0.7.1¶

Released March 2021

HIGHLIGHTS¶

New atlas fetcher nilearn.datasets.fetch_atlas_difumo to download Dictionaries of Functional Modes, or “DiFuMo”, that can serve as atlases to extract functional signals with different dimensionalities (64, 128, 256, 512, and 1024). These modes are optimized to represent well raw BOLD timeseries, over a with range of experimental conditions.
nilearn.decoding.Decoder and nilearn.decoding.DecoderRegressor is now implemented with random predictions to estimate a chance level.
The functions nilearn.plotting.plot_epi, nilearn.plotting.plot_roi, nilearn.plotting.plot_stat_map, nilearn.plotting.plot_prob_atlas is now implemented with new display mode Mosaic. That implies plotting 3D maps in multiple columns and rows in a single axes.
nilearn.plotting.plot_carpet now supports discrete atlases. When an atlas is used, a colorbar is added to the figure, optionally with labels corresponding to the different values in the atlas.

NEW¶

New atlas fetcher nilearn.datasets.fetch_atlas_difumo to download Dictionaries of Functional Modes, or “DiFuMo”, that can serve as atlases to extract functional signals with different dimensionalities (64, 128, 256, 512, and 1024). These modes are optimized to represent well raw BOLD timeseries, over a with range of experimental conditions.
nilearn.glm.Contrast.one_minus_pvalue was added to ensure numerical stability of p-value estimation. It computes 1 - p-value using the Cumulative Distribution Function in the same way as nilearn.glm.Contrast.p_value computes the p-value using the Survival Function.

Fixes¶

Fix altered, non-zero baseline in design matrices where multiple events in the same condition end at the same time (https://github.com/nilearn/nilearn/issues/2674).
Fix testing issues on ARM machine.

Enhancements¶

nilearn.decoding.Decoder and nilearn.decoding.DecoderRegressor is now implemented with random predictions to estimate a chance level.
nilearn.decoding.Decoder, nilearn.decoding.DecoderRegressor, nilearn.decoding.FREMRegressor, and nilearn.decoding.FREMClassifier now override the score method to use whatever scoring strategy was defined through the scoring attribute instead of the sklearn default. If the scoring attribute of the decoder is set to None, the scoring strategy will default to accuracy for classifiers and to r2 score for regressors.
nilearn.plotting.plot_surf and deriving functions like nilearn.plotting.plot_surf_roi now accept an optional argument cbar_tick_format to specify how numbers should be displayed on the colorbar of surface plots. The default format is scientific notation except for nilearn.plotting.plot_surf_roi for which it is set as integers.
nilearn.plotting.plot_carpet now supports discrete atlases. When an atlas is used, a colorbar is added to the figure, optionally with labels corresponding to the different values in the atlas.
nilearn.input_data.NiftiMasker, nilearn.input_data.NiftiLabelsMasker, nilearn.input_data.MultiNiftiMasker, nilearn.input_data.NiftiMapsMasker, and nilearn.input_data.NiftiSpheresMasker can now compute high variance confounds on the images provided to transform and regress them out automatically. This behaviour is controlled through the high_variance_confounds boolean parameter of these maskers which default to False.
nilearn.input_data.NiftiLabelsMasker now automatically replaces NaNs in input data with zeros, to match the behavior of other maskers.
nilearn.datasets.fetch_neurovault now implements a resample boolean argument to either perform a fixed resampling during download or keep original images. This can be handy to reduce disk usage. By default, the downloaded images are not resampled.
The functions nilearn.plotting.plot_epi, nilearn.plotting.plot_roi, nilearn.plotting.plot_stat_map, nilearn.plotting.plot_prob_atlas is now implemented with new display mode Mosaic. That implies plotting 3D maps in multiple columns and rows in a single axes.
psc standardization option of nilearn.signal.clean now allows time series with negative mean values.
nilearn.reporting.make_glm_report and nilearn.reporting.get_clusters_table have a new argument, “two_sided”, which allows for two-sided thresholding, which is disabled by default.

0.7.0¶

Released November 2020

HIGHLIGHTS¶

Nilearn now includes the functionality of Nistats as nilearn.glm. This module is experimental, hence subject to change in any future release. Here’s a guide to replacing Nistats imports to work in Nilearn.
New decoder object nilearn.decoding.Decoder (for classification) and nilearn.decoding.DecoderRegressor (for regression) implement a model selection scheme that averages the best models within a cross validation loop.
New FREM object nilearn.decoding.FREMClassifier (for classification) and nilearn.decoding.FREMRegressor (for regression) extend the decoder object with one fast clustering step at the beginning and aggregates a high number of estimators trained on various splits of the training set.
New plotting functions:
- nilearn.plotting.plot_event to visualize events file.
- nilearn.plotting.plot_roi can now plot ROIs in contours with view_type argument.
- nilearn.plotting.plot_carpet generates a “carpet plot” (also known as a “Power plot” or a “grayplot”)
- nilearn.plotting.plot_img_on_surf generates multiple views of nilearn.plotting.plot_surf_stat_map in a single figure.
- nilearn.plotting.plot_markers shows network nodes (markers) on a glass brain template
- nilearn.plotting.plot_surf_contours plots the contours of regions of interest on the surface

Warning

Minimum required version of Joblib is now 0.12.

NEW¶

Nilearn now includes the functionality of Nistats. Here’s a guide to replacing Nistats imports to work in Nilearn.
New decoder object nilearn.decoding.Decoder (for classification) and nilearn.decoding.DecoderRegressor (for regression) implement a model selection scheme that averages the best models within a cross validation loop. The resulting average model is the one used as a classifier or a regressor. These two objects also leverage the NiftiMaskers to provide a direct interface with the Nifti files on disk.
New FREM object nilearn.decoding.FREMClassifier (for classification) and nilearn.decoding.FREMRegressor (for regression) extend the decoder object pipeline with one fast clustering step at the beginning (yielding an implicit spatial regularization) and aggregates a high number of estimators trained on various splits of the training set. This returns a state-of-the-art decoding pipeline at a low computational cost. These two objects also leverage the NiftiMaskers to provide a direct interface with the Nifti files on disk.
Plot events file Use nilearn.plotting.plot_event to visualize events file. The function accepts the BIDS events file read using pandas utilities.
Plotting function nilearn.plotting.plot_roi can now plot ROIs in contours with view_type argument.
New plotting function nilearn.plotting.plot_carpet generates a “carpet plot” (also known as a “Power plot” or a “grayplot”), for visualizing global patterns in 4D functional data over time.
New plotting function nilearn.plotting.plot_img_on_surf generates multiple views of nilearn.plotting.plot_surf_stat_map in a single figure.
nilearn.plotting.plot_markers shows network nodes (markers) on a glass brain template and color code them according to provided nodal measure (i.e. connection strength). This function will replace nilearn.plotting.plot_connectome_strength.
New plotting function nilearn.plotting.plot_surf_contours plots the contours of regions of interest on the surface, optionally overlayed on top of a statistical map.
The position annotation on the plot methods now implements the decimals option to enable annotation of a slice coordinate position with the float.
New example in Cortical surface-based searchlight decoding to demo how to do cortical surface-based searchlight decoding with Nilearn.
confounds or additional regressors for design matrix can be specified as numpy arrays or pandas DataFrames interchangeably
The decomposition estimators will now accept argument per_component with score method to explain the variance for each component.

