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:
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A class that computes different kinds of functional connectivity matrices. |
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Covariance and precision matrix estimator. |
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Sparse inverse covariance w/ cross-validated choice of the parameter. |
Functions:
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Return the flattened lower triangular part of an array. |
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Return the symmetric matrix given its flattened lower triangular part. |
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Compute sparse precision matrices and covariance matrices. |
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Return correlation matrix for a given covariance matrix. |
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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:
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Download and return file names for the Craddock 2012 parcellation |
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Download and load the Destrieux cortical atlas (dated 2009) |
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Load Harvard-Oxford parcellations from FSL. |
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Download and load the MSDL brain atlas. |
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Fetch DiFuMo brain atlas |
Download and load the Power et al. brain atlas composed of 264 ROIs. |
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Load the Seitzman et al. 300 ROIs. |
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Download and load the Smith ICA and BrainMap atlas (dated 2009). |
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Download and return file names for the Yeo 2011 parcellation. |
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Downloads and returns the AAL template for SPM 12. |
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Downloads and loads multiscale functional brain parcellations |
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Download and return file names for the Allen and MIALAB ICA atlas (dated 2011). |
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Download the Pauli et al. (2017) atlas with in total 12 subcortical nodes. |
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Load the Dosenbach et al. 160 ROIs. |
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Fetch ABIDE dataset. |
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Download and load the ADHD resting-state dataset. |
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Fetch movie watching based brain development dataset (fMRI) |
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Download and loads complete haxby dataset. |
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Download and load the ICBM152 template (dated 2009). |
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Downloads ICBM152 template first, then loads ‘gm’ mask image. |
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Fetch left vs right button press contrast maps from the localizer. |
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Download and load Brainomics/Localizer dataset (94 subjects). |
Fetch calculation task contrast maps from the localizer. |
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Download and loads Miyawaki et al. 2008 dataset (153MB). |
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Download and load the NKI enhanced resting-state dataset, preprocessed and projected to the fsaverage5 space surface. |
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Download a Freesurfer fsaverage surface. |
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Download and load Destrieux et al, 2010 cortical atlas |
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Download the Talairach atlas. |
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Download and return file names for the Schaefer 2018 parcellation |
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Download and load Oasis “cross-sectional MRI” dataset (416 subjects). |
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Downloads and returns Network Matrices data from MegaTrawls release in HCP. |
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DEPRECATED: ‘fetch_cobre’ has been deprecated and will be removed in release 0.9 . |
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Download data from neurovault.org that match certain criteria. |
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Download specific images and collections from neurovault.org. |
Fetch a contrast map from NeuroVault showing the effect of mental subtraction upon auditory instructions |
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Fetch left vs right button press group contrast map from NeuroVault. |
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Returns the directories in which nilearn looks for data. |
Load skullstripped 2mm version of the MNI152 originally distributed with FSL. |
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Load brain mask from MNI152 T1 template |
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Download language localizer demo dataset. |
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Download language localizer example bids dataset. |
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Download a file with OpenNeuro BIDS dataset index. |
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Select subset of urls with given filters. |
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Add symlinks for files not named according to latest BIDS conventions. |
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Download OpenNeuro BIDS dataset. |
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Download a first-level localizer fMRI dataset |
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Function to fetch SPM auditory single-subject data. |
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Fetcher for Multi-modal Face Dataset. |
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Download a first-level fiac fMRI dataset (2 sessions) |
8.3. nilearn.decoding: Decoding¶
Decoding tools and algorithms.
Classes:
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A wrapper for popular classification strategies in neuroimaging. |
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A wrapper for popular regression strategies in neuroimaging. |
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State of the art decoding scheme applied to usual classifiers. |
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State of the art decoding scheme applied to usual regression estimators. |
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Classification learners with sparsity and spatial priors. |
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Regression learners with sparsity and spatial priors. |
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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:
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Perform Canonical Independent Component Analysis [R637c2563345c-1] [R637c2563345c-2]. |
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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:
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Improve SNR on masked fMRI signals. |
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Concatenate a list of 3D/4D niimgs of varying lengths. |
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Convert the x, y, z coordinates from one image space to another |
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Copy an image to a nibabel.Nifti1Image. |
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Crops an image as much as possible. |
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Get the image data as a |
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Return confounds signals extracted from input signals with highest variance. |
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Indexes into a 4D Niimg-like object in the fourth dimension. |
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Iterates over a 4D Niimg-like object in the fourth dimension. |
Return the largest connected component of an image or list of images. |
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Load a Niimg-like object from filenames or list of filenames. |
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Interpret a numpy based string formula using niimg in named parameters. |
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Compute the mean of the images over time or the 4th dimension. |
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Create a new image of the same class as the reference image |
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Resample a Niimg-like object |
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Resample a Niimg-like source image on a target Niimg-like image (no registration is performed: the image should already be aligned). |
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Returns an image with the affine diagonal (by permuting axes). |
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Smooth images by applying a Gaussian filter. |
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Performs swapping of hemispheres in the indicated NIfTI image. |
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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:
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Applying a mask to extract time-series from Niimg-like objects. |
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Class for masking of Niimg-like objects. |
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Class for masking of Niimg-like objects. |
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Class for masking of Niimg-like objects. |
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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:
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Compute a brain mask from fMRI data in 3D or 4D ndarrays. |
