated NIfTI image. |
| Threshold the given input image, mostly statistical or atlas images. |
8.6. nilearn.interfaces: Loading components from interfaces¶
Interfaces for Nilearn.
8.6.1. nilearn.interfaces.bids¶
Functions for working with BIDS datasets.
Functions:
| Search for files in a BIDS dataset following given constraints. |
| Return dictionary with parsed information from file path. |
8.6.2. nilearn.interfaces.fmriprep¶
The nilearn.interfaces.fmriprep module includes tools to preprocess neuroimaging data and access fMRIPrep generated confounds.
Functions:
| Use confounds from fMRIPrep. |
| Use preset strategy to load confounds from fMRIPrep. |
8.6.3. nilearn.interfaces.fsl¶
Functions for working with the FSL library.
Functions:
| Extract design matrix dataframe from FSL mat file. |
8.7. 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:
Base class for NiftiMaskers. | |
| Applying a mask to extract time-series from Niimg-like objects. |
| Class for masking of Niimg-like objects. |
| Class for masking of Niimg-like objects. |
| Class for masking of Niimg-like objects. |
| Class for masking of Niimg-like objects using seeds. |
8.8. 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 a brain mask from fMRI data in 3D or 4D |
| Compute a common mask for several sessions or subjects of fMRI data. |
| Compute the whole-brain, grey-matter or white-matter mask. |
| Compute the whole-brain, grey-matter or white-matter mask for a list of images. |
| Compute a brain mask for the images by guessing the value of the background from the border of the image. |
| Compute a common mask for several sessions or subjects of data. |
| Compute intersection of several masks. |
| Extract signals from images using specified mask. |
| Take masked data and bring them back into 3D/4D. |
8.9. nilearn.regions: Operating on Regions¶
The nilearn.regions class module includes region extraction procedure on a 4D statistical/atlas maps and its function.
Functions:
| Extraction of brain connected regions into separate regions. |
| Extract connected regions from a brain atlas image defined by labels (integers). |
| Extract region signals from image. |
| Create image from region signals defined as labels. |
| Extract region signals from image. |
| Create image from region signals defined as maps. |
Classes:
| Class for brain region extraction. |
| Learn parcellations on fMRI images. |
| Recursive Neighbor Agglomeration (ReNA): Recursively merges the pair of clusters according to 1-nearest neighbors criterion. |
| Hierarchical KMeans: First clusterize the samples into big clusters. |
8.10. nilearn.mass_univariate: Mass-Univariate Analysis¶
Defines a Massively Univariate Linear Model estimated with OLS and permutation test
Functions:
| Massively univariate group analysis with permuted OLS. |
8.11. nilearn.plotting: Plotting Brain Data¶
Plotting code for nilearn
Functions:
| Find ‘good’ cross-section slicing positions along a given axis. |
| Find the center of the largest activation connected component. |
| Return coordinates of center of mass of 3D parcellation atlas. |
| Return coordinates of center probabilistic atlas 4D image. |
| Plot cuts of an anatomical image (by default 3 cuts: Frontal, Axial, and Lateral) |
| Plot cuts of a given image (by default Frontal, Axial, and Lateral) |
| Plot cuts of an EPI image (by default 3 cuts: Frontal, Axial, and Lateral) |
| Plot the given matrix. |
| Plot cuts of an ROI/mask image (by default 3 cuts: Frontal, Axial, and Lateral) |
| Plot cuts of an ROI/mask image (by default 3 cuts: Frontal, Axial, and Lateral) |
| Plot 2d projections of an ROI/mask image (by default 3 projections: Frontal, Axial, and Lateral). |
| Plot connectome on top of the brain glass schematics. |
| Plot network nodes (markers) on top of the brain glass schematics. |
| Plot the probabilistic atlases onto the anatomical image by default MNI template |
| Plot an image representation of voxel intensities across time. |
| Plotting of surfaces with optional background and data |
| Plotting ROI on a surface mesh with optional background |
| Plotting contours of ROIs on a surface, optionally over a statistical map. |
| Plotting a stats map on a surface mesh with optional background |
| Convenience function to plot multiple views of plot_surf_stat_map in a single figure. |
| Creates plots to compare two lists of images and measure correlation. |
| Plot a design matrix provided as a |
| Creates plot for event visualization. |
| Creates plot for contrast definition. |
| Insert a surface plot of a surface map into an HTML page. |
| Insert a surface plot of a statistical map into an HTML page. |
| Insert a 3d plot of a connectome into an HTML page. |
| Insert a 3d plot of markers in a brain into an HTML page. |
| Interactive html viewer of a statistical map, with optional background. |
| Show all the figures generated by nilearn and/or matplotlib. |
8.11.31. nilearn.plotting.displays: Interacting with figures¶
Display objects and utilities.