Fixes¶

nilearn.input_data.NiftiLabelsMasker no longer ignores its mask_img
nilearn.masking.compute_brain_mask has replaced nilearn.masking.compute_gray_matter_mask. Features remained the same but some corrections regarding its description were made in the docstring.
the default background (MNI template) in plotting functions now has the correct orientation; before left and right were inverted.
first level modelling can deal with regressors having multiple events which share onsets or offsets. Previously, such cases could lead to an erroneous baseline shift.
nilearn.mass_univariate.permuted_ols no longer returns transposed t-statistic arrays when no permutations are performed.
Fix decomposition estimators returning explained variance score as 0. based on all components i.e., when per_component=False.
Fix readme file of the Destrieux 2009 atlas.

Changes¶

nilearn.datasets.fetch_cobre has been deprecated and will be removed in release 0.9 .
nilearn.plotting.plot_connectome_strength has been deprecated and will be removed in release 0.9 .
nilearn.connectome.ConnectivityMeasure can now remove confounds in its transform step.
nilearn.surface.vol_to_surf can now sample between two nested surfaces (eg white matter and pial surfaces) at specific cortical depths
nilearn.datasets.fetch_surf_fsaverage now also downloads white matter surfaces.

0.6.2¶

ENHANCEMENTS¶

Generated documentation now includes Binder links to launch examples interactively in the browser
nilearn.input_data.NiftiSpheresMasker now has an inverse transform, projecting spheres to the corresponding mask_img.

Fixes¶

More robust matplotlib backend selection
Typo in example fixed

Changes¶

Atlas nilearn.datasets.fetch_nyu_rest has been deprecated and wil be removed in Nilearn 0.8.0 .

Contributors¶

The following people contributed to this release:

Elizabeth DuPre
Franz Liem
Gael Varoquaux
Jon Haitz Legarreta Gorroño
Joshua Teves
Kshitij Chawla (kchawla-pi)
Zvi Baratz
Simon R. Steinkamp

0.6.1¶

ENHANCEMENTS¶

html pages use the user-provided plot title, if any, as their title

Fixes¶

Fetchers for developmental_fmri and localizer datasets resolve URLs correctly.

Contributors¶

The following people contributed to this release:

Elizabeth DuPre
Jerome Dockes
Kshitij Chawla (kchawla-pi)

0.6.0¶

Released December 2019

HIGHLIGHTS¶

Warning

Python2 and 3.4 are no longer supported. We recommend upgrading to Python 3.6 minimum.

Support for Python3.5 wil be removed in the 0.7.x release.
Users with a Python3.5 environment will be warned at their first Nilearn import.

joblib is now a dependency

Minimum supported versions of packages have been bumped up.
- Matplotlib – v2.0
- Scikit-learn – v0.19
- Scipy – v0.19

NEW¶

A new method for nilearn.input_data.NiftiMasker instances for generating reports viewable in a web browser, Jupyter Notebook, or VSCode.
A new function nilearn.image.get_data to replace the deprecated nibabel method Nifti1Image.get_data. Now use nilearn.image.get_data(img) rather than img.get_data(). This is because Nibabel is removing the get_data method. You may also consider using the Nibabel Nifti1Image.get_fdata, which returns the data cast to floating-point. See https://github.com/nipy/nibabel/wiki/BIAP8 . As a benefit, the get_data function works on niimg-like objects such as filenames (see http://nilearn.github.io/manipulating_images/input_output.html ).
Parcellation method ReNA: Fast agglomerative clustering based on recursive nearest neighbor grouping. Yields very fast & accurate models, without creation of giant clusters. nilearn.regions.ReNA
Plot connectome strength Use nilearn.plotting.plot_connectome_strength to plot the strength of a connectome on a glass brain. Strength is absolute sum of the edges at a node.
Optimization to image resampling
New brain development fMRI dataset fetcher nilearn.datasets.fetch_development_fmri can be used to download movie-watching data in children and adults. A light-weight dataset implemented for teaching and usage in the examples. All the connectivity examples are changed from ADHD to brain development fmri dataset.

ENHANCEMENTS¶

nilearn.plotting.view_img_on_surf, nilearn.plotting.view_surf and nilearn.plotting.view_connectome can display a title, and allow disabling the colorbar, and setting its height and the fontsize of its ticklabels.
Rework of the standardize-options of nilearn.signal.clean and the various Maskers in nilearn.input_data. You can now set standardize to zscore or psc. psc stands for Percent Signal Change, which can be a meaningful metric for BOLD.
Class nilearn.input_data.NiftiLabelsMasker now accepts an optional strategy parameter which allows it to change the function used to reduce values within each labelled ROI. Available functions include mean, median, minimum, maximum, standard_deviation and variance. This change is also introduced in nilearn.regions.img_to_signals_labels.
nilearn.plotting.view_surf now accepts surface data provided as a file path.

CHANGES¶

nilearn.plotting.plot_img now has explicit keyword arguments bg_img, vmin and vmax to control the background image and the bounds of the colormap. These arguments were already accepted in kwargs but not documented before.

FIXES¶

nilearn.input_data.NiftiLabelsMasker no longer truncates region means to their integral part when input images are of integer type.
The arg version=’det’ in nilearn.datasets.fetch_atlas_pauli_2017 now works as expected.
pip install nilearn now installs the necessary dependencies.

Lots of other fixes in documentation and examples. More detailed change list follows:

0.6.0rc NEW — .. warning:

- :func:`nilearn.plotting.view_connectome` no longer accepts old parameter names.
  Instead of `coords`, `threshold`, `cmap`, and `marker_size`,
  use `node_coords`, `edge_threshold`, `edge_cmap`, `node_size` respectively.

- :func:`nilearn.plotting.view_markers` no longer accepts old parameter names.
  Instead of `coord` and `color`, use `marker_coords` and `marker_color` respectively.

Support for Python3.5 wil be removed in the 0.7.x release. Users with a Python3.5 environment will be warned at their first Nilearn import.

Changes¶

Add a warning to nilearn.regions.Parcellations if the generated number of parcels does not match the requested number of parcels.
Class nilearn.input_data.NiftiLabelsMasker now accepts an optional strategy parameter which allows it to change the function used to reduce values within each labelled ROI. Available functions include mean, median, minimum, maximum, standard_deviation and variance. This change is also introduced in nilearn.regions.img_to_signals_labels.

Fixes¶

nilearn.input_data.NiftiLabelsMasker no longer truncates region means to their integral part when input images are of integer type.
func
nilearn.image.smooth_image no longer fails if fwhm is a numpy.ndarray.
pip install nilearn now installs the necessary dependencies.
nilearn.image.new_img_like no longer attempts to copy non-iterable headers. (PR #2212)
Nilearn no longer raises ImportError for nose when Matplotlib is not installed.
The arg version=’det’ in nilearn.datasets.fetch_atlas_pauli_2017 now works as expected.
nilearn.input_data.NiftiLabelsMasker.inverse_transform now works without the need to call transform first.