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Compute a common mask for several sessions or subjects of fMRI data. |
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Compute the whole-brain mask. |
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Compute a mask corresponding to the gray matter part of the brain for a list of images. |
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Compute a brain mask for the images by guessing the value of the background from the border of the image. |
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Compute a common mask for several sessions or subjects of data. |
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Compute intersection of several masks |
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Extract signals from images using specified mask. |
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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:
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Extraction of brain connected regions into separate regions. |
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Extract connected regions from a brain atlas image defined by labels (integers). |
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Extract region signals from image. |
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Create image from region signals defined as labels. |
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Extract region signals from image. |
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Create image from region signals defined as maps. |
Classes:
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Class for brain region extraction. |
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Learn parcellations on fMRI images. |
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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:
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Massively univariate group analysis with permuted OLS. |
8.10. nilearn.plotting: Plotting Brain Data¶
Plotting code for nilearn
Functions:
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Find ‘good’ cross-section slicing positions along a given axis. |
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Find the center of the largest activation connected component. |
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Return coordinates of center of mass of 3D parcellation atlas. |
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Return coordinates of center probabilistic atlas 4D image. |
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Plot cuts of an anatomical image (by default 3 cuts: Frontal, Axial, and Lateral) |
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Plot cuts of a given image (by default Frontal, Axial, and Lateral) |
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Plot cuts of an EPI image (by default 3 cuts: Frontal, Axial, and Lateral) |
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Plot the given matrix. |
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Plot cuts of an ROI/mask image (by default 3 cuts: Frontal, Axial, and Lateral) |
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Plot cuts of an ROI/mask image (by default 3 cuts: Frontal, Axial, and Lateral) |
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Plot 2d projections of an ROI/mask image (by default 3 projections: Frontal, Axial, and Lateral). |
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Plot connectome on top of the brain glass schematics. |
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Plot connectome strength on top of the brain glass schematics. |
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Plot network nodes (markers) on top of the brain glass schematics. |
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Plot the probabilistic atlases onto the anatomical image by default MNI template |
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Plot an image representation of voxel intensities across time. |
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Plotting of surfaces with optional background and data |
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Plotting ROI on a surface mesh with optional background |
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Plotting contours of ROIs on a surface, optionally over a statistical map. |
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Plotting a stats map on a surface mesh with optional background |
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Convenience function to plot multiple views of plot_surf_stat_map in a single figure. |
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Creates plots to compare two lists of images and measure correlation. |
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Plot a design matrix provided as a DataFrame |
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Creates plot for event visualization. |
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Creates plot for contrast definition. |
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Insert a surface plot of a surface map into an HTML page. |
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Insert a surface plot of a statistical map into an HTML page. |
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Insert a 3d plot of a connectome into an HTML page. |
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Insert a 3d plot of markers in a brain into an HTML page. |
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Interactive html viewer of a statistical map, with optional background. |
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Show all the figures generated by nilearn and/or matplotlib. |
Classes:
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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:
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Improve SNR on masked fMRI signals. |
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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:
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The contrast class handles the estimation of statistical contrasts on a given model: student (t) or Fisher (F). |
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Results from an F contrast of coefficients in a parametric model. |
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Results from a t contrast of coefficients in a parametric model. |
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A regression model with an AR(p) covariance structure. |
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A simple ordinary least squares model. |
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Class to contain results from likelihood models. |
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This class summarizes the fit of a linear regression model. |
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This class contains only information of the model fit necessary for contast computation. |
Functions:
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Compute the specified contrast given an estimated glm |
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Compute the fixed effects, given images of effects and variance |
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Converts a string describing a contrast to a contrast vector |
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Return the Benjamini-Hochberg FDR threshold for the input z_vals |
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Report the proportion of active voxels for all clusters defined by the input threshold. |
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Compute the required threshold level and return the thresholded map |
8.12.15. nilearn.glm.first_level¶
Classes:
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Implementation of the General Linear Model for single session fMRI data. |
Functions:
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Check that the provided DataFrame is indeed a valid design matrix descriptor, and returns a triplet of fields |
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This is the main function to convolve regressors with hrf model |
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Create FirstLevelModel objects and fit arguments from a BIDS dataset. |
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Implementation of the Glover dispersion derivative hrf model |
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Implementation of the Glover hrf model |
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Implementation of the Glover time derivative hrf (dhrf) model |
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Generate a design matrix from the input parameters |
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Scaling of the data to have percent of baseline change along the specified axis |
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GLM fit for an fMRI data matrix |
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Implementation of the SPM dispersion derivative hrf model |
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Implementation of the SPM hrf model |
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Implementation of the SPM time derivative hrf (dhrf) model |
8.12.16. nilearn.glm.second_level¶
Classes:
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Implementation of the General Linear Model for multiple subject fMRI data |
Functions:
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Sets up a second level design. |
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Generate p-values corresponding to the contrasts provided based on permutation testing. |
8.13. nilearn.reporting: Reporting Functions¶
Reporting code for nilearn
Functions:
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Creates pandas dataframe with img cluster statistics. |
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Returns HTMLReport 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:
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Loading data to be represented on a surface mesh. |
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Loading a surface mesh geometry |
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Extract surface data from a Nifti image. |