Skip to main content

fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data.

Project description

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. fMRIPrep is an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for task-based and resting fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. fMRIPrep robustly produces high-quality results on diverse fMRI data. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.

The workflow is based on Nipype and encompases a large set of tools from well-known neuroimaging packages, including FSL, ANTs, FreeSurfer, AFNI, and Nilearn. This pipeline was designed to provide the best software implementation for each state of preprocessing, and will be updated as newer and better neuroimaging software becomes available.

fMRIPrep performs basic preprocessing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skullstripping etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state fMRI, graph theory measures, surface or volume-based statistics, etc. fMRIPrep allows you to easily do the following:

  • Take fMRI data from unprocessed (only reconstructed) to ready for analysis.

  • Implement tools from different software packages.

  • Achieve optimal data processing quality by using the best tools available.

  • Generate preprocessing-assessment reports, with which the user can easily identify problems.

  • Receive verbose output concerning the stage of preprocessing for each subject, including meaningful errors.

  • Automate and parallelize processing steps, which provides a significant speed-up from typical linear, manual processing.

[Nat Meth doi:10.1038/s41592-018-0235-4] [Documentation fmriprep.org] [Software doi:10.5281/zenodo.852659] [Support neurostars.org]

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fmriprep-1.5.0rc2.tar.gz (115.8 kB view details)

Uploaded Source

Built Distributions

fmriprep-1.5.0rc2-cp37-cp37m-manylinux1_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.7m

fmriprep-1.5.0rc2-cp36-cp36m-manylinux1_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.6m

File details

Details for the file fmriprep-1.5.0rc2.tar.gz.

File metadata

  • Download URL: fmriprep-1.5.0rc2.tar.gz
  • Upload date:
  • Size: 115.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/20.10.1 requests-toolbelt/0.9.1 tqdm/4.34.0 CPython/3.5.2

File hashes

Hashes for fmriprep-1.5.0rc2.tar.gz
Algorithm Hash digest
SHA256 7613c31e78bd1aa686d35ec1cd0fca35e770142ff9c31acbaef0566b85c4f8ed
MD5 a81e429eda86b133d1aaae47683dc565
BLAKE2b-256 a102b5004a3076b689b2387729b57e60e53043001649ad0e05a7138995263b34

See more details on using hashes here.

Provenance

File details

Details for the file fmriprep-1.5.0rc2-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: fmriprep-1.5.0rc2-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 5.7 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/20.10.1 requests-toolbelt/0.9.1 tqdm/4.34.0 CPython/3.5.2

File hashes

Hashes for fmriprep-1.5.0rc2-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 d744c50e250cdedf2b716d222d4f20518197ae357d93585eb0d0659252a9fe34
MD5 8db3d516ebb297b6d4a93d9ca81acc5e
BLAKE2b-256 2041099d43dc84fbd2c87d3dfd2a63e62364b22eefc02802825d52a1caa73a18

See more details on using hashes here.

Provenance

File details

Details for the file fmriprep-1.5.0rc2-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: fmriprep-1.5.0rc2-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 5.7 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/20.10.1 requests-toolbelt/0.9.1 tqdm/4.34.0 CPython/3.5.2

File hashes

Hashes for fmriprep-1.5.0rc2-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 91457c4589a3b91626710566d0a40070893cf93f10b3281a17eb7eb6907de414
MD5 b3df2028c8c6269597edf98d3b30d5d8
BLAKE2b-256 25dbc1ac34d00f11770f5122f7974a6ca010db7c6110f049f33a895840bac947

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page