Contributors¶

The following people contributed to this release (in alphabetical order):

Chris Markiewicz
Dan Gale
Daniel Gomez
Derek Pisner
Elizabeth DuPre
Eric Larson
Gael Varoquaux
Jerome Dockes
JohannesWiesner
Kshitij Chawla (kchawla-pi)
Paula Sanz-Leon
ltetrel
ryanhammonds

0.6.0b0¶

Released November 2019

Warning

Python2 and 3.4 are no longer supported. Pip will raise an error in these environments.
Minimum supported version of Python is now 3.5 .
We recommend upgrading to Python 3.6 .

NEW¶

A new function nilearn.image.get_data to replace the deprecated nibabel method Nifti1Image.get_data. Now use nilearn.image.get_data(img) rather than img.get_data(). This is because Nibabel is removing the get_data method. You may also consider using the Nibabel Nifti1Image.get_fdata, which returns the data cast to floating-point. See https://github.com/nipy/nibabel/wiki/BIAP8 . As a benefit, the get_data function works on niimg-like objects such as filenames (see http://nilearn.github.io/manipulating_images/input_output.html ).

Changes¶

All functions and examples now use nilearn.image.get_data rather than the deprecated method nibabel.Nifti1Image.get_data.
nilearn.datasets.fetch_neurovault now does not filter out images that have their metadata field is_valid cleared by default.
Users can now specify fetching data for adults, children, or both from nilearn.datasets.fetch_development_fmri .

Fixes¶

nilearn.plotting.plot_connectome now correctly displays marker size on ‘l’ and ‘r’ orientations, if an array or a list is passed to the function.

Contributors¶

The following people contributed to this release (in alphabetical order):

Jake Vogel
Jerome Dockes
Kshitij Chawla (kchawla-pi)
Roberto Guidotti

0.6.0a0¶

Released October 2019

NEW¶

Warning

Python2 and 3.4 are no longer supported. We recommend upgrading to Python 3.6 minimum.

Minimum supported versions of packages have been bumped up.
- Matplotlib – v2.0
- Scikit-learn – v0.19
- Scipy – v0.19

A new method for nilearn.input_data.NiftiMasker instances for generating reports viewable in a web browser, Jupyter Notebook, or VSCode.
joblib is now a dependency
Parcellation method ReNA: Fast agglomerative clustering based on recursive nearest neighbor grouping. Yields very fast & accurate models, without creation of giant clusters. nilearn.regions.ReNA
Plot connectome strength Use nilearn.plotting.plot_connectome_strength to plot the strength of a connectome on a glass brain. Strength is absolute sum of the edges at a node.
Optimization to image resampling nilearn.image.resample_img has been optimized to pad rather than resample images in the special case when there is only a translation between two spaces. This is a common case in nilearn.input_data.NiftiMasker when using the mask_strategy=”template” option for brains in MNI space.
New brain development fMRI dataset fetcher nilearn.datasets.fetch_development_fmri can be used to download movie-watching data in children and adults; a light-weight dataset implemented for teaching and usage in the examples.
New example in examples/05_advanced/plot_age_group_prediction_cross_val.py to compare methods for classifying subjects into age groups based on functional connectivity. Similar example in examples/03_connectivity/plot_group_level_connectivity.py simplified.
Merged examples/03_connectivity/plot_adhd_spheres.py and examples/03_connectivity/plot_sphere_based_connectome.py to remove duplication across examples. The improved examples/03_connectivity/plot_sphere_based_connectome.py contains concepts previously reviewed in both examples.
Merged examples/03_connectivity/plot_compare_decomposition.py and examples/03_connectivity/plot_canica_analysis.py into an improved examples/03_connectivity/plot_compare_decomposition.py.
The Localizer dataset now follows the BIDS organization.

Changes¶

All the connectivity examples are changed from ADHD to brain development fmri dataset.
Examples plot_decoding_tutorial, plot_haxby_decoder, plot_haxby_different_estimators, plot_haxby_full_analysis, plot_oasis_vbm now use nilearn.decoding.Decoder and nilearn.decoding.DecoderRegressor instead of sklearn SVC and SVR.
nilearn.plotting.view_img_on_surf, nilearn.plotting.view_surf and nilearn.plotting.view_connectome now allow disabling the colorbar, and setting its height and the fontsize of its ticklabels.
nilearn.plotting.view_img_on_surf, nilearn.plotting.view_surf and nilearn.plotting.view_connectome can now display a title.
Rework of the standardize-options of nilearn.signal.clean and the various Maskers in nilearn.input_data. You can now set standardize to zscore or psc. psc stands for Percent Signal Change, which can be a meaningful metric for BOLD.
nilearn.plotting.plot_img now has explicit keyword arguments bg_img, vmin and vmax to control the background image and the bounds of the colormap. These arguments were already accepted in kwargs but not documented before.
nilearn.plotting.view_connectome now converts NaNs in the adjacency matrix to 0.
Removed the plotting connectomes example which used the Seitzman atlas from examples/03_connectivity/plot_sphere_based_connectome.py. The atlas data is unsuitable for the method & the example is redundant.

Fixes¶

nilearn.plotting.plot_glass_brain with colorbar=True does not crash when images have NaNs.
add_contours now accepts threshold argument for filled=False. Now threshold is equally applied when asked for fillings in the contours.
nilearn.plotting.plot_surf and nilearn.plotting.plot_surf_stat_map no longer threshold zero values when no threshold is given.
When nilearn.plotting.plot_surf_stat_map is used with a thresholded map but without a background map, the surface mesh is displayed in half-transparent grey to maintain a 3D perception.
nilearn.plotting.view_surf now accepts surface data provided as a file path.
nilearn.plotting.plot_glass_brain now correctly displays the left ‘l’ orientation even when the given images are completely masked (empty images).
nilearn.plotting.plot_matrix providing labels=None, False, or an empty list now correctly disables labels.
nilearn.plotting.plot_surf_roi now takes vmin, vmax parameters
nilearn.datasets.fetch_surf_nki_enhanced is now downloading the correct left and right functional surface data for each subject
nilearn.datasets.fetch_atlas_schaefer_2018 now downloads from release version 0.14.3 (instead of 0.8.1) by default, which includes corrected region label names along with 700 and 900 region parcelations.
Colormap creation functions have been updated to avoid matplotlib deprecation warnings about colormap reversal.
Neurovault fetcher no longer fails if unable to update dataset metadata file due to faulty permissions.

