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-1.4.1rc5.tar.gz (126.5 kB view details)

Uploaded Source

Built Distributions

fmriprep-1.4.1rc5-cp37-cp37m-manylinux1_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.7m

fmriprep-1.4.1rc5-cp36-cp36m-manylinux1_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.6m

File details

Details for the file fmriprep-1.4.1rc5.tar.gz.

File metadata

  • Download URL: fmriprep-1.4.1rc5.tar.gz
  • Upload date:
  • Size: 126.5 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/4.32.2 CPython/3.5.2

File hashes

Hashes for fmriprep-1.4.1rc5.tar.gz
Algorithm Hash digest
SHA256 fb5b06a9e2921e8cb14d60d52cb1be718ec410f82d32593f6129c1621a6ab518
MD5 cefe0b5b58f2000828c25c9a68e48ca2
BLAKE2b-256 aad4102498db6e10f5da95cda092bb7d45ec5c6571b492d296aa5d0a2d5ebfd5

See more details on using hashes here.

Provenance

File details

Details for the file fmriprep-1.4.1rc5-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: fmriprep-1.4.1rc5-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/4.32.2 CPython/3.5.2

File hashes

Hashes for fmriprep-1.4.1rc5-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 5c92b6c122a2385835a6b82715ec1155c94e9342640efbbd1dd67c077514001c
MD5 3ad21ce851ef69dfaf4e5abd871e7c52
BLAKE2b-256 7df88d3a666432ec8d39ba5e19652378ab6567a93f73b9a501f631cc25782826

See more details on using hashes here.

Provenance

File details

Details for the file fmriprep-1.4.1rc5-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: fmriprep-1.4.1rc5-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/4.32.2 CPython/3.5.2

File hashes

Hashes for fmriprep-1.4.1rc5-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 9bbe6283df4d4d42c9a9f7b8b2c854e77d7b580de6e2c14327f7f5f3c4343c3a
MD5 bdd976c8873534a4d380e0373bdc913c
BLAKE2b-256 19803a042632fdf93db49a630f901d9478dcf1fb4401fcbb03efde617612c02f

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