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Nilearn specifications


Unique identifier OMICS_28655
Name Nilearn
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Version 0.4.1
Stability Stable
setuptools, Numpy, SciPy, Scikit-learn, Nibabel
Maintained Yes




No version available



  • person_outline Alexandre Abraham <>

Additional information

Publication for Nilearn

Nilearn citations


Action and object words are differentially anchored in the sensory motor system A perspective on cognitive embodiment

PMCID: 5919964
PMID: 29700312
DOI: 10.1038/s41598-018-24475-z

[…] unit-tested implementations of state-of-the-art statistical learning algorithms ( this general-purpose machine-learning library was interfaced with the neuroimaging-specific nilearn library for high-dimensional neuroimaging datasets ( theano was used for automatic, numerically stable differentiation of symbolic computation graphs,. […]


Spatial band pass filtering aids decoding musical genres from auditory cortex 7T fMRI

PMCID: 5887073
PMID: 29707198
DOI: 10.5256/f1000research.14869.r30541

[…] terms of the size of the gaussian filter kernel(s) described by their fwhm in mm (a conversion of this unit to (cycles/mm) is shown in supplementary figure 5 in ). the image_smooth() function in the nilearn package was used to implement all spatial smoothing procedures. the implementations of gaussian low-pass (lp), and high-pass (hp) filters, as well as the dog filters for bandpass (bp) […]


Contrasting resting state fMRI abnormalities from sickle and non sickle anemia

PMCID: 5628803
PMID: 28981541
DOI: 10.1371/journal.pone.0184860

[…] noisy regions-of-non-interest, namely white matter, cerebral spinal fluid, and matter outside the brain, were used as regressors, which were calculated by the compcor method [] as implemented in nilearn []., the alff maps [] were calculated by band-pass filtering the time-series of the functional volumes in a window of 0.008–0.09 hz, estimating the power spectrum via a fast fourier […]


The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception

PMCID: 5612994
PMID: 28947835
DOI: 10.1038/s41598-017-12559-1

[…] extended > between”. after fitting, we performed parametric bootstrapping to obtain 95% bootstrapped confidence intervals on the model parameters., volumetric results were visualized using nilearn, and projected on template surfaces using afni and suma,., non-thresholded statistical maps can be found on at the following url: […]


Preprocessed Consortium for Neuropsychiatric Phenomics dataset

PMCID: 5664981
PMID: 29152222
DOI: 10.5256/f1000research.12934.r24599

[…] a very good overlap. all of the issues observed while processing the dataset are listed in ., a selection of the tested contrasts in the task analyses is shown in . figures were generated using nilearn ., the preprocessed images were deposited along the original dataset in the openfmri repository – accession number: ds000030 , under the revision 1.0.4. the preprocessed data is organized […]


Advancing functional dysconnectivity and atrophy in progressive supranuclear palsy

PMCID: 5605489
PMID: 28951832
DOI: 10.1016/j.nicl.2017.09.008

[…] diagrams were rendered using networkx (, bar plots and line plots were created with seaborn (, and brain overlay images were produced with nilearn (, patients were first assessed for clinical severity at baseline and follow up using the psprs to assess the degree of heterogeneity in clinical progression. […]

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Nilearn institution(s)
Parietal Team, INRIA Saclay-Île-de-France, Saclay, France; Neurospin, I 2BM, DSV, CEA, Gif-Sur-Yvette, France; Institute of Computer Science VI, University of Bonn, Bonn, Germany; Department of Computing, Imperial College London, London, UK; Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, Paris, France
Nilearn funding source(s)
Supported by the NiConnect project and NIDA R21 DA034954, SUBSample project from the DIGITEO Institute, France.

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