Contributors¶

The following people contributed to this release (in alphabetical order):

Alexandre Abraham
Alexandre Gramfort
Ana Luisa
Ana Luisa Pinho
Andrés Hoyos Idrobo
Antoine Grigis
BAZEILLE Thomas
Bertrand Thirion
Colin Reininger
Céline Delettre
Dan Gale
Daniel Gomez
Elizabeth DuPre
Eric Larson
Franz Liem
Gael Varoquaux
Gilles de Hollander
Greg Kiar
Guillaume Lemaitre
Ian Abenes
Jake Vogel
Jerome Dockes
Jerome-Alexis Chevalier
Julia Huntenburg
Kamalakar Daddy
Kshitij Chawla (kchawla-pi)
Mehdi Rahim
Moritz Boos
Sylvain Takerkart

0.5.2¶

Released April 2019

NEW¶

Warning

This is the last release supporting Python2 and 3.4 .
The lowest Python version supported is now Python3.5.
We recommend switching to Python3.6 .

Fixes¶

Plotting .mgz files in MNE broke in 0.5.1 and has been fixed.

Contributors¶

The following people contributed to this release:

Kshitij Chawla (kchawla-pi)
Gael Varoquaux
Alexandre Gramfort

0.5.1¶

Released April 2019

NEW¶

Support for Python2 & Python3.4 wil be removed in the next release. We recommend Python 3.6 and up. Users with a Python2 or Python3.4 environment will be warned at their first Nilearn import.
Calculate image data dtype from header information
New display mode ‘tiled’ which allows 2x2 plot arrangement when plotting three cuts (see Plotting brain images).
NiftiLabelsMasker now consumes less memory when extracting the signal from a 3D/4D image. This is especially noteworthy when extracting signals from large 4D images.
New function nilearn.datasets.fetch_atlas_schaefer_2018
New function nilearn.datasets.fetch_coords_seitzman_2018

Changes¶

Lighting used for interactive surface plots changed; plots may look a bit different.
nilearn.plotting.view_connectome default colormap is bwr, consistent with plot_connectome.
nilearn.plotting.view_connectome parameter names are consistent with plot_connectome:
- coords is now node_coord
- marker_size is noe node_size
- cmap is now edge_cmap
- threshold is now edge_threshold
nilearn.plotting.view_markers and nilearn.plotting.view_connectome can accept different marker sizes for each node / marker.
nilearn.plotting.view_markers default marker color is now ‘red’, consistent with add_markers().
nilearn.plotting.view_markers parameter names are consistent with add_markers():
- coords is now marker_coords
- colors is now marker_color
nilearn.plotting.view_img_on_surf now accepts a symmetric_cmap argument to control whether the colormap is centered around 0 and a vmin argument.
Users can now control the size and fontsize of colorbars in interactive surface and connectome plots, or disable the colorbar.

Fixes¶

Example plot_seed_to_voxel_correlation now really saves z-transformed maps.
region_extractor.connected_regions and regions.RegionExtractor now correctly use the provided mask_img.
load_niimg no longer drops header if dtype is changed.
NiftiSpheresMasker no longer silently ignores voxels if no mask_img is specified.
Interactive brainsprites generated from view_img are correctly rendered in Jupyter Book.

Known Issues¶

On Python2, nilearn.plotting.view_connectome & nilearn.plotting.view_markers do not show parameters names in function signature when using help() and similar features. Please refer to their docstrings for this information.
Plotting .mgz files in MNE is broken.

Contributors¶

The following people contributed to this release:

Bertrand Thirion
Kshitij Chawla (kchawla-pi)
fliem
Jerome Dockes
Gael Varoquaux
Salma Bougacha
himanshupathak21061998
Elizabeth DuPre
Eric Larson
Pierre Bellec

0.5.0¶

Released November 2018

NEW¶

interactive plotting functions, eg for use in a notebook.

New functions nilearn.plotting.view_surf and nilearn.plotting.view_img_on_surf for interactive visualization of maps on the cortical surface in a web browser.
New functions nilearn.plotting.view_connectome and nilearn.plotting.view_markers for interactive visualization of connectomes and seed locations in 3D
New function nilearn.plotting.view_img for interactive visualization of volumes with 3 orthogonal cuts.

Note: nilearn.plotting.view_img was nilearn.plotting.view_stat_map in alpha and beta releases.

nilearn.plotting.find_parcellation_cut_coords for extraction of coordinates on brain parcellations denoted as labels.
Added nilearn.plotting.find_probabilistic_atlas_cut_coords for extraction of coordinates on brain probabilistic maps.

Minimum supported versions of packages have been bumped up.

scikit-learn – v0.18
scipy – v0.17
pandas – v0.18
numpy – v1.11
matplotlib – v1.5.1

Nilearn Python2 support is being removed in the near future.

Users with a Python2 environment will be warned at their first Nilearn import.

Additional dataset downloaders for examples and tutorials.

ENHANCEMENTS¶

nilearn.image.clean_img now accepts a mask to restrict the cleaning of the image, reducing memory load and computation time.
NiftiMaskers now have a dtype parameter, by default keeping the same data type as the input data.
Displays by plotting functions can now add a scale bar (see Plotting brain images)

IMPROVEMENTS¶

Lots of other fixes in documentation and examples.
A cleaner layout and improved navigation for the website, with a better introduction.
Dataset fetchers are now more reliable, less verbose.
Searchlight().fit() now accepts 4D niimgs.
Anaconda link in the installation documentation updated.
Scipy is listed as a dependency for Nilearn installation.

Notable Changes¶

Default value of t_r in nilearn.signal.clean and nilearn.image.clean_img is None and cannot be None if low_pass or high_pass is specified.

Lots of changes and improvements. Detailed change list for each release follows.

0.5.0 rc¶

Highlights¶

nilearn.plotting.view_img (formerly nilearn.plotting.view_stat_map in Nilearn 0.5.0 pre-release versions) generates significantly smaller notebooks and HTML pages while getting a more consistent look and feel with Nilearn’s plotting functions. Huge shout out to Pierre Bellec (pbellec) for making a great feature awesome and for sportingly accommodating all our feedback.

nilearn.image.clean_img now accepts a mask to restrict the cleaning of: the image. This approach can help to reduce the memory load and computation time. Big thanks to Michael Notter (miykael).

Enhancements¶

nilearn.plotting.view_img is now using the brainsprite.js library, which results in much smaller notebooks or html pages. The interactive viewer also looks more similar to the plots generated by nilearn.plotting.plot_stat_map, and most parameters found in plot_stat_map are now supported in view_img.
nilearn.image.clean_img now accepts a mask to restrict the cleaning of the image. This approach can help to reduce the memory load and computation time.
nilearn.decoding.SpaceNetRegressor.fit raises a meaningful error in regression tasks if the target Y contains all 1s.

Changes¶

Default value of t_r in nilearn.signal.clean and nilearn.image.clean_img is changed from 2.5 to None. If low_pass or high_pass is specified, then t_r needs to be specified as well otherwise it will raise an error.
Order of filters in nilearn.signal.clean and nilearn.image.clean_img has changed to detrend, low- and high-pass filter, remove confounds and standardize. To ensure orthogonality between temporal filter and confound removal, an additional temporal filter will be applied on the confounds before removing them. This is according to Lindquist et al. (2018).
nilearn.image.clean_img now accepts a mask to restrict the cleaning of the image. This approach can help to reduce the memory load and computation time.
nilearn.plotting.view_img is now using the brainsprite.js library, which results in much smaller notebooks or html pages. The interactive viewer also looks more similar to the plots generated by nilearn.plotting.plot_stat_map, and most parameters found in plot_stat_map are now supported in view_img.

