Nilearn statistics

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Citations per year

Number of citations per year for the bioinformatics software tool Nilearn
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Tool usage distribution map

This map represents all the scientific publications referring to Nilearn per scientific context
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Associated diseases

This word cloud represents Nilearn usage per disease context
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Popular tool citations

chevron_left Functional brain connectivity Image visualization 3D image analysis Image segmentation chevron_right
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Protocols

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

 (14)
call_split

Automatic lesion detection and segmentation of 18F FET PET in gliomas: A full 3D U Net convolutional neural network study

2018
PLoS One
PMCID: 5898737
PMID: 29652908
DOI: 10.1371/journal.pone.0195798
call_split See protocol

[…] times the mean value of an hemispheric swap of the predicted U-Net mask to match the procedure that was performed for ground truth. All these computations were performed using python 2.7 with numpy, nilearn and scikit-learn packages []. […]

library_books

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

2018
F1000Res
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) and ban […]

library_books

Contrasting resting state fMRI abnormalities from sickle and non sickle anemia

2017
PLoS One
PMCID: 5628803
PMID: 28981541
DOI: 10.1371/journal.pone.0184860

[…] from 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 []. […]

library_books

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

2017
Sci Rep
PMCID: 5612994
PMID: 28947835
DOI: 10.1038/s41598-017-12559-1

[…] Volumetric results were visualized using Nilearn, and projected on template surfaces using AFNI and SUMA,. […]

library_books

Advancing functional dysconnectivity and atrophy in progressive supranuclear palsy

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

[…] work 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/). […]

library_books

Task relevance modulates the cortical representation of feature conjunctions in the target template

2017
Sci Rep
PMCID: 5495750
PMID: 28674392
DOI: 10.1038/s41598-017-04123-8

[…] nd vice versa), and we did not want to make such assumptions. For the model-driven searchlights, we smoothed the data with a 4 mm FWHM Gaussian kernel (to keep fine spatial patterns intact) using the Nilearn Python package, and fitted an event-related hemodynamic response function (HRF) model to the smoothed dataset using NiPy. Regressors for the HRF model were determined separately for the cue pe […]


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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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