These objects are returned by plotting functions from the plotting module.
Functions:
| Retrieve a projector from a given display mode. |
| Retrieve a slicer from a given display mode. |
Classes:
| A class to create linked axes for plotting orthogonal projections of 3D maps. |
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| An MPL axis-like object that displays a 2D view of 3D volumes. |
| An MPL axis-like object that displays a cut of 3D volumes. |
| An MPL axis-like object that displays a 2D projection of 3D volumes with a schematic view of the brain. |
| BaseSlicer implementation which main purpose is to auto adjust the axes size to the data with different layout of cuts. |
| A class to create 3 linked axes for plotting orthogonal cuts of 3D maps. |
| Implementation of a surface figure obtained with plotly engine. |
| A class to create 3 axes for plotting orthogonal cuts of 3D maps, organized in a 2x2 grid. |
| A class to create 3 |
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8.12. nilearn.signal: Preprocessing Time Series¶
Preprocessing functions for time series.
All functions in this module should take X matrices with samples x features
Functions:
| Apply a low-pass, high-pass or band-pass Butterworth filter. |
| |
| Return confounds time series extracted from series with highest variance. |
8.13. 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:
| The contrast class handles the estimation of statistical contrasts on a given model: student (t) or Fisher (F). |
| Results from an F contrast of coefficients in a parametric model. |
| Results from a t contrast of coefficients in a parametric model. |
| A regression model with an AR(p) covariance structure. |
| A simple ordinary least squares model. |
| Class to contain results from likelihood models. |
| This class summarizes the fit of a linear regression model. |
| This class contains only information of the model fit necessary for contrast computation. |
Functions:
| Compute the specified contrast given an estimated glm |
| Compute the fixed effects, given images of effects and variance |
| Converts a string describing a contrast to a contrast vector |
| Return the Benjamini-Hochberg FDR threshold for the input z_vals |
| Report the proportion of active voxels for all clusters defined by the input threshold. |
| Compute the required threshold level and return the thresholded map |
8.13.15. nilearn.glm.first_level¶
Classes:
| Implementation of the General Linear Model for single session fMRI data. |
Functions:
| Check that the provided DataFrame is indeed a valid design matrix descriptor, and returns a triplet of fields |
| This is the main function to convolve regressors with hrf model |
| Create FirstLevelModel objects and fit arguments from a BIDS dataset. |
| Implementation of the Glover dispersion derivative hrf model |
| Implementation of the Glover hrf model |
| Implementation of the Glover time derivative hrf (dhrf) model |
| Generate a design matrix from the input parameters |
| Scaling of the data to have percent of baseline change along the specified axis |
| GLM fit for an fMRI data matrix |
| Implementation of the SPM dispersion derivative hrf model |
| Implementation of the SPM hrf model |
| Implementation of the SPM time derivative hrf (dhrf) model |
8.13.16. nilearn.glm.second_level¶
Classes:
| Implementation of the General Linear Model for multiple subject fMRI data |
Functions:
| Sets up a second level design. |
| Generate p-values corresponding to the contrasts provided based on permutation testing. |
8.14. nilearn.reporting: Reporting Functions¶
Reporting code for nilearn
Functions:
| Creates pandas dataframe with img cluster statistics. |
| Returns HTMLReport object for a report which shows all important aspects of a fitted GLM. |
8.15. nilearn.surface: Manipulating Surface Data¶
Functions for surface manipulation.
Functions:
| Loading data to be represented on a surface mesh. |
| Loading a surface mesh geometry |
| Extract surface data from a Nifti image. |