Contributors¶

The following people contributed to this release:

Gael Varoquaux
Pierre Bellec
Michael Notter
Kshitij Chawla (kchawla-pi)
Kamalakar Daddy
himanshupathak21061998
Horea Christian
Jerome Dockes

0.5.0 beta¶

Highlights¶

Nilearn Python2 support is being removed in the near future. Users with a Python2 environment will be warned at their first Nilearn import.

Enhancements¶

Displays created by plotting functions can now add a scale bar: to indicate the size in mm or cm (see Plotting brain images), contributed by Oscar Esteban
Colorbars in plotting functions now have a middle gray background: suitable for use with custom colormaps with a non-unity alpha channel. Contributed by Eric Larson (larsoner)

Loads of fixes and quality of life improvements

A cleaner layout and improved navigation for the website, with a better introduction.
Less warnings and verbosity while using certain functions and during dataset downloads.
Improved backend for the dataset fetchers means more reliable dataset downloads.
Some datasets, such as the ICBM, are now compressed to take up less disk space.

Fixes¶

Searchlight().fit() now accepts 4D niimgs. Contributed by Dan Gale (danjgale).
plotting.view_markers.open_in_browser() in js_plotting_utils fixed
Brainomics dataset has been replaced in several examples.
Lots of other fixes in documentation and examples.

Changes¶

In nilearn.regions.img_to_signals_labels, the See Also section in documentation now also points to NiftiLabelsMasker and NiftiMapsMasker
Scipy is listed as a dependency for Nilearn installation.
Anaconda link in the installation documentation updated.

Contributors¶

The following people contributed to this release:

Gael Varoquaux
Kshitij Chawla (kchawla-pi)
Jerome Dockes
oesteban
Eric Larson
Kamalakar Daddy
Bertrand Thirion
Alexandre Abadie
Sourav Singh
Alex Rothberg
AnaLu
Demian Wassermann
Horea Christian
Jason Gors
Jean Remi King
MADHYASTHA Meghana
SRSteinkamp
Simon Steinkamp
jerome-alexis_chevalier
salma
sfvnMAC
Akshay
Daniel Gomez
Guillaume Lemaitre
Pierre Bellec
arokem
erramuzpe
foucault
jehane
Sylvain LANNUZEL
Aki Nikolaidis
Christophe Bedetti
Dan Gale
Dillon Plunkett
Dimitri Papadopoulos Orfanos
Greg Operto
Ivan Gonzalez
Yaroslav Halchenko
dtyulman

0.5.0 alpha¶

This is an alpha release: to download it, you need to explicitly ask for the version number:

pip install nilearn==0.5.0a0

Highlights¶

Minimum supported versions of packages have been bumped up.
scikit-learn – v0.18
scipy – v0.17
pandas – v0.18
numpy – v1.11
matplotlib – v1.5.1
New interactive plotting functions, eg for use in a notebook.

Enhancements¶

All NiftiMaskers now have a dtype argument. For now the default behaviour is to keep the same data type as the input data.
Displays created by plotting functions can now add a scale bar to indicate the size in mm or cm (see Plotting brain images), contributed by Oscar Esteban
New functions nilearn.plotting.view_surf and nilearn.plotting.view_surf and nilearn.plotting.view_img_on_surf for interactive visualization of maps on the cortical surface in a web browser.
New functions nilearn.plotting.view_connectome and nilearn.plotting.view_markers to visualize connectomes and seed locations in 3D
New function nilearn.plotting.view_stat_map (renamed to nilearn.plotting.view_img in stable release) for interactive visualization of volumes with 3 orthogonal cuts.
Add nilearn.datasets.fetch_surf_fsaverage to download either fsaverage or fsaverage 5 (Freesurfer cortical meshes).
Added nilearn.datasets.fetch_atlas_pauli_2017 to download a recent subcortical neuroimaging atlas.
Added nilearn.plotting.find_parcellation_cut_coords for extraction of coordinates on brain parcellations denoted as labels.
Added nilearn.plotting.find_probabilistic_atlas_cut_coords for extraction of coordinates on brain probabilistic maps.
Added nilearn.datasets.fetch_neurovault_auditory_computation_task and nilearn.datasets.fetch_neurovault_motor_task for simple example data.

Changes¶

nilearn.datasets.fetch_surf_fsaverage5 is deprecated and will be removed in a future release. Use nilearn.datasets.fetch_surf_fsaverage, with the parameter mesh=”fsaverage5” (the default) instead.
fsaverage5 surface data files are now shipped directly with Nilearn. Look to issue #1705 for discussion.
sklearn.cross_validation and sklearn.grid_search have been replaced by sklearn.model_selection in all the examples.
Colorbars in plotting functions now have a middle gray background suitable for use with custom colormaps with a non-unity alpha channel.

Contributors¶

The following people contributed to this release:

Gael Varoquaux
Jerome Dockes
Kshitij Chawla (kchawla-pi)
SylvainLan
Kamalakar Daddy
Gilles de Hollander
Bertrand Thirion
MENUET Romuald
Moritz Boos
Peer Herholz
Pierre Bellec

0.4.2¶

Few important bugs fix release for OHBM conference.

Changes¶

Default colormaps for surface plotting functions have changed to be more consistent with slice plotting. nilearn.plotting.plot_surf_stat_map now uses “cold_hot”, as nilearn.plotting.plot_stat_map does, and nilearn.plotting.plot_surf_roi now uses “gist_ncar”, as nilearn.plotting.plot_roi does.
Improve 3D surface plotting: lock the aspect ratio of the plots and reduce the whitespace around the plots.

Bug fixes¶

Fix bug with input repetition time (TR) which had no effect in signal cleaning. Fixed by Pradeep Raamana.
Fix issues with signal extraction on list of 3D images in nilearn.regions.Parcellations.
Fix issues with raising AttributeError rather than HTTPError in datasets fetching utilities. By Jerome Dockes.
Fix issues in datasets testing function uncompression of files. By Pierre Glaser.

0.4.1¶

This bug fix release is focussed on few bug fixes and minor developments.

Enhancements¶

nilearn.decomposition.CanICA and nilearn.decomposition.DictLearning has new attribute components_img_ providing directly the components learned as a Nifti image. This avoids the step of unmasking the attribute components_ which is true for older versions.
New object nilearn.regions.Parcellations for learning brain parcellations on fmri data.
Add optional reordering of the matrix using a argument reorder with nilearn.plotting.plot_matrix.

Note
This feature is usable only if SciPy version is >= 1.0.0

Changes¶

Using output attribute components_ which is an extracted components in nilearn.decomposition.CanICA and nilearn.decomposition.DictLearning is deprecated and will be removed in next two releases. Use components_img_ instead.

