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Nilearn specifications

Information


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
Requirements
setuptools, Numpy, SciPy, Scikit-learn, Nibabel
Maintained Yes

Download


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Versioning


No version available

Documentation


Maintainer


  • person_outline Alexandre Abraham <>

Additional information


http://nilearn.github.io/user_guide.html

Publication for Nilearn

Nilearn citations

 (17)
library_books

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

2018
PMCID: 5919964
PMID: 29700312
DOI: 10.1038/s41598-018-24475-z

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

library_books

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

2018
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) […]

library_books

Contrasting resting state fMRI abnormalities from sickle and non sickle anemia

2017
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 […]

library_books

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

2017
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 neurovault.org at the following url: http://neurovault.org/collections/neunablt. […]

library_books

Preprocessed Consortium for Neuropsychiatric Phenomics dataset

2017
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 […]

library_books

Advancing functional dysconnectivity and atrophy in progressive supranuclear palsy

2017
PMCID: 5605489
PMID: 28951832
DOI: 10.1016/j.nicl.2017.09.008

[…] diagrams were rendered using networkx (https://networkx.github.io/), bar plots and line plots were created with seaborn (https://seaborn.pydata.org/), and brain overlay images were produced with nilearn (http://nilearn.github.io/)., 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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