Bug fixes¶

Fix issues using nilearn.plotting.plot_connectome when string is passed in node_color with display modes left and right hemispheric cuts in the glass brain.
Fix bug while plotting only coordinates using add_markers on glass brain. See issue #1595
Fix issues with estimators in decomposition module when input images are given in glob patterns.
Fix bug loading Nifti2Images.
Fix bug while adjusting contrast of the background template while using nilearn.plotting.plot_prob_atlas
Fix colormap bug with recent matplotlib 2.2.0

0.4.0¶

Highlights:

nilearn.surface.vol_to_surf to project volume data to the surface.
nilearn.plotting.plot_matrix to display matrices, eg connectomes

Enhancements¶

New function nilearn.surface.vol_to_surf to project a 3d or 4d brain volume on the cortical surface.
New matrix plotting function, eg to display connectome matrices: nilearn.plotting.plot_matrix
Expose nilearn.image.coord_transform for end users. Useful to transform coordinates (x, y, z) from one image space to another space.
nilearn.image.resample_img now takes a linear resampling option (implemented by Joe Necus)
nilearn.datasets.fetch_atlas_talairach to fetch the Talairach atlas (http://talairach.org)
Enhancing new surface plotting functions, added new parameters “axes” and “figure” to accept user-specified instances in nilearn.plotting.plot_surf and nilearn.plotting.plot_surf_stat_map and nilearn.plotting.plot_surf_roi
nilearn.decoding.SearchLight has new parameter “groups” to do LeaveOneGroupOut type cv with new scikit-learn module model selection.
Enhancing the glass brain plotting in back view ‘y’ direction.
New parameter “resampling_interpolation” is added in most used plotting functions to have user control for faster visualizations.
Upgraded to Sphinx-Gallery 0.1.11

Bug fixes¶

Dimming factor applied to background image in plotting functions with “dim” parameter will no longer accepts as string (‘-1’). An error will be raised.
Fixed issues with matplotlib 2.1.0.
Fixed issues with SciPy 1.0.0.

Changes¶

Backward incompatible change: nilearn.plotting.find_xyz_cut_coords now takes a mask_img argument which is a niimg, rather than a mask argument, which used to be a numpy array.
The minimum required version for scipy is now 0.14
Dropped support for Nibabel older than 2.0.2.
nilearn.image.smooth_img no longer accepts smoothing parameter fwhm as 0. Behavior is changed in according to the issues with recent SciPy version 1.0.0.
“dim” factor range is slightly increased to -2 to 2 from -1 to 1. Range exceeding -1 meaning more increase in constrast should be cautiously set.
New ‘anterior’ and ‘posterior’ view added to the plot_surf family views
Using argument anat_img for placing background image in nilearn.plotting.plot_prob_atlas is deprecated. Use argument bg_img instead.
The examples now use pandas for the behavioral information.

Contributors¶

The following people contributed to this release:

Jerome Dockes
Gael Varoquaux
Kamalakar Daddy
Jeff Chiang
Elizabeth DuPre
Jona Sassenhagen
Sylvain Lan
J Necus
Pierre-Olivier Quirion
AnaLu
Jean Remi King
MADHYASTHA Meghana
Salma Bougacha
sfvnMAC
Eric Larson
Horea Christian
Moritz Boos
Alex Rothberg
Bertrand Thirion
Christophe Bedetti
John Griffiths
Mehdi Rahim
Sylvain LANNUZEL
Yaroslav Halchenko
clfs

0.3.1¶

This is a minor release for BrainHack.

Highlights¶

Dropped support for scikit-learn older than 0.14.1 Minimum supported version is now 0.15.

Changelog¶

The function sym_to_vec is deprecated and will be removed in release 0.4. Use nilearn.connectome.sym_matrix_to_vec instead.
Added argument smoothing_fwhm to nilearn.regions.RegionExtractor to control smoothing according to the resolution of atlas images.

Bug fix¶

The helper function largest_connected_component should now work with inputs of non-native data dtypes.
Fix plotting issues when non-finite values are present in background anatomical image.
A workaround to handle non-native endianess in the Nifti images passed to resampling the image.

Enhancements¶

New data fetcher functions nilearn.datasets.fetch_neurovault and nilearn.datasets.fetch_neurovault_ids help you download statistical maps from the Neurovault (http://neurovault.org) platform.
New function nilearn.connectome.vec_to_sym_matrix reshapes vectors to symmetric matrices. It acts as the reverse of function nilearn.connectome.sym_matrix_to_vec.
Add an option allowing to vectorize connectivity matrices returned by the “transform” method of nilearn.connectome.ConnectivityMeasure.
nilearn.connectome.ConnectivityMeasure now exposes an “inverse_transform” method, useful for going back from vectorized connectivity coefficients to connectivity matrices. Also, it allows to recover the covariance matrices for the “tangent” kind.
Reworking and renaming of connectivity measures example. Renamed from plot_connectivity_measures to plot_group_level_connectivity.
Tighter bounding boxes when using add_contours for plotting.
Function nilearn.image.largest_connected_component_img to directly extract the largest connected component from Nifti images.
Improvements in plotting, decoding and functional connectivity examples.

0.3.0¶

In addition, more details of this release are listed below. Please checkout in 0.3.0 beta release section for minimum version support of dependencies, latest updates, highlights, changelog and enhancements.

Changelog¶

Function nilearn.plotting.find_cut_slices now supports to accept Nifti1Image as an input for argument img.
Helper functions _get_mask_volume and _adjust_screening_percentile are now moved to param_validation file in utilties module to be used in common with Decoder object.

Bug fix¶

Fix bug uncompressing tar files with datasets fetcher.
Fixed bunch of CircleCI documentation build failures.
Fixed deprecations set_axis_bgcolor related to matplotlib in plotting functions.
Fixed bug related to not accepting a list of arrays as an input to unmask, in masking module.

Enhancements¶

ANOVA SVM example on Haxby datasets plot_haxby_anova_svm in Decoding section now uses SelectPercentile to select voxels rather than SelectKBest.
New function fast_svd implementation in base decomposition module to Automatically switch between randomized and lapack SVD (heuristic of scikit-learn).

0.3.0 beta¶

To install the beta version, use:

pip install --upgrade --pre nilearn

Highlights¶

Simple surface plotting
A function to break a parcellation into its connected components
Dropped support for scikit-learn older than 0.14.1 Minimum supported version is now 0.14.1.
Dropped support for Python 2.6
Minimum required version of NiBabel is now 1.2.0, to support loading annoted data with freesurfer.

Changelog¶

A helper function _safe_get_data as a nilearn utility now safely removes NAN values in the images with argument ensure_finite=True.
Connectome functions nilearn.connectome.cov_to_corr and nilearn.connectome.prec_to_partial can now be used.

Bug fix¶

Fix colormap issue with colorbar=True when using qualitative colormaps Fixed in according with changes of matplotlib 2.0 fixes.
Fix plotting functions to work with NAN values in the images.
Fix bug related get dtype of the images with nibabel get_data().
Fix bug in nilearn clean_img

Enhancements¶

A new function nilearn.regions.connected_label_regions to extract the connected components represented as same label to regions apart with each region labelled as unique label.
New plotting modules for surface plotting visualization. Matplotlib with version higher 1.3.1 is required for plotting surface data using these functions.
Function nilearn.plotting.plot_surf can be used for plotting surfaces mesh data with optional background.
A function nilearn.plotting.plot_surf_stat_map can be used for plotting statistical maps on a brain surface with optional background.
A function nilearn.plotting.plot_surf_roi can be used for plotting statistical maps rois onto brain surface.
A function nilearn.datasets.fetch_surf_fsaverage5 can be used for surface data object to be as background map for the above plotting functions.
A new data fetcher function nilearn.datasets.fetch_atlas_surf_destrieux can give you Destrieux et. al 2010 cortical atlas in fsaverage5 surface space.
A new functional data fetcher function nilearn.datasets.fetch_surf_nki_enhanced gives you resting state data preprocessed and projected to fsaverage5 surface space.
Two good examples in plotting gallery shows how to fetch atlas and NKI data and used for plotting on brain surface.
Helper function load_surf_mesh in surf_plotting module for loading surface mesh data into two arrays, containing (x, y, z) coordinates for mesh vertices and indices of mesh faces.
Helper function load_surf_data in surf_plotting module for loading data of numpy array to represented on a surface mesh.
Add fetcher for Allen et al. 2011 RSN atlas in nilearn.datasets.fetch_atlas_allen_2011.
A function nilearn.datasets.fetch_cobre is now updated to new light release of COBRE data (schizophrenia)
A new example to show how to extract regions on labels image in example section manipulating images.
coveralls is replaces with codecov
Upgraded to Sphinx version 0.1.7
Extensive plotting example shows how to use contours and filled contours on glass brain.

0.2.6¶

Changelog¶

This release enhances usage of several functions by fine tuning their parameters. It allows to select which Haxby subject to fetch. It also refactors documentation to make it easier to understand. Sphinx-gallery has been updated and nilearn is ready for new nibabel 2.1 version. Several bugs related to masks in Searchlight and ABIDE fetching have been resolved.

Bug fix¶

Change default dtype in nilearn.image.concat_imgs to be the original type of the data (see #1238).
Fix SearchLight that did not run without process_mask or with one voxel mask.
Fix flipping of left hemisphere when plotting glass brain.
Fix bug when downloading ABIDE timeseries

Enhancements¶

Sphinx-gallery updated to version 0.1.3.
Refactoring of examples and documentation.
Better ordering of regions in nilearn.datasets.fetch_coords_dosenbach_2010.
Remove outdated power atlas example.

API changes summary¶

The parameter ‘n_subjects’ is deprecated and will be removed in future release. Use ‘subjects’ instead in nilearn.datasets.fetch_haxby.
The function nilearn.datasets.fetch_haxby will now fetch the data accepting input given in ‘subjects’ as a list than integer.
Replace get_affine by affine with recent versions of nibabel.

0.2.5.1¶

Changelog¶

This is a bugfix release. The new minimum required version of scikit-learn is 0.14.1

API changes summary¶

default option for dim argument in plotting functions which uses MNI template as a background image is now changed to ‘auto’ mode. Meaning that an automatic contrast setting on background image is applied by default.
Scikit-learn validation tools have been imported and are now used to check consistency of input data, in SpaceNet for example.

New features¶

Add an option to select only off-diagonal elements in sym_to_vec. Also, the scaling of matrices is modified: we divide the diagonal by sqrt(2) instead of multiplying the off-diagonal elements.
Connectivity examples rely on nilearn.connectome.ConnectivityMeasure

Bug fix¶

Scipy 0.18 introduces a bug in a corner-case of resampling. Nilearn 0.2.5 can give wrong results with scipy 0.18, but this is fixed in 0.2.6.
Broken links and references fixed in docs

0.2.5¶

Changelog¶

The 0.2.5 release includes plotting for connectomes and glass brain with hemisphere-specific projection, as well as more didactic examples and improved documentation.

New features¶

New display_mode options in nilearn.plotting.plot_glass_brain and nilearn.plotting.plot_connectome. It is possible to plot right and left hemisphere projections separately.
A function to load canonical brain mask image in MNI template space, nilearn.datasets.load_mni152_brain_mask
A function to load brain grey matter mask image, nilearn.datasets.fetch_icbm152_brain_gm_mask
New function nilearn.image.load_img loads data from a filename or a list of filenames.
New function nilearn.image.clean_img applies the cleaning function nilearn.signal.clean on all voxels.
New simple data downloader nilearn.datasets.fetch_localizer_button_task to simplify some examples.
The dataset function nilearn.datasets.fetch_localizer_contrasts can now download a specific list of subjects rather than a range of subjects.
New function nilearn.datasets.get_data_dirs to check where nilearn downloads data.

Contributors¶

Contributors (from git shortlog -ns 0.2.4..0.2.5):

Gael Varoquaux
Alexandre Abraham
Martin Perez-Guevara
Kamalakar Daddy
amadeuskanaan
Alexandre Abadie
Arthur Mensch
Elvis Dohmatob
Loïc Estève
Jerome Dockes
Alexandre M. S
Bertrand Thirion
Ivan Gonzalez
robbisg

0.2.4¶

Changelog¶

The 0.2.4 is a small release focused on documentation for teaching.

New features¶

The path given to the “memory” argument of object now have their “~” expanded to the homedir
Display object created by plotting now uniformely expose an “add_markers” method.
plotting plot_connectome with colorbar is now implemented in function nilearn.plotting.plot_connectome
New function nilearn.image.resample_to_img to resample one img on another one (just resampling / interpolation, no coregistration)

API changes summary¶

Atlas fetcher nilearn.datasets.fetch_atlas_msdl now returns directly labels of the regions in output variable ‘labels’ and its coordinates in output variable ‘region_coords’ and its type of network in ‘networks’.
The output variable name ‘regions’ is now changed to ‘maps’ in AAL atlas fetcher in nilearn.datasets.fetch_atlas_aal.
AAL atlas now returns directly its labels in variable ‘labels’ and its index values in variable ‘indices’.

0.2.3¶

Changelog¶

The 0.2.3 is a small feature release for BrainHack 2016.

New features¶

Mathematical formulas based on numpy functions can be applied on an image or a list of images using nilearn.image.math_img.
Downloader for COBRE datasets of 146 rest fMRI subjects with nilearn.datasets.fetch_cobre
Downloader for Dosenbach atlas nilearn.datasets.fetch_coords_dosenbach_2010
Fetcher for multiscale functional brain parcellations (BASC) nilearn.datasets.fetch_atlas_basc_multiscale_2015

Bug fixes¶

Better dimming on white background for plotting

0.2.2¶

Changelog¶

The 0.2.2 is a bugfix + dependency update release (for sphinx gallery). It aims at preparing a renewal of the tutorials.

New features¶

Fetcher for Megatrawl Netmats dataset.

Enhancements¶

Flake8 is now run on pull requests.
Reworking of the documentation organization.
Sphinx-gallery updated to version 0.1.1
The default n_subjects=None in nilearn.datasets.fetch_adhd is now changed to n_subjects=30.

Bug fixes¶

Fix symmetric_split behavior in nilearn.datasets.fetch_atlas_harvard_oxford
Fix casting errors when providing integer data to nilearn.image.high_variance_confounds
Fix matplotlib 1.5.0 compatibility in nilearn.plotting.plot_prob_atlas
Fix matplotlib backend choice on Mac OS X.
nilearn.plotting.find_xyz_cut_coords raises a meaningful error when 4D data is provided instead of 3D.
nilearn.input_data.NiftiSpheresMasker handles radius smaller than the size of a voxel
nilearn.regions.RegionExtractor handles data containing Nans.
Confound regression does not force systematically the normalization of the confounds.
Force time series normalization in nilearn.connectome.ConnectivityMeasure and check dimensionality of the input.
nilearn._utils.numpy_conversions.csv_to_array could consider valid CSV files as invalid.

API changes summary¶

Deprecated dataset downloading function have been removed.
Download progression message refreshing rate has been lowered to sparsify CircleCI logs.

Contributors¶

Contributors (from git shortlog -ns 0.2.1..0.2.2):

Kamalakar Daddy
Alexandre Abraham
Loïc Estève
Gael Varoquaux
Alexandre Abadie
Salma
Danilo Bzdok
Arthur Mensch
Ben Cipollini
Elvis Dohmatob
Óscar Nájera

0.2.1¶

Changelog¶

Small bugfix for more flexible input types (targetter in particular at making code easier in nistats).

0.2¶

Changelog¶

The new minimum required version of scikit-learn is 0.13

New features¶

The new module nilearn.connectome now has class nilearn.connectome.ConnectivityMeasure can be useful for computing functional connectivity matrices.
The function nilearn.connectome.sym_to_vec in same module nilearn.connectome is also implemented as a helper function to nilearn.connectome.ConnectivityMeasure.
The class nilearn.decomposition.DictLearning in nilearn.decomposition is a decomposition method similar to ICA that imposes sparsity on components instead of independence between them.
Integrating back references template from sphinx-gallery of 0.0.11 version release.
Globbing expressions can now be used in all nilearn functions expecting a list of files.
The new module nilearn.regions now has class nilearn.regions.RegionExtractor which can be used for post processing brain regions of interest extraction.
The function nilearn.regions.connected_regions in nilearn.regions is also implemented as a helper function to nilearn.regions.RegionExtractor.
The function nilearn.image.threshold_img in nilearn.image is implemented to use it for thresholding statistical maps.

Enhancements¶

Making website a bit elaborated & modernise by using sphinx-gallery.
Documentation enhancement by integrating sphinx-gallery notebook style examples.
Documentation about nilearn.input_data.NiftiSpheresMasker.

Bug fixes¶

Fixed bug to control the behaviour when cut_coords=0. in function nilearn.plotting.plot_stat_map in nilearn.plotting. See issue # 784.
Fixed bug in nilearn.image.copy_img occured while caching the Nifti images. See issue # 793.
Fixed bug causing an IndexError in fast_abs_percentile. See issue # 875

API changes summary¶

The utilities in function group_sparse_covariance has been moved into nilearn.connectome.
The default value for number of cuts (n_cuts) in function nilearn.plotting.find_cut_slices in nilearn.plotting has been changed from 12 to 7 i.e. n_cuts=7.

Contributors¶

Contributors (from git shortlog -ns 0.1.4..0.2.0):

Elvis Dohmatob
Gael Varoquaux
Alexandre Abraham
Loïc Estève
Kamalakar Daddy
Alexandre Abadie
Chris Filo Gorgolewski
Salma BOUGACHA
Danilo Bzdok
Martin Perez-Guevara
Mehdi Rahim
Óscar Nájera
martin
Arthur Mensch
Ben Cipollini
ainafp
juhuntenburg
Martin_Perez_Guevara
Michael Hanke
arokem
Bertrand Thirion
Dimitri Papadopoulos Orfanos

0.1.4¶

Changelog¶

Highlights:

NiftiSpheresMasker: extract signals from balls specified by their coordinates
Obey Debian packaging rules
Add the Destrieux 2009 and Power 2011 atlas
Better caching in maskers

Contributors (from git shortlog -ns 0.1.3..0.1.4):

Alexandre Abraham
Gael Varoquaux
Loïc Estève
Arthur Mensch
Danilo Bzdok
Michael Hanke
Mehdi Rahim

0.1.3¶

Changelog¶

The 0.1.3 release is a bugfix release that fixes a lot of minor bugs. It also includes a full rewamp of the documentation, and support for Python 3.

Minimum version of supported packages are now:

numpy 1.6.1
scipy 0.9.0
scikit-learn 0.12.1
Matplotlib 1.1.1 (optional)

A non exhaustive list of issues fixed:

Dealing with NaNs in plot_connectome
Fix extreme values in colorbar were sometimes brok
Fix confounds removal with single confounds
Fix frequency filtering
Keep header information in images
add_overlay finds vmin and vmax automatically
vmin and vmax support in plot_connectome
detrending 3D images no longer puts them to zero

Contributors (from git shortlog -ns 0.1.2..0.1.3):

Alexandre Abraham
Loïc Estève
Gael Varoquaux
Ben Cipollini
Danilo Bzdok
Elvis Dohmatob
Óscar Nájera
Salma BOUGACHA
Alexandre Gramfort
Kamalakar Daddy
Demian Wassermann
Bertrand Thirion

0.1.2¶

Changelog¶

The 0.1.2 release is a bugfix release, specifically to fix the NiftiMapsMasker.

0.1.1¶

Changelog¶

The main change compared to 0.1 is the addition of connectome plotting via the nilearn.plotting.plot_connectome function. See the plotting documentation for more details.

Contributors (from git shortlog -ns 0.1..0.1.1):

Loïc Estève
Alexandre Abraham
Danilo Bzdok
Ben Cipollini
Gaël Varoquaux

0.1¶

Changelog¶

First release of nilearn.

Contributors (from git shortlog -ns 0.1):

Gaël Varoquaux
Alexandre Abraham
Loïc Estève
Philippe Gervais
Virgile Fritsch
Michael Eickenberg
Jean Kossaifi
Jaques Grobler
Danilo Bzdok
Chris Filo Gorgolewski
Ronald Phlypo
Ben Cipollini
Bertrand Thirion
Alexandre Gramfort
Fabian Pedregosa
Yannick Schwartz
Mehdi Rahim
Óscar Nájera
Elvis Dohmatob
Konstantin Shmelkov
Jason Gors
Salma Bougacha
Alexandre Savio
Jan Margeta
Matthias Ekman
Michael Waskom
Vincent Michel

Nilearn:

Statistics for NeuroImaging in Python

0.7.1¶

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0.7.0¶

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0.6.2¶

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0.6.1¶

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0.6.0¶

HIGHLIGHTS¶

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0.6.0b0¶

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0.6.0a0¶

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0.5.2¶

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0.5.1¶

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0.5.0¶

NEW¶

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IMPROVEMENTS¶

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0.5.0 rc¶

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0.5.0 beta¶

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0.5.0 alpha¶

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0.4.2¶

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0.4.1¶

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0.4.0¶

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